limitations of using case study in research

10 Case Study Advantages and Disadvantages

Chris Drew (PhD)

Dr. Chris Drew is the founder of the Helpful Professor. He holds a PhD in education and has published over 20 articles in scholarly journals. He is the former editor of the Journal of Learning Development in Higher Education. [Image Descriptor: Photo of Chris]

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A case study in academic research is a detailed and in-depth examination of a specific instance or event, generally conducted through a qualitative approach to data.

The most common case study definition that I come across is is Robert K. Yin’s (2003, p. 13) quote provided below:

“An empirical inquiry that investigates a contemporary phenomenon within its real-life context, especially when the boundaries between phenomenon and context are not clearly evident.”

Researchers conduct case studies for a number of reasons, such as to explore complex phenomena within their real-life context, to look at a particularly interesting instance of a situation, or to dig deeper into something of interest identified in a wider-scale project.

While case studies render extremely interesting data, they have many limitations and are not suitable for all studies. One key limitation is that a case study’s findings are not usually generalizable to broader populations because one instance cannot be used to infer trends across populations.

Case Study Advantages and Disadvantages

1. in-depth analysis of complex phenomena.

Case study design allows researchers to delve deeply into intricate issues and situations.

By focusing on a specific instance or event, researchers can uncover nuanced details and layers of understanding that might be missed with other research methods, especially large-scale survey studies.

As Lee and Saunders (2017) argue,

“It allows that particular event to be studies in detail so that its unique qualities may be identified.”

This depth of analysis can provide rich insights into the underlying factors and dynamics of the studied phenomenon.

2. Holistic Understanding

Building on the above point, case studies can help us to understand a topic holistically and from multiple angles.

This means the researcher isn’t restricted to just examining a topic by using a pre-determined set of questions, as with questionnaires. Instead, researchers can use qualitative methods to delve into the many different angles, perspectives, and contextual factors related to the case study.

We can turn to Lee and Saunders (2017) again, who notes that case study researchers “develop a deep, holistic understanding of a particular phenomenon” with the intent of deeply understanding the phenomenon.

3. Examination of rare and Unusual Phenomena

We need to use case study methods when we stumble upon “rare and unusual” (Lee & Saunders, 2017) phenomena that would tend to be seen as mere outliers in population studies.

Take, for example, a child genius. A population study of all children of that child’s age would merely see this child as an outlier in the dataset, and this child may even be removed in order to predict overall trends.

So, to truly come to an understanding of this child and get insights into the environmental conditions that led to this child’s remarkable cognitive development, we need to do an in-depth study of this child specifically – so, we’d use a case study.

4. Helps Reveal the Experiences of Marginalzied Groups

Just as rare and unsual cases can be overlooked in population studies, so too can the experiences, beliefs, and perspectives of marginalized groups.

As Lee and Saunders (2017) argue, “case studies are also extremely useful in helping the expression of the voices of people whose interests are often ignored.”

Take, for example, the experiences of minority populations as they navigate healthcare systems. This was for many years a “hidden” phenomenon, not examined by researchers. It took case study designs to truly reveal this phenomenon, which helped to raise practitioners’ awareness of the importance of cultural sensitivity in medicine.

5. Ideal in Situations where Researchers cannot Control the Variables

Experimental designs – where a study takes place in a lab or controlled environment – are excellent for determining cause and effect . But not all studies can take place in controlled environments (Tetnowski, 2015).

When we’re out in the field doing observational studies or similar fieldwork, we don’t have the freedom to isolate dependent and independent variables. We need to use alternate methods.

Case studies are ideal in such situations.

A case study design will allow researchers to deeply immerse themselves in a setting (potentially combining it with methods such as ethnography or researcher observation) in order to see how phenomena take place in real-life settings.

6. Supports the generation of new theories or hypotheses

While large-scale quantitative studies such as cross-sectional designs and population surveys are excellent at testing theories and hypotheses on a large scale, they need a hypothesis to start off with!

This is where case studies – in the form of grounded research – come in. Often, a case study doesn’t start with a hypothesis. Instead, it ends with a hypothesis based upon the findings within a singular setting.

The deep analysis allows for hypotheses to emerge, which can then be taken to larger-scale studies in order to conduct further, more generalizable, testing of the hypothesis or theory.

7. Reveals the Unexpected

When a largescale quantitative research project has a clear hypothesis that it will test, it often becomes very rigid and has tunnel-vision on just exploring the hypothesis.

Of course, a structured scientific examination of the effects of specific interventions targeted at specific variables is extermely valuable.

But narrowly-focused studies often fail to shine a spotlight on unexpected and emergent data. Here, case studies come in very useful. Oftentimes, researchers set their eyes on a phenomenon and, when examining it closely with case studies, identify data and come to conclusions that are unprecedented, unforeseen, and outright surprising.

As Lars Meier (2009, p. 975) marvels, “where else can we become a part of foreign social worlds and have the chance to become aware of the unexpected?”

Disadvantages

1. not usually generalizable.

Case studies are not generalizable because they tend not to look at a broad enough corpus of data to be able to infer that there is a trend across a population.

As Yang (2022) argues, “by definition, case studies can make no claims to be typical.”

Case studies focus on one specific instance of a phenomenon. They explore the context, nuances, and situational factors that have come to bear on the case study. This is really useful for bringing to light important, new, and surprising information, as I’ve already covered.

But , it’s not often useful for generating data that has validity beyond the specific case study being examined.

2. Subjectivity in interpretation

Case studies usually (but not always) use qualitative data which helps to get deep into a topic and explain it in human terms, finding insights unattainable by quantitative data.

But qualitative data in case studies relies heavily on researcher interpretation. While researchers can be trained and work hard to focus on minimizing subjectivity (through methods like triangulation), it often emerges – some might argue it’s innevitable in qualitative studies.

So, a criticism of case studies could be that they’re more prone to subjectivity – and researchers need to take strides to address this in their studies.

3. Difficulty in replicating results

Case study research is often non-replicable because the study takes place in complex real-world settings where variables are not controlled.

So, when returning to a setting to re-do or attempt to replicate a study, we often find that the variables have changed to such an extent that replication is difficult. Furthermore, new researchers (with new subjective eyes) may catch things that the other readers overlooked.

Replication is even harder when researchers attempt to replicate a case study design in a new setting or with different participants.

Comprehension Quiz for Students

Question 1: What benefit do case studies offer when exploring the experiences of marginalized groups?

a) They provide generalizable data. b) They help express the voices of often-ignored individuals. c) They control all variables for the study. d) They always start with a clear hypothesis.

Question 2: Why might case studies be considered ideal for situations where researchers cannot control all variables?

a) They provide a structured scientific examination. b) They allow for generalizability across populations. c) They focus on one specific instance of a phenomenon. d) They allow for deep immersion in real-life settings.

Question 3: What is a primary disadvantage of case studies in terms of data applicability?

a) They always focus on the unexpected. b) They are not usually generalizable. c) They support the generation of new theories. d) They provide a holistic understanding.

Question 4: Why might case studies be considered more prone to subjectivity?

a) They always use quantitative data. b) They heavily rely on researcher interpretation, especially with qualitative data. c) They are always replicable. d) They look at a broad corpus of data.

Question 5: In what situations are experimental designs, such as those conducted in labs, most valuable?

a) When there’s a need to study rare and unusual phenomena. b) When a holistic understanding is required. c) When determining cause-and-effect relationships. d) When the study focuses on marginalized groups.

Question 6: Why is replication challenging in case study research?

a) Because they always use qualitative data. b) Because they tend to focus on a broad corpus of data. c) Due to the changing variables in complex real-world settings. d) Because they always start with a hypothesis.

Lee, B., & Saunders, M. N. K. (2017). Conducting Case Study Research for Business and Management Students. SAGE Publications.

Meir, L. (2009). Feasting on the Benefits of Case Study Research. In Mills, A. J., Wiebe, E., & Durepos, G. (Eds.). Encyclopedia of Case Study Research (Vol. 2). London: SAGE Publications.

Tetnowski, J. (2015). Qualitative case study research design.  Perspectives on fluency and fluency disorders ,  25 (1), 39-45. ( Source )

Yang, S. L. (2022). The War on Corruption in China: Local Reform and Innovation . Taylor & Francis.

Yin, R. (2003). Case Study research. Thousand Oaks, CA: Sage.

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The Advantages and Limitations of Single Case Study Analysis

As Andrew Bennett and Colin Elman have recently noted, qualitative research methods presently enjoy “an almost unprecedented popularity and vitality… in the international relations sub-field”, such that they are now “indisputably prominent, if not pre-eminent” (2010: 499). This is, they suggest, due in no small part to the considerable advantages that case study methods in particular have to offer in studying the “complex and relatively unstructured and infrequent phenomena that lie at the heart of the subfield” (Bennett and Elman, 2007: 171). Using selected examples from within the International Relations literature[1], this paper aims to provide a brief overview of the main principles and distinctive advantages and limitations of single case study analysis. Divided into three inter-related sections, the paper therefore begins by first identifying the underlying principles that serve to constitute the case study as a particular research strategy, noting the somewhat contested nature of the approach in ontological, epistemological, and methodological terms. The second part then looks to the principal single case study types and their associated advantages, including those from within the recent ‘third generation’ of qualitative International Relations (IR) research. The final section of the paper then discusses the most commonly articulated limitations of single case studies; while accepting their susceptibility to criticism, it is however suggested that such weaknesses are somewhat exaggerated. The paper concludes that single case study analysis has a great deal to offer as a means of both understanding and explaining contemporary international relations.

The term ‘case study’, John Gerring has suggested, is “a definitional morass… Evidently, researchers have many different things in mind when they talk about case study research” (2006a: 17). It is possible, however, to distil some of the more commonly-agreed principles. One of the most prominent advocates of case study research, Robert Yin (2009: 14) defines it as “an empirical enquiry that investigates a contemporary phenomenon in depth and within its real-life context, especially when the boundaries between phenomenon and context are not clearly evident”. What this definition usefully captures is that case studies are intended – unlike more superficial and generalising methods – to provide a level of detail and understanding, similar to the ethnographer Clifford Geertz’s (1973) notion of ‘thick description’, that allows for the thorough analysis of the complex and particularistic nature of distinct phenomena. Another frequently cited proponent of the approach, Robert Stake, notes that as a form of research the case study “is defined by interest in an individual case, not by the methods of inquiry used”, and that “the object of study is a specific, unique, bounded system” (2008: 443, 445). As such, three key points can be derived from this – respectively concerning issues of ontology, epistemology, and methodology – that are central to the principles of single case study research.

First, the vital notion of ‘boundedness’ when it comes to the particular unit of analysis means that defining principles should incorporate both the synchronic (spatial) and diachronic (temporal) elements of any so-called ‘case’. As Gerring puts it, a case study should be “an intensive study of a single unit… a spatially bounded phenomenon – e.g. a nation-state, revolution, political party, election, or person – observed at a single point in time or over some delimited period of time” (2004: 342). It is important to note, however, that – whereas Gerring refers to a single unit of analysis – it may be that attention also necessarily be given to particular sub-units. This points to the important difference between what Yin refers to as an ‘holistic’ case design, with a single unit of analysis, and an ’embedded’ case design with multiple units of analysis (Yin, 2009: 50-52). The former, for example, would examine only the overall nature of an international organization, whereas the latter would also look to specific departments, programmes, or policies etc.

Secondly, as Tim May notes of the case study approach, “even the most fervent advocates acknowledge that the term has entered into understandings with little specification or discussion of purpose and process” (2011: 220). One of the principal reasons for this, he argues, is the relationship between the use of case studies in social research and the differing epistemological traditions – positivist, interpretivist, and others – within which it has been utilised. Philosophy of science concerns are obviously a complex issue, and beyond the scope of much of this paper. That said, the issue of how it is that we know what we know – of whether or not a single independent reality exists of which we as researchers can seek to provide explanation – does lead us to an important distinction to be made between so-called idiographic and nomothetic case studies (Gerring, 2006b). The former refers to those which purport to explain only a single case, are concerned with particularisation, and hence are typically (although not exclusively) associated with more interpretivist approaches. The latter are those focused studies that reflect upon a larger population and are more concerned with generalisation, as is often so with more positivist approaches[2]. The importance of this distinction, and its relation to the advantages and limitations of single case study analysis, is returned to below.

Thirdly, in methodological terms, given that the case study has often been seen as more of an interpretivist and idiographic tool, it has also been associated with a distinctly qualitative approach (Bryman, 2009: 67-68). However, as Yin notes, case studies can – like all forms of social science research – be exploratory, descriptive, and/or explanatory in nature. It is “a common misconception”, he notes, “that the various research methods should be arrayed hierarchically… many social scientists still deeply believe that case studies are only appropriate for the exploratory phase of an investigation” (Yin, 2009: 6). If case studies can reliably perform any or all three of these roles – and given that their in-depth approach may also require multiple sources of data and the within-case triangulation of methods – then it becomes readily apparent that they should not be limited to only one research paradigm. Exploratory and descriptive studies usually tend toward the qualitative and inductive, whereas explanatory studies are more often quantitative and deductive (David and Sutton, 2011: 165-166). As such, the association of case study analysis with a qualitative approach is a “methodological affinity, not a definitional requirement” (Gerring, 2006a: 36). It is perhaps better to think of case studies as transparadigmatic; it is mistaken to assume single case study analysis to adhere exclusively to a qualitative methodology (or an interpretivist epistemology) even if it – or rather, practitioners of it – may be so inclined. By extension, this also implies that single case study analysis therefore remains an option for a multitude of IR theories and issue areas; it is how this can be put to researchers’ advantage that is the subject of the next section.

Having elucidated the defining principles of the single case study approach, the paper now turns to an overview of its main benefits. As noted above, a lack of consensus still exists within the wider social science literature on the principles and purposes – and by extension the advantages and limitations – of case study research. Given that this paper is directed towards the particular sub-field of International Relations, it suggests Bennett and Elman’s (2010) more discipline-specific understanding of contemporary case study methods as an analytical framework. It begins however, by discussing Harry Eckstein’s seminal (1975) contribution to the potential advantages of the case study approach within the wider social sciences.

Eckstein proposed a taxonomy which usefully identified what he considered to be the five most relevant types of case study. Firstly were so-called configurative-idiographic studies, distinctly interpretivist in orientation and predicated on the assumption that “one cannot attain prediction and control in the natural science sense, but only understanding ( verstehen )… subjective values and modes of cognition are crucial” (1975: 132). Eckstein’s own sceptical view was that any interpreter ‘simply’ considers a body of observations that are not self-explanatory and “without hard rules of interpretation, may discern in them any number of patterns that are more or less equally plausible” (1975: 134). Those of a more post-modernist bent, of course – sharing an “incredulity towards meta-narratives”, in Lyotard’s (1994: xxiv) evocative phrase – would instead suggest that this more free-form approach actually be advantageous in delving into the subtleties and particularities of individual cases.

Eckstein’s four other types of case study, meanwhile, promote a more nomothetic (and positivist) usage. As described, disciplined-configurative studies were essentially about the use of pre-existing general theories, with a case acting “passively, in the main, as a receptacle for putting theories to work” (Eckstein, 1975: 136). As opposed to the opportunity this presented primarily for theory application, Eckstein identified heuristic case studies as explicit theoretical stimulants – thus having instead the intended advantage of theory-building. So-called p lausibility probes entailed preliminary attempts to determine whether initial hypotheses should be considered sound enough to warrant more rigorous and extensive testing. Finally, and perhaps most notably, Eckstein then outlined the idea of crucial case studies , within which he also included the idea of ‘most-likely’ and ‘least-likely’ cases; the essential characteristic of crucial cases being their specific theory-testing function.

Whilst Eckstein’s was an early contribution to refining the case study approach, Yin’s (2009: 47-52) more recent delineation of possible single case designs similarly assigns them roles in the applying, testing, or building of theory, as well as in the study of unique cases[3]. As a subset of the latter, however, Jack Levy (2008) notes that the advantages of idiographic cases are actually twofold. Firstly, as inductive/descriptive cases – akin to Eckstein’s configurative-idiographic cases – whereby they are highly descriptive, lacking in an explicit theoretical framework and therefore taking the form of “total history”. Secondly, they can operate as theory-guided case studies, but ones that seek only to explain or interpret a single historical episode rather than generalise beyond the case. Not only does this therefore incorporate ‘single-outcome’ studies concerned with establishing causal inference (Gerring, 2006b), it also provides room for the more postmodern approaches within IR theory, such as discourse analysis, that may have developed a distinct methodology but do not seek traditional social scientific forms of explanation.

Applying specifically to the state of the field in contemporary IR, Bennett and Elman identify a ‘third generation’ of mainstream qualitative scholars – rooted in a pragmatic scientific realist epistemology and advocating a pluralistic approach to methodology – that have, over the last fifteen years, “revised or added to essentially every aspect of traditional case study research methods” (2010: 502). They identify ‘process tracing’ as having emerged from this as a central method of within-case analysis. As Bennett and Checkel observe, this carries the advantage of offering a methodologically rigorous “analysis of evidence on processes, sequences, and conjunctures of events within a case, for the purposes of either developing or testing hypotheses about causal mechanisms that might causally explain the case” (2012: 10).

Harnessing various methods, process tracing may entail the inductive use of evidence from within a case to develop explanatory hypotheses, and deductive examination of the observable implications of hypothesised causal mechanisms to test their explanatory capability[4]. It involves providing not only a coherent explanation of the key sequential steps in a hypothesised process, but also sensitivity to alternative explanations as well as potential biases in the available evidence (Bennett and Elman 2010: 503-504). John Owen (1994), for example, demonstrates the advantages of process tracing in analysing whether the causal factors underpinning democratic peace theory are – as liberalism suggests – not epiphenomenal, but variously normative, institutional, or some given combination of the two or other unexplained mechanism inherent to liberal states. Within-case process tracing has also been identified as advantageous in addressing the complexity of path-dependent explanations and critical junctures – as for example with the development of political regime types – and their constituent elements of causal possibility, contingency, closure, and constraint (Bennett and Elman, 2006b).

Bennett and Elman (2010: 505-506) also identify the advantages of single case studies that are implicitly comparative: deviant, most-likely, least-likely, and crucial cases. Of these, so-called deviant cases are those whose outcome does not fit with prior theoretical expectations or wider empirical patterns – again, the use of inductive process tracing has the advantage of potentially generating new hypotheses from these, either particular to that individual case or potentially generalisable to a broader population. A classic example here is that of post-independence India as an outlier to the standard modernisation theory of democratisation, which holds that higher levels of socio-economic development are typically required for the transition to, and consolidation of, democratic rule (Lipset, 1959; Diamond, 1992). Absent these factors, MacMillan’s single case study analysis (2008) suggests the particularistic importance of the British colonial heritage, the ideology and leadership of the Indian National Congress, and the size and heterogeneity of the federal state.

Most-likely cases, as per Eckstein above, are those in which a theory is to be considered likely to provide a good explanation if it is to have any application at all, whereas least-likely cases are ‘tough test’ ones in which the posited theory is unlikely to provide good explanation (Bennett and Elman, 2010: 505). Levy (2008) neatly refers to the inferential logic of the least-likely case as the ‘Sinatra inference’ – if a theory can make it here, it can make it anywhere. Conversely, if a theory cannot pass a most-likely case, it is seriously impugned. Single case analysis can therefore be valuable for the testing of theoretical propositions, provided that predictions are relatively precise and measurement error is low (Levy, 2008: 12-13). As Gerring rightly observes of this potential for falsification:

“a positivist orientation toward the work of social science militates toward a greater appreciation of the case study format, not a denigration of that format, as is usually supposed” (Gerring, 2007: 247, emphasis added).

In summary, the various forms of single case study analysis can – through the application of multiple qualitative and/or quantitative research methods – provide a nuanced, empirically-rich, holistic account of specific phenomena. This may be particularly appropriate for those phenomena that are simply less amenable to more superficial measures and tests (or indeed any substantive form of quantification) as well as those for which our reasons for understanding and/or explaining them are irreducibly subjective – as, for example, with many of the normative and ethical issues associated with the practice of international relations. From various epistemological and analytical standpoints, single case study analysis can incorporate both idiographic sui generis cases and, where the potential for generalisation may exist, nomothetic case studies suitable for the testing and building of causal hypotheses. Finally, it should not be ignored that a signal advantage of the case study – with particular relevance to international relations – also exists at a more practical rather than theoretical level. This is, as Eckstein noted, “that it is economical for all resources: money, manpower, time, effort… especially important, of course, if studies are inherently costly, as they are if units are complex collective individuals ” (1975: 149-150, emphasis added).

Limitations

Single case study analysis has, however, been subject to a number of criticisms, the most common of which concern the inter-related issues of methodological rigour, researcher subjectivity, and external validity. With regard to the first point, the prototypical view here is that of Zeev Maoz (2002: 164-165), who suggests that “the use of the case study absolves the author from any kind of methodological considerations. Case studies have become in many cases a synonym for freeform research where anything goes”. The absence of systematic procedures for case study research is something that Yin (2009: 14-15) sees as traditionally the greatest concern due to a relative absence of methodological guidelines. As the previous section suggests, this critique seems somewhat unfair; many contemporary case study practitioners – and representing various strands of IR theory – have increasingly sought to clarify and develop their methodological techniques and epistemological grounding (Bennett and Elman, 2010: 499-500).

A second issue, again also incorporating issues of construct validity, concerns that of the reliability and replicability of various forms of single case study analysis. This is usually tied to a broader critique of qualitative research methods as a whole. However, whereas the latter obviously tend toward an explicitly-acknowledged interpretive basis for meanings, reasons, and understandings:

“quantitative measures appear objective, but only so long as we don’t ask questions about where and how the data were produced… pure objectivity is not a meaningful concept if the goal is to measure intangibles [as] these concepts only exist because we can interpret them” (Berg and Lune, 2010: 340).

The question of researcher subjectivity is a valid one, and it may be intended only as a methodological critique of what are obviously less formalised and researcher-independent methods (Verschuren, 2003). Owen (1994) and Layne’s (1994) contradictory process tracing results of interdemocratic war-avoidance during the Anglo-American crisis of 1861 to 1863 – from liberal and realist standpoints respectively – are a useful example. However, it does also rest on certain assumptions that can raise deeper and potentially irreconcilable ontological and epistemological issues. There are, regardless, plenty such as Bent Flyvbjerg (2006: 237) who suggest that the case study contains no greater bias toward verification than other methods of inquiry, and that “on the contrary, experience indicates that the case study contains a greater bias toward falsification of preconceived notions than toward verification”.

The third and arguably most prominent critique of single case study analysis is the issue of external validity or generalisability. How is it that one case can reliably offer anything beyond the particular? “We always do better (or, in the extreme, no worse) with more observation as the basis of our generalization”, as King et al write; “in all social science research and all prediction, it is important that we be as explicit as possible about the degree of uncertainty that accompanies out prediction” (1994: 212). This is an unavoidably valid criticism. It may be that theories which pass a single crucial case study test, for example, require rare antecedent conditions and therefore actually have little explanatory range. These conditions may emerge more clearly, as Van Evera (1997: 51-54) notes, from large-N studies in which cases that lack them present themselves as outliers exhibiting a theory’s cause but without its predicted outcome. As with the case of Indian democratisation above, it would logically be preferable to conduct large-N analysis beforehand to identify that state’s non-representative nature in relation to the broader population.

There are, however, three important qualifiers to the argument about generalisation that deserve particular mention here. The first is that with regard to an idiographic single-outcome case study, as Eckstein notes, the criticism is “mitigated by the fact that its capability to do so [is] never claimed by its exponents; in fact it is often explicitly repudiated” (1975: 134). Criticism of generalisability is of little relevance when the intention is one of particularisation. A second qualifier relates to the difference between statistical and analytical generalisation; single case studies are clearly less appropriate for the former but arguably retain significant utility for the latter – the difference also between explanatory and exploratory, or theory-testing and theory-building, as discussed above. As Gerring puts it, “theory confirmation/disconfirmation is not the case study’s strong suit” (2004: 350). A third qualification relates to the issue of case selection. As Seawright and Gerring (2008) note, the generalisability of case studies can be increased by the strategic selection of cases. Representative or random samples may not be the most appropriate, given that they may not provide the richest insight (or indeed, that a random and unknown deviant case may appear). Instead, and properly used , atypical or extreme cases “often reveal more information because they activate more actors… and more basic mechanisms in the situation studied” (Flyvbjerg, 2006). Of course, this also points to the very serious limitation, as hinted at with the case of India above, that poor case selection may alternatively lead to overgeneralisation and/or grievous misunderstandings of the relationship between variables or processes (Bennett and Elman, 2006a: 460-463).

As Tim May (2011: 226) notes, “the goal for many proponents of case studies […] is to overcome dichotomies between generalizing and particularizing, quantitative and qualitative, deductive and inductive techniques”. Research aims should drive methodological choices, rather than narrow and dogmatic preconceived approaches. As demonstrated above, there are various advantages to both idiographic and nomothetic single case study analyses – notably the empirically-rich, context-specific, holistic accounts that they have to offer, and their contribution to theory-building and, to a lesser extent, that of theory-testing. Furthermore, while they do possess clear limitations, any research method involves necessary trade-offs; the inherent weaknesses of any one method, however, can potentially be offset by situating them within a broader, pluralistic mixed-method research strategy. Whether or not single case studies are used in this fashion, they clearly have a great deal to offer.

References 

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Seawright, J. and Gerring, J. (2008) ‘Case Selection Techniques in Case Study Research: A Menu of Qualitative and Quantitative Options’, Political Research Quarterly , 61, 2, 294-308.

Stake, R. E. (2008) Qualitative Case Studies. In N. K. Denzin and Y. S. Lincoln (eds) Strategies of Qualitative Inquiry . Sage Publications: Los Angeles. Ch. 17.

Van Evera, S. (1997) Guide to Methods for Students of Political Science . Cornell University Press: Ithaca.

Verschuren, P. J. M. (2003) ‘Case study as a research strategy: some ambiguities and opportunities’, International Journal of Social Research Methodology , 6, 2, 121-139.

Yin, R. K. (2009) Case Study Research: Design and Methods . SAGE Publications Ltd: London.

[1] The paper follows convention by differentiating between ‘International Relations’ as the academic discipline and ‘international relations’ as the subject of study.

[2] There is some similarity here with Stake’s (2008: 445-447) notion of intrinsic cases, those undertaken for a better understanding of the particular case, and instrumental ones that provide insight for the purposes of a wider external interest.

[3] These may be unique in the idiographic sense, or in nomothetic terms as an exception to the generalising suppositions of either probabilistic or deterministic theories (as per deviant cases, below).

[4] Although there are “philosophical hurdles to mount”, according to Bennett and Checkel, there exists no a priori reason as to why process tracing (as typically grounded in scientific realism) is fundamentally incompatible with various strands of positivism or interpretivism (2012: 18-19). By extension, it can therefore be incorporated by a range of contemporary mainstream IR theories.

— Written by: Ben Willis Written at: University of Plymouth Written for: David Brockington Date written: January 2013

Further Reading on E-International Relations

• Identity in International Conflicts: A Case Study of the Cuban Missile Crisis
• Imperialism’s Legacy in the Study of Contemporary Politics: The Case of Hegemonic Stability Theory
• Recreating a Nation’s Identity Through Symbolism: A Chinese Case Study
• Ontological Insecurity: A Case Study on Israeli-Palestinian Conflict in Jerusalem
• Terrorists or Freedom Fighters: A Case Study of ETA
• A Critical Assessment of Eco-Marxism: A Ghanaian Case Study

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Home » Case Study – Methods, Examples and Guide

Case Study – Methods, Examples and Guide

Table of Contents

A case study is a research method that involves an in-depth examination and analysis of a particular phenomenon or case, such as an individual, organization, community, event, or situation.

It is a qualitative research approach that aims to provide a detailed and comprehensive understanding of the case being studied. Case studies typically involve multiple sources of data, including interviews, observations, documents, and artifacts, which are analyzed using various techniques, such as content analysis, thematic analysis, and grounded theory. The findings of a case study are often used to develop theories, inform policy or practice, or generate new research questions.

Types of Case Study

Types and Methods of Case Study are as follows:

Single-Case Study

A single-case study is an in-depth analysis of a single case. This type of case study is useful when the researcher wants to understand a specific phenomenon in detail.

For Example , A researcher might conduct a single-case study on a particular individual to understand their experiences with a particular health condition or a specific organization to explore their management practices. The researcher collects data from multiple sources, such as interviews, observations, and documents, and uses various techniques to analyze the data, such as content analysis or thematic analysis. The findings of a single-case study are often used to generate new research questions, develop theories, or inform policy or practice.

Multiple-Case Study

A multiple-case study involves the analysis of several cases that are similar in nature. This type of case study is useful when the researcher wants to identify similarities and differences between the cases.

For Example, a researcher might conduct a multiple-case study on several companies to explore the factors that contribute to their success or failure. The researcher collects data from each case, compares and contrasts the findings, and uses various techniques to analyze the data, such as comparative analysis or pattern-matching. The findings of a multiple-case study can be used to develop theories, inform policy or practice, or generate new research questions.

Exploratory Case Study

An exploratory case study is used to explore a new or understudied phenomenon. This type of case study is useful when the researcher wants to generate hypotheses or theories about the phenomenon.

For Example, a researcher might conduct an exploratory case study on a new technology to understand its potential impact on society. The researcher collects data from multiple sources, such as interviews, observations, and documents, and uses various techniques to analyze the data, such as grounded theory or content analysis. The findings of an exploratory case study can be used to generate new research questions, develop theories, or inform policy or practice.

Descriptive Case Study

A descriptive case study is used to describe a particular phenomenon in detail. This type of case study is useful when the researcher wants to provide a comprehensive account of the phenomenon.

For Example, a researcher might conduct a descriptive case study on a particular community to understand its social and economic characteristics. The researcher collects data from multiple sources, such as interviews, observations, and documents, and uses various techniques to analyze the data, such as content analysis or thematic analysis. The findings of a descriptive case study can be used to inform policy or practice or generate new research questions.

Instrumental Case Study

An instrumental case study is used to understand a particular phenomenon that is instrumental in achieving a particular goal. This type of case study is useful when the researcher wants to understand the role of the phenomenon in achieving the goal.

For Example, a researcher might conduct an instrumental case study on a particular policy to understand its impact on achieving a particular goal, such as reducing poverty. The researcher collects data from multiple sources, such as interviews, observations, and documents, and uses various techniques to analyze the data, such as content analysis or thematic analysis. The findings of an instrumental case study can be used to inform policy or practice or generate new research questions.

Case Study Data Collection Methods

Here are some common data collection methods for case studies:

Interviews involve asking questions to individuals who have knowledge or experience relevant to the case study. Interviews can be structured (where the same questions are asked to all participants) or unstructured (where the interviewer follows up on the responses with further questions). Interviews can be conducted in person, over the phone, or through video conferencing.

Observations

Observations involve watching and recording the behavior and activities of individuals or groups relevant to the case study. Observations can be participant (where the researcher actively participates in the activities) or non-participant (where the researcher observes from a distance). Observations can be recorded using notes, audio or video recordings, or photographs.

Documents can be used as a source of information for case studies. Documents can include reports, memos, emails, letters, and other written materials related to the case study. Documents can be collected from the case study participants or from public sources.

Surveys involve asking a set of questions to a sample of individuals relevant to the case study. Surveys can be administered in person, over the phone, through mail or email, or online. Surveys can be used to gather information on attitudes, opinions, or behaviors related to the case study.

Artifacts are physical objects relevant to the case study. Artifacts can include tools, equipment, products, or other objects that provide insights into the case study phenomenon.

How to conduct Case Study Research

Conducting a case study research involves several steps that need to be followed to ensure the quality and rigor of the study. Here are the steps to conduct case study research:

• Define the research questions: The first step in conducting a case study research is to define the research questions. The research questions should be specific, measurable, and relevant to the case study phenomenon under investigation.
• Select the case: The next step is to select the case or cases to be studied. The case should be relevant to the research questions and should provide rich and diverse data that can be used to answer the research questions.
• Collect data: Data can be collected using various methods, such as interviews, observations, documents, surveys, and artifacts. The data collection method should be selected based on the research questions and the nature of the case study phenomenon.
• Analyze the data: The data collected from the case study should be analyzed using various techniques, such as content analysis, thematic analysis, or grounded theory. The analysis should be guided by the research questions and should aim to provide insights and conclusions relevant to the research questions.
• Draw conclusions: The conclusions drawn from the case study should be based on the data analysis and should be relevant to the research questions. The conclusions should be supported by evidence and should be clearly stated.
• Validate the findings: The findings of the case study should be validated by reviewing the data and the analysis with participants or other experts in the field. This helps to ensure the validity and reliability of the findings.
• Write the report: The final step is to write the report of the case study research. The report should provide a clear description of the case study phenomenon, the research questions, the data collection methods, the data analysis, the findings, and the conclusions. The report should be written in a clear and concise manner and should follow the guidelines for academic writing.

Examples of Case Study

Here are some examples of case study research:

• The Hawthorne Studies : Conducted between 1924 and 1932, the Hawthorne Studies were a series of case studies conducted by Elton Mayo and his colleagues to examine the impact of work environment on employee productivity. The studies were conducted at the Hawthorne Works plant of the Western Electric Company in Chicago and included interviews, observations, and experiments.
• The Stanford Prison Experiment: Conducted in 1971, the Stanford Prison Experiment was a case study conducted by Philip Zimbardo to examine the psychological effects of power and authority. The study involved simulating a prison environment and assigning participants to the role of guards or prisoners. The study was controversial due to the ethical issues it raised.
• The Challenger Disaster: The Challenger Disaster was a case study conducted to examine the causes of the Space Shuttle Challenger explosion in 1986. The study included interviews, observations, and analysis of data to identify the technical, organizational, and cultural factors that contributed to the disaster.
• The Enron Scandal: The Enron Scandal was a case study conducted to examine the causes of the Enron Corporation’s bankruptcy in 2001. The study included interviews, analysis of financial data, and review of documents to identify the accounting practices, corporate culture, and ethical issues that led to the company’s downfall.
• The Fukushima Nuclear Disaster : The Fukushima Nuclear Disaster was a case study conducted to examine the causes of the nuclear accident that occurred at the Fukushima Daiichi Nuclear Power Plant in Japan in 2011. The study included interviews, analysis of data, and review of documents to identify the technical, organizational, and cultural factors that contributed to the disaster.

Application of Case Study

Case studies have a wide range of applications across various fields and industries. Here are some examples:

Business and Management

Case studies are widely used in business and management to examine real-life situations and develop problem-solving skills. Case studies can help students and professionals to develop a deep understanding of business concepts, theories, and best practices.

Case studies are used in healthcare to examine patient care, treatment options, and outcomes. Case studies can help healthcare professionals to develop critical thinking skills, diagnose complex medical conditions, and develop effective treatment plans.

Case studies are used in education to examine teaching and learning practices. Case studies can help educators to develop effective teaching strategies, evaluate student progress, and identify areas for improvement.

Social Sciences

Case studies are widely used in social sciences to examine human behavior, social phenomena, and cultural practices. Case studies can help researchers to develop theories, test hypotheses, and gain insights into complex social issues.

Law and Ethics

Case studies are used in law and ethics to examine legal and ethical dilemmas. Case studies can help lawyers, policymakers, and ethical professionals to develop critical thinking skills, analyze complex cases, and make informed decisions.

Purpose of Case Study

The purpose of a case study is to provide a detailed analysis of a specific phenomenon, issue, or problem in its real-life context. A case study is a qualitative research method that involves the in-depth exploration and analysis of a particular case, which can be an individual, group, organization, event, or community.

The primary purpose of a case study is to generate a comprehensive and nuanced understanding of the case, including its history, context, and dynamics. Case studies can help researchers to identify and examine the underlying factors, processes, and mechanisms that contribute to the case and its outcomes. This can help to develop a more accurate and detailed understanding of the case, which can inform future research, practice, or policy.

Case studies can also serve other purposes, including:

• Illustrating a theory or concept: Case studies can be used to illustrate and explain theoretical concepts and frameworks, providing concrete examples of how they can be applied in real-life situations.
• Developing hypotheses: Case studies can help to generate hypotheses about the causal relationships between different factors and outcomes, which can be tested through further research.
• Providing insight into complex issues: Case studies can provide insights into complex and multifaceted issues, which may be difficult to understand through other research methods.
• Informing practice or policy: Case studies can be used to inform practice or policy by identifying best practices, lessons learned, or areas for improvement.

Advantages of Case Study Research

There are several advantages of case study research, including:

• In-depth exploration: Case study research allows for a detailed exploration and analysis of a specific phenomenon, issue, or problem in its real-life context. This can provide a comprehensive understanding of the case and its dynamics, which may not be possible through other research methods.
• Rich data: Case study research can generate rich and detailed data, including qualitative data such as interviews, observations, and documents. This can provide a nuanced understanding of the case and its complexity.
• Holistic perspective: Case study research allows for a holistic perspective of the case, taking into account the various factors, processes, and mechanisms that contribute to the case and its outcomes. This can help to develop a more accurate and comprehensive understanding of the case.
• Theory development: Case study research can help to develop and refine theories and concepts by providing empirical evidence and concrete examples of how they can be applied in real-life situations.
• Practical application: Case study research can inform practice or policy by identifying best practices, lessons learned, or areas for improvement.
• Contextualization: Case study research takes into account the specific context in which the case is situated, which can help to understand how the case is influenced by the social, cultural, and historical factors of its environment.

Limitations of Case Study Research

There are several limitations of case study research, including:

• Limited generalizability : Case studies are typically focused on a single case or a small number of cases, which limits the generalizability of the findings. The unique characteristics of the case may not be applicable to other contexts or populations, which may limit the external validity of the research.
• Biased sampling: Case studies may rely on purposive or convenience sampling, which can introduce bias into the sample selection process. This may limit the representativeness of the sample and the generalizability of the findings.
• Subjectivity: Case studies rely on the interpretation of the researcher, which can introduce subjectivity into the analysis. The researcher’s own biases, assumptions, and perspectives may influence the findings, which may limit the objectivity of the research.
• Limited control: Case studies are typically conducted in naturalistic settings, which limits the control that the researcher has over the environment and the variables being studied. This may limit the ability to establish causal relationships between variables.
• Time-consuming: Case studies can be time-consuming to conduct, as they typically involve a detailed exploration and analysis of a specific case. This may limit the feasibility of conducting multiple case studies or conducting case studies in a timely manner.
• Resource-intensive: Case studies may require significant resources, including time, funding, and expertise. This may limit the ability of researchers to conduct case studies in resource-constrained settings.

About the author

Muhammad Hassan

Researcher, Academic Writer, Web developer

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The Ultimate Guide to Qualitative Research - Part 1: The Basics

• Introduction and overview
• What is qualitative research?
• What is qualitative data?
• Examples of qualitative data
• Qualitative vs. quantitative research
• Mixed methods
• Qualitative research preparation
• Theoretical perspective
• Theoretical framework
• Literature reviews

Research question

• Conceptual framework
• Conceptual vs. theoretical framework

Data collection

• Qualitative research methods
• Focus groups
• Observational research

What is a case study?

Applications for case study research, what is a good case study, process of case study design, benefits and limitations of case studies.

• Ethnographical research
• Ethical considerations
• Confidentiality and privacy
• Power dynamics
• Reflexivity

Case studies

Case studies are essential to qualitative research , offering a lens through which researchers can investigate complex phenomena within their real-life contexts. This chapter explores the concept, purpose, applications, examples, and types of case studies and provides guidance on how to conduct case study research effectively.

Whereas quantitative methods look at phenomena at scale, case study research looks at a concept or phenomenon in considerable detail. While analyzing a single case can help understand one perspective regarding the object of research inquiry, analyzing multiple cases can help obtain a more holistic sense of the topic or issue. Let's provide a basic definition of a case study, then explore its characteristics and role in the qualitative research process.

Definition of a case study

A case study in qualitative research is a strategy of inquiry that involves an in-depth investigation of a phenomenon within its real-world context. It provides researchers with the opportunity to acquire an in-depth understanding of intricate details that might not be as apparent or accessible through other methods of research. The specific case or cases being studied can be a single person, group, or organization – demarcating what constitutes a relevant case worth studying depends on the researcher and their research question .

Among qualitative research methods , a case study relies on multiple sources of evidence, such as documents, artifacts, interviews , or observations , to present a complete and nuanced understanding of the phenomenon under investigation. The objective is to illuminate the readers' understanding of the phenomenon beyond its abstract statistical or theoretical explanations.

Characteristics of case studies

Case studies typically possess a number of distinct characteristics that set them apart from other research methods. These characteristics include a focus on holistic description and explanation, flexibility in the design and data collection methods, reliance on multiple sources of evidence, and emphasis on the context in which the phenomenon occurs.

Furthermore, case studies can often involve a longitudinal examination of the case, meaning they study the case over a period of time. These characteristics allow case studies to yield comprehensive, in-depth, and richly contextualized insights about the phenomenon of interest.

The role of case studies in research

Case studies hold a unique position in the broader landscape of research methods aimed at theory development. They are instrumental when the primary research interest is to gain an intensive, detailed understanding of a phenomenon in its real-life context.

In addition, case studies can serve different purposes within research - they can be used for exploratory, descriptive, or explanatory purposes, depending on the research question and objectives. This flexibility and depth make case studies a valuable tool in the toolkit of qualitative researchers.

Remember, a well-conducted case study can offer a rich, insightful contribution to both academic and practical knowledge through theory development or theory verification, thus enhancing our understanding of complex phenomena in their real-world contexts.

What is the purpose of a case study?

Case study research aims for a more comprehensive understanding of phenomena, requiring various research methods to gather information for qualitative analysis . Ultimately, a case study can allow the researcher to gain insight into a particular object of inquiry and develop a theoretical framework relevant to the research inquiry.

Why use case studies in qualitative research?

Using case studies as a research strategy depends mainly on the nature of the research question and the researcher's access to the data.

Conducting case study research provides a level of detail and contextual richness that other research methods might not offer. They are beneficial when there's a need to understand complex social phenomena within their natural contexts.

The explanatory, exploratory, and descriptive roles of case studies

Case studies can take on various roles depending on the research objectives. They can be exploratory when the research aims to discover new phenomena or define new research questions; they are descriptive when the objective is to depict a phenomenon within its context in a detailed manner; and they can be explanatory if the goal is to understand specific relationships within the studied context. Thus, the versatility of case studies allows researchers to approach their topic from different angles, offering multiple ways to uncover and interpret the data .

The impact of case studies on knowledge development

Case studies play a significant role in knowledge development across various disciplines. Analysis of cases provides an avenue for researchers to explore phenomena within their context based on the collected data.

This can result in the production of rich, practical insights that can be instrumental in both theory-building and practice. Case studies allow researchers to delve into the intricacies and complexities of real-life situations, uncovering insights that might otherwise remain hidden.

Types of case studies

In qualitative research , a case study is not a one-size-fits-all approach. Depending on the nature of the research question and the specific objectives of the study, researchers might choose to use different types of case studies. These types differ in their focus, methodology, and the level of detail they provide about the phenomenon under investigation.

Understanding these types is crucial for selecting the most appropriate approach for your research project and effectively achieving your research goals. Let's briefly look at the main types of case studies.

Exploratory case studies

Exploratory case studies are typically conducted to develop a theory or framework around an understudied phenomenon. They can also serve as a precursor to a larger-scale research project. Exploratory case studies are useful when a researcher wants to identify the key issues or questions which can spur more extensive study or be used to develop propositions for further research. These case studies are characterized by flexibility, allowing researchers to explore various aspects of a phenomenon as they emerge, which can also form the foundation for subsequent studies.

Descriptive case studies

Descriptive case studies aim to provide a complete and accurate representation of a phenomenon or event within its context. These case studies are often based on an established theoretical framework, which guides how data is collected and analyzed. The researcher is concerned with describing the phenomenon in detail, as it occurs naturally, without trying to influence or manipulate it.

Explanatory case studies

Explanatory case studies are focused on explanation - they seek to clarify how or why certain phenomena occur. Often used in complex, real-life situations, they can be particularly valuable in clarifying causal relationships among concepts and understanding the interplay between different factors within a specific context.

Intrinsic, instrumental, and collective case studies

These three categories of case studies focus on the nature and purpose of the study. An intrinsic case study is conducted when a researcher has an inherent interest in the case itself. Instrumental case studies are employed when the case is used to provide insight into a particular issue or phenomenon. A collective case study, on the other hand, involves studying multiple cases simultaneously to investigate some general phenomena.

Each type of case study serves a different purpose and has its own strengths and challenges. The selection of the type should be guided by the research question and objectives, as well as the context and constraints of the research.

The flexibility, depth, and contextual richness offered by case studies make this approach an excellent research method for various fields of study. They enable researchers to investigate real-world phenomena within their specific contexts, capturing nuances that other research methods might miss. Across numerous fields, case studies provide valuable insights into complex issues.

Critical information systems research

Case studies provide a detailed understanding of the role and impact of information systems in different contexts. They offer a platform to explore how information systems are designed, implemented, and used and how they interact with various social, economic, and political factors. Case studies in this field often focus on examining the intricate relationship between technology, organizational processes, and user behavior, helping to uncover insights that can inform better system design and implementation.

Health research

Health research is another field where case studies are highly valuable. They offer a way to explore patient experiences, healthcare delivery processes, and the impact of various interventions in a real-world context.

Case studies can provide a deep understanding of a patient's journey, giving insights into the intricacies of disease progression, treatment effects, and the psychosocial aspects of health and illness.

Asthma research studies

Specifically within medical research, studies on asthma often employ case studies to explore the individual and environmental factors that influence asthma development, management, and outcomes. A case study can provide rich, detailed data about individual patients' experiences, from the triggers and symptoms they experience to the effectiveness of various management strategies. This can be crucial for developing patient-centered asthma care approaches.

Other fields

Apart from the fields mentioned, case studies are also extensively used in business and management research, education research, and political sciences, among many others. They provide an opportunity to delve into the intricacies of real-world situations, allowing for a comprehensive understanding of various phenomena.

Case studies, with their depth and contextual focus, offer unique insights across these varied fields. They allow researchers to illuminate the complexities of real-life situations, contributing to both theory and practice.

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Understanding the key elements of case study design is crucial for conducting rigorous and impactful case study research. A well-structured design guides the researcher through the process, ensuring that the study is methodologically sound and its findings are reliable and valid. The main elements of case study design include the research question , propositions, units of analysis, and the logic linking the data to the propositions.

The research question is the foundation of any research study. A good research question guides the direction of the study and informs the selection of the case, the methods of collecting data, and the analysis techniques. A well-formulated research question in case study research is typically clear, focused, and complex enough to merit further detailed examination of the relevant case(s).

Propositions

Propositions, though not necessary in every case study, provide a direction by stating what we might expect to find in the data collected. They guide how data is collected and analyzed by helping researchers focus on specific aspects of the case. They are particularly important in explanatory case studies, which seek to understand the relationships among concepts within the studied phenomenon.

Units of analysis

The unit of analysis refers to the case, or the main entity or entities that are being analyzed in the study. In case study research, the unit of analysis can be an individual, a group, an organization, a decision, an event, or even a time period. It's crucial to clearly define the unit of analysis, as it shapes the qualitative data analysis process by allowing the researcher to analyze a particular case and synthesize analysis across multiple case studies to draw conclusions.

Argumentation

This refers to the inferential model that allows researchers to draw conclusions from the data. The researcher needs to ensure that there is a clear link between the data, the propositions (if any), and the conclusions drawn. This argumentation is what enables the researcher to make valid and credible inferences about the phenomenon under study.

Understanding and carefully considering these elements in the design phase of a case study can significantly enhance the quality of the research. It can help ensure that the study is methodologically sound and its findings contribute meaningful insights about the case.

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Conducting a case study involves several steps, from defining the research question and selecting the case to collecting and analyzing data . This section outlines these key stages, providing a practical guide on how to conduct case study research.

Defining the research question

The first step in case study research is defining a clear, focused research question. This question should guide the entire research process, from case selection to analysis. It's crucial to ensure that the research question is suitable for a case study approach. Typically, such questions are exploratory or descriptive in nature and focus on understanding a phenomenon within its real-life context.

Selecting and defining the case

The selection of the case should be based on the research question and the objectives of the study. It involves choosing a unique example or a set of examples that provide rich, in-depth data about the phenomenon under investigation. After selecting the case, it's crucial to define it clearly, setting the boundaries of the case, including the time period and the specific context.

Previous research can help guide the case study design. When considering a case study, an example of a case could be taken from previous case study research and used to define cases in a new research inquiry. Considering recently published examples can help understand how to select and define cases effectively.

Developing a detailed case study protocol

A case study protocol outlines the procedures and general rules to be followed during the case study. This includes the data collection methods to be used, the sources of data, and the procedures for analysis. Having a detailed case study protocol ensures consistency and reliability in the study.

The protocol should also consider how to work with the people involved in the research context to grant the research team access to collecting data. As mentioned in previous sections of this guide, establishing rapport is an essential component of qualitative research as it shapes the overall potential for collecting and analyzing data.

Collecting data

Gathering data in case study research often involves multiple sources of evidence, including documents, archival records, interviews, observations, and physical artifacts. This allows for a comprehensive understanding of the case. The process for gathering data should be systematic and carefully documented to ensure the reliability and validity of the study.

Analyzing and interpreting data

The next step is analyzing the data. This involves organizing the data , categorizing it into themes or patterns , and interpreting these patterns to answer the research question. The analysis might also involve comparing the findings with prior research or theoretical propositions.

Writing the case study report

The final step is writing the case study report . This should provide a detailed description of the case, the data, the analysis process, and the findings. The report should be clear, organized, and carefully written to ensure that the reader can understand the case and the conclusions drawn from it.

Each of these steps is crucial in ensuring that the case study research is rigorous, reliable, and provides valuable insights about the case.

The type, depth, and quality of data in your study can significantly influence the validity and utility of the study. In case study research, data is usually collected from multiple sources to provide a comprehensive and nuanced understanding of the case. This section will outline the various methods of collecting data used in case study research and discuss considerations for ensuring the quality of the data.

Interviews are a common method of gathering data in case study research. They can provide rich, in-depth data about the perspectives, experiences, and interpretations of the individuals involved in the case. Interviews can be structured , semi-structured , or unstructured , depending on the research question and the degree of flexibility needed.

Observations

Observations involve the researcher observing the case in its natural setting, providing first-hand information about the case and its context. Observations can provide data that might not be revealed in interviews or documents, such as non-verbal cues or contextual information.

Documents and artifacts

Documents and archival records provide a valuable source of data in case study research. They can include reports, letters, memos, meeting minutes, email correspondence, and various public and private documents related to the case.

These records can provide historical context, corroborate evidence from other sources, and offer insights into the case that might not be apparent from interviews or observations.

Physical artifacts refer to any physical evidence related to the case, such as tools, products, or physical environments. These artifacts can provide tangible insights into the case, complementing the data gathered from other sources.

Ensuring the quality of data collection

Determining the quality of data in case study research requires careful planning and execution. It's crucial to ensure that the data is reliable, accurate, and relevant to the research question. This involves selecting appropriate methods of collecting data, properly training interviewers or observers, and systematically recording and storing the data. It also includes considering ethical issues related to collecting and handling data, such as obtaining informed consent and ensuring the privacy and confidentiality of the participants.

Data analysis

Analyzing case study research involves making sense of the rich, detailed data to answer the research question. This process can be challenging due to the volume and complexity of case study data. However, a systematic and rigorous approach to analysis can ensure that the findings are credible and meaningful. This section outlines the main steps and considerations in analyzing data in case study research.

Organizing the data

The first step in the analysis is organizing the data. This involves sorting the data into manageable sections, often according to the data source or the theme. This step can also involve transcribing interviews, digitizing physical artifacts, or organizing observational data.

Categorizing and coding the data

Once the data is organized, the next step is to categorize or code the data. This involves identifying common themes, patterns, or concepts in the data and assigning codes to relevant data segments. Coding can be done manually or with the help of software tools, and in either case, qualitative analysis software can greatly facilitate the entire coding process. Coding helps to reduce the data to a set of themes or categories that can be more easily analyzed.

Identifying patterns and themes

After coding the data, the researcher looks for patterns or themes in the coded data. This involves comparing and contrasting the codes and looking for relationships or patterns among them. The identified patterns and themes should help answer the research question.

Interpreting the data

Once patterns and themes have been identified, the next step is to interpret these findings. This involves explaining what the patterns or themes mean in the context of the research question and the case. This interpretation should be grounded in the data, but it can also involve drawing on theoretical concepts or prior research.

Verification of the data

The last step in the analysis is verification. This involves checking the accuracy and consistency of the analysis process and confirming that the findings are supported by the data. This can involve re-checking the original data, checking the consistency of codes, or seeking feedback from research participants or peers.

Like any research method , case study research has its strengths and limitations. Researchers must be aware of these, as they can influence the design, conduct, and interpretation of the study.

Understanding the strengths and limitations of case study research can also guide researchers in deciding whether this approach is suitable for their research question . This section outlines some of the key strengths and limitations of case study research.

Benefits include the following:

• Rich, detailed data: One of the main strengths of case study research is that it can generate rich, detailed data about the case. This can provide a deep understanding of the case and its context, which can be valuable in exploring complex phenomena.
• Flexibility: Case study research is flexible in terms of design , data collection , and analysis . A sufficient degree of flexibility allows the researcher to adapt the study according to the case and the emerging findings.
• Real-world context: Case study research involves studying the case in its real-world context, which can provide valuable insights into the interplay between the case and its context.
• Multiple sources of evidence: Case study research often involves collecting data from multiple sources , which can enhance the robustness and validity of the findings.

On the other hand, researchers should consider the following limitations:

• Generalizability: A common criticism of case study research is that its findings might not be generalizable to other cases due to the specificity and uniqueness of each case.
• Time and resource intensive: Case study research can be time and resource intensive due to the depth of the investigation and the amount of collected data.
• Complexity of analysis: The rich, detailed data generated in case study research can make analyzing the data challenging.
• Subjectivity: Given the nature of case study research, there may be a higher degree of subjectivity in interpreting the data , so researchers need to reflect on this and transparently convey to audiences how the research was conducted.

Being aware of these strengths and limitations can help researchers design and conduct case study research effectively and interpret and report the findings appropriately.

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What are the benefits and drawbacks of case study research?

Posted by Mark Murphy | May 24, 2014 | Method , Research Students | 0

There should be no doubt that with case studies what you gain in depth you lose in breadth – this is the unavoidable compromise that needs to be understood from the beginning of the research process. So this is neither an advantage nor a disadvantage as one aspect cancels out the benefits/drawbacks of the other – there are other benefits and drawbacks that need attention however …

• Their flexibility: case studies are popular for a number of reasons, one being that they can be conducted at various points in the research process. Researchers are known to favour them as a way to develop ideas for more extensive research in the future – pilot studies often take the form of case studies. They are also effective conduits for a broad range of research methods; in that sense they are non-prejudicial against any particular type of research – focus groups are just as welcome in case study research as are questionnaires or participant observation.
• Capturing reality: One of their key benefits is their ability to capture what Hodkinson and Hodkinson call ‘lived reality’ (2001: 3). As they put it, case studies have the potential, when applied successfully, to ‘retain more of the “noise” of real life than many other types of research’ (Hodkinson and Hodkinson, 2001: 3). The importance of ‘noise’ and its place in research is especially important in contexts such as education, for example in schools where background noise is unavoidable. Educational contexts are always complex, and as a result it is difficult to exclude other unwanted variables, ‘some of which may only have real significance for one of their students’ (Hodkinson and Hodkinson, 2001, 4).
• The challenge of generality: At the same time, given their specificity, care needs to be taken when attempting to generalise from the findings. While there’s no inherent flaw in case study design that precludes its broader application, it is preferable that researchers choose their case study sites carefully, while also basing their analysis within existing research findings that have been generated via other research designs. No design is infallible but so often has the claim against case studies been made, that some of the criticism (unwarranted and unfair in many cases) has stuck.
• Suspicion of amateurism: Less partisan researchers might wonder whether the case study offers the time and finance-strapped researcher a convenient and pragmatic source of data, providing findings and recommendations that, given the nature of case studies, can neither be confirmed nor denied, in terms of utility or veracity. Who is to say that case studies offer anything more than a story to tell, and nothing more than that?
• But alongside this suspicion is another more insiduous one – a notion that ‘stories’ are not what social science research is about. This can be a concern for those who favour  case study research, as the political consequences can be hard to ignore. That said, so much research is based either on peoples’ lives or the impact of other issues (poverty, institutional policy) on their lives, so the stories of what actually occurs in their lives or in professional environments tend to be an invaluable source of evidence. The fact is that stories (individual, collective, institutional) have a vital role to play in the world of research. And to play the specific v. general card against case study design suggests a tendency towards forms of research fundamentalism as opposed to any kind of rational and objective take on case study’s strengths and limitations.
• Preciousness: Having said that, researchers should not fall into the trap (surprising how often this happens) of assuming that case study data speaks for itself – rarely is this ever the case, an assumption that is as patronising to research subjects as it is false. The role of the researcher is both to describe social phenomena and also to explain – i.e., interpret. Without interpretation the research findings lack meaningful presentation – they present themselves as fact when of course the reality of ‘facts’ is one of the reasons why such research is carried out.
• Conflation of political/research objectives: Another trap that case study researchers sometimes fall into is presenting research findings as if they were self-evidently true, as if the stories were beyond criticism. This is often accompanied by a vague attachment to the notion that research is a political process – one that is performed as a form of liberation against for example policies that seek to ignore the stories of those who ‘suffer’ at the hands of overbearing political or economic imperatives. Case study design should not be viewed as a mechanism for providing a ‘local’ bulwark against the ‘global’ – bur rather as a mechanism for checking the veracity of universalist claims (at least one of its objectives). The valorisation of particularism can only get you so far in social research.

[This post is adapted from material in ‘Research and Education’ (Curtis, Murphy and Shields , Routledge 2014), pp. 80-82].

Reference: Hodkinson, P. and H. Hodkinson (2001). The strengths and limitations of case study research. Paper presented to the Learning and Skills Development Agency conference, Making an impact on policy and practice , Cambridge, 5-7 December 2001, downloaded from h ttp://education.exeter.ac.uk/tlc/docs/publications/LE_PH_PUB_05.12.01.rtf.26.01.2013

About The Author

Mark Murphy

Mark Murphy is a Reader in Education and Public Policy at the University of Glasgow. He previously worked as an academic at King’s College, London, University of Chester, University of Stirling, National University of Ireland, Maynooth, University College Dublin and Northern Illinois University. Mark is an active researcher in the fields of education and public policy. His research interests include educational sociology, critical theory, accountability in higher education, and public sector reform.

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Case Study Method – 18 Advantages and Disadvantages

The case study method uses investigatory research as a way to collect data about specific demographics. This approach can apply to individuals, businesses, groups, or events. Each participant receives an equal amount of participation, offering information for collection that can then find new insights into specific trends, ideas, of hypotheses.

Interviews and research observation are the two standard methods of data collection used when following the case study method.

Researchers initially developed the case study method to develop and support hypotheses in clinical medicine. The benefits found in these efforts led the approach to transition to other industries, allowing for the examination of results through proposed decisions, processes, or outcomes. Its unique approach to information makes it possible for others to glean specific points of wisdom that encourage growth.

Several case study method advantages and disadvantages can appear when researchers take this approach.

List of the Advantages of the Case Study Method

1. It requires an intensive study of a specific unit. Researchers must document verifiable data from direct observations when using the case study method. This work offers information about the input processes that go into the hypothesis under consideration. A casual approach to data-gathering work is not effective if a definitive outcome is desired. Each behavior, choice, or comment is a critical component that can verify or dispute the ideas being considered.

Intensive programs can require a significant amount of work for researchers, but it can also promote an improvement in the data collected. That means a hypothesis can receive immediate verification in some situations.

2. No sampling is required when following the case study method. This research method studies social units in their entire perspective instead of pulling individual data points out to analyze them. That means there is no sampling work required when using the case study method. The hypothesis under consideration receives support because it works to turn opinions into facts, verifying or denying the proposals that outside observers can use in the future.

Although researchers might pay attention to specific incidents or outcomes based on generalized behaviors or ideas, the study itself won’t sample those situations. It takes a look at the “bigger vision” instead.

3. This method offers a continuous analysis of the facts. The case study method will look at the facts continuously for the social group being studied by researchers. That means there aren’t interruptions in the process that could limit the validity of the data being collected through this work. This advantage reduces the need to use assumptions when drawing conclusions from the information, adding validity to the outcome of the study over time. That means the outcome becomes relevant to both sides of the equation as it can prove specific suppositions or invalidate a hypothesis under consideration.

This advantage can lead to inefficiencies because of the amount of data being studied by researchers. It is up to the individuals involved in the process to sort out what is useful and meaningful and what is not.

4. It is a useful approach to take when formulating a hypothesis. Researchers will use the case study method advantages to verify a hypothesis under consideration. It is not unusual for the collected data to lead people toward the formulation of new ideas after completing this work. This process encourages further study because it allows concepts to evolve as people do in social or physical environments. That means a complete data set can be gathered based on the skills of the researcher and the honesty of the individuals involved in the study itself.

Although this approach won’t develop a societal-level evaluation of a hypothesis, it can look at how specific groups will react in various circumstances. That information can lead to a better decision-making process in the future for everyone involved.

5. It provides an increase in knowledge. The case study method provides everyone with analytical power to increase knowledge. This advantage is possible because it uses a variety of methodologies to collect information while evaluating a hypothesis. Researchers prefer to use direct observation and interviews to complete their work, but it can also advantage through the use of questionnaires. Participants might need to fill out a journal or diary about their experiences that can be used to study behaviors or choices.

Some researchers incorporate memory tests and experimental tasks to determine how social groups will interact or respond in specific situations. All of this data then works to verify the possibilities that a hypothesis proposes.

6. The case study method allows for comparisons. The human experience is one that is built on individual observations from group situations. Specific demographics might think, act, or respond in particular ways to stimuli, but each person in that group will also contribute a small part to the whole. You could say that people are sponges that collect data from one another every day to create individual outcomes.

The case study method allows researchers to take the information from each demographic for comparison purposes. This information can then lead to proposals that support a hypothesis or lead to its disruption.

7. Data generalization is possible using the case study method. The case study method provides a foundation for data generalization, allowing researches to illustrate their statistical findings in meaningful ways. It puts the information into a usable format that almost anyone can use if they have the need to evaluate the hypothesis under consideration. This process makes it easier to discover unusual features, unique outcomes, or find conclusions that wouldn’t be available without this method. It does an excellent job of identifying specific concepts that relate to the proposed ideas that researchers were verifying through their work.

Generalization does not apply to a larger population group with the case study method. What researchers can do with this information is to suggest a predictable outcome when similar groups are placed in an equal situation.

8. It offers a comprehensive approach to research. Nothing gets ignored when using the case study method to collect information. Every person, place, or thing involved in the research receives the complete attention of those seeking data. The interactions are equal, which means the data is comprehensive and directly reflective of the group being observed.

This advantage means that there are fewer outliers to worry about when researching an idea, leading to a higher level of accuracy in the conclusions drawn by the researchers.

9. The identification of deviant cases is possible with this method. The case study method of research makes it easier to identify deviant cases that occur in each social group. These incidents are units (people) that behave in ways that go against the hypothesis under consideration. Instead of ignoring them like other options do when collecting data, this approach incorporates the “rogue” behavior to understand why it exists in the first place.

This advantage makes the eventual data and conclusions gathered more reliable because it incorporates the “alternative opinion” that exists. One might say that the case study method places as much emphasis on the yin as it does the yang so that the whole picture becomes available to the outside observer.

10. Questionnaire development is possible with the case study method. Interviews and direct observation are the preferred methods of implementing the case study method because it is cheap and done remotely. The information gathered by researchers can also lead to farming questionnaires that can farm additional data from those being studied. When all of the data resources come together, it is easier to formulate a conclusion that accurately reflects the demographics.

Some people in the case study method may try to manipulate the results for personal reasons, but this advantage makes it possible to identify this information readily. Then researchers can look into the thinking that goes into the dishonest behaviors observed.

List of the Disadvantages of the Case Study Method

1. The case study method offers limited representation. The usefulness of the case study method is limited to a specific group of representatives. Researchers are looking at a specific demographic when using this option. That means it is impossible to create any generalization that applies to the rest of society, an organization, or a larger community with this work. The findings can only apply to other groups caught in similar circumstances with the same experiences.

It is useful to use the case study method when attempting to discover the specific reasons why some people behave in a specific way. If researchers need something more generalized, then a different method must be used.

2. No classification is possible with the case study method. This disadvantage is also due to the sample size in the case study method. No classification is possible because researchers are studying such a small unit, group, or demographic. It can be an inefficient process since the skills of the researcher help to determine the quality of the data being collected to verify the validity of a hypothesis. Some participants may be unwilling to answer or participate, while others might try to guess at the outcome to support it.

Researchers can get trapped in a place where they explore more tangents than the actual hypothesis with this option. Classification can occur within the units being studied, but this data cannot extrapolate to other demographics.

3. The case study method still offers the possibility of errors. Each person has an unconscious bias that influences their behaviors and choices. The case study method can find outliers that oppose a hypothesis fairly easily thanks to its emphasis on finding facts, but it is up to the researchers to determine what information qualifies for this designation. If the results from the case study method are surprising or go against the opinion of participating individuals, then there is still the possibility that the information will not be 100% accurate.

Researchers must have controls in place that dictate how data gathering work occurs. Without this limitation in place, the results of the study cannot be guaranteed because of the presence of bias.

4. It is a subjective method to use for research. Although the purpose of the case study method of research is to gather facts, the foundation of what gets gathered is still based on opinion. It uses the subjective method instead of the objective one when evaluating data, which means there can be another layer of errors in the information to consider.

Imagine that a researcher interprets someone’s response as “angry” when performing direct observation, but the individual was feeling “shame” because of a decision they made. The difference between those two emotions is profound, and it could lead to information disruptions that could be problematic to the eventual work of hypothesis verification.

5. The processes required by the case study method are not useful for everyone. The case study method uses a person’s memories, explanations, and records from photographs and diaries to identify interactions on influences on psychological processes. People are given the chance to describe what happens in the world around them as a way for researchers to gather data. This process can be an advantage in some industries, but it can also be a worthless approach to some groups.

If the social group under study doesn’t have the information, knowledge, or wisdom to provide meaningful data, then the processes are no longer useful. Researchers must weigh the advantages and disadvantages of the case study method before starting their work to determine if the possibility of value exists. If it does not, then a different method may be necessary.

6. It is possible for bias to form in the data. It’s not just an unconscious bias that can form in the data when using the case study method. The narrow study approach can lead to outright discrimination in the data. Researchers can decide to ignore outliers or any other information that doesn’t support their hypothesis when using this method. The subjective nature of this approach makes it difficult to challenge the conclusions that get drawn from this work, and the limited pool of units (people) means that duplication is almost impossible.

That means unethical people can manipulate the results gathered by the case study method to their own advantage without much accountability in the process.

7. This method has no fixed limits to it. This method of research is highly dependent on situational circumstances rather than overarching societal or corporate truths. That means the researcher has no fixed limits of investigation. Even when controls are in place to limit bias or recommend specific activities, the case study method has enough flexibility built into its structures to allow for additional exploration. That means it is possible for this work to continue indefinitely, gathering data that never becomes useful.

Scientists began to track the health of 268 sophomores at Harvard in 1938. The Great Depression was in its final years at that point, so the study hoped to reveal clues that lead to happy and healthy lives. It continues still today, now incorporating the children of the original participants, providing over 80 years of information to sort through for conclusions.

8. The case study method is time-consuming and expensive. The case study method can be affordable in some situations, but the lack of fixed limits and the ability to pursue tangents can make it a costly process in most situations. It takes time to gather the data in the first place, and then researchers must interpret the information received so that they can use it for hypothesis evaluation. There are other methods of data collection that can be less expensive and provide results faster.

That doesn’t mean the case study method is useless. The individualization of results can help the decision-making process advance in a variety of industries successfully. It just takes more time to reach the appropriate conclusion, and that might be a resource that isn’t available.

The advantages and disadvantages of the case study method suggest that the helpfulness of this research option depends on the specific hypothesis under consideration. When researchers have the correct skills and mindset to gather data accurately, then it can lead to supportive data that can verify ideas with tremendous accuracy.

This research method can also be used unethically to produce specific results that can be difficult to challenge.

When bias enters into the structure of the case study method, the processes become inefficient, inaccurate, and harmful to the hypothesis. That’s why great care must be taken when designing a study with this approach. It might be a labor-intensive way to develop conclusions, but the outcomes are often worth the investments needed.

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Methodology or method? A critical review of qualitative case study reports

Despite on-going debate about credibility, and reported limitations in comparison to other approaches, case study is an increasingly popular approach among qualitative researchers. We critically analysed the methodological descriptions of published case studies. Three high-impact qualitative methods journals were searched to locate case studies published in the past 5 years; 34 were selected for analysis. Articles were categorized as health and health services ( n= 12), social sciences and anthropology ( n= 7), or methods ( n= 15) case studies. The articles were reviewed using an adapted version of established criteria to determine whether adequate methodological justification was present, and if study aims, methods, and reported findings were consistent with a qualitative case study approach. Findings were grouped into five themes outlining key methodological issues: case study methodology or method, case of something particular and case selection, contextually bound case study, researcher and case interactions and triangulation, and study design inconsistent with methodology reported. Improved reporting of case studies by qualitative researchers will advance the methodology for the benefit of researchers and practitioners.

Case study research is an increasingly popular approach among qualitative researchers (Thomas, 2011 ). Several prominent authors have contributed to methodological developments, which has increased the popularity of case study approaches across disciplines (Creswell, 2013b ; Denzin & Lincoln, 2011b ; Merriam, 2009 ; Ragin & Becker, 1992 ; Stake, 1995 ; Yin, 2009 ). Current qualitative case study approaches are shaped by paradigm, study design, and selection of methods, and, as a result, case studies in the published literature vary. Differences between published case studies can make it difficult for researchers to define and understand case study as a methodology.

Experienced qualitative researchers have identified case study research as a stand-alone qualitative approach (Denzin & Lincoln, 2011b ). Case study research has a level of flexibility that is not readily offered by other qualitative approaches such as grounded theory or phenomenology. Case studies are designed to suit the case and research question and published case studies demonstrate wide diversity in study design. There are two popular case study approaches in qualitative research. The first, proposed by Stake ( 1995 ) and Merriam ( 2009 ), is situated in a social constructivist paradigm, whereas the second, by Yin ( 2012 ), Flyvbjerg ( 2011 ), and Eisenhardt ( 1989 ), approaches case study from a post-positivist viewpoint. Scholarship from both schools of inquiry has contributed to the popularity of case study and development of theoretical frameworks and principles that characterize the methodology.

The diversity of case studies reported in the published literature, and on-going debates about credibility and the use of case study in qualitative research practice, suggests that differences in perspectives on case study methodology may prevent researchers from developing a mutual understanding of practice and rigour. In addition, discussion about case study limitations has led some authors to query whether case study is indeed a methodology (Luck, Jackson, & Usher, 2006 ; Meyer, 2001 ; Thomas, 2010 ; Tight, 2010 ). Methodological discussion of qualitative case study research is timely, and a review is required to analyse and understand how this methodology is applied in the qualitative research literature. The aims of this study were to review methodological descriptions of published qualitative case studies, to review how the case study methodological approach was applied, and to identify issues that need to be addressed by researchers, editors, and reviewers. An outline of the current definitions of case study and an overview of the issues proposed in the qualitative methodological literature are provided to set the scene for the review.

Definitions of qualitative case study research

Case study research is an investigation and analysis of a single or collective case, intended to capture the complexity of the object of study (Stake, 1995 ). Qualitative case study research, as described by Stake ( 1995 ), draws together “naturalistic, holistic, ethnographic, phenomenological, and biographic research methods” in a bricoleur design, or in his words, “a palette of methods” (Stake, 1995 , pp. xi–xii). Case study methodology maintains deep connections to core values and intentions and is “particularistic, descriptive and heuristic” (Merriam, 2009 , p. 46).

As a study design, case study is defined by interest in individual cases rather than the methods of inquiry used. The selection of methods is informed by researcher and case intuition and makes use of naturally occurring sources of knowledge, such as people or observations of interactions that occur in the physical space (Stake, 1998 ). Thomas ( 2011 ) suggested that “analytical eclecticism” is a defining factor (p. 512). Multiple data collection and analysis methods are adopted to further develop and understand the case, shaped by context and emergent data (Stake, 1995 ). This qualitative approach “explores a real-life, contemporary bounded system (a case ) or multiple bounded systems (cases) over time, through detailed, in-depth data collection involving multiple sources of information … and reports a case description and case themes ” (Creswell, 2013b , p. 97). Case study research has been defined by the unit of analysis, the process of study, and the outcome or end product, all essentially the case (Merriam, 2009 ).

The case is an object to be studied for an identified reason that is peculiar or particular. Classification of the case and case selection procedures informs development of the study design and clarifies the research question. Stake ( 1995 ) proposed three types of cases and study design frameworks. These include the intrinsic case, the instrumental case, and the collective instrumental case. The intrinsic case is used to understand the particulars of a single case, rather than what it represents. An instrumental case study provides insight on an issue or is used to refine theory. The case is selected to advance understanding of the object of interest. A collective refers to an instrumental case which is studied as multiple, nested cases, observed in unison, parallel, or sequential order. More than one case can be simultaneously studied; however, each case study is a concentrated, single inquiry, studied holistically in its own entirety (Stake, 1995 , 1998 ).

Researchers who use case study are urged to seek out what is common and what is particular about the case. This involves careful and in-depth consideration of the nature of the case, historical background, physical setting, and other institutional and political contextual factors (Stake, 1998 ). An interpretive or social constructivist approach to qualitative case study research supports a transactional method of inquiry, where the researcher has a personal interaction with the case. The case is developed in a relationship between the researcher and informants, and presented to engage the reader, inviting them to join in this interaction and in case discovery (Stake, 1995 ). A postpositivist approach to case study involves developing a clear case study protocol with careful consideration of validity and potential bias, which might involve an exploratory or pilot phase, and ensures that all elements of the case are measured and adequately described (Yin, 2009 , 2012 ).

Current methodological issues in qualitative case study research

The future of qualitative research will be influenced and constructed by the way research is conducted, and by what is reviewed and published in academic journals (Morse, 2011 ). If case study research is to further develop as a principal qualitative methodological approach, and make a valued contribution to the field of qualitative inquiry, issues related to methodological credibility must be considered. Researchers are required to demonstrate rigour through adequate descriptions of methodological foundations. Case studies published without sufficient detail for the reader to understand the study design, and without rationale for key methodological decisions, may lead to research being interpreted as lacking in quality or credibility (Hallberg, 2013 ; Morse, 2011 ).

There is a level of artistic license that is embraced by qualitative researchers and distinguishes practice, which nurtures creativity, innovation, and reflexivity (Denzin & Lincoln, 2011b ; Morse, 2009 ). Qualitative research is “inherently multimethod” (Denzin & Lincoln, 2011a , p. 5); however, with this creative freedom, it is important for researchers to provide adequate description for methodological justification (Meyer, 2001 ). This includes paradigm and theoretical perspectives that have influenced study design. Without adequate description, study design might not be understood by the reader, and can appear to be dishonest or inaccurate. Reviewers and readers might be confused by the inconsistent or inappropriate terms used to describe case study research approach and methods, and be distracted from important study findings (Sandelowski, 2000 ). This issue extends beyond case study research, and others have noted inconsistencies in reporting of methodology and method by qualitative researchers. Sandelowski ( 2000 , 2010 ) argued for accurate identification of qualitative description as a research approach. She recommended that the selected methodology should be harmonious with the study design, and be reflected in methods and analysis techniques. Similarly, Webb and Kevern ( 2000 ) uncovered inconsistencies in qualitative nursing research with focus group methods, recommending that methodological procedures must cite seminal authors and be applied with respect to the selected theoretical framework. Incorrect labelling using case study might stem from the flexibility in case study design and non-directional character relative to other approaches (Rosenberg & Yates, 2007 ). Methodological integrity is required in design of qualitative studies, including case study, to ensure study rigour and to enhance credibility of the field (Morse, 2011 ).

Case study has been unnecessarily devalued by comparisons with statistical methods (Eisenhardt, 1989 ; Flyvbjerg, 2006 , 2011 ; Jensen & Rodgers, 2001 ; Piekkari, Welch, & Paavilainen, 2009 ; Tight, 2010 ; Yin, 1999 ). It is reputed to be the “the weak sibling” in comparison to other, more rigorous, approaches (Yin, 2009 , p. xiii). Case study is not an inherently comparative approach to research. The objective is not statistical research, and the aim is not to produce outcomes that are generalizable to all populations (Thomas, 2011 ). Comparisons between case study and statistical research do little to advance this qualitative approach, and fail to recognize its inherent value, which can be better understood from the interpretive or social constructionist viewpoint of other authors (Merriam, 2009 ; Stake, 1995 ). Building on discussions relating to “fuzzy” (Bassey, 2001 ), or naturalistic generalizations (Stake, 1978 ), or transference of concepts and theories (Ayres, Kavanaugh, & Knafl, 2003 ; Morse et al., 2011 ) would have more relevance.

Case study research has been used as a catch-all design to justify or add weight to fundamental qualitative descriptive studies that do not fit with other traditional frameworks (Merriam, 2009 ). A case study has been a “convenient label for our research—when we ‘can't think of anything ‘better”—in an attempt to give it [qualitative methodology] some added respectability” (Tight, 2010 , p. 337). Qualitative case study research is a pliable approach (Merriam, 2009 ; Meyer, 2001 ; Stake, 1995 ), and has been likened to a “curious methodological limbo” (Gerring, 2004 , p. 341) or “paradigmatic bridge” (Luck et al., 2006 , p. 104), that is on the borderline between postpositivist and constructionist interpretations. This has resulted in inconsistency in application, which indicates that flexibility comes with limitations (Meyer, 2001 ), and the open nature of case study research might be off-putting to novice researchers (Thomas, 2011 ). The development of a well-(in)formed theoretical framework to guide a case study should improve consistency, rigour, and trust in studies published in qualitative research journals (Meyer, 2001 ).

Assessment of rigour

The purpose of this study was to analyse the methodological descriptions of case studies published in qualitative methods journals. To do this we needed to develop a suitable framework, which used existing, established criteria for appraising qualitative case study research rigour (Creswell, 2013b ; Merriam, 2009 ; Stake, 1995 ). A number of qualitative authors have developed concepts and criteria that are used to determine whether a study is rigorous (Denzin & Lincoln, 2011b ; Lincoln, 1995 ; Sandelowski & Barroso, 2002 ). The criteria proposed by Stake ( 1995 ) provide a framework for readers and reviewers to make judgements regarding case study quality, and identify key characteristics essential for good methodological rigour. Although each of the factors listed in Stake's criteria could enhance the quality of a qualitative research report, in Table I we present an adapted criteria used in this study, which integrates more recent work by Merriam ( 2009 ) and Creswell ( 2013b ). Stake's ( 1995 ) original criteria were separated into two categories. The first list of general criteria is “relevant for all qualitative research.” The second list, “high relevance to qualitative case study research,” was the criteria that we decided had higher relevance to case study research. This second list was the main criteria used to assess the methodological descriptions of the case studies reviewed. The complete table has been preserved so that the reader can determine how the original criteria were adapted.

Framework for assessing quality in qualitative case study research.

Adapted from Stake ( 1995 , p. 131).

Study design

The critical review method described by Grant and Booth ( 2009 ) was used, which is appropriate for the assessment of research quality, and is used for literature analysis to inform research and practice. This type of review goes beyond the mapping and description of scoping or rapid reviews, to include “analysis and conceptual innovation” (Grant & Booth, 2009 , p. 93). A critical review is used to develop existing, or produce new, hypotheses or models. This is different to systematic reviews that answer clinical questions. It is used to evaluate existing research and competing ideas, to provide a “launch pad” for conceptual development and “subsequent testing” (Grant & Booth, 2009 , p. 93).

Qualitative methods journals were located by a search of the 2011 ISI Journal Citation Reports in Social Science, via the database Web of Knowledge (see m.webofknowledge.com). No “qualitative research methods” category existed in the citation reports; therefore, a search of all categories was performed using the term “qualitative.” In Table II , we present the qualitative methods journals located, ranked by impact factor. The highest ranked journals were selected for searching. We acknowledge that the impact factor ranking system might not be the best measure of journal quality (Cheek, Garnham, & Quan, 2006 ); however, this was the most appropriate and accessible method available.

International Journal of Qualitative Studies on Health and Well-being.

Search strategy

In March 2013, searches of the journals, Qualitative Health Research , Qualitative Research , and Qualitative Inquiry were completed to retrieve studies with “case study” in the abstract field. The search was limited to the past 5 years (1 January 2008 to 1 March 2013). The objective was to locate published qualitative case studies suitable for assessment using the adapted criterion. Viewpoints, commentaries, and other article types were excluded from review. Title and abstracts of the 45 retrieved articles were read by the first author, who identified 34 empirical case studies for review. All authors reviewed the 34 studies to confirm selection and categorization. In Table III , we present the 34 case studies grouped by journal, and categorized by research topic, including health sciences, social sciences and anthropology, and methods research. There was a discrepancy in categorization of one article on pedagogy and a new teaching method published in Qualitative Inquiry (Jorrín-Abellán, Rubia-Avi, Anguita-Martínez, Gómez-Sánchez, & Martínez-Mones, 2008 ). Consensus was to allocate to the methods category.

Outcomes of search of qualitative methods journals.

In Table III , the number of studies located, and final numbers selected for review have been reported. Qualitative Health Research published the most empirical case studies ( n= 16). In the health category, there were 12 case studies of health conditions, health services, and health policy issues, all published in Qualitative Health Research . Seven case studies were categorized as social sciences and anthropology research, which combined case study with biography and ethnography methodologies. All three journals published case studies on methods research to illustrate a data collection or analysis technique, methodological procedure, or related issue.

The methodological descriptions of 34 case studies were critically reviewed using the adapted criteria. All articles reviewed contained a description of study methods; however, the length, amount of detail, and position of the description in the article varied. Few studies provided an accurate description and rationale for using a qualitative case study approach. In the 34 case studies reviewed, three described a theoretical framework informed by Stake ( 1995 ), two by Yin ( 2009 ), and three provided a mixed framework informed by various authors, which might have included both Yin and Stake. Few studies described their case study design, or included a rationale that explained why they excluded or added further procedures, and whether this was to enhance the study design, or to better suit the research question. In 26 of the studies no reference was provided to principal case study authors. From reviewing the description of methods, few authors provided a description or justification of case study methodology that demonstrated how their study was informed by the methodological literature that exists on this approach.

The methodological descriptions of each study were reviewed using the adapted criteria, and the following issues were identified: case study methodology or method; case of something particular and case selection; contextually bound case study; researcher and case interactions and triangulation; and, study design inconsistent with methodology. An outline of how the issues were developed from the critical review is provided, followed by a discussion of how these relate to the current methodological literature.

Case study methodology or method

A third of the case studies reviewed appeared to use a case report method, not case study methodology as described by principal authors (Creswell, 2013b ; Merriam, 2009 ; Stake, 1995 ; Yin, 2009 ). Case studies were identified as a case report because of missing methodological detail and by review of the study aims and purpose. These reports presented data for small samples of no more than three people, places or phenomenon. Four studies, or “case reports” were single cases selected retrospectively from larger studies (Bronken, Kirkevold, Martinsen, & Kvigne, 2012 ; Coltart & Henwood, 2012 ; Hooghe, Neimeyer, & Rober, 2012 ; Roscigno et al., 2012 ). Case reports were not a case of something, instead were a case demonstration or an example presented in a report. These reports presented outcomes, and reported on how the case could be generalized. Descriptions focussed on the phenomena, rather than the case itself, and did not appear to study the case in its entirety.

Case reports had minimal in-text references to case study methodology, and were informed by other qualitative traditions or secondary sources (Adamson & Holloway, 2012 ; Buzzanell & D'Enbeau, 2009 ; Nagar-Ron & Motzafi-Haller, 2011 ). This does not suggest that case study methodology cannot be multimethod, however, methodology should be consistent in design, be clearly described (Meyer, 2001 ; Stake, 1995 ), and maintain focus on the case (Creswell, 2013b ).

To demonstrate how case reports were identified, three examples are provided. The first, Yeh ( 2013 ) described their study as, “the examination of the emergence of vegetarianism in Victorian England serves as a case study to reveal the relationships between boundaries and entities” (p. 306). The findings were a historical case report, which resulted from an ethnographic study of vegetarianism. Cunsolo Willox, Harper, Edge, ‘My Word’: Storytelling and Digital Media Lab, and Rigolet Inuit Community Government (2013) used “a case study that illustrates the usage of digital storytelling within an Inuit community” (p. 130). This case study reported how digital storytelling can be used with indigenous communities as a participatory method to illuminate the benefits of this method for other studies. This “case study was conducted in the Inuit community” but did not include the Inuit community in case analysis (Cunsolo Willox et al., 2013 , p. 130). Bronken et al. ( 2012 ) provided a single case report to demonstrate issues observed in a larger clinical study of aphasia and stroke, without adequate case description or analysis.

Case study of something particular and case selection

Case selection is a precursor to case analysis, which needs to be presented as a convincing argument (Merriam, 2009 ). Descriptions of the case were often not adequate to ascertain why the case was selected, or whether it was a particular exemplar or outlier (Thomas, 2011 ). In a number of case studies in the health and social science categories, it was not explicit whether the case was of something particular, or peculiar to their discipline or field (Adamson & Holloway, 2012 ; Bronken et al., 2012 ; Colón-Emeric et al., 2010 ; Jackson, Botelho, Welch, Joseph, & Tennstedt, 2012 ; Mawn et al., 2010 ; Snyder-Young, 2011 ). There were exceptions in the methods category ( Table III ), where cases were selected by researchers to report on a new or innovative method. The cases emerged through heuristic study, and were reported to be particular, relative to the existing methods literature (Ajodhia-Andrews & Berman, 2009 ; Buckley & Waring, 2013 ; Cunsolo Willox et al., 2013 ; De Haene, Grietens, & Verschueren, 2010 ; Gratton & O'Donnell, 2011 ; Sumsion, 2013 ; Wimpenny & Savin-Baden, 2012 ).

Case selection processes were sometimes insufficient to understand why the case was selected from the global population of cases, or what study of this case would contribute to knowledge as compared with other possible cases (Adamson & Holloway, 2012 ; Bronken et al., 2012 ; Colón-Emeric et al., 2010 ; Jackson et al., 2012 ; Mawn et al., 2010 ). In two studies, local cases were selected (Barone, 2010 ; Fourie & Theron, 2012 ) because the researcher was familiar with and had access to the case. Possible limitations of a convenience sample were not acknowledged. Purposeful sampling was used to recruit participants within the case of one study, but not of the case itself (Gallagher et al., 2013 ). Random sampling was completed for case selection in two studies (Colón-Emeric et al., 2010 ; Jackson et al., 2012 ), which has limited meaning in interpretive qualitative research.

To demonstrate how researchers provided a good justification for the selection of case study approaches, four examples are provided. The first, cases of residential care homes, were selected because of reported occurrences of mistreatment, which included residents being locked in rooms at night (Rytterström, Unosson, & Arman, 2013 ). Roscigno et al. ( 2012 ) selected cases of parents who were admitted for early hospitalization in neonatal intensive care with a threatened preterm delivery before 26 weeks. Hooghe et al. ( 2012 ) used random sampling to select 20 couples that had experienced the death of a child; however, the case study was of one couple and a particular metaphor described only by them. The final example, Coltart and Henwood ( 2012 ), provided a detailed account of how they selected two cases from a sample of 46 fathers based on personal characteristics and beliefs. They described how the analysis of the two cases would contribute to their larger study on first time fathers and parenting.

Contextually bound case study

The limits or boundaries of the case are a defining factor of case study methodology (Merriam, 2009 ; Ragin & Becker, 1992 ; Stake, 1995 ; Yin, 2009 ). Adequate contextual description is required to understand the setting or context in which the case is revealed. In the health category, case studies were used to illustrate a clinical phenomenon or issue such as compliance and health behaviour (Colón-Emeric et al., 2010 ; D'Enbeau, Buzzanell, & Duckworth, 2010 ; Gallagher et al., 2013 ; Hooghe et al., 2012 ; Jackson et al., 2012 ; Roscigno et al., 2012 ). In these case studies, contextual boundaries, such as physical and institutional descriptions, were not sufficient to understand the case as a holistic system, for example, the general practitioner (GP) clinic in Gallagher et al. ( 2013 ), or the nursing home in Colón-Emeric et al. ( 2010 ). Similarly, in the social science and methods categories, attention was paid to some components of the case context, but not others, missing important information required to understand the case as a holistic system (Alexander, Moreira, & Kumar, 2012 ; Buzzanell & D'Enbeau, 2009 ; Nairn & Panelli, 2009 ; Wimpenny & Savin-Baden, 2012 ).

In two studies, vicarious experience or vignettes (Nairn & Panelli, 2009 ) and images (Jorrín-Abellán et al., 2008 ) were effective to support description of context, and might have been a useful addition for other case studies. Missing contextual boundaries suggests that the case might not be adequately defined. Additional information, such as the physical, institutional, political, and community context, would improve understanding of the case (Stake, 1998 ). In Boxes 1 and 2 , we present brief synopses of two studies that were reviewed, which demonstrated a well bounded case. In Box 1 , Ledderer ( 2011 ) used a qualitative case study design informed by Stake's tradition. In Box 2 , Gillard, Witt, and Watts ( 2011 ) were informed by Yin's tradition. By providing a brief outline of the case studies in Boxes 1 and 2 , we demonstrate how effective case boundaries can be constructed and reported, which may be of particular interest to prospective case study researchers.

Article synopsis of case study research using Stake's tradition

Ledderer ( 2011 ) used a qualitative case study research design, informed by modern ethnography. The study is bounded to 10 general practice clinics in Denmark, who had received federal funding to implement preventative care services based on a Motivational Interviewing intervention. The researcher question focussed on “why is it so difficult to create change in medical practice?” (Ledderer, 2011 , p. 27). The study context was adequately described, providing detail on the general practitioner (GP) clinics and relevant political and economic influences. Methodological decisions are described in first person narrative, providing insight on researcher perspectives and interaction with the case. Forty-four interviews were conducted, which focussed on how GPs conducted consultations, and the form, nature and content, rather than asking their opinion or experience (Ledderer, 2011 , p. 30). The duration and intensity of researcher immersion in the case enhanced depth of description and trustworthiness of study findings. Analysis was consistent with Stake's tradition, and the researcher provided examples of inquiry techniques used to challenge assumptions about emerging themes. Several other seminal qualitative works were cited. The themes and typology constructed are rich in narrative data and storytelling by clinic staff, demonstrating individual clinic experiences as well as shared meanings and understandings about changing from a biomedical to psychological approach to preventative health intervention. Conclusions make note of social and cultural meanings and lessons learned, which might not have been uncovered using a different methodology.

Article synopsis of case study research using Yin's tradition

Gillard et al. ( 2011 ) study of camps for adolescents living with HIV/AIDs provided a good example of Yin's interpretive case study approach. The context of the case is bounded by the three summer camps of which the researchers had prior professional involvement. A case study protocol was developed that used multiple methods to gather information at three data collection points coinciding with three youth camps (Teen Forum, Discover Camp, and Camp Strong). Gillard and colleagues followed Yin's ( 2009 ) principles, using a consistent data protocol that enhanced cross-case analysis. Data described the young people, the camp physical environment, camp schedule, objectives and outcomes, and the staff of three youth camps. The findings provided a detailed description of the context, with less detail of individual participants, including insight into researcher's interpretations and methodological decisions throughout the data collection and analysis process. Findings provided the reader with a sense of “being there,” and are discovered through constant comparison of the case with the research issues; the case is the unit of analysis. There is evidence of researcher immersion in the case, and Gillard reports spending significant time in the field in a naturalistic and integrated youth mentor role.

This case study is not intended to have a significant impact on broader health policy, although does have implications for health professionals working with adolescents. Study conclusions will inform future camps for young people with chronic disease, and practitioners are able to compare similarities between this case and their own practice (for knowledge translation). No limitations of this article were reported. Limitations related to publication of this case study were that it was 20 pages long and used three tables to provide sufficient description of the camp and program components, and relationships with the research issue.

Researcher and case interactions and triangulation

Researcher and case interactions and transactions are a defining feature of case study methodology (Stake, 1995 ). Narrative stories, vignettes, and thick description are used to provoke vicarious experience and a sense of being there with the researcher in their interaction with the case. Few of the case studies reviewed provided details of the researcher's relationship with the case, researcher–case interactions, and how these influenced the development of the case study (Buzzanell & D'Enbeau, 2009 ; D'Enbeau et al., 2010 ; Gallagher et al., 2013 ; Gillard et al., 2011 ; Ledderer, 2011 ; Nagar-Ron & Motzafi-Haller, 2011 ). The role and position of the researcher needed to be self-examined and understood by readers, to understand how this influenced interactions with participants, and to determine what triangulation is needed (Merriam, 2009 ; Stake, 1995 ).

Gillard et al. ( 2011 ) provided a good example of triangulation, comparing data sources in a table (p. 1513). Triangulation of sources was used to reveal as much depth as possible in the study by Nagar-Ron and Motzafi-Haller ( 2011 ), while also enhancing confirmation validity. There were several case studies that would have benefited from improved range and use of data sources, and descriptions of researcher–case interactions (Ajodhia-Andrews & Berman, 2009 ; Bronken et al., 2012 ; Fincham, Scourfield, & Langer, 2008 ; Fourie & Theron, 2012 ; Hooghe et al., 2012 ; Snyder-Young, 2011 ; Yeh, 2013 ).

Study design inconsistent with methodology

Good, rigorous case studies require a strong methodological justification (Meyer, 2001 ) and a logical and coherent argument that defines paradigm, methodological position, and selection of study methods (Denzin & Lincoln, 2011b ). Methodological justification was insufficient in several of the studies reviewed (Barone, 2010 ; Bronken et al., 2012 ; Hooghe et al., 2012 ; Mawn et al., 2010 ; Roscigno et al., 2012 ; Yeh, 2013 ). This was judged by the absence, or inadequate or inconsistent reference to case study methodology in-text.

In six studies, the methodological justification provided did not relate to case study. There were common issues identified. Secondary sources were used as primary methodological references indicating that study design might not have been theoretically sound (Colón-Emeric et al., 2010 ; Coltart & Henwood, 2012 ; Roscigno et al., 2012 ; Snyder-Young, 2011 ). Authors and sources cited in methodological descriptions were inconsistent with the actual study design and practices used (Fourie & Theron, 2012 ; Hooghe et al., 2012 ; Jorrín-Abellán et al., 2008 ; Mawn et al., 2010 ; Rytterström et al., 2013 ; Wimpenny & Savin-Baden, 2012 ). This occurred when researchers cited Stake or Yin, or both (Mawn et al., 2010 ; Rytterström et al., 2013 ), although did not follow their paradigmatic or methodological approach. In 26 studies there were no citations for a case study methodological approach.

The findings of this study have highlighted a number of issues for researchers. A considerable number of case studies reviewed were missing key elements that define qualitative case study methodology and the tradition cited. A significant number of studies did not provide a clear methodological description or justification relevant to case study. Case studies in health and social sciences did not provide sufficient information for the reader to understand case selection, and why this case was chosen above others. The context of the cases were not described in adequate detail to understand all relevant elements of the case context, which indicated that cases may have not been contextually bounded. There were inconsistencies between reported methodology, study design, and paradigmatic approach in case studies reviewed, which made it difficult to understand the study methodology and theoretical foundations. These issues have implications for methodological integrity and honesty when reporting study design, which are values of the qualitative research tradition and are ethical requirements (Wager & Kleinert, 2010a ). Poorly described methodological descriptions may lead the reader to misinterpret or discredit study findings, which limits the impact of the study, and, as a collective, hinders advancements in the broader qualitative research field.

The issues highlighted in our review build on current debates in the case study literature, and queries about the value of this methodology. Case study research can be situated within different paradigms or designed with an array of methods. In order to maintain the creativity and flexibility that is valued in this methodology, clearer descriptions of paradigm and theoretical position and methods should be provided so that study findings are not undervalued or discredited. Case study research is an interdisciplinary practice, which means that clear methodological descriptions might be more important for this approach than other methodologies that are predominantly driven by fewer disciplines (Creswell, 2013b ).

Authors frequently omit elements of methodologies and include others to strengthen study design, and we do not propose a rigid or purist ideology in this paper. On the contrary, we encourage new ideas about using case study, together with adequate reporting, which will advance the value and practice of case study. The implications of unclear methodological descriptions in the studies reviewed were that study design appeared to be inconsistent with reported methodology, and key elements required for making judgements of rigour were missing. It was not clear whether the deviations from methodological tradition were made by researchers to strengthen the study design, or because of misinterpretations. Morse ( 2011 ) recommended that innovations and deviations from practice are best made by experienced researchers, and that a novice might be unaware of the issues involved with making these changes. To perpetuate the tradition of case study research, applications in the published literature should have consistencies with traditional methodological constructions, and deviations should be described with a rationale that is inherent in study conduct and findings. Providing methodological descriptions that demonstrate a strong theoretical foundation and coherent study design will add credibility to the study, while ensuring the intrinsic meaning of case study is maintained.

The value of this review is that it contributes to discussion of whether case study is a methodology or method. We propose possible reasons why researchers might make this misinterpretation. Researchers may interchange the terms methods and methodology, and conduct research without adequate attention to epistemology and historical tradition (Carter & Little, 2007 ; Sandelowski, 2010 ). If the rich meaning that naming a qualitative methodology brings to the study is not recognized, a case study might appear to be inconsistent with the traditional approaches described by principal authors (Creswell, 2013a ; Merriam, 2009 ; Stake, 1995 ; Yin, 2009 ). If case studies are not methodologically and theoretically situated, then they might appear to be a case report.

Case reports are promoted by university and medical journals as a method of reporting on medical or scientific cases; guidelines for case reports are publicly available on websites ( http://www.hopkinsmedicine.org/institutional_review_board/guidelines_policies/guidelines/case_report.html ). The various case report guidelines provide a general criteria for case reports, which describes that this form of report does not meet the criteria of research, is used for retrospective analysis of up to three clinical cases, and is primarily illustrative and for educational purposes. Case reports can be published in academic journals, but do not require approval from a human research ethics committee. Traditionally, case reports describe a single case, to explain how and what occurred in a selected setting, for example, to illustrate a new phenomenon that has emerged from a larger study. A case report is not necessarily particular or the study of a case in its entirety, and the larger study would usually be guided by a different research methodology.

This description of a case report is similar to what was provided in some studies reviewed. This form of report lacks methodological grounding and qualities of research rigour. The case report has publication value in demonstrating an example and for dissemination of knowledge (Flanagan, 1999 ). However, case reports have different meaning and purpose to case study, which needs to be distinguished. Findings of our review suggest that the medical understanding of a case report has been confused with qualitative case study approaches.

In this review, a number of case studies did not have methodological descriptions that included key characteristics of case study listed in the adapted criteria, and several issues have been discussed. There have been calls for improvements in publication quality of qualitative research (Morse, 2011 ), and for improvements in peer review of submitted manuscripts (Carter & Little, 2007 ; Jasper, Vaismoradi, Bondas, & Turunen, 2013 ). The challenging nature of editor and reviewers responsibilities are acknowledged in the literature (Hames, 2013 ; Wager & Kleinert, 2010b ); however, review of case study methodology should be prioritized because of disputes on methodological value.

Authors using case study approaches are recommended to describe their theoretical framework and methods clearly, and to seek and follow specialist methodological advice when needed (Wager & Kleinert, 2010a ). Adequate page space for case study description would contribute to better publications (Gillard et al., 2011 ). Capitalizing on the ability to publish complementary resources should be considered.

Limitations of the review

There is a level of subjectivity involved in this type of review and this should be considered when interpreting study findings. Qualitative methods journals were selected because the aims and scope of these journals are to publish studies that contribute to methodological discussion and development of qualitative research. Generalist health and social science journals were excluded that might have contained good quality case studies. Journals in business or education were also excluded, although a review of case studies in international business journals has been published elsewhere (Piekkari et al., 2009 ).

The criteria used to assess the quality of the case studies were a set of qualitative indicators. A numerical or ranking system might have resulted in different results. Stake's ( 1995 ) criteria have been referenced elsewhere, and was deemed the best available (Creswell, 2013b ; Crowe et al., 2011 ). Not all qualitative studies are reported in a consistent way and some authors choose to report findings in a narrative form in comparison to a typical biomedical report style (Sandelowski & Barroso, 2002 ), if misinterpretations were made this may have affected the review.

Case study research is an increasingly popular approach among qualitative researchers, which provides methodological flexibility through the incorporation of different paradigmatic positions, study designs, and methods. However, whereas flexibility can be an advantage, a myriad of different interpretations has resulted in critics questioning the use of case study as a methodology. Using an adaptation of established criteria, we aimed to identify and assess the methodological descriptions of case studies in high impact, qualitative methods journals. Few articles were identified that applied qualitative case study approaches as described by experts in case study design. There were inconsistencies in methodology and study design, which indicated that researchers were confused whether case study was a methodology or a method. Commonly, there appeared to be confusion between case studies and case reports. Without clear understanding and application of the principles and key elements of case study methodology, there is a risk that the flexibility of the approach will result in haphazard reporting, and will limit its global application as a valuable, theoretically supported methodology that can be rigorously applied across disciplines and fields.

Conflict of interest and funding

The authors have not received any funding or benefits from industry or elsewhere to conduct this study.

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Case Study Research Method in Psychology

Saul McLeod, PhD

Editor-in-Chief for Simply Psychology

BSc (Hons) Psychology, MRes, PhD, University of Manchester

Saul McLeod, PhD., is a qualified psychology teacher with over 18 years of experience in further and higher education. He has been published in peer-reviewed journals, including the Journal of Clinical Psychology.

Learn about our Editorial Process

Olivia Guy-Evans, MSc

Associate Editor for Simply Psychology

BSc (Hons) Psychology, MSc Psychology of Education

Olivia Guy-Evans is a writer and associate editor for Simply Psychology. She has previously worked in healthcare and educational sectors.

On This Page:

Case studies are in-depth investigations of a person, group, event, or community. Typically, data is gathered from various sources using several methods (e.g., observations & interviews).

The case study research method originated in clinical medicine (the case history, i.e., the patient’s personal history). In psychology, case studies are often confined to the study of a particular individual.

The information is mainly biographical and relates to events in the individual’s past (i.e., retrospective), as well as to significant events that are currently occurring in his or her everyday life.

The case study is not a research method, but researchers select methods of data collection and analysis that will generate material suitable for case studies.

Freud (1909a, 1909b) conducted very detailed investigations into the private lives of his patients in an attempt to both understand and help them overcome their illnesses.

This makes it clear that the case study is a method that should only be used by a psychologist, therapist, or psychiatrist, i.e., someone with a professional qualification.

There is an ethical issue of competence. Only someone qualified to diagnose and treat a person can conduct a formal case study relating to atypical (i.e., abnormal) behavior or atypical development.

 Famous Case Studies

• Anna O – One of the most famous case studies, documenting psychoanalyst Josef Breuer’s treatment of “Anna O” (real name Bertha Pappenheim) for hysteria in the late 1800s using early psychoanalytic theory.
• Little Hans – A child psychoanalysis case study published by Sigmund Freud in 1909 analyzing his five-year-old patient Herbert Graf’s house phobia as related to the Oedipus complex.
• Bruce/Brenda – Gender identity case of the boy (Bruce) whose botched circumcision led psychologist John Money to advise gender reassignment and raise him as a girl (Brenda) in the 1960s.
• Genie Wiley – Linguistics/psychological development case of the victim of extreme isolation abuse who was studied in 1970s California for effects of early language deprivation on acquiring speech later in life.
• Phineas Gage – One of the most famous neuropsychology case studies analyzes personality changes in railroad worker Phineas Gage after an 1848 brain injury involving a tamping iron piercing his skull.

Clinical Case Studies

• Studying the effectiveness of psychotherapy approaches with an individual patient
• Assessing and treating mental illnesses like depression, anxiety disorders, PTSD
• Neuropsychological cases investigating brain injuries or disorders

Child Psychology Case Studies

• Studying psychological development from birth through adolescence
• Cases of learning disabilities, autism spectrum disorders, ADHD
• Effects of trauma, abuse, deprivation on development

Types of Case Studies

• Explanatory case studies : Used to explore causation in order to find underlying principles. Helpful for doing qualitative analysis to explain presumed causal links.
• Exploratory case studies : Used to explore situations where an intervention being evaluated has no clear set of outcomes. It helps define questions and hypotheses for future research.
• Descriptive case studies : Describe an intervention or phenomenon and the real-life context in which it occurred. It is helpful for illustrating certain topics within an evaluation.
• Multiple-case studies : Used to explore differences between cases and replicate findings across cases. Helpful for comparing and contrasting specific cases.
• Intrinsic : Used to gain a better understanding of a particular case. Helpful for capturing the complexity of a single case.
• Collective : Used to explore a general phenomenon using multiple case studies. Helpful for jointly studying a group of cases in order to inquire into the phenomenon.

Where Do You Find Data for a Case Study?

There are several places to find data for a case study. The key is to gather data from multiple sources to get a complete picture of the case and corroborate facts or findings through triangulation of evidence. Most of this information is likely qualitative (i.e., verbal description rather than measurement), but the psychologist might also collect numerical data.

1. Primary sources

• Interviews – Interviewing key people related to the case to get their perspectives and insights. The interview is an extremely effective procedure for obtaining information about an individual, and it may be used to collect comments from the person’s friends, parents, employer, workmates, and others who have a good knowledge of the person, as well as to obtain facts from the person him or herself.
• Observations – Observing behaviors, interactions, processes, etc., related to the case as they unfold in real-time.
• Documents & Records – Reviewing private documents, diaries, public records, correspondence, meeting minutes, etc., relevant to the case.

2. Secondary sources

• News/Media – News coverage of events related to the case study.
• Academic articles – Journal articles, dissertations etc. that discuss the case.
• Government reports – Official data and records related to the case context.
• Books/films – Books, documentaries or films discussing the case.

3. Archival records

Searching historical archives, museum collections and databases to find relevant documents, visual/audio records related to the case history and context.

Public archives like newspapers, organizational records, photographic collections could all include potentially relevant pieces of information to shed light on attitudes, cultural perspectives, common practices and historical contexts related to psychology.

4. Organizational records

Organizational records offer the advantage of often having large datasets collected over time that can reveal or confirm psychological insights.

Of course, privacy and ethical concerns regarding confidential data must be navigated carefully.

However, with proper protocols, organizational records can provide invaluable context and empirical depth to qualitative case studies exploring the intersection of psychology and organizations.

• Organizational/industrial psychology research : Organizational records like employee surveys, turnover/retention data, policies, incident reports etc. may provide insight into topics like job satisfaction, workplace culture and dynamics, leadership issues, employee behaviors etc.
• Clinical psychology : Therapists/hospitals may grant access to anonymized medical records to study aspects like assessments, diagnoses, treatment plans etc. This could shed light on clinical practices.
• School psychology : Studies could utilize anonymized student records like test scores, grades, disciplinary issues, and counseling referrals to study child development, learning barriers, effectiveness of support programs, and more.

How do I Write a Case Study in Psychology?

Follow specified case study guidelines provided by a journal or your psychology tutor. General components of clinical case studies include: background, symptoms, assessments, diagnosis, treatment, and outcomes. Interpreting the information means the researcher decides what to include or leave out. A good case study should always clarify which information is the factual description and which is an inference or the researcher’s opinion.

1. Introduction

• Provide background on the case context and why it is of interest, presenting background information like demographics, relevant history, and presenting problem.
• Compare briefly to similar published cases if applicable. Clearly state the focus/importance of the case.

2. Case Presentation

• Describe the presenting problem in detail, including symptoms, duration,and impact on daily life.
• Include client demographics like age and gender, information about social relationships, and mental health history.
• Describe all physical, emotional, and/or sensory symptoms reported by the client.
• Use patient quotes to describe the initial complaint verbatim. Follow with full-sentence summaries of relevant history details gathered, including key components that led to a working diagnosis.
• Summarize clinical exam results, namely orthopedic/neurological tests, imaging, lab tests, etc. Note actual results rather than subjective conclusions. Provide images if clearly reproducible/anonymized.
• Clearly state the working diagnosis or clinical impression before transitioning to management.

3. Management and Outcome

• Indicate the total duration of care and number of treatments given over what timeframe. Use specific names/descriptions for any therapies/interventions applied.
• Present the results of the intervention,including any quantitative or qualitative data collected.
• For outcomes, utilize visual analog scales for pain, medication usage logs, etc., if possible. Include patient self-reports of improvement/worsening of symptoms. Note the reason for discharge/end of care.

4. Discussion

• Analyze the case, exploring contributing factors, limitations of the study, and connections to existing research.
• Analyze the effectiveness of the intervention,considering factors like participant adherence, limitations of the study, and potential alternative explanations for the results.
• Identify any questions raised in the case analysis and relate insights to established theories and current research if applicable. Avoid definitive claims about physiological explanations.
• Offer clinical implications, and suggest future research directions.

5. Additional Items

• Thank specific assistants for writing support only. No patient acknowledgments.
• References should directly support any key claims or quotes included.
• Use tables/figures/images only if substantially informative. Include permissions and legends/explanatory notes.
• Provides detailed (rich qualitative) information.
• Provides insight for further research.
• Permitting investigation of otherwise impractical (or unethical) situations.

Case studies allow a researcher to investigate a topic in far more detail than might be possible if they were trying to deal with a large number of research participants (nomothetic approach) with the aim of ‘averaging’.

Because of their in-depth, multi-sided approach, case studies often shed light on aspects of human thinking and behavior that would be unethical or impractical to study in other ways.

Research that only looks into the measurable aspects of human behavior is not likely to give us insights into the subjective dimension of experience, which is important to psychoanalytic and humanistic psychologists.

Case studies are often used in exploratory research. They can help us generate new ideas (that might be tested by other methods). They are an important way of illustrating theories and can help show how different aspects of a person’s life are related to each other.

The method is, therefore, important for psychologists who adopt a holistic point of view (i.e., humanistic psychologists ).

Limitations

• Lacking scientific rigor and providing little basis for generalization of results to the wider population.
• Researchers’ own subjective feelings may influence the case study (researcher bias).
• Difficult to replicate.
• Time-consuming and expensive.
• The volume of data, together with the time restrictions in place, impacted the depth of analysis that was possible within the available resources.

Because a case study deals with only one person/event/group, we can never be sure if the case study investigated is representative of the wider body of “similar” instances. This means the conclusions drawn from a particular case may not be transferable to other settings.

Because case studies are based on the analysis of qualitative (i.e., descriptive) data , a lot depends on the psychologist’s interpretation of the information she has acquired.

This means that there is a lot of scope for Anna O , and it could be that the subjective opinions of the psychologist intrude in the assessment of what the data means.

For example, Freud has been criticized for producing case studies in which the information was sometimes distorted to fit particular behavioral theories (e.g., Little Hans ).

This is also true of Money’s interpretation of the Bruce/Brenda case study (Diamond, 1997) when he ignored evidence that went against his theory.

Breuer, J., & Freud, S. (1895).  Studies on hysteria . Standard Edition 2: London.

Curtiss, S. (1981). Genie: The case of a modern wild child .

Diamond, M., & Sigmundson, K. (1997). Sex Reassignment at Birth: Long-term Review and Clinical Implications. Archives of Pediatrics & Adolescent Medicine , 151(3), 298-304

Freud, S. (1909a). Analysis of a phobia of a five year old boy. In The Pelican Freud Library (1977), Vol 8, Case Histories 1, pages 169-306

Freud, S. (1909b). Bemerkungen über einen Fall von Zwangsneurose (Der “Rattenmann”). Jb. psychoanal. psychopathol. Forsch ., I, p. 357-421; GW, VII, p. 379-463; Notes upon a case of obsessional neurosis, SE , 10: 151-318.

Harlow J. M. (1848). Passage of an iron rod through the head.  Boston Medical and Surgical Journal, 39 , 389–393.

Harlow, J. M. (1868).  Recovery from the Passage of an Iron Bar through the Head .  Publications of the Massachusetts Medical Society. 2  (3), 327-347.

Money, J., & Ehrhardt, A. A. (1972).  Man & Woman, Boy & Girl : The Differentiation and Dimorphism of Gender Identity from Conception to Maturity. Baltimore, Maryland: Johns Hopkins University Press.

Money, J., & Tucker, P. (1975). Sexual signatures: On being a man or a woman.

Further Information

• Case Study Approach
• Case Study Method
• Enhancing the Quality of Case Studies in Health Services Research
• “We do things together” A case study of “couplehood” in dementia
• Using mixed methods for evaluating an integrative approach to cancer care: a case study

• Open access
• Published: 27 June 2011

The case study approach

• Sarah Crowe 1 ,
• Kathrin Cresswell 2 ,
• Ann Robertson 2 ,
• Guro Huby 3 ,
• Anthony Avery 1 &
• Aziz Sheikh 2  

BMC Medical Research Methodology volume  11 , Article number:  100 ( 2011 ) Cite this article

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The case study approach allows in-depth, multi-faceted explorations of complex issues in their real-life settings. The value of the case study approach is well recognised in the fields of business, law and policy, but somewhat less so in health services research. Based on our experiences of conducting several health-related case studies, we reflect on the different types of case study design, the specific research questions this approach can help answer, the data sources that tend to be used, and the particular advantages and disadvantages of employing this methodological approach. The paper concludes with key pointers to aid those designing and appraising proposals for conducting case study research, and a checklist to help readers assess the quality of case study reports.

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Introduction

The case study approach is particularly useful to employ when there is a need to obtain an in-depth appreciation of an issue, event or phenomenon of interest, in its natural real-life context. Our aim in writing this piece is to provide insights into when to consider employing this approach and an overview of key methodological considerations in relation to the design, planning, analysis, interpretation and reporting of case studies.

The illustrative 'grand round', 'case report' and 'case series' have a long tradition in clinical practice and research. Presenting detailed critiques, typically of one or more patients, aims to provide insights into aspects of the clinical case and, in doing so, illustrate broader lessons that may be learnt. In research, the conceptually-related case study approach can be used, for example, to describe in detail a patient's episode of care, explore professional attitudes to and experiences of a new policy initiative or service development or more generally to 'investigate contemporary phenomena within its real-life context' [ 1 ]. Based on our experiences of conducting a range of case studies, we reflect on when to consider using this approach, discuss the key steps involved and illustrate, with examples, some of the practical challenges of attaining an in-depth understanding of a 'case' as an integrated whole. In keeping with previously published work, we acknowledge the importance of theory to underpin the design, selection, conduct and interpretation of case studies[ 2 ]. In so doing, we make passing reference to the different epistemological approaches used in case study research by key theoreticians and methodologists in this field of enquiry.

This paper is structured around the following main questions: What is a case study? What are case studies used for? How are case studies conducted? What are the potential pitfalls and how can these be avoided? We draw in particular on four of our own recently published examples of case studies (see Tables 1 , 2 , 3 and 4 ) and those of others to illustrate our discussion[ 3 – 7 ].

What is a case study?

A case study is a research approach that is used to generate an in-depth, multi-faceted understanding of a complex issue in its real-life context. It is an established research design that is used extensively in a wide variety of disciplines, particularly in the social sciences. A case study can be defined in a variety of ways (Table 5 ), the central tenet being the need to explore an event or phenomenon in depth and in its natural context. It is for this reason sometimes referred to as a "naturalistic" design; this is in contrast to an "experimental" design (such as a randomised controlled trial) in which the investigator seeks to exert control over and manipulate the variable(s) of interest.

Stake's work has been particularly influential in defining the case study approach to scientific enquiry. He has helpfully characterised three main types of case study: intrinsic , instrumental and collective [ 8 ]. An intrinsic case study is typically undertaken to learn about a unique phenomenon. The researcher should define the uniqueness of the phenomenon, which distinguishes it from all others. In contrast, the instrumental case study uses a particular case (some of which may be better than others) to gain a broader appreciation of an issue or phenomenon. The collective case study involves studying multiple cases simultaneously or sequentially in an attempt to generate a still broader appreciation of a particular issue.

These are however not necessarily mutually exclusive categories. In the first of our examples (Table 1 ), we undertook an intrinsic case study to investigate the issue of recruitment of minority ethnic people into the specific context of asthma research studies, but it developed into a instrumental case study through seeking to understand the issue of recruitment of these marginalised populations more generally, generating a number of the findings that are potentially transferable to other disease contexts[ 3 ]. In contrast, the other three examples (see Tables 2 , 3 and 4 ) employed collective case study designs to study the introduction of workforce reconfiguration in primary care, the implementation of electronic health records into hospitals, and to understand the ways in which healthcare students learn about patient safety considerations[ 4 – 6 ]. Although our study focusing on the introduction of General Practitioners with Specialist Interests (Table 2 ) was explicitly collective in design (four contrasting primary care organisations were studied), is was also instrumental in that this particular professional group was studied as an exemplar of the more general phenomenon of workforce redesign[ 4 ].

What are case studies used for?

According to Yin, case studies can be used to explain, describe or explore events or phenomena in the everyday contexts in which they occur[ 1 ]. These can, for example, help to understand and explain causal links and pathways resulting from a new policy initiative or service development (see Tables 2 and 3 , for example)[ 1 ]. In contrast to experimental designs, which seek to test a specific hypothesis through deliberately manipulating the environment (like, for example, in a randomised controlled trial giving a new drug to randomly selected individuals and then comparing outcomes with controls),[ 9 ] the case study approach lends itself well to capturing information on more explanatory ' how ', 'what' and ' why ' questions, such as ' how is the intervention being implemented and received on the ground?'. The case study approach can offer additional insights into what gaps exist in its delivery or why one implementation strategy might be chosen over another. This in turn can help develop or refine theory, as shown in our study of the teaching of patient safety in undergraduate curricula (Table 4 )[ 6 , 10 ]. Key questions to consider when selecting the most appropriate study design are whether it is desirable or indeed possible to undertake a formal experimental investigation in which individuals and/or organisations are allocated to an intervention or control arm? Or whether the wish is to obtain a more naturalistic understanding of an issue? The former is ideally studied using a controlled experimental design, whereas the latter is more appropriately studied using a case study design.

Case studies may be approached in different ways depending on the epistemological standpoint of the researcher, that is, whether they take a critical (questioning one's own and others' assumptions), interpretivist (trying to understand individual and shared social meanings) or positivist approach (orientating towards the criteria of natural sciences, such as focusing on generalisability considerations) (Table 6 ). Whilst such a schema can be conceptually helpful, it may be appropriate to draw on more than one approach in any case study, particularly in the context of conducting health services research. Doolin has, for example, noted that in the context of undertaking interpretative case studies, researchers can usefully draw on a critical, reflective perspective which seeks to take into account the wider social and political environment that has shaped the case[ 11 ].

How are case studies conducted?

Here, we focus on the main stages of research activity when planning and undertaking a case study; the crucial stages are: defining the case; selecting the case(s); collecting and analysing the data; interpreting data; and reporting the findings.

Defining the case

Carefully formulated research question(s), informed by the existing literature and a prior appreciation of the theoretical issues and setting(s), are all important in appropriately and succinctly defining the case[ 8 , 12 ]. Crucially, each case should have a pre-defined boundary which clarifies the nature and time period covered by the case study (i.e. its scope, beginning and end), the relevant social group, organisation or geographical area of interest to the investigator, the types of evidence to be collected, and the priorities for data collection and analysis (see Table 7 )[ 1 ]. A theory driven approach to defining the case may help generate knowledge that is potentially transferable to a range of clinical contexts and behaviours; using theory is also likely to result in a more informed appreciation of, for example, how and why interventions have succeeded or failed[ 13 ].

For example, in our evaluation of the introduction of electronic health records in English hospitals (Table 3 ), we defined our cases as the NHS Trusts that were receiving the new technology[ 5 ]. Our focus was on how the technology was being implemented. However, if the primary research interest had been on the social and organisational dimensions of implementation, we might have defined our case differently as a grouping of healthcare professionals (e.g. doctors and/or nurses). The precise beginning and end of the case may however prove difficult to define. Pursuing this same example, when does the process of implementation and adoption of an electronic health record system really begin or end? Such judgements will inevitably be influenced by a range of factors, including the research question, theory of interest, the scope and richness of the gathered data and the resources available to the research team.

Selecting the case(s)

The decision on how to select the case(s) to study is a very important one that merits some reflection. In an intrinsic case study, the case is selected on its own merits[ 8 ]. The case is selected not because it is representative of other cases, but because of its uniqueness, which is of genuine interest to the researchers. This was, for example, the case in our study of the recruitment of minority ethnic participants into asthma research (Table 1 ) as our earlier work had demonstrated the marginalisation of minority ethnic people with asthma, despite evidence of disproportionate asthma morbidity[ 14 , 15 ]. In another example of an intrinsic case study, Hellstrom et al.[ 16 ] studied an elderly married couple living with dementia to explore how dementia had impacted on their understanding of home, their everyday life and their relationships.

For an instrumental case study, selecting a "typical" case can work well[ 8 ]. In contrast to the intrinsic case study, the particular case which is chosen is of less importance than selecting a case that allows the researcher to investigate an issue or phenomenon. For example, in order to gain an understanding of doctors' responses to health policy initiatives, Som undertook an instrumental case study interviewing clinicians who had a range of responsibilities for clinical governance in one NHS acute hospital trust[ 17 ]. Sampling a "deviant" or "atypical" case may however prove even more informative, potentially enabling the researcher to identify causal processes, generate hypotheses and develop theory.

In collective or multiple case studies, a number of cases are carefully selected. This offers the advantage of allowing comparisons to be made across several cases and/or replication. Choosing a "typical" case may enable the findings to be generalised to theory (i.e. analytical generalisation) or to test theory by replicating the findings in a second or even a third case (i.e. replication logic)[ 1 ]. Yin suggests two or three literal replications (i.e. predicting similar results) if the theory is straightforward and five or more if the theory is more subtle. However, critics might argue that selecting 'cases' in this way is insufficiently reflexive and ill-suited to the complexities of contemporary healthcare organisations.

The selected case study site(s) should allow the research team access to the group of individuals, the organisation, the processes or whatever else constitutes the chosen unit of analysis for the study. Access is therefore a central consideration; the researcher needs to come to know the case study site(s) well and to work cooperatively with them. Selected cases need to be not only interesting but also hospitable to the inquiry [ 8 ] if they are to be informative and answer the research question(s). Case study sites may also be pre-selected for the researcher, with decisions being influenced by key stakeholders. For example, our selection of case study sites in the evaluation of the implementation and adoption of electronic health record systems (see Table 3 ) was heavily influenced by NHS Connecting for Health, the government agency that was responsible for overseeing the National Programme for Information Technology (NPfIT)[ 5 ]. This prominent stakeholder had already selected the NHS sites (through a competitive bidding process) to be early adopters of the electronic health record systems and had negotiated contracts that detailed the deployment timelines.

It is also important to consider in advance the likely burden and risks associated with participation for those who (or the site(s) which) comprise the case study. Of particular importance is the obligation for the researcher to think through the ethical implications of the study (e.g. the risk of inadvertently breaching anonymity or confidentiality) and to ensure that potential participants/participating sites are provided with sufficient information to make an informed choice about joining the study. The outcome of providing this information might be that the emotive burden associated with participation, or the organisational disruption associated with supporting the fieldwork, is considered so high that the individuals or sites decide against participation.

In our example of evaluating implementations of electronic health record systems, given the restricted number of early adopter sites available to us, we sought purposively to select a diverse range of implementation cases among those that were available[ 5 ]. We chose a mixture of teaching, non-teaching and Foundation Trust hospitals, and examples of each of the three electronic health record systems procured centrally by the NPfIT. At one recruited site, it quickly became apparent that access was problematic because of competing demands on that organisation. Recognising the importance of full access and co-operative working for generating rich data, the research team decided not to pursue work at that site and instead to focus on other recruited sites.

Collecting the data

In order to develop a thorough understanding of the case, the case study approach usually involves the collection of multiple sources of evidence, using a range of quantitative (e.g. questionnaires, audits and analysis of routinely collected healthcare data) and more commonly qualitative techniques (e.g. interviews, focus groups and observations). The use of multiple sources of data (data triangulation) has been advocated as a way of increasing the internal validity of a study (i.e. the extent to which the method is appropriate to answer the research question)[ 8 , 18 – 21 ]. An underlying assumption is that data collected in different ways should lead to similar conclusions, and approaching the same issue from different angles can help develop a holistic picture of the phenomenon (Table 2 )[ 4 ].

Brazier and colleagues used a mixed-methods case study approach to investigate the impact of a cancer care programme[ 22 ]. Here, quantitative measures were collected with questionnaires before, and five months after, the start of the intervention which did not yield any statistically significant results. Qualitative interviews with patients however helped provide an insight into potentially beneficial process-related aspects of the programme, such as greater, perceived patient involvement in care. The authors reported how this case study approach provided a number of contextual factors likely to influence the effectiveness of the intervention and which were not likely to have been obtained from quantitative methods alone.

In collective or multiple case studies, data collection needs to be flexible enough to allow a detailed description of each individual case to be developed (e.g. the nature of different cancer care programmes), before considering the emerging similarities and differences in cross-case comparisons (e.g. to explore why one programme is more effective than another). It is important that data sources from different cases are, where possible, broadly comparable for this purpose even though they may vary in nature and depth.

Analysing, interpreting and reporting case studies

Making sense and offering a coherent interpretation of the typically disparate sources of data (whether qualitative alone or together with quantitative) is far from straightforward. Repeated reviewing and sorting of the voluminous and detail-rich data are integral to the process of analysis. In collective case studies, it is helpful to analyse data relating to the individual component cases first, before making comparisons across cases. Attention needs to be paid to variations within each case and, where relevant, the relationship between different causes, effects and outcomes[ 23 ]. Data will need to be organised and coded to allow the key issues, both derived from the literature and emerging from the dataset, to be easily retrieved at a later stage. An initial coding frame can help capture these issues and can be applied systematically to the whole dataset with the aid of a qualitative data analysis software package.

The Framework approach is a practical approach, comprising of five stages (familiarisation; identifying a thematic framework; indexing; charting; mapping and interpretation) , to managing and analysing large datasets particularly if time is limited, as was the case in our study of recruitment of South Asians into asthma research (Table 1 )[ 3 , 24 ]. Theoretical frameworks may also play an important role in integrating different sources of data and examining emerging themes. For example, we drew on a socio-technical framework to help explain the connections between different elements - technology; people; and the organisational settings within which they worked - in our study of the introduction of electronic health record systems (Table 3 )[ 5 ]. Our study of patient safety in undergraduate curricula drew on an evaluation-based approach to design and analysis, which emphasised the importance of the academic, organisational and practice contexts through which students learn (Table 4 )[ 6 ].

Case study findings can have implications both for theory development and theory testing. They may establish, strengthen or weaken historical explanations of a case and, in certain circumstances, allow theoretical (as opposed to statistical) generalisation beyond the particular cases studied[ 12 ]. These theoretical lenses should not, however, constitute a strait-jacket and the cases should not be "forced to fit" the particular theoretical framework that is being employed.

When reporting findings, it is important to provide the reader with enough contextual information to understand the processes that were followed and how the conclusions were reached. In a collective case study, researchers may choose to present the findings from individual cases separately before amalgamating across cases. Care must be taken to ensure the anonymity of both case sites and individual participants (if agreed in advance) by allocating appropriate codes or withholding descriptors. In the example given in Table 3 , we decided against providing detailed information on the NHS sites and individual participants in order to avoid the risk of inadvertent disclosure of identities[ 5 , 25 ].

What are the potential pitfalls and how can these be avoided?

The case study approach is, as with all research, not without its limitations. When investigating the formal and informal ways undergraduate students learn about patient safety (Table 4 ), for example, we rapidly accumulated a large quantity of data. The volume of data, together with the time restrictions in place, impacted on the depth of analysis that was possible within the available resources. This highlights a more general point of the importance of avoiding the temptation to collect as much data as possible; adequate time also needs to be set aside for data analysis and interpretation of what are often highly complex datasets.

Case study research has sometimes been criticised for lacking scientific rigour and providing little basis for generalisation (i.e. producing findings that may be transferable to other settings)[ 1 ]. There are several ways to address these concerns, including: the use of theoretical sampling (i.e. drawing on a particular conceptual framework); respondent validation (i.e. participants checking emerging findings and the researcher's interpretation, and providing an opinion as to whether they feel these are accurate); and transparency throughout the research process (see Table 8 )[ 8 , 18 – 21 , 23 , 26 ]. Transparency can be achieved by describing in detail the steps involved in case selection, data collection, the reasons for the particular methods chosen, and the researcher's background and level of involvement (i.e. being explicit about how the researcher has influenced data collection and interpretation). Seeking potential, alternative explanations, and being explicit about how interpretations and conclusions were reached, help readers to judge the trustworthiness of the case study report. Stake provides a critique checklist for a case study report (Table 9 )[ 8 ].

Conclusions

The case study approach allows, amongst other things, critical events, interventions, policy developments and programme-based service reforms to be studied in detail in a real-life context. It should therefore be considered when an experimental design is either inappropriate to answer the research questions posed or impossible to undertake. Considering the frequency with which implementations of innovations are now taking place in healthcare settings and how well the case study approach lends itself to in-depth, complex health service research, we believe this approach should be more widely considered by researchers. Though inherently challenging, the research case study can, if carefully conceptualised and thoughtfully undertaken and reported, yield powerful insights into many important aspects of health and healthcare delivery.

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Acknowledgements

We are grateful to the participants and colleagues who contributed to the individual case studies that we have drawn on. This work received no direct funding, but it has been informed by projects funded by Asthma UK, the NHS Service Delivery Organisation, NHS Connecting for Health Evaluation Programme, and Patient Safety Research Portfolio. We would also like to thank the expert reviewers for their insightful and constructive feedback. Our thanks are also due to Dr. Allison Worth who commented on an earlier draft of this manuscript.

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AS conceived this article. SC, KC and AR wrote this paper with GH, AA and AS all commenting on various drafts. SC and AS are guarantors.

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Health worker perspectives on barriers and facilitators of tuberculosis investigation coverage among index case contacts in rural Southwestern Uganda: a qualitative study

• Paddy Mutungi Tukamuhebwa 1 ,
• Pascalia Munyewende 1 ,
• Nazarius Mbona Tumwesigye 2 ,
• Juliet Nabirye 3 &
• Ntombizodwa Ndlovu 1  

BMC Infectious Diseases volume  24 , Article number:  867 ( 2024 ) Cite this article

Metrics details

In 2012, the World Health Organization recommended screening and investigation of contacts of index tuberculosis patients as a strategy to accelerate detection of tuberculosis (TB) cases. Nine years after the adoption of this recommendation, coverage of TB contact investigations in Uganda remains low. The objective of this study was to examine health care providers’ perceptions of factors influencing coverage of TB contact investigations in three selected rural health facilities in Mbarara district, southwestern Uganda.

This study identified provider opinions on the barriers and facilitators to implementation of TB contact investigation using the Consolidated Framework for Implementation Research. Using an exploratory qualitative study design, semi-structured interviews with 19 health workers involved in the TB program at district, health facility and community levels were conducted from April 2020 and July 2020. Analysis was conducted inductively using reflexive thematic analysis in six iterative steps: familiarizing with the data, creating initial codes, searching for themes, reviewing themes, developing theme definitions, and writing the report.

Nineteen health care workers participated in this study which translates to a 100% response rate. These included two district TB and leprosy supervisors, five nurses, five clinical officers, six village health team members and one laboratory technician. The three themes that emerged from the analysis were intervention-related, health system and contextual factors. Health system-related barriers included inadequate or delayed government funding for the TB program, shortage of human resources, insufficient personal protective equipment, and a stock-out of supplies such as Xpert MTB cartridges. Contextual barriers included steep terrain, poverty or low income, and the stigma associated with TB and COVID-19. Facilitators comprised increased knowledge and understanding of the intervention, performance review and on-the-job training of health workers.

Conclusions

This study found that most of the factors affecting TB contact investigations in this rural community were related to health system constraints such as inadequate or delayed funding and human resource shortages. This can be addressed by strengthening the foundational elements of the health system - health financing and human resources - to establish a comprehensive TB control program that will enable the efficient identification of missing TB patients.

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Introduction

An estimated 10 million people suffer from active tuberculosis (TB) every year [ 1 ]. The disease continues to be the leading infectious cause of death globally, causing about 1.5 million deaths—95% of which occurred in low- and middle-income countries [ 2 , 3 ]. Although the African region has 9% of the world population, the region contributed 25% of all new TB cases in 2019, becoming the continent with the second-highest TB cases after South-East Asia. In Africa TB is mainly driven by the HIV pandemic, with about 50% of TB cases co-infected with HIV, and is the top cause of death among patients with HIV, causing more than 30% of all AIDS-related deaths [ 4 , 5 ].

In 2012, the WHO recommended the screening and evaluation of contacts of persons with infectious TB as an intervention for increasing TB case detection [ 6 ]. The intervention also provides an opportunity to diagnose latent TB and to scale-up TB preventive therapy among the eligible contacts, such as, children below five years, HIV positive patients, and other high-risk groups [ 7 , 8 ]. Five years later, in 2017, the Uganda Ministry of Health (MoH) adopted these WHO recommendations as high-level policy, and integrated them into the Manual for Management and Control of TB and Leprosy in Uganda [ 9 ]. Furthermore, in 2019, detailed operational guidelines were developed by the Uganda National Tuberculosis and Leprosy Program (NTLP) to guide and standardize TB contact investigation processes at health facility and community levels [ 8 ].

Despite the WHO policy guidance, coverage of TB contact investigation in many TB high burden countries such as Uganda, Kenya, Lao Republic, Pakistan and Yemen is still low [ 10 ]. A meta-analysis conducted in 2015 by Block et al., showed low TB contact investigation coverage in five countries (2.8% in the Lao Republic, 4.8% in Kenya, 14.9% in Pakistan and 15.1% in Uganda) and high coverage in one country (91.7% in the Democratic Republic of Congo) [ 10 ]. Armstrong et al. (2017), in a prospective multi-center observational study conducted in Kampala, Uganda, reported significant drop-out rates across the steps in the contact investigation cascade [ 11 ]. Among the 338 clients eligible for TB contact investigation, only 61% were scheduled for home visits, and only 50% of them were visited [ 11 ]. Furthermore, among the 131 people who were screened for TB and required definitive evaluation, only 20% were evaluated [ 11 ].

In rural Uganda, the coverage of TB contact investigation is much lower (15.1%) than that in urban areas such as Kampala (20%), and yet many of the missing TB cases are in such hard to reach and underserved rural areas [ 10 , 11 ]. This low coverage increases undiagnosed and untreated TB patients, thus perpetuating the TB pandemic. Furthermore, without TB contact investigation, many TB patients might infect other people in the household and the community, or die from TB related complications [ 12 ]. The low contact investigation coverage contributes to a high numbers of missed diagnoses in Uganda (400,000 in 2014), and high TB transmission rates, which hamper progress towards achievement of the third United Nations Sustainable Development Goal of ending the TB epidemic by 2030 [ 13 ].

Implementation research helps to connect research and practice by speeding-up the development and provision of public health interventions [ 14 ]. Given that urban settings have been the primary focus of the majority of implementation research studies in Africa and that the burden of TB differs between urban and rural areas [ 7 , 15 , 16 ], this study used implementation research tools to investigate the barriers to and enablers of TB contact investigation coverage in rural southwestern Uganda [ 3 ]. Although 82% of the Ugandan population lives in rural areas, there is inadequate information about TB contact investigation coverage, and its barriers and facilitators in rural settings [ 17 ]. The purpose of this study was to investigate the barriers and facilitators of investigation coverage among contacts of TB patients in rural Uganda.

The Consolidated Framework for Implementation Research (CFIR) by Damschroder et al. was used to explore barriers and facilitators of implementation in this study [ 18 ]. The framework consists of 39 constructs and five domains: characteristics of the intervention, inner setting, outer setting, individuals involved and implementation process [ 18 ]. The framework has been widely used across the globe to identify the barriers and facilitators of implementation outcomes in various healthcare settings, for example, evaluation of the online frailty tool in primary health care in Canada, integration of hypertension-HIV management in three Ugandan HIV clinics, examining the task shifting strategy for hypertension control at 32 hospitals and community health centers in Ghana and evaluating the implementation context of a quality improvement program for increasing vaccination coverage in Nigeria [ 19 , 20 , 21 , 22 ].

Study setting

This study was conducted in the rural district of Mbarara, located in the southwestern region of Uganda, about 270 km southwest of the capital city, Kampala. According to the 2014 population and housing census, the district had a population of 472,629 (Land area 1785.6 km 2 ), of which 59% resided in rural areas [ 23 ]. In total the district had 87 health facilities including 48 government owned, 26 private clinics and 13 nonprofit health facilities [ 24 ]. There were no data on TB contact investigation available at district level. Health Centres (HC) in Uganda are ranked II, III or IV based on the administrative zone served by the health facility with level II serving a parish, level III serving a sub-county and level IV serving a county [ 25 ]. A HC IV is expected to serve a population of at least 100,000 people. The services offered included general outpatient clinic (including TB and HIV care), immunization, antenatal care, maternity services, inpatient, laboratory, emergency surgery and blood transfusion [ 25 ].

The Ugandan health system operates on a referral basis, with the lowest level of health care provided by community health workers called Village Health Teams (VHTs) and the highest level of care offered at highly specialized hospitals called National Referral Hospitals. Levels of health care increase with complexity in terms of the packages of services offered, staffing levels, and the size of the population served. Three health facilities where the study was conducted were purposively selected due to their rural location, level of care (IV), and significant volume of patients compared to lower levels (II and III).

Coordination of TB services in the district was done by the District TB and Leprosy Supervisor (DTLS), who is responsible for 26 TB diagnostic and treatment centers. Regional coordination of TB activities is done by the Zonal TB and Leprosy Supervisor (ZTLS), while national level coordination and policy formulation is done by the National TB and Leprosy Program (NTLP) [ 15 ].

Study design and study population

A qualitative, exploratory study design was conducted to identify barriers and facilitators to implementing TB contact investigations between April and July 2020. Semi-structured interviews were conducted with all 19 health workers who were purposively selected based on their direct participation in the implementation of TB interventions since they were likely to have the most knowledge and experience with TB contact investigations. These included TB focal persons at the health facilities, clinical officers, nurses, laboratory staff, VHTs, and District TB and Leprosy Supervisors. Health workers who were not in the health facility during the data collection period were excluded from the study. The Consolidated criteria for reporting qualitative studies (COREQ) were applied to comply with the reporting standards (Table S2 ) [ 26 ].

Data collection

Semi-structured interview guides were developed and included background information about study participants and questions developed according to the five domains of the CFIR. The VHT interview guides were translated into the regional dialect and put through a pilot test to ensure that the questions were understood and to gauge how long the interviews would take. Two health facilities that provided comparable research sites in terms of staffing levels and services were used for the pilot testing.

Physical interviews for the study participants were conducted by the lead researcher (PT) in either English or Runyankore and each interview was tape recorded while a trained research assistant took field notes. Data collection for each category of study participants was continued until saturation was reached [ 27 ]. Since data collection took place during the first wave of the COVID-19 pandemic, precautions were taken to prevent COVID-19 cross-infection on both the researcher and the participants. Interviews were conducted at the selected health facilities in well-ventilated spaces, with both the interviewer and the participant wearing N-95 respirators, and surgical masks, respectively. Each interview lasted between 30 and 45 min and no repeat interviews were conducted.

Data management and analysis

Data were transcribed verbatim by the research team and the lead researcher listened to each audio recording while reading through the transcripts to correct errors in transcription and familiarize himself with the data. Transcripts were not given back to the participants for review or comments because evidence suggests that interviewee transcript review does not add value to the quality and rigor of qualitative research [ 28 ]. PT and JN reviewed the transcripts and made initial notes of interesting features or potential codes and themes in the data. The transcripts were then uploaded into MAXQDA 2020, and analyzed using reflexive thematic analysis in six iterative and recursive steps as described by Braun and Clarke [ 29 ]. The six steps included (1) familiarization with the data, (2) coding, (3) searching for themes, (4) reviewing the themes, (5) naming and defining the themes, and (6) writing the report [ 29 ]. The first step of the analysis was to look at the participants’ own words and expressions, without preconceived notions or classifications. The researchers then examined the language used by each participant in relation to the five domains of the CFIR. To ensure the reliability and credibility of the research analysis, both researchers PT and JN developed the themes by reading the transcripts independently to establish inter-coder agreement [ 30 ]. After the initial coding, the two-member team met to discuss the independently developed codes and themes and to reach an agreement on the themes. The transcribed texts and quotes were then grouped into themes, and the lead researcher used a reflexive approach to identify similarities or differences among CFIR domains and constructs. This iterative and recursive process provided space for reflexivity and ensured the credibility of the research findings. Themes were then defined and further refined to reflect the challenges and enablers of contact investigation coverage.

The research team and reflexivity

The field research team consisted of the principal investigator (PT), a male master’s student at the University of the Witwatersrand, and a female research assistant (GA), who is trained in population studies and monitoring and evaluation, and she was not employed at the time of this study. The principal investigator is a medical doctor who has training and experience in TB care and is familiar with WHO TB guidelines for contact investigations. He was not affiliated with the District Health Department or the Ministry of Health NTLP and is therefore unlikely to have influenced participant responses. Prior to the study, the principal investigator received training in qualitative research methods at the University of the Witwatersrand, so he was aware of how a researcher’s background, location, and assumptions can influence a qualitative study. The research team did not know the participants beforehand, and they were not directly involved in patient care in a way that would have influenced their responses.

Ethical considerations

This study was cleared by the Human Research Ethics Committees (Medical) at the University of the Witwatersrand (M200101), and Mbarara University of Science and Technology (MUREC 1/7). The Uganda National Council for Science and Technology granted permission to conduct the study in Uganda (HS569ES). Administrative approval was obtained from the District Health Officer, and the health facility managers of the respective study sites. Information about the study was shared with the participants before the interviews and written informed consent for participation and audio recording was obtained from each participant. To preserve participant privacy, interviews were conducted in a private space within the outpatient units, with only the researchers and the participants present.

Characteristics of study participants

Nineteen participants took part in semi-structured interviews with a response rate of 100% and 21.1% ( n  = 4) of them were male (Table  1 ). The sample comprised five clinical officers (26.3%), five nurses (26.3%), six VHT members (31.6%), one laboratory technician (5.2%), and two DTLs (10.5%). Eight of the participants (42.1%) had over three years’ experience in offering TB care. Clinical officers were paramedics with a diploma in clinical medicine, as opposed to nurses who had a bachelor’s degree in nursing, a diploma, or a nursing certificate. VHTs were lay health workers based in the community to aid with TB interventions in the local population. Laboratory technicians had a diploma in laboratory sciences, whereas DTLSs had one in nursing or clinical medicine.

Barriers and facilitators of TB contact investigation coverage

A reflective thematic analysis of the data gave rise to three themes: health system, contextual and intervention-related factors. The barriers and facilitators identified under each of the three themes (Table S3 ). Based on the WHO’s health system building blocks, the factors affecting the health system emerged under six sub-themes: human resources, commodities, service delivery, leadership and coordination, funding, and health information systems. Contextual factors were further categorized into geographic, social, and cultural, economic, and policy-related factors. Issues affecting TB contact investigations linked to the intervention itself were covered by the final theme (intervention-associated factors).

Barriers and facilitators

Domain 1: characteristics of the intervention.

The intervention related factors reported by the participants fell under three constructs, that is: evidence-base, intervention complexity and implementation cost.

Evidence-base

Out of the 19 healthcare workers involved in this research, 16 were aware of the intervention and its effectiveness in detecting, treating, and stopping the spread of tuberculosis in the community. Some of them had even engaged in relevant programs at the district, health facility, and community levels to improve uptake, such as support supervision, enlisting household contacts, home visits, health education, screening, and sputum sample collection. The DTLSs reported that training and regular orientation on several aspects of TB management, including TB contact investigation, provided easy access to knowledge and information. The district provided training on TB contact investigation to health workers in different platforms, including quarterly performance review meetings. As a result, they had the necessary information, abilities, or confidence to carry out contact investigation tasks.

“Even in meetings , we talk about contact tracing and investigation. Because for us we do meetings quarterly , all those meetings we…include a training in contact tracing and investigation” (Respondent 1—Nurse).

Intervention complexity

Three VHTs reported that TB contact investigations had multiple processes and therefore required a team to go for community visits, which interfered with other ongoing interventions at the health facility, such as TB screening at outpatient clinics, linking positive patients to treatment, providing community-based DOTs for patients on treatment, and following up with clients who defaulted on treatment. They also assisted with other medical services, such as immunizations, prenatal care, and providing ART refills to stable HIV patients. Therefore, during contact investigations, VHTs were mostly involved in community activities, leaving some of the basic facility-based interventions unattended.

“…it interferes with other programs… Now I am here working at the health facility , collecting sputum , screening and… I have many patients attending immunization , antenatal , ART (HIV clinic) , and I am the one who works on them too. And after that , I want to go and do contact tracing… Sometimes I ignore some of the facility activities so that I spare some time to go and do contact tracing in the community” (Respondent 4—VHT) .

Cost of the intervention

During TB contact investigations, it may be required to phone many patients or contacts. It is frequently necessary to call people who have appointments but do not show up at the health facility. Healthcare workers find it challenging to make these calls due to the high airtime requirements of this intervention and the associated cost.

“…some of these contacts need to be contacted on the phone several times because someone tells you he is coming tomorrow; and he doesn’t come. And the person keeps giving appointments without coming. And we do not have all that airtime…” (Respondent 5—Clinical Officer) .

Domain 2: outer setting

Funding from external entities: inadequate funding.

Multiple funding related challenges were reported at national, district and health facility levels. Funding for TB contact investigation was provided, through the Primary Health Care grants released from the Ministry of Health to public health facilities. Additional funds for contact investigation came from USAID through the Regional Health Integration to Enhance Services in Southwestern Uganda; a program for scaling up access to comprehensive HIV, TB and reproductive health services in the region.

Health workers believed that TB was not considered a priority by the Ministry of Health, which led to underfunding of the NTLP, and eventually underfunded TB work at district, health facility and community levels. TB interventions were not integrated into the annual budgeting processes like other interventions. For example, Malaria and sanitation interventions received funds, while TB remained unfunded, since 2014. The DTLS reported that the sanitation program was prioritized and funded better than the TB program, because of the advocacy by the sanitation program.

“…I think if the government says , ‘let us fight this disease’ , they need to put in (funds). Let them consider TB across the board. Let them budget for it like the way they budget for other conditions. Malaria is budgeted for , sanitation…receives money every quarter. But it is like six years (since 2014) when there was money for TB…and it was for only one quarter” (Respondent 1—Nurse).

The DTLSs reported insufficient funds for TB support supervision at the district level, which limited the amount of time the district TB supervisor spends in each health facility for supervision visits. Eventually, the quality of the supervision was compromised because teams did not have sufficient resources to train, mentor and supervise health facility teams.

“Because of the funds being little , we are forced , like in a day , to move to about four facilities. Remember , in TB , there are six indicators that you need to focus on and get to understand what the problem is. So , you find we do not have sufficient time to spend in the facility and support it.” (Respondent 2—Clinical Officer).

Health facility level funding challenges included delayed reimbursement of funds, and inadequate funds for home visits. In some cases, health facilities rely on NGOs for extra funds to conduct contact investigations, because of insufficient funds from the Primary Health Care (PHC) fund.

“…but when you do not have that NGO , things are challenging because you know that PHC money cannot be enough. You find that the PHC money is for only two patients , yet you have like six of them (to follow-up). So , when you do not have that money from NGOs , you cannot do it smoothly.” (Respondent 2—Clinical Officer) .

Some participants reported that they used their own money to trace index TB contacts; however, this money takes a long time to be refunded. Some participants even had a pay gap of about five months, which lowered their morale to continue with community visits.

“Most of the cases , we use our own money… you want to do your job , but transport facilitation (is missing)! Even…when they decide to refund it (money) , it takes so long…for example , since January we have never got that transport (money). We did contact tracing in January , February , March , April and May; we gave them reports , and they see that we are working , but we do not see our transport (refund)” (Respondent 16—VHT).

Critical incidents: COVID-19 pandemic related factors

This study was conducted during the first wave of the COVID-19 pandemic a lockdown policy was implemented by the government. This was characterized by suspension of public and private transportation, some health workers, TB patients and their contacts were unable to access health facilities. These restrictions affected the mobility of the health workers and patients to the health facility, and undermined TB contact investigation efforts. Besides lockdown measures, the COVID-19 pandemic was also associated with stigma among patients and health workers. Some TB contacts were afraid to report cough, in fear of being suspected of having COVID-19 and having to be quarantined for 14 days as per the MOH recommendations at the time. COVID-19 heightened the stigma associated with TB, because the two conditions have similar symptoms. Health workers could not tell who had COVID-19 or TB and, therefore, avoided anyone presenting with cough, because they feared it might be COVID-19. Some laboratory personnel declined to examine sputum samples because they were concerned that the samples might contain COVID-19 and increase their risk of getting the virus.

“Now with corona (COVID-19) , we would come here and not find any patient or health worker because they did not have transport means during the lockdown. Most of our people stayed at home. Even if you had your own motorcycle , they would not allow you to ride it…” (Respondent 13—Clinical officer).

Partnerships and connections: collaboration with NGOs and community-based organizations

Health workers and VHTs reported that the district and health facilities are networked with NGOs and community-based organizations which support the implementation of TB contact investigation and other health interventions. The primary implementing partner was Regional Health Integration to Enhance Services in Southwestern (RHITES-SW) Uganda, which supports the district with transportation and materials, while doing household visits.

Along with funding TB contact investigation, district-based NGOs also sponsored radio airtime to increase awareness and create demand for TB services.

“…RHITES-SW provides us with materials to use , like carrier bags. They provide us with transport to do contact tracing and the information. They normally update us on each and everything that is current in contact tracing and investigation” (Respondent 5—Clinical officer) . “Other stakeholders are working hand in hand with the government and our implementing partners. I see them working as a team to sponsor airtime on radios to create awareness and give some financial assistance.” (Respondent 12 , Clinical Officer).

Policies and laws: availability of updated operational guidelines

The district established favorable communication networks at district and health facility levels, facilitating efficient communication of guidelines, reference materials, and patients’ results. For example, the district had a WhatsApp group, specifically for the district TB team, to share information and monitor district activities.

“…we have a WhatsApp group of all the in charges and TB focal persons , where we discuss TB management and…share guidelines , so whoever needs guideline in TB management , he just goes there” (Respondent 1—Nurse).

Domain 3: inner setting

Available resources.

The barriers that emerged under available resources included, lack of personal protective equipment (PPE), stock-outs of Xpert MTB cartridges and shortage of human resources. Commodities that frequently went out of stock included toolkits for TB contact investigations and Xpert MTB cartridges for conducting Xpert MTB and RIF tests. At times health facilities spend about two months without cartridges, and health workers were notified by the laboratory team not to send sputum samples for analysis, which weighs down contact investigation efforts. Additionally, VHTs reported the lack of essential tools for community visits, especially during extreme weather. Health facilities also frequently ran short of PPE for home-based contact screening, such as masks and gloves, which discouraged them from doing community contact tracing out of fear of acquiring TB.

“…sometimes , there are no GeneXpert (Xpert MTB) cartridges; you find that we are not doing GeneXpert (tests) because cartridges are finished… , at times we take like a month or two without cartridges and…that is not good… , the lab people tell us , ‘do not send samples this month , we do not have (cartridges)’ , which means we are missing people (patients).” (Respondent 12—Clinical Officer). “At times you go to a difficult place…in a rainy season… , you climb a hill while it is raining on you. You do not have an umbrella; you do not have boots or a bag to carry the stuff (materials)…” (Respondent 4—VHT).

Human resource shortage was also reported as barrier. Sometimes, only one health worker was available to go for community visits, yet there are multiple tasks to do, including health education, screening, and sample collection. Therefore, this scarcity of human resources affects the quality of implementation since some of the tasks are left incomplete.

“…sometimes there is a lack of manpower because…the health workers are not enough at the facility , so you find that only one person is going for contact tracing , and the work there is huge , and that person cannot do all the work alone. So , most of the things are not done. They do part of the work and leave out some” (Respondent 15—Nurse).

Two facilitators were discussed under the construct of available resources: presence of a landline telephones to aid communication and a motorcycle to support transportation during community visits. The telephones were loaded with airtime for scheduling household visits and communicating Xpert MTB/RIF results from the hub laboratory while the motorcycle helped to reduce the cost of transportation since community visits only required fuel for the motorcycle.

“We have a health facility motorcycle , which does not force us to put in a lot of money… We just consider the distance we are covering and then put in fuel and move , which is easier than getting a boda-boda (motorcycle taxi).” (Respondent 16—VHT).

Structural characteristics: rugged terrain and poor road network, paper-based reporting systems, and hub and spoke laboratory system

All six VHTs reported that some patients came from hard-to-reach areas, characterized by rugged terrain, where vehicles or motorcycles cannot reach. This makes it hard for health workers to visit such communities for contact investigations. Additionally, some places have poor roads that are impassable during the rainy season, thus affecting service delivery. In such circumstances, health workers use boda-bodas (motorcycle taxis) to a certain point, and then walk the remaining distance. Sometimes the terrain is hilly and exhausting, which discourages teams from doing community visits. Large health facility catchment areas also made it more difficult for field teams to deliver contact investigation services to distant households. As a result, contacts of index TB cases from remote places were instead asked to come to the health facility for further evaluation, however, some of them did not come.

“…for those people who come from hard-to-reach areas , going to those homes is quite challenging. Sometimes we reach a point of walking on foot because we cannot reach there using a car or a motorcycle. So , we must climb a steep hill to look for those patients” (Respondent 4—VHT). “This is a big sub-county; people come from distant areas , even neighboring districts. And of course , as a health worker , you cannot reach every homestead. So , some (contacts) are called to come to the health facility. But because of the long distances , some fail to come.” (Respondent 4—VHT).

Another barrier was the use of the paper-based reporting system. One of the TB focal persons reported that TB contact investigation reports were submitted manually using a paper-based system which affects timeliness of reporting. Submission of reports had to wait for an opportunity when someone was going to the district headquarters, which causes a delay and eventually affects re-imbursement of the payments for activities.

“Sometimes , since we are sending the reports to Mbarara , they reach late because of transport issues. It becomes hard for someone to send the report since you cannot get any transport , so you get someone going to Mbarara , give them the reports , and tell that person where they should be delivering the reports. So , it also takes a bit of time” (Respondent 8—Nurse).

The laboratory system in the district used a “hub and spoke” system, where laboratory samples are collected in peripheral laboratories and transported by motorcycle riders to the central laboratory for analysis. However, participants reported that this system was dysfunctional because of the long results turn-around time, compromised early TB diagnosis and treatment and affected TB contact investigation coverage. In some cases, health workers spent up to two months, waiting for Xpert MTB results.

“And we have a challenge with hub riders… Sometimes , the hub riders take sputum samples to Mbarara , and if they do not go back to pick the results , you will never see them. And you end up spending around two months without results” (Respondent 12—Clinical Officer).

Domain 4: individuals involved

Under characteristics of the individuals involved, participants reported the presence of internal implementation leads called TB focal persons at health facility and DTLS at district level. These were responsible for coordinating the provision of TB services and technical leadership and supervision of the TB program and different levels of care. Additionally, health workers received adequate training on various aspects of TB management including TB contact investigation. Such training sessions supported them with the adequate knowledge and skills to confidently conduct contact investigation activities.

Domain 5: implementation process

The three constructs that emerged under implementation process were planning, engaging and reflection and evaluation.

The DTLSs reported that leaders at the Ministry of Health had transferred the planning, coordination, and funding of TB interventions, including TB contact investigation. Instead, this role was left to implementing partners, usually local and international Non-Governmental Organizations (NGOs), which negatively impacted the TB program at district level. Also, participants reported that implementing partners tend to have different priorities. For example, these organizations mainly focus on HIV interventions, and less on TB. Therefore, it is challenging to divert them from their preferences and focus them on district priorities, since their priorities are often guided by donor funding.

“Also , The Ministry of Health has deliberately left this work (TB contact investigation) …to implementing partners , and it has killed everything. And in that line , I think we can eradicate TB , but if the government is putting in (effort) , not leaving this disease for the implementing partners.” (Respondent 1—Nurse). “They tell you their priority is HIV , and you cannot shift them. They have their …operational guidelines that you cannot change.” (Respondent 1 , Nurse).

Reflection and evaluation

data use to inform program decisions by the district health team was identified as a facilitator. The district held quarterly performance and reflection meetings with the participation of the district’s NGOs, community-based organizations, district health management team, and healthcare providers from the various health centers. In these meetings, attendees discussed their performance, challenges across the different technical areas, and strategies for bridging the gaps.

the involvement of all stakeholders within the district, including health facility teams, district teams, NGOs, and community-based organizations involved in the TB program, in regular engagements to review implementation progress, performance, and plan improvement strategies was reported as a facilitator. Non-Governmental Organizations are actively involved in discussions regarding potential funding opportunities for specific activities.

“…we normally have the district stakeholders meeting , where they (external stakeholders) normally come here , and we discuss performance in different areas - MCH (maternal and child health) and HIV; TB is also given a platform. We tell them about our challenges.” (Respondent 1—Nurse) .

The stigma associated with TB was reported as a common challenge by all participants in this study. For this reason, index TB patients preferred not to be visited at home by a health worker, out of fear of being stigmatized if neighbors and other community members found out that they had TB. Some index TB patients even tried to avoid being visited by giving health workers incorrect phone numbers and physical addresses. Patients with TB and HIV co-infection have an increased fear of disclosing their status because of the misconception that every TB patient has HIV. Additionally, poverty among index TB patients was also found to be a challenge because contacts of TB patients lacked funds to transport them to the health facility for assessment, diagnosis, and treatment. As a result, it was necessary for health professionals to collect sputum samples from the community and bring them to the health facility for analysis. This, however, was not always feasible, leaving some of the contacts of TB patients unevaluated.

“…some patients give us wrong telephone contacts , we call the number , it is not on , or a different person picks it. So , we fail to trace that person. Some fear health workers going to their homes. Mostly when the index TB patient is also HIV positive , they do not want people in their villages to see any health care worker coming to their home because they may identify them” (Respondent 11—VHT).

This study explored the factors influencing TB contact investigation coverage in three rural, primary health facilities in Southwestern Uganda. The study is unique in its rural focus unlike previous studies in Uganda and Kenya, which were conducted in cities [ 7 , 15 , 31 ]. The barriers and facilitators identified in this study were diverse and covered all the five domains of the CFIR. Although some studies have used other implementation research tools to identify the barriers and facilitators to implementing TB contact investigation, this study used the CFIR to explore the factors influencing TB contact investigation coverage in Africa.

The key challenges that emerged from this study included health system challenges, such as the lack of funding for TB contact investigation, insufficient PPE and inadequate Xpert MTB equipment for diagnostic testing. The rugged terrain and poor road networks in rural communities also made it difficult for health workers to access patients in the community, and vice versa. Poverty, TB- and COVID19-related stigma were also perceived as barriers. On the other hand, the facilitators to TB contact investigation included an increased awareness of TB contact investigation, adequate knowledge of the Ugandan MoH guidelines, confidence in delivering the intervention and on-the-job training of health workers. In addition, the availability of a telephone and transport to schedule and make household visits were reported as facilitators. The support of key district stakeholders involved in TB contact investigations and quarterly performance review meetings also emerged as facilitators.

The health system barriers that emerged from this research were inadequate or irregular funding, human resource shortages, lack of PPE supplies (face masks, gloves, raincoats, and gumboots), out of stock of Xpert MTB cartridges and lack of airtime for communication. In addition, inadequate or inconsistent funding limited the frequency of the DTLS visits to health facilities for supervision and caused a delay in payment of travel and allowances to field teams, causing TB contact investigation operations to be hampered. This finding is in contrast with another study conducted in urban Kenya, which found that the TB program received sustainable funding for infrastructure and health workforce for contact investigation [ 32 ]. Furthermore, this Kenyan study used the WHO health systems framework. It focused on the stakeholder perspectives of the barriers and facilitators to optimizing TB contact investigation in Nairobi, the capital of Kenya. This funding disparity between rural and urban areas could be due to a higher TB prevalence in most urban settings thus attracting the attention of policy makers to allocate more resources there [ 33 ].

Consistent with this study, three studies conducted in Botswana, Ethiopia and Uganda reported human resource shortages as a considerable hindrance to TB contact investigation coverage [ 3 , 15 , 16 ]. In urban Uganda, health workers had other competing duties in the TB clinics, thus, they did not have sufficient time for community-level activities, including household contact tracing [ 15 ]. In this study, sometimes only one health worker was available for community visits, and they could not complete multiple tasks, such as health education, screening, sample collection, HIV testing and documentation in the registers. The staff shortage is partly attributed to a small number of staff trained in TB, and assigning them responsibilities in other units outside the TB unit [ 3 ].

Another challenge identified in this study was a lack of PPE materials such as masks, gloves, raincoats and gumboots for health workers to protect themselves against TB and other infectious diseases (such as COVID-19). Health staff were hesitant to conduct household contact investigations without wearing masks and gloves, to avoid contracting TB and COVID-19. Similarly, protective gear, such as raincoats and gumboots, to be used in harsh weather conditions, were not provided to health workers. There is limited literature on the influence of PPE materials on TB contact investigation coverage and this calls for more research in this area. These findings indicate that the supply chain management system for essential infection control materials is weak. These findings emphasize the need to strengthen mechanisms to guarantee sufficient PPE supplies and sustain the supply chain for these products.

The context within which an intervention is implemented, is recognized as a significant determinant of implementation success [ 18 ]. Contextual factors refer to issues about a person or their environment that can positively or negatively affect the delivery of an intervention [ 18 ]. Socio-economic, policy-related, and geographical barriers emerged as contextual barriers in this research. The socio-economic factors included poverty, lack of phones where patients can be contacted to confirm the appointment of household visits, stigma, and fear of reporting cough in fear of being labelled as having COVID-19.

In Botswana, Kenya, Ethiopia, and Uganda, the stigma associated with Tuberculosis has been reported as a barrier to TB contact investigation. [ 3 , 7 , 15 , 16 ]. Although these studies did not specifically focus on TB contact investigation coverage, stigma hindered household visits, because index TB patients avoided home visits by health workers, out of fear of their status being disclosed to the community and discrimination from them, which could eventually affect demand and coverage of the intervention. An important observation in our study was that stigma was aggravated by the misconception that every TB patient has HIV, and the emergence of the COVID-19 pandemic. Tuberculosis and COVID-19 have common respiratory symptoms (cough, fever, and breathing difficulties), making it difficult to distinguish the two. This causes diagnostic confusion, and the health workers may also avoid such patients, in fear of contracting COVID-19 [ 34 ]. Furthermore, because of the new COVID-19 stigma, patients with a chronic cough might fear coming to the health facilities for diagnosis, thus complicating the two pandemics [ 34 ].

The COVID-19 lockdown policy implemented in 2020 by the Government of Uganda posed significant challenges to TB contact investigation efforts. Both health staff and patients could not access health facilities, due to stringent lockdown measures, including travel restrictions and public and private transportation prohibitions. Additionally, health providers could not conduct home visits to screen the contacts. Similar findings were found in another study on the impact of COVID-19 on TB programs in Western Pacific nations [ 35 ]. Other COVID-19 related problems encountered in the Western Pacific study included a change in priorities towards the COVID-19 response, as demonstrated by the relocation of TB program staff to the COVID-19 response, and a reduced willingness of patients and contacts to visit health facilities [ 35 ]. Therefore, innovative strategies are required to streamline TB contact investigation in the context of the COVID-19 pandemic.

As reported by Cattamanchi et al., geographical challenges contribute to the failure of TB patients and contacts to present at health facilities for TB care [ 36 ]. In their study, health workers reported that the physical remoteness of patients’ homes from the health facility and the rugged terrain encountered during travel, was a challenge [ 36 ]. Likewise, in this study, health workers reported that some index TB patients and contacts came from distant and challenging areas, with steep hills and poor road networks, preventing access to health facilities. This challenge was aggravated by poverty, because patients and contacts from the periphery of the county could not travel to health facilities because of high transport costs.

Facilitators

All health workers interviewed in this study reported awareness of the intervention. They had even engaged in relevant programs to improve its uptake, including enlisting household contacts, home visits, screening, and sputum sample collection. In addition, the clarification of the various steps demonstrated health workers’ adherence to the organizational protocols for TB contact investigations. The increased awareness and fidelity to the guidelines may be attributed to the development and dissemination of local contact investigation guidelines through training and the use of electronic media, such as WhatsApp. Conversely, a similar study conducted in rural Ethiopia found that awareness and adherence to the guidelines were poor because of a lack of refresher training. [ 3 ].

The health system facilitators that emerged from this study include good provider knowledge and access to information, performance review meetings at the district level, and engagement of district stakeholders to obtain their support. In contrast to other studies in Uganda, Ethiopia, and the USA, provider knowledge and confidence (self-efficacy) worked as a facilitator in this study because staff involved in TB contact investigation had received on-the-job training on various aspects of TB management, including contact investigation, diagnosis, and management [ 3 , 15 , 37 ]. In this study, health workers reported that they had the knowledge, skills, and confidence to conduct TB contact investigations successfully. These results are partly attributed to the quarterly district performance review meetings, in which an orientation on TB contact investigation was done and guidelines were shared with health workers.

Reflection and evaluation in TB contact investigation performance were demonstrated by Karamagi et al., in a Quality Improvement study to improve case finding in Northern Uganda [ 38 ]. A review meeting was held to discuss progress on active case finding and develop scale-up plans for the intervention [ 38 ]. Similarly, this study found that quarterly district review meetings were held, to discuss district and health facility performance, challenges, and improvement strategies in various program components, including TB contact investigation. These reflection meetings involved district-based stakeholders such as NGOs, health workers, TB focal persons, and health facility managers, and this promoted ownership of the interventions, and helped in resource mobilization. These meetings were also used to review quarterly TB performance, and develop action plans to improve multiple TB indicators, including TB contact investigation.

Strengths and limitations of the study

This study had the following strengths. First, we included various health provider categories at different levels of the district healthcare system, including community, health facility and district levels, to obtain different perspectives from the participants. Second, this study used implementation science methods such as the CFIR to investigate the rural perceptions of the challenges and enablers of TB contact investigation coverage. The CFIR provided a framework for developing the semi-structured interview guides and interpretation of study findings and this promotes transferability of these results to other settings.

Some weaknesses were also observed. First, index TB patients and their contacts were not interviewed; therefore, some information on the challenges and enablers of contact investigation coverage from the patients’ and caregivers’ perspective may have been missed. Second, data collection was conducted during the COVID-19 lockdown, and some health workers were inaccessible, especially laboratory personnel involved in pandemic control activities at the time. Consequently, the laboratory may have challenges that were not identified in this study. Third, the COVID-19 pandemic may have aggravated some challenges, which were not so pronounced before the pandemic. Finally, the generalizability of our results to other geographical locations may be limited, because this study was conducted in one district in Uganda, which gives it a smaller scope. However, we included three health facilities in different counties, which may improve transferability to other settings.

This study explored health providers perceptions of the barriers and facilitators of TB contact investigation in rural Mbarara district, Southwestern Uganda. This study found that most of the challenges limiting TB contact investigations in rural communities are related to health system; for-example inadequate or delayed funding and human resource shortages. The Ministry of Health in Uganda therefore must strengthen the health system building blocks, particularly health financing and human resources to establish a robust TB control program that will enable the efficient identification of missing TB patients. It also demonstrated the unique challenges affecting the rural settings regarding tuberculosis contact investigation including lack of personal protective equipment, stock-out of Xpert MTB cartridges, shortage of airtime for communication, TB-related stigma, and inconsistent funding for TB contact investigation. Further research is needed to determine the effectiveness of potential implementation strategies for eliminating these barriers in rural communities. Also, having identified the disruptive nature of the COVID-19 pandemic to the achievement of optimal TB contact investigation coverage, there is a need to develop measures for integrating both COVID-19 and TB contact investigation interventions.

Data availability

The dataset used in the current study are available from the corresponding author on reasonable request.

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Acknowledgements

I acknowledge the contribution of Grace Ayebazibwe (GA), who supported me during the data collection and analysis by taking field notes, transcription, and translation of audio recordings.

This research work was supported by TDR, the Special Program for Research and Training in Tropical Diseases, which is hosted at the World Health Organization, and co-sponsored by UNICEF, UNDP, the World Bank and WHO. TDR grant number: B40299, first author ORCID ID: 0000-0001-9722-1202. The content is solely the responsibility of the authors and does not necessarily represent the official views of the funder.

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PT, NN and PM participated in the conceptualization and design of the study, developing interview guides, writing the initial version of the manuscript, and reviewing subsequent versions, with substantial input from NMT. With assistance from NN and PM, PT and JN conducted the data analysis. Each author contributed to the writing of the manuscript, and they all reviewed and gave their approval for publishing of the final draft.

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Tukamuhebwa, P.M., Munyewende, P., Tumwesigye, N.M. et al. Health worker perspectives on barriers and facilitators of tuberculosis investigation coverage among index case contacts in rural Southwestern Uganda: a qualitative study. BMC Infect Dis 24 , 867 (2024). https://doi.org/10.1186/s12879-024-09798-9

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Federated Learning of XAI Models in Healthcare: A Case Study on Parkinson’s Disease

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• Pietro Ducange 1 ,
• Francesco Marcelloni 1 ,
• Alessandro Renda 1 &
• Fabrizio Ruffini 1  

Artificial intelligence (AI) systems are increasingly used in healthcare applications, although some challenges have not been completely overcome to make them fully trustworthy and compliant with modern regulations and societal needs. First of all, sensitive health data, essential to train AI systems, are typically stored and managed in several separate medical centers and cannot be shared due to privacy constraints, thus hindering the use of all available information in learning models. Further, transparency and explainability of such systems are becoming increasingly urgent, especially at a time when “opaque” or “black-box” models are commonly used. Recently, technological and algorithmic solutions to these challenges have been investigated: on the one hand, federated learning (FL) has been proposed as a paradigm for collaborative model training among multiple parties without any disclosure of private raw data; on the other hand, research on eXplainable AI (XAI) aims to enhance the explainability of AI systems, either through interpretable by-design approaches or post-hoc explanation techniques. In this paper, we focus on a healthcare case study, namely predicting the progression of Parkinson’s disease, and assume that raw data originate from different medical centers and data collection for centralized training is precluded due to privacy limitations. We aim to investigate how FL of XAI models can allow achieving a good level of accuracy and trustworthiness. Cognitive and biologically inspired approaches are adopted in our analysis: FL of an interpretable by-design fuzzy rule-based system and FL of a neural network explained using a federated version of the SHAP post-hoc explanation technique. We analyze accuracy, interpretability, and explainability of the two approaches, also varying the degree of heterogeneity across several data distribution scenarios. Although the neural network is generally more accurate, the results show that the fuzzy rule-based system achieves competitive performance in the federated setting and presents desirable properties in terms of interpretability and transparency.

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Introduction and Motivations

The extensive reliance on artificial intelligence (AI) and machine learning (ML) tools in the healthcare sector poses significant challenges, especially concerning the concept of trust . Any AI system must meet the requirements of robustness, fairness, and transparency throughout its whole life cycle. Furthermore, sensitive health-related data hold an intrinsic value and become a lucrative target for cyber attacks.

The concept of trustworthy AI has recently been considered also by government entities: European Union, for example, is at the forefront for AI regulation as witnessed by the proposal of the “AI ACT” Footnote 1 (2021), which is often referred to as the first law on AI and is conceived for introducing a common regulatory and legal framework for AI. The European Commission had previously promoted the definition of the “Ethics guidelines for trustworthy AI” [ 1 ], which identifies lawfulness, ethics, and robustness as key pillars for trustworthiness and describes the requirements for an AI system to be deemed trustworthy. The ethical aspects are pivotal in the healthcare domain given the sensitive nature of patient data, the disclosure of which poses serious risks. For example, discrimination based on such data can occur in the insurance field: insurance companies could decide to charge different fees depending on the individual health status. Likewise, securing the non-discrimination in financial services is nowadays perceived as an important matter, as witnessed for example by the regulations on the “right to be forgotten” for cancer survivors [ 2 ]. Finally, special attention should be paid to specific domains (e.g., mental health), due to the stigmatized nature of some types of illness.

In the pursuit of trustworthiness, data privacy and transparency emerge as pivotal enablers, especially in the healthcare domain. While data privacy is considered an invaluable right, it somehow clashes with the creation of accurate ML models that, to date, require large amounts of data in their training phase. The common scenario is in fact that many different entities (be they individuals, medical centers, or hospitals) have few or limited amounts of data and are often reluctant to share their assets and sensitive information with other parties. The processes of data mining and knowledge extraction are therefore hampered by the unfeasibility of data collection for centralized processing. The requirement of transparency encompasses the traceability of the learning process, beginning from the data gathering phase, and the ability to comprehend the structure and the functioning of the ML model itself. The latter challenge is the central focus of a branch of AI named Explainable AI (XAI) [ 3 , 4 , 5 , 6 ]. The right for explanation is explicitly mentioned both in the “Ethics guidelines for trustworthy AI" [ 1 ], “ [...] AI systems and their decisions should be explained in a manner adapted to the stakeholder concerned ”, and in the recital 71 of the General Data Protection Regulation (GDPR) [ 2 ] “ [...] the processing should be subject to suitable safeguards, which should include specific information to the data subject and the right to obtain human intervention, to express his or her point of view, to obtain an explanation of the decision reached after such assessment and to challenge the decision.” Indeed, the ability to understand the inner working of an AI system represents a cornerstone of trust and holds particular significance in high-stakes applications in the healthcare domain.

In this work, we embrace the challenge of enhancing trustworthiness of AI systems in medicine investigating technical enablers for the requirements of data privacy and explainability.

Data access limitations, driven by privacy requirements and by the need to prevent ethical risks associated with the disclosure of sensitive data, have prompted the development of new paradigms for training ML models, including federated learning (FL) [ 7 , 8 ]. FL enables multiple parties to collaboratively train an ML model without any disclosure of private raw data. Essentially, a shared global model is learned through proper aggregation of locally computed updates from remote data owners, thus removing the need of centralizing data for training purposes.

The requirement of explainability is typically addressed through two main categories of approaches [ 3 , 4 ]: the exploitation of interpretable by-design models and the adoption of post-hoc explainability techniques. Interpretability and transparency refer to inherent properties of a model and consist in the ability to understand how decisions have been taken and what is the structure of the model itself, respectively. Post-hoc explanation techniques, instead, typically address the goal of explaining why a model provides a decision. It follows that an interpretable model results to be also explainable. The property of interpretability is generally attributed to models such as decision trees (DTs) and rule-based systems (RBSs): in fact, they consist of (or can be traced back to) collection of “IF antecedent THEN consequent ” rules. As a consequence, the inference process turns out to be highly understandable. Understandability can be defined as “ the characteristic of a model to make a human understand its function (that is, how the model works) without any need for explaining its internal structure or the algorithmic means by which the model processes data internally ” [ 3 ]. It is worth emphasizing that the concept of understandability is strongly related to the targeted audience in terms of their a-priori knowledge and cognitive skills. For example, a rule-based inference requires familiarity with logic and, possibly, an adequate training depending on the specific implementation adopted in the antecedent and consequent parts of the rules.

Post-hoc techniques are applied on models which are referred to as opaque or “black boxes,” such as Neural Networks (NNs) and ensemble models, to explain their outcomes. The vast majority of existing post-hoc methods can be roughly ascribed to the following categories, which emulate different nuances of human reasoning: feature importance explanations, rule-based explanations, prototypes explanations, contrastive/counterfactual explanations, and textual or visual explanations [ 3 , 9 ]. Notably, since the field of research is constantly evolving, the list should not be considered exhaustive; in addition, different post-hoc strategies can be specifically tailored based on the different kinds of data (e.g., images or text) and based on the specific models involved [ 9 ]. In the context of XAI, a further distinction is also made between local and global explanations: the former refers to the inference process and focuses on how/why the decision is taken for any single input instance. The latter refers to structural properties of the models (thus pertaining to the concept of transparency) or to aggregated information computed over the entire dataset.

The awareness of the importance of explainability and privacy preservation has greatly increased in recent years. While FL inherently tackles the challenge of preserving data privacy in decentralized ML, it typically lacks integrated solutions for the issue of explainability [ 10 ]. Actually, FL was originally conceived for models optimized through stochastic gradient descent (SGD) (e.g., Deep Neural Networks (DNNs)), and the application of post-hoc techniques is not straightforward in the federated setting.

This work lies at the intersection between FL and XAI and contributes to a research area named Fed-XAI (acronym for fed erated learning of XAI models) [ 11 , 12 , 13 ]. We explore the adoption of Fed-XAI approaches within the healthcare domain for predicting the progression of Parkinson’s disease (PD), formulated as a regression task. We consider a plausible scenario where sensitive raw data originate from different medical centers, making centralized learning unfeasible. In particular, the task is to predict one of the most commonly used indicators for the severity of PD symptoms, namely the Unified PD Rating Scale (UPDRS, firstly introduced in 1987 [ 14 ]), by exploiting real-world voice recordings. The analysis extends a recent work [ 15 ] and encompasses two approaches for Fed-XAI in order to explore the trade-off between accuracy and trustworthiness. The first approach adopts an interpretable by-design model, learned in a federated fashion. The second one employs an opaque model, where both training and post-hoc explanation are compliant with the federated setting. On one hand, we can assess the generalization capability of the models in the regression problem by exploiting a real, publicly available dataset; on the other hand, trustworthiness is meant here as the concurrent attainment of explainability in all its nuances, from both local and global perspectives, and privacy preservation through the adoption of FL.

As for the interpretable by-design model, we employ the Takagi-Sugeno-Kang Fuzzy Rule-Based system (TSK-FRBS) [ 16 ] which is considered as a transparent and interpretable model: its inference method mimics a cognitive process typical of human reasoning in the form of if-then rules . The partitioning of numerical variables into fuzzy sets, which is one of the defining aspects of TSK-FRBS, has proven to enable competitive levels of performance for classification and regression tasks [ 17 ] and has a twofold implication. First, fuzziness in rule-based systems enhances semantic interpretability through linguistic representation of numerical variables. Second, a fuzzy set can be interpreted as a formal representation of an information granule, intended as a generic and conceptually meaningful entity [ 18 , 19 ]: in this context, a fuzzy set allows any number in the real unit interval to represent the membership degree of a feature value to the information granule. As a consequence, the adopted TSK-FRBS fits into the paradigm of granular computing and makes use of information granules in the explainable decision-making pipeline.

As for the opaque model, we employ a well-known biologically inspired model, namely Multi Layer Perceptron Neural Network (MLP-NN). FL is performed exploiting the popular federated averaging (FedAvg) aggregation strategy [ 7 ]. Furthermore, the SHAP explainer [ 20 ], purposely adapted to comply with the federated setting based on a recently proposed approach [ 21 ], is used for explaining the output of the MLP-NN by attributing the contribution (i.e., importance) of each feature to each prediction.

The main contributions of this work can be summarized as follows:

We simulate a scenario in which several medical institutions cooperate in creating a PD progression prediction model pursuing the requirements of explainability and privacy preservation;

To achieve this goal, we implement and exploit two Fed-XAI approaches, based on TSK-FRBS, and MLP-NN plus SHAP, which represent state-of-art techniques for by-design interpretability and post-hoc explainability, respectively, in the federated setting;

We discuss the accuracy of the approaches under several data distribution scenarios, considering the independent and identically distributed (i.i.d.) case and three different non-i.i.d. cases;

For each scenario, we compare the FL scheme with two baselines, namely centralized learning and local learning, to verify the suitability of the federated approach;

We discuss the explainability of MLP-NN and the interpretability of TSK-FRBS, both from a local and a global perspective;

We discuss about the consistency of explanations provided by the two approaches in the federated setting. Here, consistency is achieved when different participants in the FL process obtain the same explanation given the same input information.

The rest of the paper is organized as follows: in Section 2 , we provide a brief overview of recent works that adopt XAI and FL tools in healthcare and more specifically in the context of PD. Furthermore, we describe recent advances in the field of Fed-XAI. Section 3 describes the background related to FL, detailing the approaches for FL of TSK-FRBS and FL of MLP-NN. Furthermore, SHAP is introduced as post-hoc explainability technique, and a recent approach for exploiting SHAP in the federated setting is presented. Section  4 describes the PD progression prediction case study, providing details about the experimental setup: we outline the different data distribution scenarios, the evaluation strategies, and the configuration of the two approaches. In Section 5 , we report and discuss the experimental results. The considerations regarding interpretability and explainability are given in Section 6 . Finally, in Section 7 , we draw some conclusions.

Related Works

The adoption of AI techniques in healthcare has been widely investigated. In this section, we first discuss the most relevant works concerning the adoption of FL and XAI in this application domain. Then, we discuss existing works related to XAI in Parkinson’s disease studies. Finally, we discuss recent algorithmic efforts for combining FL paradigm and XAI approaches.

Federated Learning and XAI in Healthcare Scenarios

The opportunities and the practical utility of FL in the healthcare domain have been recently acknowledged in the specialized literature [ 22 , 23 , 24 ], with applications mainly in the fields of medical imaging [ 25 ] and precision medicine [ 26 ]. FL is presented as a solution to protect sensitive data for privacy concerns and ethical constraints [ 27 ] and also in relation to cyber attacks [ 28 ]. At the same time, the interest in XAI is increasingly widespread, especially in the attempt to “open” the so-called black-boxes [ 29 , 30 ], which have enabled unprecedented performance in the field of deep learning (DL) in medicine. The surveys on XAI for healthcare applications usually delve into the problem of how to present the AI results and their explanations to physicians, medical staff, patients, and caregivers: the explanations should be a tool to understand the outcomes of an AI system, but also a way to allow interaction and enhance stakeholders’ trust in AI (human-centered AI). The XAI goal is usually achieved through the adoption of post-hoc methods for opaque models, often concerning image data analysis (e.g., X-rays and CT scans).

Authors of a recently published survey [ 10 ] provide a review of clinical cases where post-hoc methods and interpretable by-design models are applied to more than 20 different medical case studies, spanning from COVID-19 diagnosis and early detection to diagnostic for breast cancer. Different data types are exploited, depending on the application: images (e.g., EEG, MRI) are often involved, and SHAP is among the most used post-hoc methods. Example case studies include prediction of depressive symptoms from texts with adoption of a post-hoc method for the estimation of word importance [ 31 ] and Alzheimer classification using Random Forest and SHAP [ 32 ].

In the same survey [ 10 ], the practical utility of FL in healthcare applications is discussed, especially considering DL approaches, horizontal data partitioning, and FedAvg optimization strategy. Example case studies include the detection of COVID-19 from decentralized medical data, with Convolutional Neural Networks applied on anterior and posterior chest X-rays [ 33 ]. Authors in [ 34 ] exploit tabular electronic health records (demographics, past medical history, vital signs, lab tests results) from five hospitals to predict mortality in patients diagnosed with COVID-19 within a week of hospital admission. However, it is worth noticing that the applications of FL and XAI are treated separately, emphasizing the substantial lack of works that simultaneously address the requirements of privacy through FL and transparency through XAI in the healthcare domain.

XAI in Parkinson’s Disease Studies

PD is diagnosed in about 10 million people worldwide [ 35 ]: after the Alzheimer, it is one of the most prevalent neurodegenerative diseases. Given its socioeconomic relevance, several AI methods have been proposed for supporting diagnosis and monitoring [ 35 , 36 ]. The most commonly used data types exploited for PD studies include images and speech signals [ 37 , 38 ].

A few works discuss the topic of explainability in the context of PD studies supported by AI techniques: for instance, authors in [ 39 ] apply the LIME [ 40 ] post-hoc method on a DNN used to classify healthy from not-healthy subjects using images from SPECT scanning. Authors in [ 41 ] provide explanations for different ML model outcomes using three post-hoc methods, namely LIME, SHAP, and SHAPASH (a tool for making ML models more understandable and interpretable for general audience), on a multiclass classification task. Since the aspect of data privacy holds high relevance in this context, few recent works elaborate upon the exploitation of the FL paradigm for PD-related applications [ 42 , 43 , 44 ].

In this work, we consider the Parkinson Telemonitoring dataset, which has been analyzed in several recent works for both classification [ 45 , 46 ] and regression [ 47 , 48 , 49 ] tasks. None of the works mentioned above, however, considers the aspects of privacy and explainability simultaneously.

The primary goal of our analysis is to understand the potentialities of the Fed-XAI paradigm in a PD-related application. In the following, we provide a brief overview of the most relevant approaches for Fed-XAI proposed in the literature, relaxing the constraint on the application domain.

Federated Learning of XAI Models

Explainability in FL has been pursued either using post-hoc [ 20 , 50 , 51 , 52 , 53 , 54 , 55 ] or ex-ante [ 13 , 56 , 57 , 58 , 59 ] approaches. A thorough review of such approaches has been provided in several recent works [ 11 , 12 , 60 ]. Here, we describe the most recent advances on the topic.

Bogdanova et al. [ 60 ] have proposed a novel approach (named DC-SHAP) for consistent explainability over both horizontally (different instances, same features) and vertically (different features, same instances) partitioned data for the Data Collaboration (DC) paradigm. This paradigm consists of two stages: first, participants obtain intermediate representations of data through irreversible transformations and transmit them to a central server (unlike FL, which typically shares models rather than data). Then, the server combines such intermediate representations into a single dataset, trains an ML model, and distributes it back to the participants. Unlike other ex-ante [ 58 ] and post-hoc [ 50 , 61 ] explainability approaches tailored for the decentralized setting, DC-SHAP ensures consistency of explanations: In this context, the property of consistency is met if the explanations of the same data instance for a global model are the same for different participants. As underlined by the authors in [ 60 ], model-agnostic post-hoc explainability methods are prone to misalignment of client-side explanations, since they rely on probing the global model with various inputs generated from the local data distribution (typically referred to as background or reference dataset ). In their proposal for horizontally partitioned data, they use a set of auxiliary synthetic data shared among the participants to solve the issue of different background datasets and show how this allows the mitigation of feature attribution discrepancies among the participants. The approach proposed in [ 51 ] is conceived to obtain a consistent global feature attribution score for horizontal FL. A model-specific post-hoc explainability method, namely Integrated Gradients (IGs) [ 62 ], has been adopted for computing feature relevance. The integrated gradients get averaged and thus unified among the clients; however, local explainability is not addressed.

The issue of Fed-XAI for PD has been recently discussed in [ 63 ], with the aim of identifying digital bio-markers for the progress of the disease. Three assumptions constitute the privacy model, considering a scenario with multiple hospitals, each with its own patients: (i) input records and corresponding labels are isolated; (ii) the raw inputs are isolated between patients; (iii) the target labels are isolated between hospitals.

A hierarchical framework is adopted to build the FL model: local FL processes allow to collaboratively train a model among patients in the same hospital, whereas a global FL process aggregates models from each hospital for generating the complete model. An adaptation of SHAP is then adopted as post-hoc method for feature importance explanation. To address the issue of misalignment of client-side explanations, background datasets are generated sampling from a Gaussian distribution: the parameters of such Gaussian distribution (mean and variance) are estimated for each feature in a hierarchical way, by combining the parameters estimated intra- and inter-hospitals. It is shown that the average feature importance computed in the federated fashion is qualitatively similar to but quantitatively different from that obtained in the centralized fashion, where the union of the participants’ training sets can be used as background dataset. Although the proposed method theoretically allows for it, the aspect of local explainability/interpretability is not however discussed.

Authors in [ 21 ] have proposed an approach for obtaining SHAP explanations [ 20 ] in horizontal FL. Specifically, the explanation of an instance prediction made by the federated ML model is obtained by aggregating the explanation of the participants. Such an approach ensures consistency of explanations and is shown to be a faithful approximation of the SHAP explanations obtained in a centralized setting. However, the approach requires that test instances are available to all participants, which may be undesirable or unfeasible in real-world applications where privacy must be guaranteed also at inference time.

In the framework of FL of interpretable-by-design models, TSK-FRBSs [ 13 , 57 , 59 ] and DTs [ 56 , 58 ] have been considered as XAI models to be learnt in a federated fashion. Approaches proposed in [ 57 , 59 ] for federated TSK-FRBS rely on a clustering procedure for the structure identification stage and on a federated adaptation of classical gradient-based learning schemes for adjusting the parameters of the consequent part of the rules. In this work, we consider the approach introduced in [ 13 ], which leads to more interpretable TSK-FRBSs compared to the ones considered in [ 57 ] and [ 59 ]. Additional details on such an approach are reported in Section 3.1 .

As for DTs, the IBM FL framework [ 56 ] supports, among others, a federated adaptation of the ID3 algorithm for horizontally partitioned data. Specifically, an orchestrating server grows a single decision tree by exploiting client contributions based on their local data, in an iterative, round-based, procedure. Similarly, the approach proposed in [ 58 ] allows multiple clients to collaborate in the generation of a global DT by transmitting encrypted statistics, but it refers to the vertical data partitioning scenario. Finally, Polato et al. [ 64 ] have proposed a federated version of the AdaBoost algorithm, posing minimal constraints on the learning settings of the clients, enabling a federation of DTs, and without relying on gradient-based methods.

The categorization of FL approaches is typically based on the data partitioning scheme and the scale of federation. Data partitioning can be broadly categorized into horizontal and vertical settings. In the horizontal setting, training instances from different participants refer to the same set of features, whereas in the vertical setting, the feature set itself is partitioned among participants. The scale of federation refers to the number of participants and is typically classified into cross-silo FL, involving a low number of participants with ample data and computational power, and cross-device FL, where a large number of participants, often represented by smartphones or personal equipment, may feature a relatively small amount of data and computational power.

The PD progress prediction case study discussed in this work pertains to a cross-silo horizontal FL setting. This section reports background information for the two approaches adopted to address this task, which can be ascribed to the Fed-XAI research field: federated TSK-FRBS and federated MLP-NN with post-hoc explainability.

Federated Learning of TSK-FRBS

Let \(\textbf{X}=\left\{ X_{1}, X_{2}, \dots , X_{M}\right\} \) and Y be the set of M input variables and the output variable, respectively. A generic input instance is in the form \(\textbf{x}_{i} = [x_{i,1}, x_{i,2}, \dots , x_{i,M}]^T\) and has an associated output value \(y_i\) . Let \(U_{j}\) be the universe of discourse of variable \(X_j\) and \(P_{j} =\left\{ A_{j,1}, A_{j,2}, \dots , A_{j, T_{j}} \right\} \) be a fuzzy partition over \(U_j\) with \(T_{j}\) fuzzy sets, each labeled with a linguistic term. The term \(A_{j,t}\) indicates the \(t^{th}\) fuzzy set of the fuzzy partition over the \(j^{th}\) input variable \(X_j\) . A TSK-FRBS consists of a collection of fuzzy if-then rules, where the antecedent part of each rule is a conjunction of fuzzy propositions and the consequent part implements a regression model. In case of the commonly used first-order regression model, the generic \(r^{th}\) rule is expressed as follows:

where \(\gamma _{r,j}\) (with \(j=0,\dots ,M\) ) are the coefficients of the linear model that evaluates the output prediction \(y_r\) .

The parameters of the rules are determined through a data-driven approach. The if part (antecedent) is generated either using grid-partitioning or fuzzy clustering over the input space. Once the antecedent is determined, the then part (consequent) estimation consists of local linear models obtained, for instance, through the least squares method.

At inference time, TSK-FRBS exploits the rule base as follows. Given an input instance, first the strength of activation of each rule is computed as

where \(\mu _{j, t_{r,j}}(x_{i,j})\) is the membership degree of \(x_{i,j}\) to the fuzzy set \(A_{j,t_{r,j}}\) . Then, the final output can be evaluated with either weighted average or maximum matching policy. In the former case, the TSK-FRBS output is computed as the average of the outputs of all the activated rules weighted by their strengths of activation. In the latter case, the output corresponds to the output of the rule with the maximum strength of activation.

The maximum matching policy enhances the interpretability of TSK-FRBS, since a single rule explains a predicted output for an input instance. Furthermore, the fuzzy linguistic representation of numerical variables fosters the semantic interpretability of the model itself, whose operation, based on the evaluation of rules, turns out to be highly intuitive.

From an algorithmic perspective, FL of TSK-FRBS, as well as of other families of highly interpretable models, requires ad-hoc strategies. In this work, we rely on the approach for building TSK-FRBSs in a federated fashion recently proposed in [ 13 ]. We consider horizontally partitioned data: every participant produces a local TSK-FRBS and sends it to the server. Subsequently, the server consolidates the received rule bases by juxtaposing the rules received from the participants and by resolving potential conflicts. A conflict occurs when rules from different local TSK-FRBSs have the same antecedent, thus identifying the same specific region of the input space, but they have different consequents. In this case, the federated TSK-FRBS summarizes conflicting rules in a single rule with the same antecedent as the conflicting rules and with the consequent obtained by computing the weighted average of the regression model coefficients in the consequents of the conflicting rules. Such average takes into account the weight associated with each rule, which is estimated on the local training set as the harmonic mean of its support (how many instances activate the rule), and confidence (average quality of the prediction of the rule).

With the aim of ensuring the consistency of the rules among participants and increasing the system interpretability, the input variables are partitioned by using a strong uniform fuzzy partition with triangular fuzzy sets An example of strong uniform fuzzy partition with five triangular fuzzy sets is shown in Fig.  1 . Here, each fuzzy set is associated with a meaningful label that is used to express linguistically the rules.

An example of strong uniform fuzzy partition with five triangular fuzzy sets

It should be noted that building a fuzzy system requires a careful design especially regarding the choice of its hyperparameters (e.g., number, shape, and position of fuzzy sets for each partition), also considering their impact on interpretability [ 65 ]. Uniform fuzzy partitions with triangular fuzzy sets are generally deemed highly interpretable since they satisfy the criteria of coverage, completeness, distinguishability, and complementarity [ 66 ]. However, in practical applications, a meaningful partition should be agreed with the human users who are expected to interact with the AI system and to interpret the given rules. An interesting future development of this work would consist in examining the choice of the uniform partitioning together with domain experts (e.g., physicians).

Federated Learning of MLP-NN

Models optimized through Stochastic Gradient Descent (SGD), such as NNs, can be learned in a federated fashion by exploiting an aggregation strategy based on, or derived from, the popular federated averaging (FedAvg) procedure. FedAvg is an iterative, round-based procedure, in which each round encompasses the following steps: the server sends the global model to the participants; each participant locally updates the model through SGD on its local training set and sends the updated model to the server; the server obtains an updated global model by computing the weighted average of the locally updated models, where weights are based on the local training set cardinality. Several extensions of FedAvg have been proposed in the literature, mostly aimed at addressing FL in heterogeneous settings [ 67 , 68 ]. In this paper, we focus on classical FedAvg and deal in more detail with the issue of explainability of MLP-NN learned in a federated fashion. To this end, in the following, we first describe a popular post-hoc method for explainability, namely SHAP (SHapley Additive exPlanations) method [ 20 ], and then an approach for the adoption of SHAP in the federated setting.

Post-hoc Explainability: The SHAP Method

One of the most popular post-hoc strategies used to explain a model prediction is to assess the importance of each feature in producing the output. In general, given a model f , an input instance \(\textbf{x}_{i}\) , and a predicted output \(\hat{y_i} =f(\textbf{x}_{i})\) , the explainer assigns to each input component \(x_{i,j}\) a value that reflects how much that particular feature is important for the prediction. These values are interpreted in terms of sign and magnitude: if the sign is positive (negative), then that feature contributes positively (negatively) to the prediction output; as per the magnitude, the larger it is, the higher the impact of the corresponding feature on the output.

In this work, we adopt the SHAP method [ 20 ] which is one of the most widely used approaches to assess the feature importance for both regression and classification tasks. SHAP provides local explainability, that is, it explains individual predictions. Global explainability insights can be obtained by aggregating the individual explanations over a set of data.

SHAP computes the importance of the individual features using the optimal Shapley values introduced by L. Shapley in 1953 [ 69 ] in game theory. In SHAP, the connection between game theory and explainability is that a prediction for an individual instance \(\textbf{x}_{i}\) can be explained by conceiving the features \(X_i\) as the “players” of a “game” where the prediction \(\hat{y_i}\) is the game “payout.” Intuitively, the different M players of the game (features) receive different rewards, called Shapley values \(\phi _j\) , depending on their contribution to the total prediction, i.e., \(\hat{y_i} = \phi _0 + \sum _{j=1}^M \phi _j\) where \(\phi _0\) is a reference value (baseline) computed as the average of output values. In this game-explanation analogy, the player who contributes with the larger \(\phi _j\) to the total prediction is the most important feature in the explanation.

Since the computation of the Shapley values involves testing all the possible combinations of the features ( coalitions of the players in the game theory) by perturbating the instance \(\textbf{x}_i\) , the time increases exponentially with the number of features [ 70 ]. Thus, various approaches have been proposed to estimate them efficiently, including SHAP. There are several kinds of SHAP methods, corresponding to different ways of approximating the Shapely values. In this work, we consider the widely adopted KernelExplainer variant of SHAP (KernelSHAP), as it is model-agnostic [ 21 ]. Indeed, other methods, such as TreeExplainer, result to be more efficient but are model-specific. Algorithm 1 describes the KernelSHAP procedure.

KernelSHAP algorithm, from [ 70 ].

Notably, KernelSHAP requires a background dataset that serves as a reference: whenever a feature is excluded from a coalition, its value is replaced using an instance randomly sampled from such dataset. The choice of a representative background dataset is crucial for obtaining accurate estimates of the Shapley values. For this reason, the training set is typically adopted for this purpose. However, it is not the unique possible choice: a different, generally smaller, dataset can be used at the condition of being representative of the data distribution of the training set. In the literature, representative objects such as medoids or centroids of clusters generated by applying a clustering algorithm on the training set have allowed faster estimations of the Shapley values. Additional details on SHAP and Shapley values can be found in [ 70 ].

Federated SHAP

Let H be the number of clients involved in the federation, \(\textbf{x}_{i} { aninputinstance},\,{ and}f(\textbf{x}_{i}){ thepredictiontobeexplained}.{ Inthiswork},\,{ themodel} f(\cdot ) \) is an MLP-NN learnt in a federated fashion. Following the setup proposed in [ 21 ], the goal is to achieve a federated explanation for \(f(\textbf{x}_{i}){ consideringtheinputinstancesimultaneouslyavailabletoalltheclients}.{ Itisworthunderliningthatthismaybeundesirableorunfeasibleiftheinstance}\textbf{x}_{i} { issubjecttotheprivacyconstraint}.{ Notably},\,{ insomeparticularscenarios},\,{ forexample},\,{ ifthepatientsrequiremultiplemedicalconsultations},\,{ theinstance}\textbf{x}_{i} \) could, under specific agreements, be shared to the other clients. Another scenario is represented by the presence of a benchmark dataset available to research entities, with the objective of comparing the goodness of the explainability produced by different methods.

As discussed in Section 3.2.1 , the application of the SHAP method requires a background dataset. Typically, this is the dataset used to train the prediction model. However, in the federated setting, local training sets belong to different entities and cannot be shared due to privacy issues. Therefore, the server has no reference data to be used as background. Federated SHAP proposed in [ 21 ] represents a possible strategy to overcome this issue and to achieve federated explanations by exploiting the additive property of the Shapley values.

In the federated SHAP procedure, first of all, each client estimates the Shapley values using the local dataset as background dataset; then the values are transmitted to the server that evaluates their average. In this way, the data privacy is preserved, since the raw data are never shared. Furthermore, it is shown that the average can be considered a good approximation of the Shapley values calculated if the union of the local training sets was available to the server.

Schematically, the overall procedure for FL with post-hoc explainability technique entails the following steps: (i) each participant \(h ({ with}h=1, \dots , H){ contributestothecreationofanFLmodel}.{ Attheendofthefederatedprocedure},\,{ thefederatedmodelismadeavailabletoeachparticipant};\,({ ii}){ givenanunseeninstance}\textbf{x}_{i},\,{ eachparticipant}h{ computestheShapelyvalues}\phi ^{(i,h)}_j{ with}j = 1, \dots , M,\,{ evaluatingKernelSHAPlocally},\,{ byexploitingtheprivatetrainingsetasbackgrounddataset};\,({ iii}){ theShapelyvalues}\phi ^{(i,h)}_j{ estimatedatparticipantlevelaretransmittedtotheserver},\,{ whichperformssimpleaveragingtoobtainthefederatedestimationoftheShapleyvalues}\phi ^{(i)}_j{ forexplainingtheprediction}f(\textbf{x}_i)\) .

Case Study: Federated Learning for Parkinson’s Disease Progress Prediction

In this section, we describe the case study for the evaluation of the Fed-XAI approaches. First, details about the PD telemonitoring dataset are provided. Then, we describe the experimental setup in terms of data partitioning scenarios and evaluation strategies. Finally, we give the configurations of the ML models adopted in the two Fed-XAI approaches based on MLP-NN and TSK-FRBS, respectively.

The Parkinson Telemonitoring Dataset

The Parkinson Telemonitoring dataset is a well-known regression dataset available within the UCI Machine Learning Repository [ 71 ]. The dataset is composed of 5875 instances of biomedical voice measurements from 42 patients with early-stage PD. Data are acquired remotely during a 6-month trial. Each instance corresponds to one voice recording, characterized by 22 features as reported in Table 1 . The regression task consists in predicting the total Unified PD Rating Scale score ( total_UPDRS ) associated with a given voice recording. Differently from motor_UPDRS , which is related only to the motor symptoms, total_UPDRS is related to the overall set of symptoms.

Federated Learning Scenarios

In this paper, extending the preliminary analysis performed in [ 15 ], we consider the challenging setting in which the raw dataset is not available on a single node for centralized processing, as in traditional ML, but it is instead scattered over multiple physical locations, e.g., hospitals or healthcare institutions. Specifically, we simulate several scenarios featuring 10 hospitals (cross-silo FL setting), in order to evaluate the performance of two Fed-XAI approaches under different horizontal data partitioning schemes that could be encountered in real-world situations.

In the following, we formally define the four scenarios considered in our experimental analysis. Let \(P_h(\textbf{x},y){ bethelocaldistributionofinputdata}\textbf{x} { andassociatedtargetvalues}y\) ( total_UPDRS ) for the hospital \(h,\,{ and}P(\textbf{x},y)\) the overall data distribution.

Scenario IID It is a simple independent and identically distributed ( i.i.d. ) setting; formally,

The training data of the ten hospitals follow the same distribution, with about 500 instances each.

Scenario NIID-Q (acronym for n on- i . i . d . q uantity skew). It is a non-i.i.d. setting with quantity skew [ 72 ]: different hospitals can hold different amounts of training data, which follow the same overall distribution.

Scenario NIID-F (acronym for n on- i . i . d . f eature skew). It is a non-i.i.d. setting with feature distribution skew [ 72 ] based on the \(age\) feature; formally,

Each hospital contains training data from only a specific range of ages (e.g., 56 to 57, 58 to 59, \(\dots \) , more than 75 years old). In this scenario, we aim to have training sets with as similar amount of data as possible.

Scenario NIID-FQ (acronym for n on- i . i . d . f eature and q uantity skew). It is a non-i.i.d. setting with both quantity skew and feature distribution skew based on the \(age\) feature. Each hospital contains training data from only a specific range of ages; furthermore, different hospitals can hold different amounts of data.

The four scenarios concern different partitioning schemes for training data. As for the testing data, we consider the case of an external publicly available test set, valid for all the scenarios. The test set follows the overall data distribution (i.e., representative of all age groups) and has 588 instances. The distribution of the training data in the four scenarios is summarized in Table 2 and in Fig.  2 .

As for the NIID-F and NIID-FQ scenarios, it is worth underlining that other features besides age may be affected by bias or skewness. However, this contingency still meets the definition of feature distribution skew. Therefore, the four scenarios enable a thorough and extensive evaluation of the performance of the two Fed-XAI approaches based, respectively, on MLP-NN and TSK-FRBS.

Barplot of the training data partitioning scheme over ten hospitals in the four scenarios. Data for different ages are represented in different colors

Evaluation Settings

Typically, the performance evaluation of a model in the federated setting is performed not only in absolute terms, but also comparatively against two baseline settings [ 15 , 73 , 74 ]: local learning and centralized learning. Figure  3 provides a schematic overview of the three learning settings.

Schematized representation of the three learning settings: a federated learning (FL), b local learning (LL), c centralized learning (CL)

Federated learning (FL), local learning (LL), and centralized learning (CL) can be summarized for the dataset under consideration as follows:

FL: the hospitals collaborate in obtaining a single federated model without sharing their raw data. The privacy of sensitive data is preserved.

LL: each hospital locally learns a model from its private training data. As a consequence, the privacy of sensitive data is preserved, as in the FL case, but there is no collaboration among different hospitals.

CL: training data from all hospitals are collected in a single central repository in the server and exploited to learn a global model. CL implies indeed maximum collaboration among hospitals, but violates privacy, as private sensitive data are moved from their owner to the server.

A model learned in the FL setting is expected to be more accurate than the ones learned in the LL setting. On the other hand, a model learned in the CL setting can outperform the other models (both LL and FL), in terms of accuracy, because it can rely on the union of the training datasets. The CL approach, however, is not viable in real applications where privacy protection is a mandatory constraint.

Regression Problem and Fed-XAI Models

The PD progress prediction is formulated as a regression problem where the target variable is the Total_UPDRS score. In our experiments, we replicated the preprocessing steps adopted in [ 15 ], namely, (i) a robust scaling using 0.025 and 0.975 quantiles is applied to the input features to remove outliers and clip the distribution in the range [0,1], and (ii) the output variable is normalized in the range [0,1].

Unlike in [ 15 ], the univariate feature selection procedure is carried out independently for the three learning settings, for a fair comparison of the entire regression pipelines. We select the \(G=4\) best features in terms of Mutual Information (MI) [ 75 ] with the target variable. The estimate of MI and the subsequent feature selection is done individually by each participant in the LL setting, based on the local training sets, and globally in the CL setting, based on the union of the training sets. As for the FL setting, the federated feature selection procedure is schematized in Fig.  4 , considering the example of the IID scenario. Each participant computes the MI score for all the features and transmits such information to the server. The server computes the average MI score for each feature and communicates the \(G\) best features to each participant. Thereafter, the FL process starts considering only the selected subset of features. In the example of Fig.  4 concerning the IID setting, the federated feature selection procedure selects the following features: age , test_time , DFA , and HNR . Note that the feature importance scores of each participant may change depending on the data distribution scenario: thus, the selected features may vary and may generally differ from the CL setting.

A schematic overview of the federated feature selection procedure. The example concerns the IID scenario

The choice of the \(G\) value is guided by the following considerations: a reduced number of features generally improves the explainability task, both for post-hoc and interpretable by-design approaches. In addition, TSK-FRBSs struggle to handle high dimensional datasets [ 76 ]: the set of candidate rules grows exponentially with the number of features, thus jeopardizing the accuracy and the interpretability of the system. We have verified that \(G=4\) ensures a good generalization capability for both models and an increase in the number of features does not lead to a significant improvement in performance.

In our experiments, for each data distribution scenario, we trained a TSK-FRBS and an MLP-NN according to FL, LL, and CL settings. The experimental analysis is approached from a twofold perspective: model accuracy and model explainability . We assess the accuracy of the predictions obtained by the regression models as in [ 15 ] by using two popular metrics, namely Root Mean Squared Error (RMSE) and Pearson correlation coefficient ( \(r\) ). They are defined as follows:

where \(N{ isthenumberofsamplesconsideredfortheevaluation},\,{ and}y_i{ and}\hat{y}_i{ arethegroundtruthvalueandthepredictedvalueassociatedwiththe}i\) -th instance, respectively. Finally, \(\bar{y} { isthemeanofgroundtruthvalues},\,{ and}\hat{\bar{y}} { isthemeanofthepredictedvalues}.{ Obviously},\,{ thegoalistominimizeRMSEandmaximize}r\) .

It is worth underlining that the evaluation of an FL system typically covers other aspects besides accuracy such as computation and communication efficiency. These aspects, however, represent often crucial requirements or potential bottlenecks in cross-device FL, with many devices featuring limited computational resources [ 77 ]. In a cross-silo FL scenario, as the one considered in this work, such aspects are generally deemed less critical.

Interpretable By-design Fed-XAI: TSK-FRBS Configuration

As in [ 15 ], we employ a first order TSK-FRBS model (described in Section 3.1 ). We adopt a strong uniform fuzzy partition on the features with five triangular fuzzy sets, as shown in Fig.  1 . The choice of five fuzzy set is driven by the indication of the specialized literature and by the pursuit of a reasonable trade-off between model complexity and generalization capability. The number of linguistic terms associated with a linguistic variable should be below the limit of \(7\pm 2\) [ 78 ]. Indeed, it has been shown that this represents a threshold for information processing capability, and thus exceeding it undermines the interpretability of the system [ 79 ]. With the aim of describing linguistically a given rule, the five fuzzy sets can be labeled with the following linguistic terms: VeryLow , Low , Medium , High , and VeryHigh . Furthermore, it should be noted that different features may be partitioned using a different number of fuzzy sets, e.g., by exploiting domain knowledge to enhance understandability. Although this represents an interesting future development, we did not conduct extensive hyperparameters optimization. Rather, we verified in the CL setting that beyond 5 fuzzy sets, there is a substantial increase in model complexity, without significant improvement in terms of performance metrics. The choice of 5 fuzzy sets ensures a high linguistic interpretability and represents a reasonable trade-off between model complexity and generalization capability.

TSK-FRBS: empirical cumulative distribution function (ECDF) of the differences of RMSE scores between FL and LL for the four data partitioning schemes

Post-hoc Explainable Fed-XAI: MLP-NN Configuration

The MLP-NN consists of two hidden layers with 128 neurons, each with the ReLu activation function. The Mean Squared Error (MSE) is adopted as loss function and Adam as optimizer. The minibatch size is set to 64. The overall number of epochs is set to 100 in the CL and LL settings. In the FL setting, we set the number of local epochs and the number of rounds as 5 and 20, respectively. We have not performed a thorough optimization of hyperparameters for each learning setting and each data distribution scenario individually; however, we have empirically observed that a further increase in the capacity of the models in terms of number of layers, number of neurons, and training epochs does not lead to a significant increase in the generalization capability of the MLP-NN.

Analysis of the Experimental Results

Table 3 presents the RMSEs and \(r\) coefficients obtained by the TSK-FRBS and MLP-NN models for all the learning settings and the data distribution scenarios. As regards the LL setting, we report the average values \(\pm \) standard deviation obtained by the models learned locally in each participant. In the table, we have highlighted in bold the best results for each row, considering the comparison between TSK-FRBS and MLP-NN. Notably, in certain cases, the result is obtained from a distribution of values and is expressed in terms of mean and standard deviation: this occurs in the LL setting when ten local models are evaluated on the test sets and, regardless of the learning setting, when performance metrics are evaluated on ten training sets from as many hospitals. In such cases, we highlighted in bold the best result only if there exists a statistical difference in metrics values between TSK-FRBS and MLP-NN. The statistical significance has been assessed through a pairwise Wilcoxon signed-rank test [ 80 ] with confidence level \(\alpha =0.05.{ Ingeneral},\,{ federatedmodelsoutperformthelocalcounterparts},\,{ bothintermsofRMSEand}r\) . The benefit of FL over the LL setting is particularly evident for the TSK-FRBS and especially in the non-i.i.d. settings.

Federated TSK-FRBS and federated MLP-NN achieve comparable performances. The most noticeable difference occurs in the NIID-F setting, in which the two metrics provide diverse insights: on one hand, RMSE indicates that the deviation of predictions from true values is lower for the MLP-NN (10.268) compared to the TSK-FRBS (16.848); on the other hand, predictions and true values are more correlated for TSK-FRBS ( \(r=0.461\) ) than for MLP-NN ( \(r=0.205\) ).

The non-i.i.d. setting with quantity skew (NIID-Q) does not harm particularly the performance of the models: RMSE and \(r\) values are comparable to those of the IID setting, for both TSK-FRBS and MLP-NN. In the case of TSK-FRBS, the simple average of the performances measured on the training sets shows poor performance for the LL setting, where some models likely suffer from low data availability. The aggregation strategy based on the rule weight (defined as a combination of confidence and support) ensures that this unfavorable situation is mitigated in the FL setting.

Scenarios with feature distribution skew (NIID-F and NIID-FQ) turn out to be the most challenging for both models. The generalization capability of models built in the LL setting is rather poor, due to exposure to data from a limited age range during training: both TSK-FRBS and MLP-NN perfectly model the training data (resulting in low RMSE values and an \(r\) coefficient around 0.9 on training sets) but fail in properly predicting the total_UPDRS score of the test instances (resulting in high RMSE and low \(r).{ DiscrepancyinRMSEand}r\) values between training and test sets is noticeable in the LL setting, whereas it is limited or negligible in the FL setting.

It is worth highlighting that the performances in the FL setting are always worse than those obtained in the CL setting, both for TSK-FRBS and MLP-NN. In general, the centralized MLP-NN is able to achieve the best performance with a slight improvement in terms of RMSE and \(r\) over the centralized TSK-FRBS. The superior performance of the centralized model can be attributed to the utilization of all data for conventional training. However, it is deemed unfeasible when privacy preservation represents a critical requirement.

The results in Table 3 provide an aggregate view of the LL setting. A better understanding of the outcomes can be gained by analyzing the specific performance obtained in each hospital: such detailed results are illustrated in Figs.  5 and 6 through the empirical cumulative distribution function (ECDF) for the RMSE metric.

MLP-NN: empirical cumulative distribution function (ECDF) of the differences of RMSE scores between FL and LL for the four data partitioning schemes

Number of rules of the TSK-FRBS for each learning setting and each data partitioning scheme. Error bar represents the standard deviation

For both models, the ECDF is reported for the values of the difference, for each hospital, of the RMSE achieved in the FL setting and the 10 locally computed values of RMSE obtained in the LL setting: each plot, therefore, is made up of 10 points. The plot can be interpreted as follows: if a point lies in the negative half-plane (negative RMSE difference), then the RMSE value of the FL model is lower (and therefore the FL model is better) than the RMSE value of the LL model. The fine-grained analysis shown in Figs.  5 and 6 confirms that the FL setting generally outperforms the LL setting.

Finally, we report on the overall complexity of the models, which will be further discussed in Section 6 . In the case of the MLP-NN, the network architecture is fixed: the complexity, intended as the number of parameters, does not change with the learning setting. In the case of TSK-FRBS, the complexity can be assessed in terms of the number of rules in the rule base. Figure  7 shows the complexity for each data distribution scenario and each learning setting.

The number of rules of the federated TSK-FRBS never exceeds, by construction, that of the centralized TSK-FRBS, which in any case is limited (433 rules). As expected, the federated TSK-FRBS is more complex than locally learned ones. In the IID and NIID-Q scenarios (in which each hospital has data representative of the entire distribution), the 10 local models have approximately half the number of rules of the federated ones, indicating that common antecedents are often found in the rule aggregation phase. The complexity of locally learned TSK-FRBSs in the presence of feature distribution skew (NIID-F and NIID-FQ) is significantly lower than the one in IID setting, and the gain in accuracy provided by the federated models comes at a cost in terms of number of rules, which is approximately five times higher.

Explainability Analysis

The extensive adoption of AI systems in the healthcare field depends not only on the achievement of adequate levels of accuracy, but also on how much they are perceived to be trustable. In particular, the ability to explain how the outcomes have been produced by the models is more and more required and represents the main driver of XAI. This section discusses the aspect of transparency of AI systems, focusing on how it is defined for the two Fed-XAI approaches analyzed in this paper. First, we analyze the explainability of the MLP-NN model, in which SHAP is used as post-hoc method. Then, we discuss the interpretability by-design of the TSK-FRBS. We consider models built in a federated fashion according to the IID data partitioning scheme: the discussion of the outcomes is limited to such case, but the pipeline for explainability analysis can be easily replicated for any data distribution scenario. Furthermore, we recall that all the input variables and the output variable are normalized in the unit interval \([0,1]\) : the considerations in this section refer to the predicted values before inverse transformation.

Post-hoc Explainability of MLP-NN

In this section, we discuss the explainability of the MLP-NN after the application of the agnostic post-hoc method SHAP. We recall that, given an input instance, the Shapley value associated with each feature represents the contribution given by such feature to the predicted value. In this sense, for each prediction, SHAP explains why the model produces a particular output.

We adopt the Federated SHAP approach proposed in [ 21 ] and introduced in Section 3.2.2 . KernelSHAP is employed in each hospital to estimate the Shapley values considering the full local training set as background dataset.

It is worth underlining a first crucial aspect concerning the explainability of MLP-NN: unlike interpretable-by-design Fed-XAI approaches, the post-hoc method affects the overall efficiency of the systems, both from a computation and a communication point of view. As for the former aspect, the estimation of the Shapley values with KernelShap is time consuming and the runtime increases with the number of features and the size of the adopted background dataset [ 70 ]. As for the latter aspect, Federated SHAP introduces a communication overhead, as Shapley values need to be transmitted by the participants for central aggregation. Conversely, the interpretable-by-design TSK-FRBS has no computation and communication overhead for generating the explanations.

MLP-NN: Global Insights

Globally, an MLP-NN is generally considered “opaque,” due to the presence of several layers of non-linear information processing. In our case, the structure consists of two hidden layers with 128 neurons, resulting in 17,281 trainable parameters. The high number of parameters and the relations among these parameters make very hard to provide a global explanation of the model. Thus, indirect methods based, for instance, on feature importance are typically used to provide global explainability information [ 3 ].

As Shapley values represent additive feature importance scores for each particular prediction, the overall feature importance can be assessed by computing the average of the absolute Shapely values across the data [ 70 ]. Of course, the larger the average absolute value of the contribution given by a feature, the greater the importance of that feature. The assessment of the feature importance of a model is typically independent of the test data. In the case of the MLP-NN, it can be estimated as follows. First, each hospital \(h{ evaluatestheimportance}I{ ofthefeature}i\) on its training data:

where \(\phi _j^{(i,h)} { istheShapelyvalueforafeature}j{ andatraininginstance}i{ athospital}h,\,{ and}N_h{ isthesizeofthetrainingsetathospital}h\) . Then, each client can transmit locally computed features importance to the server, and the overall features importance for the federated model can be computed by the server as follows:

where \(H{ isthenumberofhospitalsand}N = \sum _{h=1}^{H}N_h\) .

Figure  8 shows the global feature importance scores for the MLP-NN, as per ( 10 ): in the IID setting, the most relevant feature is age , while test_time seems to be less relevant than vocal features, namely DFA and HNR , which in turn are of similar relevance.

Feature importance scores for the MLP-NN

MLP-NN: Local Insights

Figure  9 reports the SHAP values for two instances of the test set; they correspond to two cases where both models (MLP-NN and TSK-FRBS) obtain high and low errors, respectively. The absolute error (AE) made by the MLP-NN is around 0.55 for instance #2496 and around 0 for instance #2323.

MLP-NN local explainability: Shapley values for two instances in the test set. The absolute error for each instance is reported along with the baseline value

The Shapley values reveal, a-posteriori, the relevance of the corresponding features in the prediction performed by the model: as expected, they are different when considering different instances. In the former case (Fig.  9 a), age has little negative influence, while the other features have a large and positive impact on the output value. In the latter case (Fig.  9 b), the most influential feature is test_time , which has a negative impact on the output. Notably, the SHAP values for individual features are evaluated, for both instances, with respect to the same baseline value \(\phi _0 = 0.46\) .

Interpretability By-design of TSK-FRBS

TSK-FRBSs are often considered as “ light gray box” models [ 81 ]: their operation is highly interpretable, since they consist of a collection of linguistic, fuzzy, if-then rules. However, in the first-order TSK-FRBSs used in this paper, the adoption of a linear model in the consequent part, which certainly improves the accuracy with respect to the zero-order TSK-FRBS, makes the interpretation of a single rule less intuitive than the zero-order counterpart.

A substantial difference with respect to the MLP-NN model analyzed in Section 6.1 is that interpretability information is given without additional overhead in terms of computation and communication (as it is the case for the calculation of Shapley values on the MLP-NN model).

The following analysis aims to characterize both global and local interpretability of TSK-FRBS learnt in a federated fashion.

TSK-FRBS: Global Insights

The global interpretability of TSK-FRBSs can be quantitatively assessed by measuring the complexity of the system in terms of the number of rules and/or parameters. Less complex models, i.e., those with fewer rules, can be generally considered more interpretable [ 66 ]. Figure  7 reports the number of rules for each learning setting and each data partitioning scenario. As underlined in Section 5 for the IID case, the number of rules in the FL setting is rather limited (i.e., 397) and just double that in the LL case (despite the presence of 10 participants).

The model is therefore comprehensively described by the rule base, which can be represented in the intelligible form reported in the following.

The number of parameters of a rule in a TSK-FRBS can be estimated as follows, considering the presence of four input variables: it is given by the sum of four parameters for the antecedent part (one for each input variable, to identify a fuzzy set of the a-priori partitioning) and five parameters for the linear model of the consequent part. Ultimately, the considered TSK-FRBS has 3573 parameters overall.

In the case of TSK-FRBS, a measure of feature importance can be obtained by averaging the absolute values of the coefficients of the linear models in the rule base. Formally, the importance \(I{ ofthefeature}j\) is evaluated as follows:

where \(\gamma _{r,j} { isthecoefficientforthevariable}j{ intherule}r\) .

Feature importance scores for the TSK-FRBS

Figure  10 suggests that feature importance computed for the TSK-FRBS model is consistent with the one computed for the MLP-NN by the SHAP method in the IID setting. Age and test_time are identified as the most and the least relevant features, respectively. Furthermore, the importance of HNR and DFA is similar, consistently with what is observed for the MLP-NN.

A direct comparison of the importance values between Fig.  8 (MLP-NN) and 10 (TSK-FRBS) is not meaningful. Indeed, in the case of MLP-NN, Fig.  8 shows the average absolute contribution for each feature with respect to the baseline value. In case of TSK-FRBS, Fig.  10 represents the average absolute value of the coefficients of the linear models used in the case of TSK-FRBS.

It is worth underlining that model-agnostic nature of SHAP can be exploited to compute post-hoc explanations also on the TSK-FRBS model. Thus, we can directly compare the feature importance obtained by averaging the absolute values of the coefficients of the linear models in the rule base and reported in Fig.  10 , with that obtained by averaging the absolute Shapely values across the data for TSK-FRBS ( 9 ) and ( 10 ). The latter approach results in the scores reported in Fig.  11 .

Feature importance scores for the TSK-FRBS evaluated in terms of Shapley values

It is interesting to note that age and test_time are still identified as the most and least important features, indicating a summary agreement among the results obtained with different important attribution methods in the IID scenario. There is a discrepancy, on the other hand, between the relative values of DFA and HNR , possibly because the two approaches estimate importance values with different criteria. Furthermore, it is worth noting that feature importance scores computed for TSK-FRBS using SHAP are consistent with those computed for MLP-NN and shown in Fig.  8 , also in terms of range of values.

The identification of age as the most important feature regardless of the model and the attribution method adopted is not surprising. First, in the IID scenario, each hospital can have data for all age ranges and therefore the feature entails a high variability. Second, such evidence is reflected in the specialized literature, which indicates that age is the best predictor of the progression of Parkinson’s disease and the most important risk factor for the development of the disease [ 82 ].

TSK-FRBS: Local Insights

The interpretability of the TSK-FRBS derives from its structure and the type of inference strategy we use. Indeed, for any given input instance, the predicted output depends on a single rule: the antecedent part isolates a region (a hypercube) of the input space, where its consequent part defines a local linear model. The coefficients of this model indicate how the input features contribute to form the TSK-FRBS output in that region: a positive (negative) coefficient for a given feature indicates that the output increases (decreases) with that feature. Notably, all instances belonging to that region will refer to the same linear model.

Given an instance \(\textbf{x}_{i} { andarule}r,\,{ whichistheonewiththemaximumstrengthofactivationfor}\textbf{x}_{i} \) , the actual contribution of each feature to the prediction \(\hat{y_{i}} { isobtainedas}\gamma _{r,j}\cdot x_{i,j} \) . In other words, contributions are obtained as the element-wise product between the coefficients of the linear model and the feature values of an instance. Figure  12 reports both the feature contributions and the coefficients of the linear model of the TSK-FRBS for the same two instances of the test set analyzed in the case of the MLP-NN (instances #2496 and #2323).

TSK-FRBS local explainability: coefficients of the linear model and actual feature contributions for two instances in the test set. The absolute error for each instance is reported above the relevant plot along with the ID of the rule considered for the prediction and the term \(\gamma _0\) of the linear model

The two instances activate different rules: as a consequence, the contributions are significantly different. Furthermore, in general, the contributions are reduced compared to the coefficients, since each feature is normalized in the unit interval.

Shapley values calculated for the MLP-NN model on the instances in the test set that activate rule \(R_{233} \) of the TSK-FRBS. The absolute error (AE) for each instance and for each model is reported above the relevant plot

Local Explanations: Comparison Between TSK-FRBS and MLP-NN

A relevant outcome can be drawn by comparing the barplots of Figs.  9 and 12 : although the absolute errors are similar (we have verified that the predicted values are similar as well), the two models “reason” differently and assign different—sometimes diametrically opposed—contributions to the features, also because the term \(\gamma _0\) is different from the baseline value of SHAP.

To better examine this aspect, we focus on a set of instances and analyze the explanations provided by the two models. Specifically, we consider the instance resulting in a high AE for both models (ID #2496) and all the instances of the test set that activate the same rule (namely, \(R_{223} \) ) of the TSK-FRBS. In this way, we isolate four instances (ID #827, ID #2266, ID #2496, ID #5146) which are inevitably close to each other in the input space.

The local interpretability of the TSK-FRBS is straightforward: predictions are obtained by applying the following rule:

In the case of MLP-NN, the prediction is explained through the Shapley values: Fig.  13 shows the contributions for the MLP-NN considering the four instances of the test set that activate rule \(R_{233} \) of the TSK-FRBS system.

It is evident that the Shapley values for the MLP-NN vary greatly even though the instances are fairly close in the input space: as an example, age has a negative contribution for ID #827 and a positive one for ID #5146. For this reason, it is equally evident that a correspondence cannot be found between the explanations offered by SHAP for the MLP-NN and the interpretation of the linear model of the TSK-FRBS. For example, SHAP always assigns a positive contribution to DFA , while the relevant coefficient is negative for TSK-FRBS.

As noted above, the divergence in explanations between TSK-FRBS and MLP-NN in the IID scenario does not correspond to a divergence in output values. We have verified that the predicted outputs are similar (and indeed the reported AE values are similar): different models, which achieve similar results, lead to different explanations from a local point of view. This is not to be considered odd: our analysis entails different feature importance methods (inherent and post-hoc) and different models (TSK-FRBS and MLP-NN, respectively). Actually, it has been empirically shown that the feature importance score may suffer from numerical instability (when model, instance and attribution method are the same), solution diversity (if different models are considered, but on the same instance with the same attribution methods), or disagreement problem (if different attribution methods are considered, but on the same instance and the same model) [ 83 ]. These scenarios are due to the so-called Rashomon effect [ 84 ], whereby for a given dataset there may exist many models with equally good performance but with different solution strategies.

Consistency of Explanations

As mentioned in Section 2.3 , the property of consistency in the FL setting, introduced in [ 60 ], is met if different participants receive the same explanation of an output obtained with the federated model given the same data instance.

Evidently, for all the different operative scenarios discussed in this paper, the explanations for the federated TSK-FRBS are consistent: any local explanation obtained in a given hospital depends only on the input instance and on the activated rule of the federated model.

Conversely, the approach proposed in [ 21 ] and adopted in this work as post-hoc technique for the MLP-NN explainability does ensure the consistency of the explanations only in the situation where the test instances are shareable to all the clients. The local explanation obtained in a given hospital, in fact, depends not only on the input instance and the federated MLP-NN, but also on the background dataset used for estimating the Shapley values. Since each hospital has its own private dataset, the Shapley values for the same input instance may differ, in general, from one hospital to another. The Federated SHAP approach allows obtaining an explanation for each test instance by averaging the local explanations from different hospitals. On one hand, this ensures that a unique and unambiguous explanation is obtained. On the other hand, this requires that any test instance is shared with other hospitals at inference time, which may be problematic due to privacy and/or latency issues.

Shapley values for the MLP-NN for instance ID #2496 for each hospital. IID Scenario

Shapley values for the MLP-NN for instance ID #2496 for each hospital. NIID-FQ scenario

In the following, we quantitatively assess the misalignment of client-side explanations obtained with SHAP for the MLP-NN, before applying the averaging operation that characterizes Federated SHAP. We consider an input test instance (ID# 2496, discussed also in the previous examples) and suppose that it is available at every hospital. Specifically, we evaluate how the prediction for such instance would be explained on different clients, in case that sharing Shapley values for averaging is precluded for privacy reason.

Figure  14 reports the Shapley values for each hospital and each feature in the IID scenario.

The barplot suggests that the explanations are consistent in the IID scenario, albeit showing some slight variability, which is reasonable since the background datasets are identically distributed. Indeed, explanations are in line with the average pattern reported in Fig.  14 c.

The variability among client-side explanations turns out to be substantial in non-i.i.d. scenarios. Figure  15 shows the Shapley values for the same instance for each hospital and each feature when considering the NIID-FQ scenario. We recall that such scenario entails both a quantity skew and a feature skew on the age feature. Furthermore, it is worth mentioning that the feature selection process is part of the FL pipeline. As a consequence, the set of selected features depends on the data distribution scenario: this explains the presence of different features compared to the IID case, with Jitter(Abs) replacing HNR .

Figure  15 suggests that the misalignment of explanations is severe, especially for the contribution assigned to the age feature by different hospitals. The relevant Shapley value goes from negative in hospitals with younger patients to positive in hospitals with older patients. Probing the global model with data from heterogeneous distributions results in a difference also in the importance assigned to the DFA feature. In summary, the same instance, analyzed with the same model, is explained in very different ways on different hospitals. Thus, the property of consistency among explanations is not achieved.

The consistency of SHAP explanations in the FL setting can be achieved by avoiding the use of private training data as background: Chen et al. [ 63 ], for example, propose to use synthetic background datasets generated sampling from a Gaussian distribution whose parameters are estimated on the server side based on the contributions of all participants. However, such explanations may be different from those obtained using actual training data. Ensuring both consistency and accuracy of explanations, intended as agreement with the centralized case, is one of the interesting future developments of this work.

Conclusions

In this paper, we have addressed the problem of developing a trustworthy AI system for a healthcare application, with specific focus on a Parkinson’s disease (PD) progression prediction task. For this purpose, we designed two approaches that simultaneously meet the requirements of data privacy preservation and explainability, which are usually deemed crucial for enabling trustworthiness. The first approach adopts a Takagi-Sugeno-Kang Fuzzy Rule-Based system (TSK-FRBS), which is interpretable by-design. TSK-FRBSs make use of fuzzy sets as information granules, thus guaranteeing high semantic interpretability. The second approach employs a Multi Layer Perceptron Neural Network (MLP-NN): as a “black-box” model, it requires the adoption of a post-hoc technique for explainability purposes. In this paper, we have adopted SHAP, which is considered as a state of art feature importance explainability method.

For both approaches, the federated learning (FL) paradigm has been exploited as it inherently enables privacy preservation during global model training procedures in decentralized settings. In detail, we devised an experimental setting assuming that sensitive data originate from ten hospitals and cannot be shared for privacy reasons. In order to cover several real-world situations, four (one i.i.d. and three non-i.i.d.) scenarios with different degrees of heterogeneity are simulated.

The critical analysis of the two approaches has concerned the following aspects: (i) the accuracy of the models, in terms of Root Mean Squared Error (RMSE) and Pearson correlation coefficient \(r,\,{ dependingonthelearningsetting}({ federatedlearning},\,{ locallearning},\,{ centralizedlearning}){ andthefourdatadistributionscenarios},\,{ and}({ ii}){ theexplainabilityofthemodelsatglobalandlocallevels}.{ Thekeyfindingscanbesummarizedasfollows}.{ Fromtheperspectiveofperformancemetrics},\,{ resultshighlightthatthefederatedmodelsareabletooutperformtheoneslearnedbyusingonlylocaldata},\,{ bothintermsofRMSEand}r\) values, hence highlighting the benefits of the federation. This is particularly evident in the non-i.i.d. settings. Also, results suggest that federated TSK-FRBS and federated MLP-NN achieve comparable performance, within the context of the considered case study.

As regards explainability, we have presented the results of the post-hoc explainability of MLP-NN and of the by-design interpretability of TSK-FRBS, also providing a comparative analysis of the two approaches. It turns out that the two approaches can lead to different local explanations, even if the underlying models achieve similar results in terms of regression metrics. A first major difference between the two approaches lies in their nature: the TSK-FRBS model provides insights about “how” an outcome is obtained, whereas the post-hoc method provides insight about “why” an outcome is provided. Consequently, a qualitative comparison is more reasonable than a quantitative one. We have to take into account, however, that if a model is able to provide a glimpse on how an outcome has been obtained, implicitly it is also making manifest why that outcome has been computed from the inputs.

More precisely, the TSK-FRBS is a collection of linguistic if-then rules: its global interpretability is usually assessed in terms of the number of rules and/or parameters: a lower number of rules corresponds to systems considered more interpretable. An equivalent global picture is not immediately available in the case of MLP-NN: the adoption of the SHAP method enables local explainability (i.e., it explains individual predictions), and a global explainability indication can be obtained in terms of feature importance, by aggregating the individual explanations over a set of data. The comparison of the feature importance obtained for the MLP-NN (as the average of the absolute Shapley values) and for the TSK-FRBS (as the average of the absolute values of the coefficients of the linear models in the rule base) in the IID setting reveals a fair agreement: both methods identify age as the most important feature and test_time as the least important one.

Local explanations of the two approaches convey inherently different messages. For the MLP-NN, the Shapley values represent the importance of each feature to each prediction with respect to a baseline value, computed as the average output value. For the TSK-FRBS, the predicted output depends, in our setting, just on the mostly activated rule: the antecedent part isolates a region of the input space and is expressed using linguistic terms associated with fuzzy sets (information granules), whereas its consequent part defines a local linear model whose coefficients indicate how the input features contribute to form the output. Focusing on a specific region of the input space, corresponding to the one isolated by the antecedent of a rule in the TSK-FRBS, we have evaluated the explanations provided by the two models for the instances located within that region. As expected, the explanations obtained with SHAP for the MLP-NN are generally different for the different instances and do not match what is expressed by the relevant rule of the TSK-FRBS. In essence, different models, which produce similar outputs and achieve similar overall results, lead to different explanations from a local point of view.

Finally, it is worth underlining that the property of consistency holds for the federated TSK-FRBS: for the same input data, different participants get the same explanation from the common federated model. In the case of the SHAP post-hoc technique, this is not as straightforward: if participants use their local training data as background dataset for the estimation of the Shapley values, the feature importance scores are necessarily different; if the federated SHAP strategy of averaging locally computed Shapley values is adopted, a single value is obtained. This ensures consistency of explanation but requires that the privacy preservation constraint is relaxed at test time. How to achieve consistency of post-hoc explanation and simultaneously to ensure privacy preservation represents an interesting future development of this work.

Overall, the Fed-XAI approach offers remarkable potential and can have important practical implications in high-stake applications such as healthcare: in this domain, in fact, data centralization for ML model training is not only difficult to implement from a technical perspective, but it also raises ethical concerns related to privacy and is subject to limitations imposed by regulatory policies. On one hand, FL removes the need for data centralization while still allowing ML models training to benefit from ample and diverse data, which is crucial to address urgent challenges such as health disparities, under-served populations, and rare diseases [ 85 ]. On the other hand, the adoption of XAI tools endow the AI system with the capability of explaining its decisions, which is paramount in these kinds of applications.

We have discussed strengths and weaknesses of the two federated approaches for learning XAI models. In the future, we aim to enrich the comparative analysis with an additional level of assessment by collecting feedbacks on explainability from human experts (e.g., physicians) and by considering other case studies.

Data Availability

The dataset is publicly available at https://archive.ics.uci.edu/dataset/189/parkinsons+telemonitoring .

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Open access funding provided by Università di Pisa within the CRUI-CARE Agreement. This work has been partly funded by the PON 2014–2021 “Research and Innovation,” DM MUR 1062/2021, Project title: “Progettazione e sperimentazione di algoritmi di federated learning per data stream mining,” PNRR - M4C2 - Investimento 1.3, Partenariato Esteso PE00000013 - “FAIR - Future Artificial Intelligence Research” - Spoke 1 “Human-centered AI” and the PNRR “Tuscany Health Ecosystem” (THE) (Ecosistemi dell’Innovazione) - Spoke 6 - Precision Medicine & Personalized Healthcare (CUP I53C22000780001) under the NextGeneration EU programme, and by the Italian Ministry of University and Research (MUR) in the framework of the FoReLab and CrossLab projects (Departments of Excellence).

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All authors contributed to the study conception and design. Material preparation, data collection, and analysis were performed by Alessandro Renda and Fabrizio Ruffini. The first draft of the manuscript was written by Francesco Marcelloni, Alessandro Renda, and Fabrizio Ruffini, and all authors commented on previous versions of the manuscript. All authors read and approved the final manuscript.

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Ducange, P., Marcelloni, F., Renda, A. et al. Federated Learning of XAI Models in Healthcare: A Case Study on Parkinson’s Disease. Cogn Comput (2024). https://doi.org/10.1007/s12559-024-10332-x

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ALLin4IPE- an international research study on interprofessional health professions education: a protocol for an ethnographic multiple-case study of practice architectures in sites of students’ interprofessional clinical placements across four universities

• Annika Lindh Falk 1 ,
• Madeleine Abrandt Dahlgren 1 ,
• Johanna Dahlberg 1 ,
• Bente Norbye 2 ,
• Anita Iversen 2 ,
• Kylie J. Mansfield 3 ,
• Eileen McKinlay 4 ,
• Sonya Morgan 4 ,
• Julia Myers 4 &
• Linda Gulliver 4  

BMC Medical Education volume  24 , Article number:  940 ( 2024 ) Cite this article

Metrics details

The global discourse on future health care emphasises that learning to collaborate across professions is crucial to assure patient safety and meet the changing demands of health care. The research on interprofessional education (IPE) is diverse but with gaps in curricula design and how IPE is enacted in practice.

Purpose and aims

This research project will identify. 1) how IPE in clinical placements emerges, evolves, and is enacted by students when embedded in local health care practices, 2) factors critical for the design of IPE for students at clinical placements across the four countries.

A study involving four countries (Sweden, Norway, Australia and New Zealand) using the theory of practice architectures will be undertaken between 2023 and 2027. The project is designed as an international, collaborative multiple-case ethnographic study, using the theoretical framework of practice architectures (TPA). It will include four ethnographic case studies of IPE, one in each country. Data will be collected in the following sequence: (1) participant observation of students during interprofessional placements, (2) interviews with students at clinical placement and stakeholders/professionals, (3) Non-clinical documents may be used to support the analysis, and collection of photos may be use as memory aids for documenting context. An analysis of “sayings, doings and relatings” will address features of the cultural- discursive, material-economic, social-political elements making up the three key dimensions of TPA. Each of the four international cases will be analysed separately. A cross case analysis will be undertaken to establish common learning and critical IPE design elements across the four collaborating universities.

The use of TPA framework and methodology in the analysis of data will make it possible to identify comparable dimensions across the four research sites, enabling core questions to be addressed critical for the design of IPE. The ethnographic field studies will generate detailed descriptions that take account of country-specific cultural and practice contexts. The study will also generate new knowledge as to how IPE can be collaboratively researched.

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The global discourse on future health care emphasizes interprofessional collaborative capability as being crucial to meet changing demands on health care systems. These demands are the result of aging populations, increasing inequities in health care outcomes, the increasing number of those with complex health conditions and shortage of health care personnel [ 1 , 2 ]. The World Health Organisation (WHO) [ 2 ] states interprofessional education (IPE) “occurs when students from two or more professions learn about , from and with each other to enable effective collaboration and improve health outcomes” (p. 10), signalling that IPE involves interaction between students in learning activities. When the students understand the value of collaborative practice, they are better prepared to become a member of the collaborative practice team and provide better health services. The rationale for IPE, according to WHO [ 2 ] is that health professions should strive to design IPE activities to develop and optimize students’ collaborative competences to prepare them for the above challenges in their future working life [ 2 ], something that is also emphasized in the Winterthur/Doha declaration of Interprofessional. Global 2023 [ 3 ].

Efforts to explore IPE from the international research community are rapidly growing [ 4 ]. Meta-analyses and scoping reviews of IPE initiatives indicate a diverse picture of IPE programmes [ 5 , 6 , 7 ]. Vuurberg et al. [ 8 ], in their review of research studies on IPE between 1970 and 2017, point to a paucity of research regarding the influence of collaborative work on the development of professional interpersonal skills. In recent years it has been argued that there is a potential to offer IPE in clinical placements thus providing authentic learning opportunities for students in the context of complex health care practices [ 9 ]. Interprofessional learning during clinical placements is a step forward to develop and strengthen students’ interprofessional competencies, professional identity, and confidence [ 10 , 11 ].

Several reviews regarding students’ perceptions about IPE in clinical placements mostly report positive experiences, e.g., increased communication skills and increased knowledge of each other’s roles [ 12 , 13 , 14 ]. Results also indicate increased abilities with regard to working within a team and improved communication [e.g., 15 – 16 ]. Longer periods of IPE activities seem to strengthen students’ professional identity formation and overcome traditional hierarchical prejudices that can exist in interprofessional teams [e.g., 17 – 18 ]. On the other hand, it has also been suggested [ 19 ] that the lack of attention to power and conflict in the IPE literature might indicate a neglect of the impact of organizational, structural and institutional issues; and thereby might veil the very problems that IPE attempts to solve.

Published examples of IPE activities in clinical placements have covered a wide range of types of activities as well as numbers of hours and days. Initiatives have been developed that extend over a few hours or a day. Students taking responsibility for a team round in clinical placement [ 20 ] or structured interprofessional workshops about falls prevention [ 21 ], are both examples of formal activities arranged during clinical placement periods. A workplace-driven, informal, arrangement where students on uni-professional clinical placement were engaged in interprofessional teamwork for one day [ 22 ] is another example. Interprofessional activities where students practice together for a longer period have been developed and implemented during the past 25 years. Interprofessional training wards where students work together, often for a period of around two weeks, with the overall responsibility for patients’ care, have been a successful activity developed worldwide [ 23 , 24 , 25 ]. The heterogeneity of activities, educational approaches, and outcome measures, makes it difficult to compare between programmes, both at national and international levels [ 26 , 27 , 28 , 29 , 30 ]. To overcome this, the importance of international collaborative efforts to research interprofessional education practices has been emphasized [ 31 ] but to date, such collaborations are scarce. In particular, there is a need for theory-based research and observational methods to discover and understand the basis of interprofessional actions and interactions [ 7 , 32 ]. Moreover, multiple site studies are needed to inform IPE educational design, since the heterogeneity of learning activities and practices varies with the different health care systems. Visser, et al. [ 33 ] in their systematic review, described barriers and enablers of IPE at an individual level but also at a process/curricular and cultural/organizational level of the educational programmes, while Pullon et al. [ 34 ] discussed the importance of paying attention to both individual and contextual factors for sustainable collaborative practice. This indicates a need for research approaches that allow broader perspectives considering not only the experiences of the individual, but also those of the local contexts where IPE is occurring. Recent theories on research on professional learning emphasize the importance of considering the complexity and dynamics of the practices and contexts, i.e., the social and material conditions under which the learning takes place [ 35 , 36 , 37 ]. A scoping review highlighted the use of socio-material approaches as a theoretical lens to understand professional learning practices in IPE and interprofessional collaboration (IPC) [ 38 ]. Using a socio-material perspective makes it possible to gain a deeper understanding of how IPE practices emerge within a clinical setting, and furthermore, to develop an understanding of complex situations such as power relationships, human resource shortages in health care, patient safety and more.

In this study, the focus is on identifying how interprofessional collaboration and learning emerge when embedded in clinical practice placements designed for such purposes. The study is designed as an international, collaborative multiple-case ethnographic study. It will involve four sites of health care clinical practice situated locally in Sweden, Norway, Australia, and New Zealand. The multiple case study ethnographic research design [ 39 ] will be used in combination with Kemmis’ theory of practice architectures (TPA) [ 40 ]. This approach will make it possible to identify similarities and differences across the four countries and different sites of IPE.

Context of study

Each country has endorsed the WHO’s global call for Interprofessional Education and Collaborative Practice (IPECP) in different ways, which has been influenced by their national and local health care organization [ 2 ]. The local experience of teaching IPE, how the learning experience is designed, and for how long the students have an IPE clinical placement, varies between the four universities. Linköping University (Sweden) has long-standing experience of an IPE-curriculum including all health education programmes. UiT The Arctic University of Norway has a long history of IPE and builds on selected interprofessional learning activities including 13 health – and social programmes at the most. The University of Otago (New Zealand) has centrally organized IPE with a staged implementation strategy for all health and social services students to undertake IPE learning activities, while the University of Wollongong (Australia) is at an early phase of developing and implementing IPE across a variety of health and social programmes. The different contexts and establishment of IPE at the four sites make up a natural variation suitable for multiple case study research [ 39 ]. A summary of key contextual issues provides a background to each country (Table  1 ).

Theoretical framework – theory of practice architecture (TPA)

We will use a theoretical framework based on Kemmis’ TPA [ 36 , 40 ] (see Fig.  1 ). The TPA is increasingly being used to understand professional practice and the potential to learn in new ways. [ 36 , 37 , 40 ]. The theoretical framework uses the three recognized practice architecture dimensions of cultural-discursive, material-economic and social-political, along with their associated elements. The cultural-discursive dimension includes the interactions, discourses, and words (‘sayings’) which make the professional practice understandable; this reveals what to say and think in or about a practice, and what it means. The material-economic dimension enables and constrains how people can act and interact in physical and material space (‘doings’); this reveals the different types of activities and work performed by the professionals within a physical environment and the way these ‘doings’ influence others in the same practice. The social-political dimension describes the relationships that form between individuals and groups (‘relatings’); this reveals how relationships between certain arrangements of professionals develop, their roles, and whether and how relations continue to exist or not [ 44 ]. The emphasis is therefore on the relationships between material arrangements and human actions and what these produce [ 37 ], and that these relationships are more, or less likely to happen, in certain circumstances [ 45 ].

Kemmis´ theory of practice architectures (TPA) [ 40 ] p.97. (with permission from the author)

According to TPA, IPE in clinical placements can be viewed as an organized set of actions and interactions embedded in a professional practice. This means that both human and non-human factors are considered. The focus of the study is the students’ sayings, doings and relatings with fellow students, patients, supervisors, and staff, in the complex dynamic and relational dimensions of practice, i.e. the social and material conditions under which the clinical placement or learning activity is arranged.

The aims of this research project are to identify:

how IPE in clinical placements emerges, evolves, and is enacted by students when embedded in local health care practices,

factors critical for the design of IPE for students at clinical placements across the four countries.

Four research questions (RQ) will be explored:

How do interprofessional clinical placements enable students collaborative learning activities? RQ2. How do students’ sayings, doings and relatings in practice shape interprofessional collaboration and learning?

What challenges do interprofessional clinical practice placements bring to established health care practices?

What lessons from the case studies can inform the global discourse on interprofessional educational practice?

Case study site selection

Each case study site has been purposively selected within each country and across the four countries (see Table  2 ). Purposive selection has been used to ensure maximal variation [ 46 ].

Data collection

Methodology.

Four case studies will be undertaken, one each by the local research groups based in Sweden, Norway, Australia, and New Zealand and using a common ethnographic methodology.

An ethnographic approach focuses on understanding the social processes and cultures of different contexts [ 47 ], and usually comprises a range of qualitative methods. It is recognized as a suitable research method for acquiring knowledge about how practices are arranged and interrelated within naturally occurring physical and social environments, and about the contexts in which activities and knowledge-sharing can take place [ 45 , 48 ].

The initial site visits by each respective country’s local research team will take place in late 2023 and early 2024. At each case site the researcher(s), all connected to health profession education, will use the case study observational research (CSOR) method where non-participant observation guides data collection. In the CSOR method, the direct observations of participants’ behaviours and interactions are given priority and precedence over self-reported forms of data collection, and collection of non-observational data is informed by the analysis of the observational data to enable further investigation of observations [ 49 , 50 ].

Direct observation allows the researcher to see what is occurring rather than having participants describe what they do through interviews. Observations of students will follow the naturally occurring rhythm of interprofessional activities during the day. Examples of such activities are the students planning together their daily work, encounters with patients, deliberations following their work on what seems to be proper treatment and advice for the patient in question, students interactions with staff and supervisors, and their daily reflections on how they have been working together and what they have learned. Each case site is different, and the IPE learning activities is of different length and with different learning outcomes. In each case site, researchers will act as observers of interprofessional students in action and write detailed fieldnotes or record audio memos on the interactions and their context. Field notes will also incorporate the researcher’s reflections “including feelings, actions and responses to the situation” [ 39 ]. Brief informal conversations with students may be conducted during or immediately after the observations if clarity is needed about what has been observed, and these will be recorded in the field notes [ 47 ]. Non-clinical documents may be used to support the analysis, and photographs may be collected for documenting context and to aid recall. These comprehensive observations will facilitate the systematic collection of data while still acknowledging the influence and interpretations of the researcher in the data collection process. The CSOR method will make it possible to gain access to observed actions, interactions and discussions that take place between students (sayings, doings and relatings), and between students and patients, staff, and others.

In each case, the observational data and field notes (and if needed non-clinical documentation and/or photographs for context) will be immediately circulated to the local research team and reflexive feedback provided for inclusion in the analysis. Following this rapid analysis of observational data and guided by what further data is needed or needing to be corroborated, formal interviews will be booked as soon as possible with students, patients, clinical tutors, IPE teachers and others, Formal interviews (audio recorded) will be guided by a template of core questions developed by the research project team. This common template will be augmented by other questions informed by each initial case analysis. Data will be transcribed either selectively or fully; English language translation will occur where data are being analysed for comparative analysis.

Theoretical approach

Data analysis will use TPA [ 40 ] including an analysis “tool kit” [ 51 ]. The tool kit is a theory and method package to investigate practices by the systematic interpretation of the case study data. A “zooming in – zooming out” methodological approach [ 51 ], will make visible details in a specific local practice; “zooming in” allows getting close to the practices being observed (to answer RQ1 and RQ2) and then “zooming out” allows the researcher to expand their scope and look for connections between different practices (RQ3 and RQ4). The connections between practices in the research study will be identified through focusing on the three dimensions of practice architectures: the cultural-discursive, material-economic, and social-political. The agreed tool kit approach will include a layered, purposeful constant comparative analysis [ 52 ], comprising three phases of individual and collaborative activities, using English as the common language. First the systematic collection and analysis of observations and field notes of those observations and other qualitative data by each local research team, will be guided by the theoretical perspective on how students interact in relation to social and material arrangements. Second, the data in each of the four case study sites will be analysed by each local research team and verified locally and collectively; this will lead to site-specific findings. Third, comparisons will be made between the four different sites by cross-checking and developing and refining the interpretations of all the data.

Practical approach

Each country will follow the data collection and analysis process outlined in the methods for their case site and each case site will be analysed separately. Each local research team will have regular meetings to ensure that a reflexive, but uniform approach is undertaken as data is collected. These meetings will also include workshops for collaborative data analysis. Monthly meetings will also be held between the four countries’ project research teams as case data collection and analysis progresses and a similar reflexive process used. This will ensure the analyses of each case follows the same process and will provide assurance of mutual understanding across sites. To enable this, anonymized observational data (and fieldnotes), interview data and photographic or document extracts will be shared, analysed and reviewed in workshops. Following completion of each case study in the four different countries, a cross-case process [ 39 ] will be undertaken. Each local research teams will first have undertaken the primary analysis, combining data from fieldnotes and interview transcript generating preliminary themes to identify the sayings, doings and relatings are emerging and connected in the efforts of collaborate around the patient. As the findings are first collated, observed aspects from students’ sayings, doings and relatings, projects and dispositions will be revealed. As a second layer of analysis, the findings will analytically be connected to practice architectures, such as the cultural-discursive, the material-economic and the socio-political arrangements. The use of a common scheme for how to document the analysis is important for comparative reasons and indicate points for shared analyses across the research teams to consider the respective results, identify similarities and differences across the four sites, and explore any learning principles that might apply to IPE internationally. It is intended for each country to use the same processes to anonymize, catalogue and code the transcribed data. The research agreement also includes a process to enable sharing of selected portions of data and coding software databases using password-protected systems [ 53 ].

Ethical approval and consent

The research group in each country will be responsible for (1) seeking ethical approval for their respective case, (2) gaining consent from each local site to undertake the respective case study, (3) establishing rules for storage of the data. The following countries have received ethical approval to proceed: Sweden (Dnr 2023-02277-01), Norway (No.889163), New Zealand (No. H23/035), Australia (underway).

Establishing trustworthiness

The following processes and definitions proposed by Korstjens and Moser [ 54 ] based on Lincoln and Guba [ 55 ] will be used to ensure trustworthiness in the implementation of this study (Table  3 ).

A timeline for the research project has been established (Table  4 ).

This research project is innovative as it takes an international approach to a globally identified educational challenge regarding methods to design and implement IPE in clinical practice settings. The approach, using case studies in four different countries, will explicitly acknowledge that educational phenomena and learning are contextually bound and situated and that although each country involved is different, common learning can be gained.

It is hoped that the four case studies will lead to new understanding and conceptualization of how IPE can be arranged within and across diverse contexts, languages, and local conditions. Furthermore, the cases may establish some of the challenges interprofessional clinical placements for students may bring to existing or established health care practices.

It is recognized however that while each country’s case will lead to new understanding for that country, it may be challenging to establish cross country learning as the context of each may be very different. Although English language will be adopted for communication, there may be subtle differences in how language is used and understood between English and non-English speaking countries, as well as between English speaking countries.

Taking account of local context as well as developing joint findings will be a challenge. The TPA will give opportunities to identify and analyse how students´ interprofessional clinical activities are embedded in the complex practice of routine health care at a local level within each country, and between countries. The theory will make it possible to capture how the students act in practice and how they relate to each other in clinical placements. It is hoped it will also show how clinical and interprofessional practices are influenced through the three different dimensions (cultural-discursive, material-economic, and social-political) and if these may construct, enable, or constrain practice work and knowledge-sharing. Possible examples may include: (1) the influence of a discipline’s language or discourse; the way of speaking that forms the framework for understanding themselves and others, (2) the arrangement of a health care setting; the way the environment influences where students can meet and work together (e.g. patient care rooms, rooms used for ward rounds and corridors), and (3) the development of relationships; the way social norms and political influences impact on relationships between different disciplines and groups [ 40 , 45 ]. It is possible when the analysis progresses that the three dimensions referred to above may show nuanced differences between countries which previously have been difficult to articulate and account for.

Undertaking this international collaborative research is important for IPE research going forward. International collaborative research projects in IPE are rare but have been recommended for the consolidation and growth of the IPE research knowledge base [ 31 ].

The design of IPE in clinical placements should be informed by evidence and best practice. This includes using theoretical approaches which can be replicated or further developed, such as the TPA.

This research will advance a model of IPE based on TPA. It will provide new understanding and conceptualization of how IPE can be arranged across diverse contexts and local conditions, but with a common aim to provide collaborative practice-ready graduates able to respond to the increasing healthcare demands of the future.

Therefore, the broader impact of the proposed study is expected to contribute to: (1) the local and international educational IPE community regarding design of IPE in clinical practice, and (2) the international IPE research community regarding how IPE in practice can be collaboratively researched.

Data availability

Selected data will be reported in the Results section but will not be available as datasets.

Abbreviations

Case Study Observational Research

Interprofessional Education

Interprofessional Education and Collaborative Practice

Interprofessional Collaboration

Theory of Practice Architectures

World Health Organisation

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Acknowledgements

The research team gratefully acknowledge the contribution of the advisor: Nick Hopwood, Professor of Professional Learning, University of Technology, Sydney, Australia.

Open access funding provided by Linköping University. This study is funded by the Swedish Research Council: 2022–03210. The funder had no role in the study design, collection, analysis and interpretation of the data; writing of the protocol, or in the decision to submit the paper for publication.

Open access funding provided by Linköping University.

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ALF, MAD, JD, BN, AI, KM, EM & LG contributed to the conception and design of the overall study. ALF is the overall Principal investigator (PI) and PI of the Swedish case study; AI is the PI of the Norwegian case study, KM is the PI of the Australian case study, EM is the PI of the New Zealand case study. ALF, MAD & JD developed the analysis plan. JD drafted the initial protocol. ALF, MAD, JD, BN, AI, KM, EM, LG, SM & JM revised the protocol critically for important intellectual content and read and approved the final version of the manuscript to be published.

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Correspondence to Annika Lindh Falk .

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Ethical approval.

Sweden: Approved by Swedish Ethical Review Authority. Dnr 2023-02277-01. Each participant will be asked to give signed consent to take part in the case study.

Norway: Approved by Norwegian Agency for Shared Services in Education and Research, reference number: 889163. Each participant will be asked to give signed consent to take part in the case study.

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Lindh Falk, A., Abrandt Dahlgren, M., Dahlberg, J. et al. ALLin4IPE- an international research study on interprofessional health professions education: a protocol for an ethnographic multiple-case study of practice architectures in sites of students’ interprofessional clinical placements across four universities. BMC Med Educ 24 , 940 (2024). https://doi.org/10.1186/s12909-024-05902-4

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Ensuring sustainable digital inclusion among the elderly: a comprehensive analysis.

1. Introduction

• To conduct a thorough literature review to discover factors influencing the adoption of digital technologies among the elderly, with a focus on the current state of digital exclusion.
• To explore the causes of the digital divide and identify key contributors that can lead to sustainable digital inclusion.
• To perform in-depth analysis of various data sources to investigate patterns and trends and to obtain a global perspective on socio-economic factors and their link to social sustainability.
• RQ1: What is the current state of digital exclusion among the elderly, particularly in terms of competency with devices such as computers, smartphones, and other digital tools?
• RQ2: What are the primary factors affecting the digital divide among elderly people, and how can these factors ensure sustainable digital inclusion among the elderly?
• RQ3: How does the digital exclusion vary among different countries and cultures, and are there any variations in digital exclusion in High-Income Countries (HICs) and Lower Middle-Income Countries (LMICs)?
• The HICs and LMICs selected for this study were analyzed independently to determine the differences in digital exclusion between them, taking into account how diverse social and economic conditions influence technology usage among older people. It describes how different country features influence digital exclusion.
• The list of factors influencing the digital divide among the elderly includes socio-economic factors, health-related issues, and age-related limitations. By connecting these characteristics to theories such as socio-economic and ecological systems [ 9 ], the research provides insights into how they interact and how they affect digital inclusion.
• This study examines the technological challenges faced by elderly people by considering the “Digital Divide Theory” [ 10 ] to assess how digital literacy factors influence digital exclusion.
• The analysis of five cohorts from diverse regions to identify how regional and environmental characteristics affect digital exclusion.
• Applying different statistical analyses, such as Principal Component Analysis (PCA), component matrix, and pattern matrix, to understand the major factors affecting digital exclusion. These methods help to analyze how diverse factors contribute to a better understanding of digital exclusion.
• The use of the factor analysis method identified the primary factors influencing digital exclusion among the elderly as socio-economic, age-related limitations and health-related issues. This finding supports the Ecological Systems Theory [ 9 ], providing evidence about how these factors affect digital exclusion among the elderly.
• Analyzing the linear trend in the association between digital exclusion and country type.
• The study identifies a linear trend in the relationship between age group and country type and illustrates the interaction between age and socio-economic factors in the context of digital exclusion. This finding supports the Ecological Systems Theory [ 9 ] showing how multiple factors like age- and country-specific factors impacts digital exclusion among elderly.

2. Research Background

2.1. current state of digital exclusion, 2.2. factors influencing the digital divide, 2.3. technological challenges faced by the elderly, 2.4. initiatives to reduce digital divide, 2.5. previous research studies.

• H1: There is a significant percentage of elderly people who lack the skills to use computers, smartphones, and digital tools.
• H2: Less access to digital devices and the internet, as well as lower education levels, are significantly associated with higher rates of digital exclusion among the elderly.
• H3: Digital exclusion is influenced by cultural settings, and the digital exclusion rate is higher in LMICs compared to HICs.

3. Research Strategy

3.1. data collection, 3.2. type of research design.

• Research journals, databases, academic sources and organizational websites, and newspapers were utilized to get the relevant information needed for the research. The Leeds Beckett Online Library, Google Scholar, PubMed, and SCOPUS were mainly considered to identify the research papers.
• The terms like “digital exclusion of the elderly”, “digital divide”, “older people digital needs”, “digital literacy among older adults” were used to search and identify the relevant knowledge.
• The initiatives to reduce digital exclusion in each country was identified through Google search and collecting details from the respective websites.
• The statistical data from government organizations such as Office for National Statistics were collected for providing insights.

3.3. Quantitative Analysis

3.3.1. data collection and transformation.

• Some records lacked age information but had year-of-birth information available. So, using Microsoft Excel (v2403), the actual age was calculated based on the year of birth and the year of the interview.
• As the research focused on older adults aged 55 and above, details for those under 55 were omitted.
• Some records with missing gender fields were updated to ‘Not known’.
• The null values for health issues (diabetes, high blood pressure, tumor, lungs, heart) were updated as ‘NA’.
• The digitally excluded persons were identified by analyzing the different values from each dataset, such as internet, mobile, and social media usage.
• A new field, “digitally excluded”, was created to represent people who had not used social media or the internet in over a month.
• After cleaning and transforming the dataset, the commonly available variables from these five datasets were merged.
• The records were then categorized according to different age groups (55–64, 65–74, and 75+) considering the age of each individual and a new field was given for representing the age group of each record. This was helpful to analyze the variations in digital exclusion between the different age groups.
• Finally, we introduced a new field to represent the country type (HICs and LMICs) and updated it according to the country.

3.3.2. Descriptive Statistics

• The details of age (mean, median, and standard deviation (SD)); age groups (55–64, 65–74, 75+); gender (male and female); and fields like age preventing the performance of actions, usually feel left out/lonely, feel lack of control, feel stressed/anxious, completed high school, living with partner, widowed, area lived (rural area or village, town, a big city/the suburbs or outskirts of big city, nursing home or care facility), reported poor health rating, health issues (diabetes, hypertension, tumor, lungs, heart), sight impaired, attended training courses within last 12 months, and digital exclusion rate was added in the descriptive statistics for each country.
• The descriptive analysis included factors such as health, education, family situation, wealth, and psychological well-being.
• The details for the area lived: a big city/the suburbs or outskirts of a big city was not available from the datasets for the UK, India, Brazil, and Mexico. Therefore, it is recorded as blank.
• Similarly, the details of attended training courses within the last 12 months are not recorded for India and Mexico since it was not available from the datasets.

3.3.3. Primary Factors Affecting Digital Exclusion

• All the available variables were taken for factor analysis, and the PCA analysis was conducted with the ‘factor’ option under Analyze > component reduction in SPSS.
• From the ‘Descriptives’ tab, KMO and Bartlett’s test of sphericity was chosen; we selected ‘Principal components’ as the extraction method, chose a correlation matrix for analysis, and selected scree plots, and extraction was performed based on an Eigenvalue > 1.
• The ‘Promax’ method was chosen for rotation, and the coefficients less than 0.3 were suppressed.
• The variables having commonalities of less than 0.3 were removed from factor analysis to focus on the variables which are more related to the underlying factors and to obtain clear and meaningful results.

3.4. Comparative Analysis

• Variations in digital exclusion.
• Variations in life satisfaction among digitally excluded people.
• Impact of health rating by the interaction of digital exclusion and country type.

4. Results and Discussion

4.1. statistical summary, 4.1.1. hics, 4.1.2. lmics, 4.2. rq1: what is the current state of digital exclusion among the elderly, particularly in terms of competency with devices such as computers, smartphones, and other digital tools, 4.2.1. internet usage vs. internet connection, 4.2.2. device ownership vs. internet use, 4.2.3. digital exclusion vs. age, 4.2.4. digital exclusion vs. country type, 4.2.5. digital exclusion vs. country type and age group, 4.2.6. digital exclusion vs. life satisfaction, 4.3. rq2: what are the primary factors affecting the digital divide among the elderly people, and how these factors can ensure sustainable digital inclusion among the elderly, 4.3.1. factor analysis using pca (principal component analysis), 4.3.2. total variance, 4.3.3. scree plot, 4.3.4. component matrices, 4.4. convergent and discriminant validity, 4.4.1. convergent validity, 4.4.2. discriminant validity, 4.4.3. factors affecting digital exclusion, 4.5. rq3: how does digital exclusion vary among different countries and cultures, and are there any variations in digital exclusion in high-income countries (hics) and lower middle-income countries (lmics), 4.5.1. variations of digital exclusion between hics and lmics.

• Pearson’s chi-square statistics are highly significant ( p < 0.001), which indicates that there is a strong association between country type and digital exclusion [ 63 ].
• Continuity correction is a modification of the chi-square test for 2 × 2 contingency tables, and the p -value (<0.001) confirms the strong association found in chi-square tests.
• Likelihood ratio compares how well the observed data fit the null hypothesis to a model in which the variables are independent, and the p -value (<0.001) explains that the data fit the model better than the null hypothesis [ 64 ].
• Fisher’s exact test is used for a small sample size, and it confirms that there is a strong association found in the above tests [ 65 ].
• Linear-by-linear association indicates the linear trend in the association between these variables, and the p -value is <0.001, indicating a significant linear trend in the association [ 66 ].

4.5.2. Variation of Digital Exclusion between Different Age Groups in HICs and LMICs

• Pearson’s chi-square statistics value of 4557.933 with a p -value (<0.001) suggests that there is a significant association between the age group and country types [ 63 ].
• The likelihood ratio also has a p -value (<0.001), which represents a highly significant association [ 64 ].
• The linear-by-linear association of chi-square statistics is 4000.725, which indicates there is a linear trend in the association between these variables [ 66 ].

4.5.3. Variation in Life Satisfaction among HIC and LMIC of Digitally Excluded People

4.5.4. how the health rating is impacted by the interaction of digital exclusion and hics and lmics, 5. conclusions and future work, author contributions, institutional review board statement, informed consent statement, data availability statement, conflicts of interest.

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Mohan, R.; Saleem, F.; Voderhobli, K.; Sheikh-Akbari, A. Ensuring Sustainable Digital Inclusion among the Elderly: A Comprehensive Analysis. Sustainability 2024 , 16 , 7485. https://doi.org/10.3390/su16177485

Mohan R, Saleem F, Voderhobli K, Sheikh-Akbari A. Ensuring Sustainable Digital Inclusion among the Elderly: A Comprehensive Analysis. Sustainability . 2024; 16(17):7485. https://doi.org/10.3390/su16177485

Mohan, Rinku, Farrukh Saleem, Kiran Voderhobli, and Akbar Sheikh-Akbari. 2024. "Ensuring Sustainable Digital Inclusion among the Elderly: A Comprehensive Analysis" Sustainability 16, no. 17: 7485. https://doi.org/10.3390/su16177485

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