research paper on health sector

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• Published: 06 January 2022

The use of Big Data Analytics in healthcare

• Kornelia Batko   ORCID: orcid.org/0000-0001-6561-3826 1 &
• Andrzej Ślęzak 2  

Journal of Big Data volume  9 , Article number:  3 ( 2022 ) Cite this article

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The introduction of Big Data Analytics (BDA) in healthcare will allow to use new technologies both in treatment of patients and health management. The paper aims at analyzing the possibilities of using Big Data Analytics in healthcare. The research is based on a critical analysis of the literature, as well as the presentation of selected results of direct research on the use of Big Data Analytics in medical facilities. The direct research was carried out based on research questionnaire and conducted on a sample of 217 medical facilities in Poland. Literature studies have shown that the use of Big Data Analytics can bring many benefits to medical facilities, while direct research has shown that medical facilities in Poland are moving towards data-based healthcare because they use structured and unstructured data, reach for analytics in the administrative, business and clinical area. The research positively confirmed that medical facilities are working on both structural data and unstructured data. The following kinds and sources of data can be distinguished: from databases, transaction data, unstructured content of emails and documents, data from devices and sensors. However, the use of data from social media is lower as in their activity they reach for analytics, not only in the administrative and business but also in the clinical area. It clearly shows that the decisions made in medical facilities are highly data-driven. The results of the study confirm what has been analyzed in the literature that medical facilities are moving towards data-based healthcare, together with its benefits.

Introduction

The main contribution of this paper is to present an analytical overview of using structured and unstructured data (Big Data) analytics in medical facilities in Poland. Medical facilities use both structured and unstructured data in their practice. Structured data has a predetermined schema, it is extensive, freeform, and comes in variety of forms [ 27 ]. In contrast, unstructured data, referred to as Big Data (BD), does not fit into the typical data processing format. Big Data is a massive amount of data sets that cannot be stored, processed, or analyzed using traditional tools. It remains stored but not analyzed. Due to the lack of a well-defined schema, it is difficult to search and analyze such data and, therefore, it requires a specific technology and method to transform it into value [ 20 , 68 ]. Integrating data stored in both structured and unstructured formats can add significant value to an organization [ 27 ]. Organizations must approach unstructured data in a different way. Therefore, the potential is seen in Big Data Analytics (BDA). Big Data Analytics are techniques and tools used to analyze and extract information from Big Data. The results of Big Data analysis can be used to predict the future. They also help in creating trends about the past. When it comes to healthcare, it allows to analyze large datasets from thousands of patients, identifying clusters and correlation between datasets, as well as developing predictive models using data mining techniques [ 60 ].

This paper is the first study to consolidate and characterize the use of Big Data from different perspectives. The first part consists of a brief literature review of studies on Big Data (BD) and Big Data Analytics (BDA), while the second part presents results of direct research aimed at diagnosing the use of big data analyses in medical facilities in Poland.

Healthcare is a complex system with varied stakeholders: patients, doctors, hospitals, pharmaceutical companies and healthcare decision-makers. This sector is also limited by strict rules and regulations. However, worldwide one may observe a departure from the traditional doctor-patient approach. The doctor becomes a partner and the patient is involved in the therapeutic process [ 14 ]. Healthcare is no longer focused solely on the treatment of patients. The priority for decision-makers should be to promote proper health attitudes and prevent diseases that can be avoided [ 81 ]. This became visible and important especially during the Covid-19 pandemic [ 44 ].

The next challenges that healthcare will have to face is the growing number of elderly people and a decline in fertility. Fertility rates in the country are found below the reproductive minimum necessary to keep the population stable [ 10 ]. The reflection of both effects, namely the increase in age and lower fertility rates, are demographic load indicators, which is constantly growing. Forecasts show that providing healthcare in the form it is provided today will become impossible in the next 20 years [ 70 ]. It is especially visible now during the Covid-19 pandemic when healthcare faced quite a challenge related to the analysis of huge data amounts and the need to identify trends and predict the spread of the coronavirus. The pandemic showed it even more that patients should have access to information about their health condition, the possibility of digital analysis of this data and access to reliable medical support online. Health monitoring and cooperation with doctors in order to prevent diseases can actually revolutionize the healthcare system. One of the most important aspects of the change necessary in healthcare is putting the patient in the center of the system.

Technology is not enough to achieve these goals. Therefore, changes should be made not only at the technological level but also in the management and design of complete healthcare processes and what is more, they should affect the business models of service providers. The use of Big Data Analytics is becoming more and more common in enterprises [ 17 , 54 ]. However, medical enterprises still cannot keep up with the information needs of patients, clinicians, administrators and the creator’s policy. The adoption of a Big Data approach would allow the implementation of personalized and precise medicine based on personalized information, delivered in real time and tailored to individual patients.

To achieve this goal, it is necessary to implement systems that will be able to learn quickly about the data generated by people within clinical care and everyday life. This will enable data-driven decision making, receiving better personalized predictions about prognosis and responses to treatments; a deeper understanding of the complex factors and their interactions that influence health at the patient level, the health system and society, enhanced approaches to detecting safety problems with drugs and devices, as well as more effective methods of comparing prevention, diagnostic, and treatment options [ 40 ].

In the literature, there is a lot of research showing what opportunities can be offered to companies by big data analysis and what data can be analyzed. However, there are few studies showing how data analysis in the area of healthcare is performed, what data is used by medical facilities and what analyses and in which areas they carry out. This paper aims to fill this gap by presenting the results of research carried out in medical facilities in Poland. The goal is to analyze the possibilities of using Big Data Analytics in healthcare, especially in Polish conditions. In particular, the paper is aimed at determining what data is processed by medical facilities in Poland, what analyses they perform and in what areas, and how they assess their analytical maturity. In order to achieve this goal, a critical analysis of the literature was performed, and the direct research was based on a research questionnaire conducted on a sample of 217 medical facilities in Poland. It was hypothesized that medical facilities in Poland are working on both structured and unstructured data and moving towards data-based healthcare and its benefits. Examining the maturity of healthcare facilities in the use of Big Data and Big Data Analytics is crucial in determining the potential future benefits that the healthcare sector can gain from Big Data Analytics. There is also a pressing need to predicate whether, in the coming years, healthcare will be able to cope with the threats and challenges it faces.

This paper is divided into eight parts. The first is the introduction which provides background and the general problem statement of this research. In the second part, this paper discusses considerations on use of Big Data and Big Data Analytics in Healthcare, and then, in the third part, it moves on to challenges and potential benefits of using Big Data Analytics in healthcare. The next part involves the explanation of the proposed method. The result of direct research and discussion are presented in the fifth part, while the following part of the paper is the conclusion. The seventh part of the paper presents practical implications. The final section of the paper provides limitations and directions for future research.

Considerations on use Big Data and Big Data Analytics in the healthcare

In recent years one can observe a constantly increasing demand for solutions offering effective analytical tools. This trend is also noticeable in the analysis of large volumes of data (Big Data, BD). Organizations are looking for ways to use the power of Big Data to improve their decision making, competitive advantage or business performance [ 7 , 54 ]. Big Data is considered to offer potential solutions to public and private organizations, however, still not much is known about the outcome of the practical use of Big Data in different types of organizations [ 24 ].

As already mentioned, in recent years, healthcare management worldwide has been changed from a disease-centered model to a patient-centered model, even in value-based healthcare delivery model [ 68 ]. In order to meet the requirements of this model and provide effective patient-centered care, it is necessary to manage and analyze healthcare Big Data.

The issue often raised when it comes to the use of data in healthcare is the appropriate use of Big Data. Healthcare has always generated huge amounts of data and nowadays, the introduction of electronic medical records, as well as the huge amount of data sent by various types of sensors or generated by patients in social media causes data streams to constantly grow. Also, the medical industry generates significant amounts of data, including clinical records, medical images, genomic data and health behaviors. Proper use of the data will allow healthcare organizations to support clinical decision-making, disease surveillance, and public health management. The challenge posed by clinical data processing involves not only the quantity of data but also the difficulty in processing it.

In the literature one can find many different definitions of Big Data. This concept has evolved in recent years, however, it is still not clearly understood. Nevertheless, despite the range and differences in definitions, Big Data can be treated as a: large amount of digital data, large data sets, tool, technology or phenomenon (cultural or technological.

Big Data can be considered as massive and continually generated digital datasets that are produced via interactions with online technologies [ 53 ]. Big Data can be defined as datasets that are of such large sizes that they pose challenges in traditional storage and analysis techniques [ 28 ]. A similar opinion about Big Data was presented by Ohlhorst who sees Big Data as extremely large data sets, possible neither to manage nor to analyze with traditional data processing tools [ 57 ]. In his opinion, the bigger the data set, the more difficult it is to gain any value from it.

In turn, Knapp perceived Big Data as tools, processes and procedures that allow an organization to create, manipulate and manage very large data sets and storage facilities [ 38 ]. From this point of view, Big Data is identified as a tool to gather information from different databases and processes, allowing users to manage large amounts of data.

Similar perception of the term ‘Big Data’ is shown by Carter. According to him, Big Data technologies refer to a new generation of technologies and architectures, designed to economically extract value from very large volumes of a wide variety of data by enabling high velocity capture, discovery and/or analysis [ 13 ].

Jordan combines these two approaches by identifying Big Data as a complex system, as it needs data bases for data to be stored in, programs and tools to be managed, as well as expertise and personnel able to retrieve useful information and visualization to be understood [ 37 ].

Following the definition of Laney for Big Data, it can be state that: it is large amount of data generated in very fast motion and it contains a lot of content [ 43 ]. Such data comes from unstructured sources, such as stream of clicks on the web, social networks (Twitter, blogs, Facebook), video recordings from the shops, recording of calls in a call center, real time information from various kinds of sensors, RFID, GPS devices, mobile phones and other devices that identify and monitor something [ 8 ]. Big Data is a powerful digital data silo, raw, collected with all sorts of sources, unstructured and difficult, or even impossible, to analyze using conventional techniques used so far to relational databases.

While describing Big Data, it cannot be overlooked that the term refers more to a phenomenon than to specific technology. Therefore, instead of defining this phenomenon, trying to describe them, more authors are describing Big Data by giving them characteristics included a collection of V’s related to its nature [ 2 , 3 , 23 , 25 , 58 ]:

Volume (refers to the amount of data and is one of the biggest challenges in Big Data Analytics),

Velocity (speed with which new data is generated, the challenge is to be able to manage data effectively and in real time),

Variety (heterogeneity of data, many different types of healthcare data, the challenge is to derive insights by looking at all available heterogenous data in a holistic manner),

Variability (inconsistency of data, the challenge is to correct the interpretation of data that can vary significantly depending on the context),

Veracity (how trustworthy the data is, quality of the data),

Visualization (ability to interpret data and resulting insights, challenging for Big Data due to its other features as described above).

Value (the goal of Big Data Analytics is to discover the hidden knowledge from huge amounts of data).

Big Data is defined as an information asset with high volume, velocity, and variety, which requires specific technology and method for its transformation into value [ 21 , 77 ]. Big Data is also a collection of information about high-volume, high volatility or high diversity, requiring new forms of processing in order to support decision-making, discovering new phenomena and process optimization [ 5 , 7 ]. Big Data is too large for traditional data-processing systems and software tools to capture, store, manage and analyze, therefore it requires new technologies [ 28 , 50 , 61 ] to manage (capture, aggregate, process) its volume, velocity and variety [ 9 ].

Undoubtedly, Big Data differs from the data sources used so far by organizations. Therefore, organizations must approach this type of unstructured data in a different way. First of all, organizations must start to see data as flows and not stocks—this entails the need to implement the so-called streaming analytics [ 48 ]. The mentioned features make it necessary to use new IT tools that allow the fullest use of new data [ 58 ]. The Big Data idea, inseparable from the huge increase in data available to various organizations or individuals, creates opportunities for access to valuable analyses, conclusions and enables making more accurate decisions [ 6 , 11 , 59 ].

The Big Data concept is constantly evolving and currently it does not focus on huge amounts of data, but rather on the process of creating value from this data [ 52 ]. Big Data is collected from various sources that have different data properties and are processed by different organizational units, resulting in creation of a Big Data chain [ 36 ]. The aim of the organizations is to manage, process and analyze Big Data. In the healthcare sector, Big Data streams consist of various types of data, namely [ 8 , 51 ]:

clinical data, i.e. data obtained from electronic medical records, data from hospital information systems, image centers, laboratories, pharmacies and other organizations providing health services, patient generated health data, physician’s free-text notes, genomic data, physiological monitoring data [ 4 ],

biometric data provided from various types of devices that monitor weight, pressure, glucose level, etc.,

financial data, constituting a full record of economic operations reflecting the conducted activity,

data from scientific research activities, i.e. results of research, including drug research, design of medical devices and new methods of treatment,

data provided by patients, including description of preferences, level of satisfaction, information from systems for self-monitoring of their activity: exercises, sleep, meals consumed, etc.

data from social media.

These data are provided not only by patients but also by organizations and institutions, as well as by various types of monitoring devices, sensors or instruments [ 16 ]. Data that has been generated so far in the healthcare sector is stored in both paper and digital form. Thus, the essence and the specificity of the process of Big Data analyses means that organizations need to face new technological and organizational challenges [ 67 ]. The healthcare sector has always generated huge amounts of data and this is connected, among others, with the need to store medical records of patients. However, the problem with Big Data in healthcare is not limited to an overwhelming volume but also an unprecedented diversity in terms of types, data formats and speed with which it should be analyzed in order to provide the necessary information on an ongoing basis [ 3 ]. It is also difficult to apply traditional tools and methods for management of unstructured data [ 67 ]. Due to the diversity and quantity of data sources that are growing all the time, advanced analytical tools and technologies, as well as Big Data analysis methods which can meet and exceed the possibilities of managing healthcare data, are needed [ 3 , 68 ].

Therefore, the potential is seen in Big Data analyses, especially in the aspect of improving the quality of medical care, saving lives or reducing costs [ 30 ]. Extracting from this tangle of given association rules, patterns and trends will allow health service providers and other stakeholders in the healthcare sector to offer more accurate and more insightful diagnoses of patients, personalized treatment, monitoring of the patients, preventive medicine, support of medical research and health population, as well as better quality of medical services and patient care while, at the same time, the ability to reduce costs (Fig.  1 ).

(Source: Own elaboration)

Healthcare Big Data Analytics applications

The main challenge with Big Data is how to handle such a large amount of information and use it to make data-driven decisions in plenty of areas [ 64 ]. In the context of healthcare data, another major challenge is to adjust big data storage, analysis, presentation of analysis results and inference basing on them in a clinical setting. Data analytics systems implemented in healthcare are designed to describe, integrate and present complex data in an appropriate way so that it can be understood better (Fig.  2 ). This would improve the efficiency of acquiring, storing, analyzing and visualizing big data from healthcare [ 71 ].

Process of Big Data Analytics

The result of data processing with the use of Big Data Analytics is appropriate data storytelling which may contribute to making decisions with both lower risk and data support. This, in turn, can benefit healthcare stakeholders. To take advantage of the potential massive amounts of data in healthcare and to ensure that the right intervention to the right patient is properly timed, personalized, and potentially beneficial to all components of the healthcare system such as the payer, patient, and management, analytics of large datasets must connect communities involved in data analytics and healthcare informatics [ 49 ]. Big Data Analytics can provide insight into clinical data and thus facilitate informed decision-making about the diagnosis and treatment of patients, prevention of diseases or others. Big Data Analytics can also improve the efficiency of healthcare organizations by realizing the data potential [ 3 , 62 ].

Big Data Analytics in medicine and healthcare refers to the integration and analysis of a large amount of complex heterogeneous data, such as various omics (genomics, epigenomics, transcriptomics, proteomics, metabolomics, interactomics, pharmacogenetics, deasomics), biomedical data, talemedicine data (sensors, medical equipment data) and electronic health records data [ 46 , 65 ].

When analyzing the phenomenon of Big Data in the healthcare sector, it should be noted that it can be considered from the point of view of three areas: epidemiological, clinical and business.

From a clinical point of view, the Big Data analysis aims to improve the health and condition of patients, enable long-term predictions about their health status and implementation of appropriate therapeutic procedures. Ultimately, the use of data analysis in medicine is to allow the adaptation of therapy to a specific patient, that is personalized medicine (precision, personalized medicine).

From an epidemiological point of view, it is desirable to obtain an accurate prognosis of morbidity in order to implement preventive programs in advance.

In the business context, Big Data analysis may enable offering personalized packages of commercial services or determining the probability of individual disease and infection occurrence. It is worth noting that Big Data means not only the collection and processing of data but, most of all, the inference and visualization of data necessary to obtain specific business benefits.

In order to introduce new management methods and new solutions in terms of effectiveness and transparency, it becomes necessary to make data more accessible, digital, searchable, as well as analyzed and visualized.

Erickson and Rothberg state that the information and data do not reveal their full value until insights are drawn from them. Data becomes useful when it enhances decision making and decision making is enhanced only when analytical techniques are used and an element of human interaction is applied [ 22 ].

Thus, healthcare has experienced much progress in usage and analysis of data. A large-scale digitalization and transparency in this sector is a key statement of almost all countries governments policies. For centuries, the treatment of patients was based on the judgment of doctors who made treatment decisions. In recent years, however, Evidence-Based Medicine has become more and more important as a result of it being related to the systematic analysis of clinical data and decision-making treatment based on the best available information [ 42 ]. In the healthcare sector, Big Data Analytics is expected to improve the quality of life and reduce operational costs [ 72 , 82 ]. Big Data Analytics enables organizations to improve and increase their understanding of the information contained in data. It also helps identify data that provides insightful insights for current as well as future decisions [ 28 ].

Big Data Analytics refers to technologies that are grounded mostly in data mining: text mining, web mining, process mining, audio and video analytics, statistical analysis, network analytics, social media analytics and web analytics [ 16 , 25 , 31 ]. Different data mining techniques can be applied on heterogeneous healthcare data sets, such as: anomaly detection, clustering, classification, association rules as well as summarization and visualization of those Big Data sets [ 65 ]. Modern data analytics techniques explore and leverage unique data characteristics even from high-speed data streams and sensor data [ 15 , 16 , 31 , 55 ]. Big Data can be used, for example, for better diagnosis in the context of comprehensive patient data, disease prevention and telemedicine (in particular when using real-time alerts for immediate care), monitoring patients at home, preventing unnecessary hospital visits, integrating medical imaging for a wider diagnosis, creating predictive analytics, reducing fraud and improving data security, better strategic planning and increasing patients’ involvement in their own health.

Big Data Analytics in healthcare can be divided into [ 33 , 73 , 74 ]:

descriptive analytics in healthcare is used to understand past and current healthcare decisions, converting data into useful information for understanding and analyzing healthcare decisions, outcomes and quality, as well as making informed decisions [ 33 ]. It can be used to create reports (i.e. about patients’ hospitalizations, physicians’ performance, utilization management), visualization, customized reports, drill down tables, or running queries on the basis of historical data.

predictive analytics operates on past performance in an effort to predict the future by examining historical or summarized health data, detecting patterns of relationships in these data, and then extrapolating these relationships to forecast. It can be used to i.e. predict the response of different patient groups to different drugs (dosages) or reactions (clinical trials), anticipate risk and find relationships in health data and detect hidden patterns [ 62 ]. In this way, it is possible to predict the epidemic spread, anticipate service contracts and plan healthcare resources. Predictive analytics is used in proper diagnosis and for appropriate treatments to be given to patients suffering from certain diseases [ 39 ].

prescriptive analytics—occurs when health problems involve too many choices or alternatives. It uses health and medical knowledge in addition to data or information. Prescriptive analytics is used in many areas of healthcare, including drug prescriptions and treatment alternatives. Personalized medicine and evidence-based medicine are both supported by prescriptive analytics.

discovery analytics—utilizes knowledge about knowledge to discover new “inventions” like drugs (drug discovery), previously unknown diseases and medical conditions, alternative treatments, etc.

Although the models and tools used in descriptive, predictive, prescriptive, and discovery analytics are different, many applications involve all four of them [ 62 ]. Big Data Analytics in healthcare can help enable personalized medicine by identifying optimal patient-specific treatments. This can influence the improvement of life standards, reduce waste of healthcare resources and save costs of healthcare [ 56 , 63 , 71 ]. The introduction of large data analysis gives new analytical possibilities in terms of scope, flexibility and visualization. Techniques such as data mining (computational pattern discovery process in large data sets) facilitate inductive reasoning and analysis of exploratory data, enabling scientists to identify data patterns that are independent of specific hypotheses. As a result, predictive analysis and real-time analysis becomes possible, making it easier for medical staff to start early treatments and reduce potential morbidity and mortality. In addition, document analysis, statistical modeling, discovering patterns and topics in document collections and data in the EHR, as well as an inductive approach can help identify and discover relationships between health phenomena.

Advanced analytical techniques can be used for a large amount of existing (but not yet analytical) data on patient health and related medical data to achieve a better understanding of the information and results obtained, as well as to design optimal clinical pathways [ 62 ]. Big Data Analytics in healthcare integrates analysis of several scientific areas such as bioinformatics, medical imaging, sensor informatics, medical informatics and health informatics [ 65 ]. Big Data Analytics in healthcare allows to analyze large datasets from thousands of patients, identifying clusters and correlation between datasets, as well as developing predictive models using data mining techniques [ 65 ]. Discussing all the techniques used for Big Data Analytics goes beyond the scope of a single article [ 25 ].

The success of Big Data analysis and its accuracy depend heavily on the tools and techniques used to analyze the ability to provide reliable, up-to-date and meaningful information to various stakeholders [ 12 ]. It is believed that the implementation of big data analytics by healthcare organizations could bring many benefits in the upcoming years, including lowering health care costs, better diagnosis and prediction of diseases and their spread, improving patient care and developing protocols to prevent re-hospitalization, optimizing staff, optimizing equipment, forecasting the need for hospital beds, operating rooms, treatments, and improving the drug supply chain [ 71 ].

Challenges and potential benefits of using Big Data Analytics in healthcare

Modern analytics gives possibilities not only to have insight in historical data, but also to have information necessary to generate insight into what may happen in the future. Even when it comes to prediction of evidence-based actions. The emphasis on reform has prompted payers and suppliers to pursue data analysis to reduce risk, detect fraud, improve efficiency and save lives. Everyone—payers, providers, even patients—are focusing on doing more with fewer resources. Thus, some areas in which enhanced data and analytics can yield the greatest results include various healthcare stakeholders (Table 1 ).

Healthcare organizations see the opportunity to grow through investments in Big Data Analytics. In recent years, by collecting medical data of patients, converting them into Big Data and applying appropriate algorithms, reliable information has been generated that helps patients, physicians and stakeholders in the health sector to identify values and opportunities [ 31 ]. It is worth noting that there are many changes and challenges in the structure of the healthcare sector. Digitization and effective use of Big Data in healthcare can bring benefits to every stakeholder in this sector. A single doctor would benefit the same as the entire healthcare system. Potential opportunities to achieve benefits and effects from Big Data in healthcare can be divided into four groups [ 8 ]:

Improving the quality of healthcare services:

assessment of diagnoses made by doctors and the manner of treatment of diseases indicated by them based on the decision support system working on Big Data collections,

detection of more effective, from a medical point of view, and more cost-effective ways to diagnose and treat patients,

analysis of large volumes of data to reach practical information useful for identifying needs, introducing new health services, preventing and overcoming crises,

prediction of the incidence of diseases,

detecting trends that lead to an improvement in health and lifestyle of the society,

analysis of the human genome for the introduction of personalized treatment.

Supporting the work of medical personnel

doctors’ comparison of current medical cases to cases from the past for better diagnosis and treatment adjustment,

detection of diseases at earlier stages when they can be more easily and quickly cured,

detecting epidemiological risks and improving control of pathogenic spots and reaction rates,

identification of patients who are predicted to have the highest risk of specific, life-threatening diseases by collating data on the history of the most common diseases, in healing people with reports entering insurance companies,

health management of each patient individually (personalized medicine) and health management of the whole society,

capturing and analyzing large amounts of data from hospitals and homes in real time, life monitoring devices to monitor safety and predict adverse events,

analysis of patient profiles to identify people for whom prevention should be applied, lifestyle change or preventive care approach,

the ability to predict the occurrence of specific diseases or worsening of patients’ results,

predicting disease progression and its determinants, estimating the risk of complications,

detecting drug interactions and their side effects.

Supporting scientific and research activity

supporting work on new drugs and clinical trials thanks to the possibility of analyzing “all data” instead of selecting a test sample,

the ability to identify patients with specific, biological features that will take part in specialized clinical trials,

selecting a group of patients for which the tested drug is likely to have the desired effect and no side effects,

using modeling and predictive analysis to design better drugs and devices.

Business and management

reduction of costs and counteracting abuse and counseling practices,

faster and more effective identification of incorrect or unauthorized financial operations in order to prevent abuse and eliminate errors,

increase in profitability by detecting patients generating high costs or identifying doctors whose work, procedures and treatment methods cost the most and offering them solutions that reduce the amount of money spent,

identification of unnecessary medical activities and procedures, e.g. duplicate tests.

According to research conducted by Wang, Kung and Byrd, Big Data Analytics benefits can be classified into five categories: IT infrastructure benefits (reducing system redundancy, avoiding unnecessary IT costs, transferring data quickly among healthcare IT systems, better use of healthcare systems, processing standardization among various healthcare IT systems, reducing IT maintenance costs regarding data storage), operational benefits (improving the quality and accuracy of clinical decisions, processing a large number of health records in seconds, reducing the time of patient travel, immediate access to clinical data to analyze, shortening the time of diagnostic test, reductions in surgery-related hospitalizations, exploring inconceivable new research avenues), organizational benefits (detecting interoperability problems much more quickly than traditional manual methods, improving cross-functional communication and collaboration among administrative staffs, researchers, clinicians and IT staffs, enabling data sharing with other institutions and adding new services, content sources and research partners), managerial benefits (gaining quick insights about changing healthcare trends in the market, providing members of the board and heads of department with sound decision-support information on the daily clinical setting, optimizing business growth-related decisions) and strategic benefits (providing a big picture view of treatment delivery for meeting future need, creating high competitive healthcare services) [ 73 ].

The above specification does not constitute a full list of potential areas of use of Big Data Analysis in healthcare because the possibilities of using analysis are practically unlimited. In addition, advanced analytical tools allow to analyze data from all possible sources and conduct cross-analyses to provide better data insights [ 26 ]. For example, a cross-analysis can refer to a combination of patient characteristics, as well as costs and care results that can help identify the best, in medical terms, and the most cost-effective treatment or treatments and this may allow a better adjustment of the service provider’s offer [ 62 ].

In turn, the analysis of patient profiles (e.g. segmentation and predictive modeling) allows identification of people who should be subject to prophylaxis, prevention or should change their lifestyle [ 8 ]. Shortened list of benefits for Big Data Analytics in healthcare is presented in paper [ 3 ] and consists of: better performance, day-to-day guides, detection of diseases in early stages, making predictive analytics, cost effectiveness, Evidence Based Medicine and effectiveness in patient treatment.

Summarizing, healthcare big data represents a huge potential for the transformation of healthcare: improvement of patients’ results, prediction of outbreaks of epidemics, valuable insights, avoidance of preventable diseases, reduction of the cost of healthcare delivery and improvement of the quality of life in general [ 1 ]. Big Data also generates many challenges such as difficulties in data capture, data storage, data analysis and data visualization [ 15 ]. The main challenges are connected with the issues of: data structure (Big Data should be user-friendly, transparent, and menu-driven but it is fragmented, dispersed, rarely standardized and difficult to aggregate and analyze), security (data security, privacy and sensitivity of healthcare data, there are significant concerns related to confidentiality), data standardization (data is stored in formats that are not compatible with all applications and technologies), storage and transfers (especially costs associated with securing, storing, and transferring unstructured data), managerial skills, such as data governance, lack of appropriate analytical skills and problems with Real-Time Analytics (health care is to be able to utilize Big Data in real time) [ 4 , 34 , 41 ].

The research is based on a critical analysis of the literature, as well as the presentation of selected results of direct research on the use of Big Data Analytics in medical facilities in Poland.

Presented research results are part of a larger questionnaire form on Big Data Analytics. The direct research was based on an interview questionnaire which contained 100 questions with 5-point Likert scale (1—strongly disagree, 2—I rather disagree, 3—I do not agree, nor disagree, 4—I rather agree, 5—I definitely agree) and 4 metrics questions. The study was conducted in December 2018 on a sample of 217 medical facilities (110 private, 107 public). The research was conducted by a specialized market research agency: Center for Research and Expertise of the University of Economics in Katowice.

When it comes to direct research, the selected entities included entities financed from public sources—the National Health Fund (23.5%), and entities operating commercially (11.5%). In the surveyed group of entities, more than a half (64.9%) are hybrid financed, both from public and commercial sources. The diversity of the research sample also applies to the size of the entities, defined by the number of employees. Taking into account proportions of the surveyed entities, it should be noted that in the sector structure, medium-sized (10–50 employees—34% of the sample) and large (51–250 employees—27%) entities dominate. The research was of all-Poland nature, and the entities included in the research sample come from all of the voivodships. The largest group were entities from Łódzkie (32%), Śląskie (18%) and Mazowieckie (18%) voivodships, as these voivodships have the largest number of medical institutions. Other regions of the country were represented by single units. The selection of the research sample was random—layered. As part of medical facilities database, groups of private and public medical facilities have been identified and the ones to which the questionnaire was targeted were drawn from each of these groups. The analyses were performed using the GNU PSPP 0.10.2 software.

The aim of the study was to determine whether medical facilities in Poland use Big Data Analytics and if so, in which areas. Characteristics of the research sample is presented in Table 2 .

The research is non-exhaustive due to the incomplete and uneven regional distribution of the samples, overrepresented in three voivodeships (Łódzkie, Mazowieckie and Śląskie). The size of the research sample (217 entities) allows the authors of the paper to formulate specific conclusions on the use of Big Data in the process of its management.

For the purpose of this paper, the following research hypotheses were formulated: (1) medical facilities in Poland are working on both structured and unstructured data (2) medical facilities in Poland are moving towards data-based healthcare and its benefits.

The paper poses the following research questions and statements that coincide with the selected questions from the research questionnaire:

From what sources do medical facilities obtain data? What types of data are used by the particular organization, whether structured or unstructured, and to what extent?

From what sources do medical facilities obtain data?

In which area organizations are using data and analytical systems (clinical or business)?

Is data analytics performed based on historical data or are predictive analyses also performed?

Determining whether administrative and medical staff receive complete, accurate and reliable data in a timely manner?

Determining whether real-time analyses are performed to support the particular organization’s activities.

Results and discussion

On the basis of the literature analysis and research study, a set of questions and statements related to the researched area was formulated. The results from the surveys show that medical facilities use a variety of data sources in their operations. These sources are both structured and unstructured data (Table 3 ).

According to the data provided by the respondents, considering the first statement made in the questionnaire, almost half of the medical institutions (47.58%) agreed that they rather collect and use structured data (e.g. databases and data warehouses, reports to external entities) and 10.57% entirely agree with this statement. As much as 23.35% of representatives of medical institutions stated “I agree or disagree”. Other medical facilities do not collect and use structured data (7.93%) and 6.17% strongly disagree with the first statement. Also, the median calculated based on the obtained results (median: 4), proves that medical facilities in Poland collect and use structured data (Table 4 ).

In turn, 28.19% of the medical institutions agreed that they rather collect and use unstructured data and as much as 9.25% entirely agree with this statement. The number of representatives of medical institutions that stated “I agree or disagree” was 27.31%. Other medical facilities do not collect and use structured data (17.18%) and 13.66% strongly disagree with the first statement. In the case of unstructured data the median is 3, which means that the collection and use of this type of data by medical facilities in Poland is lower.

In the further part of the analysis, it was checked whether the size of the medical facility and form of ownership have an impact on whether it analyzes unstructured data (Tables 4 and 5 ). In order to find this out, correlation coefficients were calculated.

Based on the calculations, it can be concluded that there is a small statistically monotonic correlation between the size of the medical facility and its collection and use of structured data (p < 0.001; τ = 0.16). This means that the use of structured data is slightly increasing in larger medical facilities. The size of the medical facility is more important according to use of unstructured data (p < 0.001; τ = 0.23) (Table 4 .).

To determine whether the form of medical facility ownership affects data collection, the Mann–Whitney U test was used. The calculations show that the form of ownership does not affect what data the organization collects and uses (Table 5 ).

Detailed information on the sources of from which medical facilities collect and use data is presented in the Table 6 .

The questionnaire results show that medical facilities are especially using information published in databases, reports to external units and transaction data, but they also use unstructured data from e-mails, medical devices, sensors, phone calls, audio and video data (Table 6 ). Data from social media, RFID and geolocation data are used to a small extent. Similar findings are concluded in the literature studies.

From the analysis of the answers given by the respondents, more than half of the medical facilities have integrated hospital system (HIS) implemented. As much as 43.61% use integrated hospital system and 16.30% use it extensively (Table 7 ). 19.38% of exanimated medical facilities do not use it at all. Moreover, most of the examined medical facilities (34.80% use it, 32.16% use extensively) conduct medical documentation in an electronic form, which gives an opportunity to use data analytics. Only 4.85% of medical facilities don’t use it at all.

Other problems that needed to be investigated were: whether medical facilities in Poland use data analytics? If so, in what form and in what areas? (Table 8 ). The analysis of answers given by the respondents about the potential of data analytics in medical facilities shows that a similar number of medical facilities use data analytics in administration and business (31.72% agreed with the statement no. 5 and 12.33% strongly agreed) as in the clinical area (33.04% agreed with the statement no. 6 and 12.33% strongly agreed). When considering decision-making issues, 35.24% agree with the statement "the organization uses data and analytical systems to support business decisions” and 8.37% of respondents strongly agree. Almost 40.09% agree with the statement that “the organization uses data and analytical systems to support clinical decisions (in the field of diagnostics and therapy)” and 15.42% of respondents strongly agree. Exanimated medical facilities use in their activity analytics based both on historical data (33.48% agree with statement 7 and 12.78% strongly agree) and predictive analytics (33.04% agrees with the statement number 8 and 15.86% strongly agree). Detailed results are presented in Table 8 .

Medical facilities focus on development in the field of data processing, as they confirm that they conduct analytical planning processes systematically and analyze new opportunities for strategic use of analytics in business and clinical activities (38.33% rather agree and 10.57% strongly agree with this statement). The situation is different with real-time data analysis, here, the situation is not so optimistic. Only 28.19% rather agree and 14.10% strongly agree with the statement that real-time analyses are performed to support an organization’s activities.

When considering whether a facility’s performance in the clinical area depends on the form of ownership, it can be concluded that taking the average and the Mann–Whitney U test depends. A higher degree of use of analyses in the clinical area can be observed in public institutions.

Whether a medical facility performs a descriptive or predictive analysis do not depend on the form of ownership (p > 0.05). It can be concluded that when analyzing the mean and median, they are higher in public facilities, than in private ones. What is more, the Mann–Whitney U test shows that these variables are dependent from each other (p < 0.05) (Table 9 ).

When considering whether a facility’s performance in the clinical area depends on its size, it can be concluded that taking the Kendall’s Tau (τ) it depends (p < 0.001; τ = 0.22), and the correlation is weak but statistically important. This means that the use of data and analytical systems to support clinical decisions (in the field of diagnostics and therapy) increases with the increase of size of the medical facility. A similar relationship, but even less powerful, can be found in the use of descriptive and predictive analyses (Table 10 ).

Considering the results of research in the area of analytical maturity of medical facilities, 8.81% of medical facilities stated that they are at the first level of maturity, i.e. an organization has developed analytical skills and does not perform analyses. As much as 13.66% of medical facilities confirmed that they have poor analytical skills, while 38.33% of the medical facility has located itself at level 3, meaning that “there is a lot to do in analytics”. On the other hand, 28.19% believe that analytical capabilities are well developed and 6.61% stated that analytics are at the highest level and the analytical capabilities are very well developed. Detailed data is presented in Table 11 . Average amounts to 3.11 and Median to 3.

The results of the research have enabled the formulation of following conclusions. Medical facilities in Poland are working on both structured and unstructured data. This data comes from databases, transactions, unstructured content of emails and documents, devices and sensors. However, the use of data from social media is smaller. In their activity, they reach for analytics in the administrative and business, as well as in the clinical area. Also, the decisions made are largely data-driven.

In summary, analysis of the literature that the benefits that medical facilities can get using Big Data Analytics in their activities relate primarily to patients, physicians and medical facilities. It can be confirmed that: patients will be better informed, will receive treatments that will work for them, will have prescribed medications that work for them and not be given unnecessary medications [ 78 ]. Physician roles will likely change to more of a consultant than decision maker. They will advise, warn, and help individual patients and have more time to form positive and lasting relationships with their patients in order to help people. Medical facilities will see changes as well, for example in fewer unnecessary hospitalizations, resulting initially in less revenue, but after the market adjusts, also the accomplishment [ 78 ]. The use of Big Data Analytics can literally revolutionize the way healthcare is practiced for better health and disease reduction.

The analysis of the latest data reveals that data analytics increase the accuracy of diagnoses. Physicians can use predictive algorithms to help them make more accurate diagnoses [ 45 ]. Moreover, it could be helpful in preventive medicine and public health because with early intervention, many diseases can be prevented or ameliorated [ 29 ]. Predictive analytics also allows to identify risk factors for a given patient, and with this knowledge patients will be able to change their lives what, in turn, may contribute to the fact that population disease patterns may dramatically change, resulting in savings in medical costs. Moreover, personalized medicine is the best solution for an individual patient seeking treatment. It can help doctors decide the exact treatments for those individuals. Better diagnoses and more targeted treatments will naturally lead to increases in good outcomes and fewer resources used, including doctors’ time.

The quantitative analysis of the research carried out and presented in this article made it possible to determine whether medical facilities in Poland use Big Data Analytics and if so, in which areas. Thanks to the results obtained it was possible to formulate the following conclusions. Medical facilities are working on both structured and unstructured data, which comes from databases, transactions, unstructured content of emails and documents, devices and sensors. According to analytics, they reach for analytics in the administrative and business, as well as in the clinical area. It clearly showed that the decisions made are largely data-driven. The results of the study confirm what has been analyzed in the literature. Medical facilities are moving towards data-based healthcare and its benefits.

In conclusion, Big Data Analytics has the potential for positive impact and global implications in healthcare. Future research on the use of Big Data in medical facilities will concern the definition of strategies adopted by medical facilities to promote and implement such solutions, as well as the benefits they gain from the use of Big Data analysis and how the perspectives in this area are seen.

Practical implications

This work sought to narrow the gap that exists in analyzing the possibility of using Big Data Analytics in healthcare. Showing how medical facilities in Poland are doing in this respect is an element that is part of global research carried out in this area, including [ 29 , 32 , 60 ].

Limitations and future directions

The research described in this article does not fully exhaust the questions related to the use of Big Data Analytics in Polish healthcare facilities. Only some of the dimensions characterizing the use of data by medical facilities in Poland have been examined. In order to get the full picture, it would be necessary to examine the results of using structured and unstructured data analytics in healthcare. Future research may examine the benefits that medical institutions achieve as a result of the analysis of structured and unstructured data in the clinical and management areas and what limitations they encounter in these areas. For this purpose, it is planned to conduct in-depth interviews with chosen medical facilities in Poland. These facilities could give additional data for empirical analyses based more on their suggestions. Further research should also include medical institutions from beyond the borders of Poland, enabling international comparative analyses.

Future research in the healthcare field has virtually endless possibilities. These regard the use of Big Data Analytics to diagnose specific conditions [ 47 , 66 , 69 , 76 ], propose an approach that can be used in other healthcare applications and create mechanisms to identify “patients like me” [ 75 , 80 ]. Big Data Analytics could also be used for studies related to the spread of pandemics, the efficacy of covid treatment [ 18 , 79 ], or psychology and psychiatry studies, e.g. emotion recognition [ 35 ].

Availability of data and materials

The datasets for this study are available on request to the corresponding author.

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This research was fully funded as statutory activity—subsidy of Ministry of Science and Higher Education granted for Technical University of Czestochowa on maintaining research potential in 2018. Research Number: BS/PB–622/3020/2014/P. Publication fee for the paper was financed by the University of Economics in Katowice.

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The role of artificial intelligence in healthcare: a structured literature review

• Silvana Secinaro 1 ,
• Davide Calandra 1 ,
• Aurelio Secinaro 2 ,
• Vivek Muthurangu 3 &
• Paolo Biancone 1  

BMC Medical Informatics and Decision Making volume  21 , Article number:  125 ( 2021 ) Cite this article

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Background/Introduction

Artificial intelligence (AI) in the healthcare sector is receiving attention from researchers and health professionals. Few previous studies have investigated this topic from a multi-disciplinary perspective, including accounting, business and management, decision sciences and health professions.

The structured literature review with its reliable and replicable research protocol allowed the researchers to extract 288 peer-reviewed papers from Scopus. The authors used qualitative and quantitative variables to analyse authors, journals, keywords, and collaboration networks among researchers. Additionally, the paper benefited from the Bibliometrix R software package.

The investigation showed that the literature in this field is emerging. It focuses on health services management, predictive medicine, patient data and diagnostics, and clinical decision-making. The United States, China, and the United Kingdom contributed the highest number of studies. Keyword analysis revealed that AI can support physicians in making a diagnosis, predicting the spread of diseases and customising treatment paths.

Conclusions

The literature reveals several AI applications for health services and a stream of research that has not fully been covered. For instance, AI projects require skills and data quality awareness for data-intensive analysis and knowledge-based management. Insights can help researchers and health professionals understand and address future research on AI in the healthcare field.

Peer Review reports

Artificial intelligence (AI) generally applies to computational technologies that emulate mechanisms assisted by human intelligence, such as thought, deep learning, adaptation, engagement, and sensory understanding [ 1 , 2 ]. Some devices can execute a role that typically involves human interpretation and decision-making [ 3 , 4 ]. These techniques have an interdisciplinary approach and can be applied to different fields, such as medicine and health. AI has been involved in medicine since as early as the 1950s, when physicians made the first attempts to improve their diagnoses using computer-aided programs [ 5 , 6 ]. Interest and advances in medical AI applications have surged in recent years due to the substantially enhanced computing power of modern computers and the vast amount of digital data available for collection and utilisation [ 7 ]. AI is gradually changing medical practice. There are several AI applications in medicine that can be used in a variety of medical fields, such as clinical, diagnostic, rehabilitative, surgical, and predictive practices. Another critical area of medicine where AI is making an impact is clinical decision-making and disease diagnosis. AI technologies can ingest, analyse, and report large volumes of data across different modalities to detect disease and guide clinical decisions [ 3 , 8 ]. AI applications can deal with the vast amount of data produced in medicine and find new information that would otherwise remain hidden in the mass of medical big data [ 9 , 10 , 11 ]. These technologies can also identify new drugs for health services management and patient care treatments [ 5 , 6 ].

Courage in the application of AI is visible through a search in the primary research databases. However, as Meskò et al. [ 7 ] find, the technology will potentially reduce care costs and repetitive operations by focusing the medical profession on critical thinking and clinical creativity. As Cho et al. and Doyle et al. [ 8 , 9 ] add, the AI perspective is exciting; however, new studies will be needed to establish the efficacy and applications of AI in the medical field [ 10 ].

Our paper will also concentrate on AI strategies for healthcare from the accounting, business, and management perspectives. The authors used the structured literature review (SLR) method for its reliable and replicable research protocol [ 11 ] and selected bibliometric variables as sources of investigation. Bibliometric usage enables the recognition of the main quantitative variables of the study stream [ 12 ]. This method facilitates the detection of the required details of a particular research subject, including field authors, number of publications, keywords for interaction between variables (policies, properties and governance) and country data [ 13 ]. It also allows the application of the science mapping technique [ 14 ]. Our paper adopted the Bibliometrix R package and the biblioshiny web interface as tools of analysis [ 14 ].

The investigation offers the following insights for future researchers and practitioners:

bibliometric information on 288 peer-reviewed English papers from the Scopus collection.

Identification of leading journals in this field, such as Journal of Medical Systems, Studies in Health Technology and Informatics, IEEE Journal of Biomedical and Health Informatics, and Decision Support Systems.

Qualitative and quantitative information on authors’ Lotka’s law, h-index, g-index, m-index, keyword, and citation data.

Research on specific countries to assess AI in the delivery and effectiveness of healthcare, quotes, and networks within each region.

A topic dendrogram study that identifies five research clusters: health services management, predictive medicine, patient data, diagnostics, and finally, clinical decision-making.

An in-depth discussion that develops theoretical and practical implications for future studies.

The paper is organised as follows. Section  2 lists the main bibliometric articles in this field. Section  3 elaborates on the methodology. Section  4 presents the findings of the bibliometric analysis. Section  5 discusses the main elements of AI in healthcare based on the study results. Section  6 concludes the article with future implications for research.

Related works and originality

As suggested by Zupic and Čater [ 15 ], a research stream can be evaluated with bibliometric methods that can introduce objectivity and mitigate researcher bias. For this reason, bibliometric methods are attracting increasing interest among researchers as a reliable and impersonal research analytical approach [ 16 , 17 ]. Recently, bibliometrics has been an essential method for analysing and predicting research trends [ 18 ]. Table  1 lists other research that has used a similar approach in the research stream investigated.

The scientific articles reported show substantial differences in keywords and research topics that have been previously studied. The bibliometric analysis of Huang et al. [ 19 ] describes rehabilitative medicine using virtual reality technology. According to the authors, the primary goal of rehabilitation is to enhance and restore functional ability and quality of life for patients with physical impairments or disabilities. In recent years, many healthcare disciplines have been privileged to access various technologies that provide tools for both research and clinical intervention.

Hao et al. [ 20 ] focus on text mining in medical research. As reported, text mining reveals new, previously unknown information by using a computer to automatically extract information from different text resources. Text mining methods can be regarded as an extension of data mining to text data. Text mining is playing an increasingly significant role in processing medical information. Similarly, the studies by dos Santos et al. [ 21 ] focus on applying data mining and machine learning (ML) techniques to public health problems. As stated in this research, public health may be defined as the art and science of preventing diseases, promoting health, and prolonging life. Using data mining and ML techniques, it is possible to discover new information that otherwise would be hidden. These two studies are related to another topic: medical big data. According to Liao et al. [ 22 ], big data is a typical “buzzword” in the business and research community, referring to a great mass of digital data collected from various sources. In the medical field, we can obtain a vast amount of data (i.e., medical big data). Data mining and ML techniques can help deal with this information and provide helpful insights for physicians and patients. More recently, Choudhury et al. [ 23 ] provide a systematic review on the use of ML to improve the care of elderly patients, demonstrating eligible studies primarily in psychological disorders and eye diseases.

Tran et al. [ 2 ] focus on the global evolution of AI research in medicine. Their bibliometric analysis highlights trends and topics related to AI applications and techniques. As stated in Connelly et al.’s [ 24 ] study, robot-assisted surgeries have rapidly increased in recent years. Their bibliometric analysis demonstrates how robotic-assisted surgery has gained acceptance in different medical fields, such as urological, colorectal, cardiothoracic, orthopaedic, maxillofacial and neurosurgery applications. Additionally, the bibliometric analysis of Guo et al. [ 25 ] provides an in-depth study of AI publications through December 2019. The paper focuses on tangible AI health applications, giving researchers an idea of how algorithms can help doctors and nurses. A new stream of research related to AI is also emerging. In this sense, Choudhury and Asan’s [ 26 ] scientific contribution provides a systematic review of the AI literature to identify health risks for patients. They report on 53 studies involving technology for clinical alerts, clinical reports, and drug safety. Considering the considerable interest within this research stream, this analysis differs from the current literature for several reasons. It aims to provide in-depth discussion, considering mainly the business, management, and accounting fields and not dealing only with medical and health profession publications.

Additionally, our analysis aims to provide a bibliometric analysis of variables such as authors, countries, citations and keywords to guide future research perspectives for researchers and practitioners, as similar analyses have done for several publications in other research streams [ 15 , 16 , 27 ]. In doing so, we use a different database, Scopus, that is typically adopted in social sciences fields. Finally, our analysis will propose and discuss a dominant framework of variables in this field, and our analysis will not be limited to AI application descriptions.

Methodology

This paper evaluated AI in healthcare research streams using the SLR method [ 11 ]. As suggested by Massaro et al. [ 11 ], an SLR enables the study of the scientific corpus of a research field, including the scientific rigour, reliability and replicability of operations carried out by researchers. As suggested by many scholars, the methodology allows qualitative and quantitative variables to highlight the best authors, journals and keywords and combine a systematic literature review and bibliometric analysis [ 27 , 28 , 29 , 30 ]. Despite its widespread use in business and management [ 16 , 31 ], the SLR is also used in the health sector based on the same philosophy through which it was originally conceived [ 32 , 33 ]. A methodological analysis of previously published articles reveals that the most frequently used steps are as follows [ 28 , 31 , 34 ]:

defining research questions;

writing the research protocol;

defining the research sample to be analysed;

developing codes for analysis; and

critically analysing, discussing, and identifying a future research agenda.

Considering the above premises, the authors believe that an SLR is the best method because it combines scientific validity, replicability of the research protocol and connection between multiple inputs.

As stated by the methodological paper, the first step is research question identification. For this purpose, we benefit from the analysis of Zupic and Čater [ 15 ], who provide several research questions for future researchers to link the study of authors, journals, keywords and citations. Therefore, RQ1 is “What are the most prominent authors, journal keywords and citations in the field of the research study?” Additionally, as suggested by Haleem et al. [ 35 ], new technologies, including AI, are changing the medical field in unexpected timeframes, requiring studies in multiple areas. Therefore, RQ2 is “How does artificial intelligence relate to healthcare, and what is the focus of the literature?” Then, as discussed by Massaro et al. [ 36 ], RQ3 is “What are the research applications of artificial intelligence for healthcare?”.

The first research question aims to define the qualitative and quantitative variables of the knowledge flow under investigation. The second research question seeks to determine the state of the art and applications of AI in healthcare. Finally, the third research question aims to help researchers identify practical and theoretical implications and future research ideas in this field.

The second fundamental step of the SLR is writing the research protocol [ 11 ]. Table  2 indicates the currently known literature elements, uniquely identifying the research focus, motivations and research strategy adopted and the results providing a link with the following points. Additionally, to strengthen the analysis, our investigation benefits from the PRISMA statement methodological article [ 37 ]. Although the SLR is a validated method for systematic reviews and meta-analyses, we believe that the workflow provided may benefit the replicability of the results [ 37 , 38 , 39 , 40 ]. Figure  1 summarises the researchers’ research steps, indicating that there are no results that can be referred to as a meta-analysis.

Source : Authors’ elaboration on Liberati et al. [ 37 ]

PRISMA workflow.

The third step is to specify the search strategy and search database. Our analysis is based on the search string “Artificial Intelligence” OR “AI” AND “Healthcare” with a focus on “Business, Management, and Accounting”, “Decision Sciences”, and “Health professions”. As suggested by [ 11 , 41 ] and motivated by [ 42 ], keywords can be selected through a top-down approach by identifying a large search field and then focusing on particular sub-topics. The paper uses data retrieved from the Scopus database, a multi-disciplinary database, which allowed the researchers to identify critical articles for scientific analysis [ 43 ]. Additionally, Scopus was selected based on Guo et al.’s [ 25 ] limitations, which suggest that “future studies will apply other databases, such as Scopus, to explore more potential papers” . The research focuses on articles and reviews published in peer-reviewed journals for their scientific relevance [ 11 , 16 , 17 , 29 ] and does not include the grey literature, conference proceedings or books/book chapters. Articles written in any language other than English were excluded [ 2 ]. For transparency and replicability, the analysis was conducted on 11 January 2021. Using this research strategy, the authors retrieved 288 articles. To strengthen the study's reliability, we publicly provide the full bibliometric extract on the Zenodo repository [ 44 , 45 ].

The fourth research phase is defining the code framework that initiates the analysis of the variables. The study will identify the following:

descriptive information of the research area;

source analysis [ 16 ];

author and citation analysis [ 28 ];

keywords and network analysis [ 14 ]; and

geographic distribution of the papers [ 14 ].

The final research phase is the article’s discussion and conclusion, where implications and future research trends will be identified.

At the research team level, the information is analysed with the statistical software R-Studio and the Bibliometrix package [ 15 ], which allows scientific analysis of the results obtained through the multi-disciplinary database.

The analysis of bibliometric results starts with a description of the main bibliometric statistics with the aim of answering RQ1, What are the most prominent authors, journal keywords and citations in the field of the research study?, and RQ2, How does artificial intelligence relate to healthcare, and what is the focus of the literature? Therefore, the following elements were thoroughly analysed: (1) type of document; (2) annual scientific production; (3) scientific sources; (4) source growth; (5) number of articles per author; (6) author’s dominance ranking; (7) author’s h-index, g-index, and m-index; (8) author’s productivity; (9) author’s keywords; (10) topic dendrogram; (11) a factorial map of the document with the highest contributions; (12) article citations; (13) country production; (14) country citations; (15) country collaboration map; and (16) country collaboration network.

Main information

Table  3 shows the information on 288 peer-reviewed articles published between 1992 and January 2021 extracted from the Scopus database. The number of keywords is 946 from 136 sources, and the number of keywords plus, referring to the number of keywords that frequently appear in an article’s title, was 2329. The analysis period covered 28 years and 1 month of scientific production and included an annual growth rate of 5.12%. However, the most significant increase in published articles occurred in the past three years (please see Fig.  2 ). On average, each article was written by three authors (3.56). Finally, the collaboration index (CI), which was calculated as the total number of authors of multi-authored articles/total number of multi-authored articles, was 3.97 [ 46 ].

Source : Authors’ elaboration

Annual scientific production.

Table  4 shows the top 20 sources related to the topic. The Journal of Medical Systems is the most relevant source, with twenty-one of the published articles. This journal's main issues are the foundations, functionality, interfaces, implementation, impacts, and evaluation of medical technologies. Another relevant source is Studies in Health Technology and Informatics, with eleven articles. This journal aims to extend scientific knowledge related to biomedical technologies and medical informatics research. Both journals deal with cloud computing, machine learning, and AI as a disruptive healthcare paradigm based on recent publications. The IEEE Journal of Biomedical and Health Informatics investigates technologies in health care, life sciences, and biomedicine applications from a broad perspective. The next journal, Decision Support Systems, aims to analyse how these technologies support decision-making from a multi-disciplinary view, considering business and management. Therefore, the analysis of the journals revealed that we are dealing with an interdisciplinary research field. This conclusion is confirmed, for example, by the presence of purely medical journals, journals dedicated to the technological growth of healthcare, and journals with a long-term perspective such as futures.

The distribution frequency of the articles (Fig.  3 ) indicates the journals dealing with the topic and related issues. Between 2008 and 2012, a significant growth in the number of publications on the subject is noticeable. However, the graph shows the results of the Loess regression, which includes the quantity and publication time of the journal under analysis as variables. This method allows the function to assume an unlimited distribution; that is, feature can consider values below zero if the data are close to zero. It contributes to a better visual result and highlights the discontinuity in the publication periods [ 47 ].

Source growth. Source : Authors’ elaboration

Finally, Fig.  4 provides an analytical perspective on factor analysis for the most cited papers. As indicated in the literature [ 48 , 49 ], using factor analysis to discover the most cited papers allows for a better understanding of the scientific world’s intellectual structure. For example, our research makes it possible to consider certain publications that effectively analyse subject specialisation. For instance, Santosh’s [ 50 ] article addresses the new paradigm of AI with ML algorithms for data analysis and decision support in the COVID-19 period, setting a benchmark in terms of citations by researchers. Moving on to the application, an article by Shickel et al. [ 51 ] begins with the belief that the healthcare world currently has much health and administrative data. In this context, AI and deep learning will support medical and administrative staff in extracting data, predicting outcomes, and learning medical representations. Finally, in the same line of research, Baig et al. [ 52 ], with a focus on wearable patient monitoring systems (WPMs), conclude that AI and deep learning may be landmarks for continuous patient monitoring and support for healthcare delivery.

Factorial map of the most cited documents.

This section identifies the most cited authors of articles on AI in healthcare. It also identifies the authors’ keywords, dominance factor (DF) ranking, h-index, productivity, and total number of citations. Table  5 identifies the authors and their publications in the top 20 rankings. As the table shows, Bushko R.G. has the highest number of publications: four papers. He is the editor-in-chief of Future of Health Technology, a scientific journal that aims to develop a clear vision of the future of health technology. Then, several authors each wrote three papers. For instance, Liu C. is a researcher active in the topic of ML and computer vision, and Sharma A. from Emory University Atlanta in the USA is a researcher with a clear focus on imaging and translational informatics. Some other authors have two publications each. While some authors have published as primary authors, most have published as co-authors. Hence, in the next section, we measure the contributory power of each author by investigating the DF ranking through the number of elements.

Authors’ dominance ranking

The dominance factor (DF) is a ratio measuring the fraction of multi-authored articles in which an author acts as the first author [ 53 ]. Several bibliometric studies use the DF in their analyses [ 46 , 54 ]. The DF ranking calculates an author’s dominance in producing articles. The DF is calculated by dividing the number of an author’s multi-authored papers as the first author (Nmf) by the author's total number of multi-authored papers (Nmt). This is omitted in the single-author case due to the constant value of 1 for single-authored articles. This formulation could lead to some distortions in the results, especially in fields where the first author is entered by surname alphabetical order [ 55 ].

The mathematical equation for the DF is shown as:

Table  6 lists the top 20 DF rankings. The data in the table show a low level of articles per author, either for first-authored or multi-authored articles. The results demonstrate that we are dealing with an emerging topic in the literature. Additionally, as shown in the table, Fox J. and Longoni C. are the most dominant authors in the field.

Authors’ impact

Table  7 shows the impact of authors in terms of the h-index [ 56 ] (i.e., the productivity and impact of citations of a researcher), g-index [ 57 ] (i.e., the distribution of citations received by a researcher's publications), m-index [ 58 ] (i.e., the h-index value per year), total citations, total paper and years of scientific publication. The H-index was introduced in the literature as a metric for the objective comparison of scientific results and depended on the number of publications and their impact [ 59 ]. The results show that the 20 most relevant authors have an h-index between 2 and 1. For the practical interpretation of the data, the authors considered data published by the London School of Economics [ 60 ]. In the social sciences, the analysis shows values of 7.6 for economic publications by professors and researchers who had been active for several years. Therefore, the youthfulness of the research area has attracted young researchers and professors. At the same time, new indicators have emerged over the years to diversify the logic of the h-index. For example, the g-index indicates an author's impact on citations, considering that a single article can generate these. The m-index, on the other hand, shows the cumulative value over the years.

The analysis, also considering the total number of citations, the number of papers published and the year of starting to publish, thus confirms that we are facing an expanding research flow.

Authors’ productivity

Figure  5 shows Lotka’s law. This mathematical formulation originated in 1926 to describe the publication frequency by authors in a specific research field [ 61 ]. In practice, the law states that the number of authors contributing to research in a given period is a fraction of the number who make up a single contribution [ 14 , 61 ].

Lotka’s law.

The mathematical relationship is expressed in reverse in the following way:

where y x is equal to the number of authors producing x articles in each research field. Therefore, C and n are constants that can be estimated in the calculation.

The figure's results are in line with Lotka's results, with an average of two publications per author in a given research field. In addition, the figure shows the percentage of authors. Our results lead us to state that we are dealing with a young and growing research field, even with this analysis. Approximately 70% of the authors had published only their first research article. Only approximately 20% had published two scientific papers.

Authors’ keywords

This section provides information on the relationship between the keywords artificial intelligence and healthcare . This analysis is essential to determine the research trend, identify gaps in the discussion on AI in healthcare, and identify the fields that can be interesting as research areas [ 42 , 62 ].

Table  8 highlights the total number of keywords per author in the top 20 positions. The ranking is based on the following elements: healthcare, artificial intelligence, and clinical decision support system . Keyword analysis confirms the scientific area of reference. In particular, we deduce the definition as “Artificial intelligence is the theory and development of computer systems able to perform tasks normally requiring human intelligence, such as visual perception, speech recognition, decision-making, and translation between languages” [ 2 , 63 ]. Panch et al. [ 4 ] find that these technologies can be used in different business and management areas. After the first keyword, the analysis reveals AI applications and related research such as machine learning and deep learning.

Additionally, data mining and big data are a step forward in implementing exciting AI applications. According to our specific interest, if we applied AI in healthcare, we would achieve technological applications to help and support doctors and medical researchers in decision-making. The link between AI and decision-making is the reason why we find, in the seventh position, the keyword clinical decision support system . AI techniques can unlock clinically relevant information hidden in the massive amount of data that can assist clinical decision-making [ 64 ]. If we analyse the following keywords, we find other elements related to decision-making and support systems.

The TreeMap below (Fig.  6 ) highlights the combination of possible keywords representing AI and healthcare.

Keywords treemap.

The topic dendrogram in Fig.  7 represents the hierarchical order and the relationship between the keywords generated by hierarchical clustering [ 42 ]. The cut in the figure and the vertical lines facilitate an investigation and interpretation of the different clusters. As stated by Andrews [ 48 ], the figure is not intended to find the perfect level of associations between clusters. However, it aims to estimate the approximate number of clusters to facilitate further discussion.

Topic dendrogram.

The research stream of AI in healthcare is divided into two main strands. The blue strand focuses on medical information systems and the internet. Some papers are related to healthcare organisations, such as the Internet of Things, meaning that healthcare organisations use AI to support health services management and data analysis. AI applications are also used to improve diagnostic and therapeutic accuracy and the overall clinical treatment process [ 2 ]. If we consider the second block, the red one, three different clusters highlight separate aspects of the topic. The first could be explained as AI and ML predictive algorithms. Through AI applications, it is possible to obtain a predictive approach that can ensure that patients are better monitored. This also allows a better understanding of risk perception for doctors and medical researchers. In the second cluster, the most frequent words are decisions , information system , and support system . This means that AI applications can support doctors and medical researchers in decision-making. Information coming from AI technologies can be used to consider difficult problems and support a more straightforward and rapid decision-making process. In the third cluster, it is vital to highlight that the ML model can deal with vast amounts of data. From those inputs, it can return outcomes that can optimise the work of healthcare organisations and scheduling of medical activities.

Furthermore, the word cloud in Fig.  8 highlights aspects of AI in healthcare, such as decision support systems, decision-making, health services management, learning systems, ML techniques and diseases. The figure depicts how AI is linked to healthcare and how it is used in medicine.

Word cloud.

Figure  9 represents the search trends based on the keywords analysed. The research started in 2012. First, it identified research topics related to clinical decision support systems. This topic was recurrent during the following years. Interestingly, in 2018, studies investigated AI and natural language processes as possible tools to manage patients and administrative elements. Finally, a new research stream considers AI's role in fighting COVID-19 [ 65 , 66 ].

Keywords frequency.

Table  9 represents the number of citations from other articles within the top 20 rankings. The analysis allows the benchmark studies in the field to be identified [ 48 ]. For instance, Burke et al. [ 67 ] writes the most cited paper and analyses efficient nurse rostering methodologies. The paper critically evaluates tangible interdisciplinary solutions that also include AI. Immediately thereafter, Ahmed M.A.'s article proposes a data-driven optimisation methodology to determine the optimal number of healthcare staff to optimise patients' productivity [ 68 ]. Finally, the third most cited article lays the groundwork for developing deep learning by considering diverse health and administrative information [ 51 ].

This section analyses the diffusion of AI in healthcare around the world. It highlights countries to show the geographies of this research. It includes all published articles, the total number of citations, and the collaboration network. The following sub-sections start with an analysis of the total number of published articles.

Country total articles

Figure  9 and Table  10 display the countries where AI in healthcare has been considered. The USA tops the list of countries with the maximum number of articles on the topic (215). It is followed by China (83), the UK (54), India (51), Australia (54), and Canada (32). It is immediately evident that the theme has developed on different continents, highlighting a growing interest in AI in healthcare. The figure shows that many areas, such as Russia, Eastern Europe and Africa except for Algeria, Egypt, and Morocco, have still not engaged in this scientific debate.

Country publications and collaboration map

This section discusses articles on AI in healthcare in terms of single or multiple publications in each country. It also aims to observe collaboration and networking between countries. Table  11 and Fig.  10 highlight the average citations by state and show that the UK, the USA, and Kuwait have a higher average number of citations than other countries. Italy, Spain and New Zealand have the most significant number of citations.

Articles per country.

Figure  11 depicts global collaborations. The blue colour on the map represents research cooperation among nations. Additionally, the pink border linking states indicates the extent of collaboration between authors. The primary cooperation between nations is between the USA and China, with two collaborative articles. Other collaborations among nations are limited to a few papers.

Collaboration map.

Artificial intelligence for healthcare: applications

This section aims to strengthen the research scope by answering RQ3: What are the research applications of artificial intelligence for healthcare?

Benefiting from the topical dendrogram, researchers will provide a development model based on four relevant variables [ 69 , 70 ]. AI has been a disruptive innovation in healthcare [ 4 ]. With its sophisticated algorithms and several applications, AI has assisted doctors and medical professionals in the domains of health information systems, geocoding health data, epidemic and syndromic surveillance, predictive modelling and decision support, and medical imaging [ 2 , 9 , 10 , 64 ]. Furthermore, the researchers considered the bibliometric analysis to identify four macro-variables dominant in the field and used them as authors' keywords. Therefore, the following sub-sections aim to explain the debate on applications in healthcare for AI techniques. These elements are shown in Fig.  12 .

Dominant variables for AI in healthcare.

Health services management

One of the notable aspects of AI techniques is potential support for comprehensive health services management. These applications can support doctors, nurses and administrators in their work. For instance, an AI system can provide health professionals with constant, possibly real-time medical information updates from various sources, including journals, textbooks, and clinical practices [ 2 , 10 ]. These applications' strength is becoming even more critical in the COVID-19 period, during which information exchange is continually needed to properly manage the pandemic worldwide [ 71 ]. Other applications involve coordinating information tools for patients and enabling appropriate inferences for health risk alerts and health outcome prediction [ 72 ]. AI applications allow, for example, hospitals and all health services to work more efficiently for the following reasons:

Clinicians can access data immediately when they need it.

Nurses can ensure better patient safety while administering medication.

Patients can stay informed and engaged in their care by communicating with their medical teams during hospital stays.

Additionally, AI can contribute to optimising logistics processes, for instance, realising drugs and equipment in a just-in-time supply system based totally on predictive algorithms [ 73 , 74 ]. Interesting applications can also support the training of personnel working in health services. This evidence could be helpful in bridging the gap between urban and rural health services [ 75 ]. Finally, health services management could benefit from AI to leverage the multiplicity of data in electronic health records by predicting data heterogeneity across hospitals and outpatient clinics, checking for outliers, performing clinical tests on the data, unifying patient representation, improving future models that can predict diagnostic tests and analyses, and creating transparency with benchmark data for analysing services delivered [ 51 , 76 ].

Predictive medicine

Another relevant topic is AI applications for disease prediction and diagnosis treatment, outcome prediction and prognosis evaluation [ 72 , 77 ]. Because AI can identify meaningful relationships in raw data, it can support diagnostic, treatment and prediction outcomes in many medical situations [ 64 ]. It allows medical professionals to embrace the proactive management of disease onset. Additionally, predictions are possible for identifying risk factors and drivers for each patient to help target healthcare interventions for better outcomes [ 3 ]. AI techniques can also help design and develop new drugs, monitor patients and personalise patient treatment plans [ 78 ]. Doctors benefit from having more time and concise data to make better patient decisions. Automatic learning through AI could disrupt medicine, allowing prediction models to be created for drugs and exams that monitor patients over their whole lives [ 79 ].

• Clinical decision-making

One of the keyword analysis main topics is that AI applications could support doctors and medical researchers in the clinical decision-making process. According to Jiang et al. [ 64 ], AI can help physicians make better clinical decisions or even replace human judgement in healthcare-specific functional areas. According to Bennett and Hauser [ 80 ], algorithms can benefit clinical decisions by accelerating the process and the amount of care provided, positively impacting the cost of health services. Therefore, AI technologies can support medical professionals in their activities and simplify their jobs [ 4 ]. Finally, as Redondo and Sandoval [ 81 ] find, algorithmic platforms can provide virtual assistance to help doctors understand the semantics of language and learning to solve business process queries as a human being would.

Patient data and diagnostics

Another challenging topic related to AI applications is patient data and diagnostics. AI techniques can help medical researchers deal with the vast amount of data from patients (i.e., medical big data ). AI systems can manage data generated from clinical activities, such as screening, diagnosis, and treatment assignment. In this way, health personnel can learn similar subjects and associations between subject features and outcomes of interest [ 64 ].

These technologies can analyse raw data and provide helpful insights that can be used in patient treatments. They can help doctors in the diagnostic process; for example, to realise a high-speed body scan, it will be simpler to have an overall patient condition image. Then, AI technology can recreate a 3D mapping solution of a patient’s body.

In terms of data, interesting research perspectives are emerging. For instance, we observed the emergence of a stream of research on patient data management and protection related to AI applications [ 82 ].

For diagnostics, AI techniques can make a difference in rehabilitation therapy and surgery. Numerous robots have been designed to support and manage such tasks. Rehabilitation robots physically support and guide, for example, a patient’s limb during motor therapy [ 83 ]. For surgery, AI has a vast opportunity to transform surgical robotics through devices that can perform semi-automated surgical tasks with increasing efficiency. The final aim of this technology is to automate procedures to negate human error while maintaining a high level of accuracy and precision [ 84 ]. Finally, the -19 period has led to increased remote patient diagnostics through telemedicine that enables remote observation of patients and provides physicians and nurses with support tools [ 66 , 85 , 86 ].

This study aims to provide a bibliometric analysis of publications on AI in healthcare, focusing on accounting, business and management, decision sciences and health profession studies. Using the SLR method of Massaro et al. [ 11 ], we provide a reliable and replicable research protocol for future studies in this field. Additionally, we investigate the trend of scientific publications on the subject, unexplored information, future directions, and implications using the science mapping workflow. Our analysis provides interesting insights.

In terms of bibliometric variables, the four leading journals, Journal of Medical Systems , Studies in Health Technology and Informatics , IEEE Journal of Biomedical and Health Informatics , and Decision Support Systems , are optimal locations for the publication of scientific articles on this topic. These journals deal mainly with healthcare, medical information systems, and applications such as cloud computing, machine learning, and AI. Additionally, in terms of h-index, Bushko R.G. and Liu C. are the most productive and impactful authors in this research stream. Burke et al.’s [ 67 ] contribution is the most cited with an analysis of nurse rostering using new technologies such as AI. Finally, in terms of keywords, co-occurrence reveals some interesting insights. For instance, researchers have found that AI has a role in diagnostic accuracy and helps in the analysis of health data by comparing thousands of medical records, experiencing automatic learning with clinical alerts, efficient management of health services and places of care, and the possibility of reconstructing patient history using these data.

Second, this paper finds five cluster analyses in healthcare applications: health services management, predictive medicine, patient data, diagnostics, and finally, clinical decision-making. These technologies can also contribute to optimising logistics processes in health services and allowing a better allocation of resources.

Third, the authors analysing the research findings and the issues under discussion strongly support AI's role in decision support. These applications, however, are demonstrated by creating a direct link to data quality management and the technology awareness of health personnel [ 87 ].

The importance of data quality for the decision-making process

Several authors have analysed AI in the healthcare research stream, but in this case, the authors focus on other literature that includes business and decision-making processes. In this regard, the analysis of the search flow reveals a double view of the literature. On the one hand, some contributions belong to the positivist literature and embrace future applications and implications of technology for health service management, data analysis and diagnostics [ 6 , 80 , 88 ]. On the other hand, some investigations also aim to understand the darker sides of technology and its impact. For example, as Carter [ 89 ] states, the impact of AI is multi-sectoral; its development, however, calls for action to protect personal data. Similarly, Davenport and Kalakota [ 77 ] focus on the ethical implications of using AI in healthcare. According to the authors, intelligent machines raise issues of accountability, transparency, and permission, especially in automated communication with patients. Our analysis does not indicate a marked strand of the literature; therefore, we argue that the discussion of elements such as the transparency of technology for patients is essential for the development of AI applications.

A large part of our results shows that, at the application level, AI can be used to improve medical support for patients (Fig.  11 ) [ 64 , 82 ]. However, we believe that, as indicated by Kalis et al. [ 90 ] on the pages of Harvard Business Review, the management of costly back-office problems should also be addressed.

The potential of algorithms includes data analysis. There is an immense quantity of data accessible now, which carries the possibility of providing information about a wide variety of medical and healthcare activities [ 91 ]. With the advent of modern computational methods, computer learning and AI techniques, there are numerous possibilities [ 79 , 83 , 84 ]. For example, AI makes it easier to turn data into concrete and actionable observations to improve decision-making, deliver high-quality patient treatment, adapt to real-time emergencies, and save more lives on the clinical front. In addition, AI makes it easier to leverage capital to develop systems and facilities and reduce expenses at the organisational level [ 78 ]. Studying contributions to the topic, we noticed that data accuracy was included in the debate, indicating that a high standard of data will benefit decision-making practitioners [ 38 , 77 ]. AI techniques are an essential instrument for studying data and the extraction of medical insight, and they may assist medical researchers in their practices. Using computational tools, healthcare stakeholders may leverage the power of data not only to evaluate past data ( descriptive analytics ) but also to forecast potential outcomes ( predictive analytics ) and to define the best actions for the present scenario ( prescriptive analytics ) [ 78 ]. The current abundance of evidence makes it easier to provide a broad view of patient health; doctors should have access to the correct details at the right time and location to provide the proper treatment [ 92 ].

Will medical technology de-skill doctors?

Further reflection concerns the skills of doctors. Studies have shown that healthcare personnel are progressively being exposed to technology for different purposes, such as collecting patient records or diagnosis [ 71 ]. This is demonstrated by the keywords (Fig.  6 ) that focus on technology and the role of decision-making with new innovative tools. In addition, the discussion expands with Lu [ 93 ], which indicates that the excessive use of technology could hinder doctors’ skills and clinical procedures' expansion. Among the main issues arising from the literature is the possible de-skilling of healthcare staff due to reduced autonomy in decision-making concerning patients [ 94 ]. Therefore, the challenges and discussion we uncovered in Fig.  11 are expanded by also considering the ethical implications of technology and the role of skills.

Implications

Our analysis also has multiple theoretical and practical implications.

In terms of theoretical contribution, this paper extends the previous results of Connelly et al., dos Santos et al, Hao et al., Huang et al., Liao et al. and Tran et al. [ 2 , 19 , 20 , 21 , 22 , 24 ] in considering AI in terms of clinical decision-making and data management quality.

In terms of practical implications, this paper aims to create a fruitful discussion with healthcare professionals and administrative staff on how AI can be at their service to increase work quality. Furthermore, this investigation offers a broad comprehension of bibliometric variables of AI techniques in healthcare. It can contribute to advancing scientific research in this field.

Limitations

Like any other, our study has some limitations that could be addressed by more in-depth future studies. For example, using only one research database, such as Scopus, could be limiting. Further analysis could also investigate the PubMed, IEEE, and Web of Science databases individually and holistically, especially the health parts. Then, the use of search terms such as "Artificial Intelligence" OR "AI" AND "Healthcare" could be too general and exclude interesting studies. Moreover, although we analysed 288 peer-reviewed scientific papers, because the new research topic is new, the analysis of conference papers could return interesting results for future researchers. Additionally, as this is a young research area, the analysis will be subject to recurrent obsolescence as multiple new research investigations are published. Finally, although bibliometric analysis has limited the subjectivity of the analysis [ 15 ], the verification of recurring themes could lead to different results by indicating areas of significant interest not listed here.

Future research avenues

Concerning future research perspectives, researchers believe that an analysis of the overall amount that a healthcare organisation should pay for AI technologies could be helpful. If these technologies are essential for health services management and patient treatment, governments should invest and contribute to healthcare organisations' modernisation. New investment funds could be made available in the healthcare world, as in the European case with the Next Generation EU programme or national investment programmes [ 95 ]. Additionally, this should happen especially in the poorest countries around the world, where there is a lack of infrastructure and services related to health and medicine [ 96 ]. On the other hand, it might be interesting to evaluate additional profits generated by healthcare organisations with AI technologies compared to those that do not use such technologies.

Further analysis could also identify why some parts of the world have not conducted studies in this area. It would be helpful to carry out a comparative analysis between countries active in this research field and countries that are not currently involved. It would make it possible to identify variables affecting AI technologies' presence or absence in healthcare organisations. The results of collaboration between countries also present future researchers with the challenge of greater exchanges between researchers and professionals. Therefore, further research could investigate the difference in vision between professionals and academics.

In the accounting, business, and management research area, there is currently a lack of quantitative analysis of the costs and profits generated by healthcare organisations that use AI technologies. Therefore, research in this direction could further increase our understanding of the topic and the number of healthcare organisations that can access technologies based on AI. Finally, as suggested in the discussion section, more interdisciplinary studies are needed to strengthen AI links with data quality management and AI and ethics considerations in healthcare.

In pursuing the philosophy of Massaro et al.’s [ 11 ] methodological article, we have climbed on the shoulders of giants, hoping to provide a bird's-eye view of the AI literature in healthcare. We performed this study with a bibliometric analysis aimed at discovering authors, countries of publication and collaboration, and keywords and themes. We found a fast-growing, multi-disciplinary stream of research that is attracting an increasing number of authors.

The research, therefore, adopts a quantitative approach to the analysis of bibliometric variables and a qualitative approach to the study of recurring keywords, which has allowed us to demonstrate strands of literature that are not purely positive. There are currently some limitations that will affect future research potential, especially in ethics, data governance and the competencies of the health workforce.

Availability of data and materials

All the data are retrieved from public scientific platforms.

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Secinaro, S., Calandra, D., Secinaro, A. et al. The role of artificial intelligence in healthcare: a structured literature review. BMC Med Inform Decis Mak 21 , 125 (2021). https://doi.org/10.1186/s12911-021-01488-9

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Objectives, methods, and results in critical health systems and policy research: evaluating the healthcare market

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Since the 1980s, markets have turned increasingly to intangible goods – healthcare, education, the arts, and justice. Over 40 years, the authors investigated healthcare commoditisation to produce policy knowledge relevant to patients, physicians, health professionals, and taxpayers. This paper revisits their objectives, methods, and results to enlighten healthcare policy design and research.

This paper meta-analyses the authors’ research that evaluated the markets impact on healthcare and professional culture and investigated how they influenced patients’ timely access to quality care and physicians’ working conditions. Based on these findings, they explored the political economic of healthcare.

In low-income countries the analysed research showed that, through loans and cooperation, multilateral agencies restricted the function of public services to disease control, with subsequent catastrophic reductions in access to care, health de-medicalisation, increased avoidable mortality, and failure to attain the narrow MDGs in Africa.

The pro-market reforms enacted in middle-income countries entailed the purchaser-provider split, privatisation of healthcare pre-financing, and government contracting of health finance management to private insurance companies. To establish the materiality of a cause-and-effect relationship, the authors compared the efficiency of Latin American national health systems according to whether or not they were pro-market and complied with international policy standards.

While pro-market health economists acknowledge that no market can offer equitable access to healthcare without effective regulation and control, the authors showed that both regulation and control were severely constrained in Asia by governance and medical secrecy issues.

In high-income countries they questioned the interest for population health of healthcare insurance companies, whilst comparing access to care and health expenditures in the European Union vs. the U.S., the Netherlands, and Switzerland. They demonstrated that commoditising healthcare increases mortality and suffering amenable to care considerably and carries professional, cultural, and ethical risks for doctors and health professionals. Pro-market policies systems cause health systems inefficiency, inequity in access to care and strain professionals’ ethics.

Policy research methodologies benefit from being inductive, as health services and systems evaluations, and population health studies are prerequisites to challenge official discourse and to explore the historical, economic, sociocultural, and political determinants of public policies.

Since the 1980s, markets have turned increasingly to intangible goods – health care, education, arts, and justice. Political changes have accompanied the transformation of health systems. After World War II, the WHO was founded to counter the health effects of devastating destruction, but over the last decades its funding by the World Health Assembly dropped to 25% of its budget. Foundations and industrial countries funded the rest, that is, their preferred programmes. The 1978 Alma Ata Declaration establishing the Primary Health Care policy had resulted from able WHO leadership and a growing social movement demanding health for all. One year later, the Selective Primary Health Care movement promoted by the Rockefeller Foundation undermined its foundations. It led the international policy exclusively to support disease control programmes in LMICs and to turn their first-line health services into epidemiological units allegedly because comprehensive primary health care was costly.

After the collapse of the “socialist” camp in 1989, the Washington Consensus, WB, and IMF conditioned low-interest loans on moves to market economy and government withdrawal from health care provision and financing. Since the 2000s, governments in industrialized countries and their private sector set up international disease control programmes called Global Health Initiatives. These were actually epidemiological public-private partnerships that replaced international cooperation in the health sector.

With the Millennium Development Goals (MDGs) and subsequent Sustainable Development Goals (SDGs), the United Nations set quite unambitious global health goals. They assigned donor-driven targets to LMIC governments, that is, controlling a limited number of pathologies, first transmissible and then increasingly chronic ones.

Over this period the authors evaluated pro-market reforms and policies and identified their determinants through the lenses of patients, physicians and health professionals, and taxpayers. Patients are concerned about accessibility to healthcare services and the price and quality of care. Physicians’ interests are, or should also be, their problem-solving capacity, professional freedom, intellectual progress, medical ethics, work environment, and income. These were the authors’ yardsticks to assess health systems and conduct policy research. These studies thus covered curative medicine, preventive medical care, and medical education but not the important field of inter-sectoral public health policies.

It all started in 1982, when J.-P. Unger discovered in Boston the Rockefeller Foundation’s long-term strategy to commoditise healthcare financing worldwide. In investigating the health marketisation motives of the “Selective Primary Healthcare” strategy in low- and middle-income countries (LMICs), he interviewed J. Walsh and K. Warren, the authors of a publication released just 1 year after the Alma Ata conference that advocated an alternative to the Primary Healthcare strategy called “selective primary healthcare” [ 1 ]. Their message was that the Primary Healthcare Strategy endorsed by the World Health Assembly in Alma Ata in 1978 was unaffordable. Instead, the Rockefeller Foundation promoted a policy that would turn low-income countries’ (LICs) public health systems into structures fit to host disease control programmes – “like Christmas ornaments festooning a Christmas tree” – rather than delivering individual health care. A field experiment in Deschapelles, Haiti [ 2 ], was a central piece of evidence supporting this strategy to make LIC health centres in public services mere disease control structures. The scenario pushed by the Rockefeller Foundation eventually came to be in LICs in the 1980s, albeit with major variants.

In 1986, we invalidated the efficiency alibi of this strategy. As an answer to the Rockefeller strategy, an action research project covering 180,000 persons in Kasongo, Congo [ 3 ] (then Zaire), enabled us to show that the cost of delivering individual health care and a few disease control and other public health interventions under a single administration could be similar to those of first-line services providing just five disease control programmes, because the former solution made it possible to keep its administration simple [ 4 ]. That prompted us to study the economic motives and public health consequences of healthcare insurance commercialisation, healthcare commoditisation, and health service privatisation and to build a case with coordinated studies. This paper meta-analyses the objectives, methods, and results of evaluations and research into market based health systems and policies spanning over 35 years ( https://pure.itg.be/en/persons/jeanpierre-unger(92d91a56-f267-4b85-82e7-9e4f8a8cffed).html ). Specifically, it aims to make sense of an array of policy studies that all relied on the same medical and public health ethical criteria already formulated in 1972 [ 5 ]; and to delineate health policy research standards relevant to physicians, health professionals, and patients’ representatives committed to the human right to health, i.e., the right to access professional care in universal health systems [ 6 ].

Research strategy

On the grounds of the Kasongo experience and aforementioned Walsh and Warren interview, we formulated in 1983 the overarching hypothesis of our decades-long policy research: Pro-market reforms of healthcare financing and management expand the healthcare delivery and disease control market to the detriment of patients, populations, doctors, health professionals, and taxpayers.

To confirm or overturn this hypothesis, we tested four secondary hypotheses and tried to show a causal relationship between pro-market policies’ characteristics and the following phenomena:

Regarding the access of patients and persons with health risks to professionally delivered healthcare, we tried to verify whether the market tended to allocate individual, “discretional” health care to the rich and public health interventions to the poor, thereby reducing the general population’s access to care significantly.

Regarding disease control, we checked whether public health programmes often failed because the market assigned them a vertical structure to be better suited to absorbing medical equipment and pharmaceuticals with public financing.

Regarding fiscal justice, we strove to determine whether health markets ran counter to social justice in health as they precluded the efficient and equitable use of taxes in the care sector.

About professionals’ ethics and personal development, we aimed to verify if care commoditisation was compatible with the physician’s reliance on professional ethics and investments in medical equipment and pharmaceuticals might antagonise the conditions of doctors’ and teams professional development.

This paper meta-analyses the authors’ research evaluating the impact of markets on health care and professional culture and investigating how they influenced patients’ timely access to quality care and physicians’ working conditions. Based on these findings, they explored the political economy of health care. However, there was no early design of a long-term research strategy. They conducted the studies according to opportunities, although some principles were adopted from the start:

Interdisciplinarity

Testing the above hypotheses required ad hoc, interdisciplinary research methods in order to build a good case for a causal relationship.

Heterogeneous research setting

The hypotheses had to be tested in a large array of health systems, from low- to high-income countries. To allow generalisations about the healthcare environment, countries and regions would be key policy analysis units.

Inductive reasoning

Historical studies would be based on public health evaluation of healthcare systems. Interpreting policy decisions critically required previous ex-post demonstration of ill-functioning services.

The authors approached qualitative research in medical care and public health policies by making use of the concept of praxeology that Bourdieu developed and adapted to sociology in his “Outline of a Theory of Practice.” [ 7 ] They took this approach because both medicine and public health, like sociology research, are combinations of practice and theory [ 8 ]. They believed that the failure to connect them was a frequent weakness of contemporary medical and public health research. An important aspect of praxeology is inductive reasoning. It builds on and evaluates propositions that are abstractions of observations of individual instances of members of the same class. In this regard, the policy evaluations were problem-based and relied on paradoxical observations of care delivery and health service management. They were the raw material of the research and prerequisites for assessing health systems and policies and then exploring the social, political, and economic determinants of faulty ones. Figure 1 depicts the inductive chain generally used in these policy analyses.

Sequencing the authors’ research on (inter-)national health policies

Deconstruction of the policy discourse

Deconstruction is a form of critical analysis concerned with the relationship between text and meaning. Jacques Derrida’s 1967 work on grammatology introduced the majority of its influential concepts. The authors set out to deconstruct public policies with qualitative, interpretative research and nested probabilistic studies. Their goal in this respect was to verify the evidence sustaining pro-market reforms in LMIC and high-income country (HIC) settings; based on these findings, expose their practical, political economy rationale; and then tentatively deconstruct the pro-market discourse of multilateral agencies and commercial organisations. Case studies of national healthcare policies and disease control programmes would provide the material required to analyse international policies and national health sector reforms [ 9 ].

Explicit research values

The authors made explicit their ethical values of social justice and medical professionalism because research methodology, policy evaluation, and interpretation depend on social, economic, and professional standards. These values, published elsewhere, were conceived of for healthcare delivery, management, planning, financing, and disease control. In particular, the authors relied on three healthcare standards with policy implications formulated in 1971, namely, holistic (biopsychosocial and patient-centred), continuous, and integrated care [ 5 ]. In Belgium, they served as an ideology to cement alliances of professionals concerned about quality and equitable access to care for more than 40 years [ 10 , 11 ]. The authors also relied on another key standard of medical practice, the Hippocratic “self-effacement” tenet (“Into whatsoever houses I enter, I will enter to help the sick, and I will abstain from all intentional wrong-doing and harm”) that is expected to deter physicians from making self-interested clinical decisions and maximising their profits with ad hoc clinical decisions, i.e., practising commercial medicine.

Evaluating disease control programmes, the hub of international and national health policies in LICs

By 2015, Africa still had not attained the modest MDGs in health. In 2007, we reviewed the grey literature issued by the main multilateral agencies active in the LIC health sector. Under the aegis of the MDGs, disease control was the conceptual and operational hub of health system reform in LICs. Our review revealed that over the preceding 25 years, virtually all the multilateral agencies active in the health sector had adopted policies restricting the function of LICs’ public services to disease control, thereby allocating individual healthcare delivery to commercial services (and private, non-profit facilities where they existed) [ 12 ].

To convince physicians and policy makers in LICs to adhere to sectoral reforms and to replace individual care delivery by disease control in public services, the Bretton Wood agencies attached conditions to their loans and projects and financed a host of local experts to produce the “scientific” justifications of this policy.

For LIC populations, the avoidable mortality, suffering, and anxiety that followed the loss of access to individual care proved immense. In Africa, virtually none of the MDGs were attained, regardless of their limited scope, precisely because in failing to deliver individual healthcare, African public services could no longer implement disease control initiatives satisfactorily.

To explain why a huge financial effort (AIDS control funds, for instance, were multiplied twentyfold between 1997 and 2007) could not achieve the MDGs in Africa, the authors

showed mathematically that successful disease control programmes required health facilities to be used by patients with various symptoms, as they represented the pool of users that these programmes needed for early case detection and follow-up [ 15 ].

studied the mechanisms whereby integrated disease control interventions hampered access to care in the services in which they were integrated and so undermined public services. Although a few AIDS and under-five programmes had been known to deliver bio-psychosocial care, disease control programmes in Africa have reduced the problem-solving capacities of health services; shrunk the professional identity and skills of physicians and nurses; reduced access to drugs to those managed by Global Health Initiatives; and limited in-service training to collective care delivery [ 16 ].

showed this to be a “catch 22” situation, with disease control programmes drastically reducing the number of users in the (public service) facilities where such programmes were implemented [ 17 ].

analysed the evidence of pro-market policies for other characteristics, such as equitable access to quality health care; mismatch of commercial healthcare delivery with medical ethics [ 18 ]; the inability of public services focusing on disease control to respond to people’s demands for individual care, thus preventing community participation; and undue restrictions on professional autonomy in health services designed as “machine bureaucracies.” [ 19 ]

The authors concluded that Hypothesis 2 was plausible because of the following:

Disease control-based reforms strained access to care in LICs without achieving their alleged epidemiological goals.

Replacing individual health care by disease control interventions in LIC public services could be the real motive of the related (inter-)national policies. This was because these reforms practically, albeit tacitly, ushered in a situation in which competition between public and private providers in delivering individual care was made impossible. Multinationals linked to charities that were focusing LMIC public services on disease control took advantage of the disappearance of publicly delivered health care to sell medical care to LMIC middle and upper classes without having to face public sector competition. International disease control programmes not only permitted the use of cooperation funds to purchase drugs and medical equipment manufactured by HIC industries, so fomenting aid-dependent pharmaceutical markets in LICs, but were also structured to foster the healthcare market in urban settings.

How do health-financing markets perform in middle-income countries? Comparing Latin American national healthcare policies and evaluating healthcare regulation in Asia

In MICs, pro-market health system reforms focused on national health care financing. Starting in Chile in the 1980s (under a military government) and in Colombia in 1993 (under an authoritarian government), the privatisation of health financing in Latin America occurred in virtually every country, even those with “socialist” governments. The two exceptions that did not undergo market reforms, Costa Rica [ 20 ] and Cuba [ 21 ], were performance outliers. However, the reform scenarios and organisation of health systems were not identical across the continent. Schematically, Insurance companies made profits whilst managing government funds, capturing the health expenditures of the healthy and wealthy middle class, and employing or contracting physicians. The political economics of health sector reforms in MICs consisted of variable combinations of

under-financing public services;

unduly favouring investments in public services over their recurrent operating costs;

putting the physicians working for publicly-oriented institutions under economic and workload stress;

separating purchasers and providers by law so as to create a niche for commercial insurance banks;

allowing commercial entities to manage public funds and possibly making this scheme mandatory;

privatising public hospitals or imposing commercial competition rules on them (the so-called “management property split”) and on contracted, self-employed physicians, too;

stimulating private financing of public hospitals (“private finance initiatives”);

limiting public services’ activities to unprofitable care, e.g., for the poor (Medicaid) and the elderly (Medicare) in HICs, and to disease control programmes in LMICs; and

liberalising investments in health care under the aegis of international trade treaties.

Given the many cultural and political similarities across Latin American countries, their health systems offered a good setting to explore strategic variants of care commoditisation. The authors assessed primarily the effects of pro-market reforms in Latin America by comparing the performance of systems abiding by international (World Bank, International Monetary Fund, Inter-American Development Bank, etc.) health policies with those that did not [ 9 ]. They thus studied the histories and functioning of some national health systems and the impact of financing options on their management, care quality, and access to care. To study health systems’ productivity, they relied on aggregated production data, population-based care accessibility and continuity rates and ratios; direct observations in healthcare services and administration; and interviews of patients, physicians, health professionals, policy makers, and public health experts.

They studied the health care and outcomes of large-scale, nationwide, in vivo experiments of care commoditisation. The ones they studied did not show any benefits for patients, physicians, health professionals, and/or public finances:

Colombia, which had been a good student, by international standards, since 1993, had a deplorable health record [ 22 ]. In our interviews we studied and compared the barriers to access to care erected in Colombia by a managed competition model with the barriers in north-eastern Brazil, where public services were severely under-financed [ 23 , 24 ]. As expected, both had very poor results.

In 2006, Chile’s public services [ 25 ], which had survived the dictatorship, covered 84% of the population with half of the country’s health expenditure. However, with just 50% of the country’s health expenditures, the public services managed to make the country a positive outlier in Latin America on many health indicators. The technical challenge of this study was to relate health system features to indicators of output (utilisation and coverage rates, for instance) and outcome (maternal mortality, for instance).

Finally, in 2001, Costa Rica, with its publicly-oriented healthcare services and financing, had about the same demographic and epidemiological features as the United States, although it spent nine times less per capita on health than the U.S. [ 20 ]

To fuel the legal and institutional dynamics of health insurance privatisation, the WHO and other UN agencies promoted a strategy called “Universal Health Coverage” (UHC) [ 26 ], that is, universal access to health insurance. Its pro-market discourse endorsed the idea that only insured populations could access health care [ 27 ], despite evidence that expanding insurance coverage might reduce service utilisation, e.g., when public-private insurance mixes were supposed to achieve universal coverage of health risks [ 28 , 29 , 30 ] and evidence of the superior effectiveness, fairness, and efficiency of Latin American off-market health systems [ 20 , 21 ].

The findings of these international comparisons led the authors to question the UHC strategy as a way to secure universal access to care. This was not only because public-private mixes in healthcare financing give rise to severe inefficiency in health systems, but because access to care was shown to be highly dependent on non-financial factors (geographical and psychological accessibility of health services, for instance) [ 31 ] otherwise neglected by the UHC strategy and possibly even undermined by it. In the absence of performance-based evidence supporting health-financing marketisation, the hypothesised centrality of an economic agenda in Latin American health reforms became plausible.

In sum, these comparisons of the Chilean, Colombian, Costa Rican, and Brazilian health systems and historical studies of Bolivia and Ecuador support Hypothesis 1 regarding the negative impact of pro-market policies on access to care and quality of care and Hypothesis 3 regarding fiscal injustice and inefficient use of public funds by commercial health services and insurance companies.

In addition, the authors’ studies of Asian health systems showed that the health care market was structurally flawed by the impossibility of regulating and controlling the activities of the private but also public health care sector in MICs properly. Whilst the Rockefeller Foundation had tacitly admitted that without regulation and control, privatising health services could not produce equitable access to care [ 32 ], the authors showed through their observations in nine (maternal health) case studies of regulations in China, India, and Vietnam [ 33 ] and theoretical discussion [ 34 ] that regulation and control of for-profit care delivery were most likely to be ineffectual in the MIC care sector.

In Vietnam, for instance, sex-selective abortion was responsible for a serious gender imbalance in spite of a decade of State regulation and control. Although a regulation against the practice had been passed in 2003 and implemented since 2006, regional disparities in gender-specific birth rates increased between 2006 and 2011. As a “critical incident”, the number of ultrasound violations detected in 2011 had been 1 positive out of 83,192 controls done in the health districts under study. And in 2016, the gender ratio still was 112 females/100 males in Vietnam. Against a background of strong social demand for sex-selective abortion in the middle class, selective abortions remained undetected in spite of the regulation and inspections because of the policy-makers’ failure to allocate sufficient resources to this exercise, weak governance, medical secrecy, conflicts of interests, dual physicians’ employment (in public and private healthcare services), the opacity of the medical market, and difficulties specifying contingency in clinical situations [ 33 ].

This set of nine studies in China, India, and Vietnam thus supports the plausibility of Hypothesis 4, as they confirm the vulnerability of medical ethics to care commoditisation policies when regulation and control of medical practice are ineffectual, which actually they are because of the socio-political and technical characteristics of middle income countries.

Assessing the impact of health markets on access to care in Europe

At the end of World War II, unionised blue-collar workers imposed social protection schemes in health. In a bipolar world, the workers’ organisations took advantage of progressive ideologies that were gathering strong followings in Europe. Whilst the weakened employers’ organisations prevailed upon the Social Democrats and Social Christians to join them in the anti-Communist struggle, they conceded the pillarization of European States. Workers’ trade unions, mutual societies, and political parties entered the parliaments (as was the case before Word War II), but also the State’s executive branches, judiciary, and social and health services, education, the police, and the army. That is how workers’ representatives limited the impact of corruption in State constituencies, i.e., preventing those who had the will and resources from buying the State’s policy and administrative decisions. They locked the sustainability of social security into government structures and secured access to professional health care in universal health systems as a human right. Admittedly, the users of healthcare services paid for this State pillarization with a dose of nepotism and its consequences. Still, European States had acquired key democratic features. Heated negotiations between representatives of social classes with opposing interests produced sectoral priorities within the overarching framework of national health budgets. In Belgium, for instance, this debate was institutionalised in the national social security organisation. Footnote 1

The macroeconomic result of this pillarization of the State can be seen in two inversely proportional numbers that show the importance of risk-pooling and solidarity in European health care financing, namely:

a government share in total health spending that long exceeded 80% and

total health expenditures that were high enough (about US$4000 per capita in 2014, of which approximately 10% was for the commercial sector) to make the publicly-oriented healthcare services Footnote 2 effective but sufficiently modest (10% of European GDP versus 17.1% of U.S. GDP in 2014) to favour economic growth outside the health sector.

That is how employees’ and employers’ taxes and social contributions made it possible to limit household expenditures on health care whilst securing one of the best geographically, financially, psychologically, and technically accessible forms of professional health care. Importantly, these schemes gave physicians sufficient professional autonomy. Access to professional care was equitable thanks to cost-redistributing, non-profit, non-actuarial health care financing and a sufficiently large proportion of non-material investments in the health sector.

With government social security schemes that included fairly comprehensive universal health insurance, Europeans enjoyed a high degree of social protection from 1945 to 1989 in Eastern Europe, until the 2008 financial crisis in Southern Europe, and even later in other countries.

Unfortunately, the institutional pillarization did not prevail at the European Union and Commission level. Rather, European politicians, civil servants, and political parties were the targets of more than 30,000 commercial lobbyists (1.4 per European Commission (EC) civil servant) [ 35 ] working to foster the interests of the international insurance banks that were investing in health, amongst other things. In contradiction to the provisions of the Treaty of Rome [ 36 ], the EC intervened in the Member States’ health care systems by negotiating international trade treaties involving investments in health care that could make healthcare management and medical practice subject to a commercial rationale. In addition, the 3% budget deficit rule that the Maastricht Treaty imposed on Member States gave political parties an opportunity and a plausible reason to cut public expenditures on health until healthcare financing would be sufficiently privatised, as the WB and IMF had done earlier in Latin America.

Public expenditure on health care was severely constrained but once health laws and regulations had been modified, as shown by the history of Dutch, Swiss, and Colombian health systems, insurance banks strove to maximise public and private expenditures on health care and governments found the needed resources through inter-branch arbitration.

In the U.S., where the health market was mature, the wealthy faced more problems accessing health care than the poor in most OECD countries, whilst the U.S. government alone spent more on health per capita than the total (public and private) per capita spending of most European countries [ 37 ]. Nevertheless, over the last 10 years, the number of uninsured Americans varied between 35 and 50 million. Many more were poorly insured. If the U.S. insurance coverage rate were applied to Europe, the number of uninsured Europeans would reach about 75 million. If the European ratio of mortality amenable to care became that of the United States, avoidable mortality would increase by up to 100,000 deaths per year.

In Latin American countries, the same financial structure yielded the same health effects as in the U.S. but, admittedly, not in the Netherlands and Switzerland. The sustainable performance of these two health systems is central to policy debates in Europe and, expectedly, insurance banks praise their functioning, except for one small detail: Since health care financing has been marketed (respectively in 1996 and 2006), the Swiss and Dutch health expenditures have skyrocketed [ 38 ].

What are the reasons to believe that health insurance markets are environments hostile to the universal right to care? The authors evaluated [ 39 ] the performances of the U.S., the Netherlands, and Switzerland, three industrial nations that pursued market-based financing models, with a focus on equity in access to care, care quality, health status, and efficiency. They then assessed the consistency of their findings with those of various research teams. Using secondary data obtained from a semi-structured review of articles from 2000 to 2017, inter alia, they discussed the hypothesis that commercial health care insurance was detrimental to access to professional health care and population health status.

The findings can be summarised as follows:

In 2010, poor Americans had twice the unmet care needs of Americans with above-average incomes and ten times more than the UK poor. The unmet care needs of the rich in the U.S. exceeded those of the poor in several industrial countries [ 40 ]. The number of Dutchmen and -women experiencing financial obstacles to health care quadrupled between 2007 and 2013 [ 41 ]. Switzerland ranked second worst in a 2016 survey of 11 countries, just ahead of the USA, with 22% of Swiss adults likely to skip needed care [ 42 ].

The most negative impacts of “managed care” on care quality were its tight constraints on physicians’ professional autonomy, large reliance on the physicians’ material motivation, the fragmentation of health services, and a tendency to apply evidence-based medicine too rigidly. In requiring strict application of clinical protocols, commercially managed care was less likely to be favourable to care quality than systems giving physicians sufficient freedom to rely on professional decision-making and medical ethics.

The prevalence of burnout amongst MDs made medical practice the riskiest occupation in the United States and one of the riskiest occupations in Europe [ 43 ]. This burnout was not related to insufficient income but to excessive workloads and to perceiving existential threats to their professional identity, ethics, and autonomy in the way health care was organised. This observation supports Hypothesis 4 because these psychological and professional status threats actually result from the commoditisation of care [ 44 ].

Countries with a commercial insurance monopoly generally remained above the maternal, infant, and neonatal mortality rates v. the health-spending regression line [ 45 ]. And the growth rates of health expenditure were the highest in the U.S. and Switzerland, with the Netherlands not far behind [ 46 ].

Controlling for the impact of the obesity confounding factor, these studies reveal that the industrialisation of care contributes to the comparatively poor performance of the U.S., Dutch, and Swiss health systems, with the Dutch first-line services being an exception made possible by the GPs’ medical culture and the low cost to patient.

International trade treaties may further worsen the mortality rates of cardiovascular and cerebrovascular conditions, diabetes, and cancers in Europe, since they favour the food industry’s market penetration [ 47 ].

These findings admittedly conflict with recent influential health system rankings, perhaps because of the ways their health indicators are constructed and a bias towards assessing first-line healthcare services.

In conclusion, the comparison of US, Dutch, and Swiss health systems with the others in Europe supports the validity of Hypotheses 1 and 3. The most inefficient system is where the insurance market has achieved its maximal development, that is, in the U.S. In general, healthcare expenditures rose faster where health insurance was commoditised. The Netherlands and Switzerland reveal that increasing expenditure on health care enables health systems based on commercial insurance to maintain decent access to professionally-delivered health care for a few years.

The sizeably better, much more equitable access to health care in Western Europe (and its demographic and epidemiological superiority over the U.S.) and its much lower cost is generally explained by redistributive laws and regulations (tax-based or mandatory social security) channelled through health care public services or mutual societies that permit solidarity in health care financing.

The analysis of the U.S. health system’s disappointing performance reveals that actuarial management of health finances and the commercial management of health services are responsible for deficient accessibility to care and services. In particular, actuarial management of health care reduces risk pooling and solidarity in health financing between men and women, the young and the elderly, the sick and the well, high and low risks, and rich and poor.

Methodological lessons for descriptive, policy studies

Identifying health services productivity shortfalls and dysfunctional structures

The authors tried to provide patients’ and physicians’ organisations with the evidence and clues about policies from the angle of the human right to care and professional endeavour. Their research assessed the influence of policies on health services’ productivity in defined historical contexts from various standpoints: those of patients (e.g., care quality and accessibility); physicians (e.g., continuing medical education and teamwork); taxpayers (efficiency and equity in use of public monies); and public health specialists (health care and disease control management).

From an inductive study perspective, documenting health services’ structural and functional deficiencies provided the raw material for assessing health systems and possibly challenging policy decisions and official discourse.

To gauge the quality of health care, the authors used medical knowledge to observe clinical practice (sometimes as mock patients) [ 48 ]. For instance, to assess the impact of managed care techniques on care quality in Costa Rica, they sat in on consultations. The research hypotheses had been formulated by the Limon region’s GPs, who suggested that there was a relationship between managed care ( compromisos de gestión ) and the lack of time available for interpersonal communication and deficient care accessibility [ 49 ]. In addition, they collected data on disease-specific indicators to explore the extent to which managed care techniques were responsible for decreasing care quality and data reliability.

To assess care accessibility, they often used the services’ routine production data, with indicators such as population-based utilisation rates of curative care in first-line services and hospital admission rates [ 31 ], referral completion rates, and preventive (vaccination, antenatal clinics, etc.) coverage rates, and then they validated them by triangulation when possible. As a proxy for the financial accessibility of health care, they used “catastrophic health expenditures.” [ 50 ] Routine data proved cheaper, readily available, and a good reflection of the services’ operations in large geographical areas, but the method had limits even when it was combined with data triangulation and controls:

In Colombia, semi-structured interviews of patients and professionals proved indispensable to gauge care accessibility [ 51 , 52 , 53 ] because networks of “sentinel physicians” were not organised to collect service utilisation and epidemiological data; population-based statistics were not available and the denominators would have consisted of populations affiliated with a myriad of health insurers and care providers; and private insurance companies were reluctant to provide data that could undermine their reputations.

The routine data were sometimes biased, such as in the case of a state administration in charge of determining regional maternal mortality rates in Asia. Aside from the technical difficulties of establishing the maternal mortality rate (MMR), middle line managers were likely to be penalised when this indicator was too high but also too low, because in the latter case the administration did not trust the data’s validity [ 33 ]. Hence a regression to the mean …

In general, the authors relied on output indicators rather than on population outcome. However, two demographic indicators proved particularly interesting for critical assessment of healthcare systems:

The Maternal Mortality Rate (MMR) reflects access to the entire healthcare system pyramid [ 54 ], particularly in LMICs [ 55 ] and probably in any situation where it exceeds 40 per thousand. This is in contrast to the Infant Mortality Rate (IMR), which in LICs often mirrors low-cost interventions that may reduce access to care (such as immunisation campaigns) [ 56 ] and biomedical/sociocultural health determinants (such as the availability of food and clean water and women’s education, respectively). Since the lower the per capita GDP, the cheaper and less reliable the demographic indicators used [ 57 ], the authors retained in practice only the gross differences when comparing the health systems’ performances in terms of MMR. In 2010, for instance, Moldova, the poorest country in Europe, had the same MMR as the U.S., despite spending 1/20 as much on health per capita.

In HICs, life expectancy and population mortality rates mirror obesity-associated pathologies but, just as importantly, access to quality health care. Up to 80% of premature deaths in Poland were explained by unsatisfactory access to health care [ 58 ]. According to Kruk and co-workers, 15.6 million excess deaths from 61 conditions occurred in LMIC in 2016. This research compared case fatality between each LMIC with corresponding numbers from 23 high-income reference countries with strong health systems. After excluding deaths that were preventable by public health measures, the authors found that 55% of excess deaths were amenable to health care and could be put down to either the receipt of poor-quality care or the non-utilisation of health care [ 59 ].

To evaluate health systems by the design and performance of their disease control programmes, the authors relied on two models:

An all-purpose disease control model (“ vertical analysis” ), designed by P. Mercenier [ 60 ] to provide standards for the design of disease-specific control programmes. It was based on the systemic representation of the disease-specific syndromes and vector development stages and biomedical and socio-cultural interventions to interrupt the disease chain in the field, from aetiology to patient death.

M. Piot’s model [ 61 ] to assess care continuity for any defined disease. It establishes the disease-specific cure rate as the product of coefficients measuring detection, diagnosis, and treatment activities. As the model reveals the health system characteristics needed to secure, say, early detection and care continuity, they used it to contrast the performances of public and private sectors in tuberculosis control in India [ 62 ] and to evaluate malaria control programmes in Mali and Sub-Saharan Africa in general [ 15 ].

Once health system productivity had been studied, the authors analysed the organisation of health services and systems. For this they relied on managerial models and standards specific to

publicly-minded care management (e.g., concerned with access to professional health care, professional autonomy and well-being, professional ethics, and public health) [ 63 ];

the systemic management of hospital(s) and first-line facilities networks [ 18 ]; and

“divisionalised adhocracy”, an organisation pattern that favours knowledge management and teamwork [ 19 ] and is suited to systems whose end-line producers are highly skilled and sufficiently autonomous professionals (as are physicians) rather than workers and technicians, as assumed by the classic generic management theories.

Studies of health financing and systems characteristics that cause low services productivity

Health system case studies and the existence of large databases in the health sector provided the opportunity to single out natural, quasi-experimental study designs – time series and non-equivalent comparisons – to contrast health systems with and without or before and after pro-market health reforms:

For non-equivalent groups (countries, regions, etc.), the authors compared the performances of national/regional health systems in Latin America compliant with the international policy standards with those of “disobedient” ones [ 9 ] and established a typology of reforms.

With time series, we showed long-standing, substandard performances in the quality, accessibility, and financing of health care (for instance, after the privatisation of health insurance in Colombia).

Time series of health services’ routine data also proved useful to reveal contradictory interactions of health activities in populations. For instance, in the late 1980s, the utilisation of medical consultations decreased steadily in Senegal whilst immunisation campaigns were implemented in health care services [ 64 ]. The challenge of the study consisted in demonstrating a causal relationship between these campaigns and the subsequent sustained deterioration of care accessibility in public services.

Beyond substandard care performances: political economics

Inductive research made it possible to deconstruct official self-apologetic discourses. The authors were then able to seek the real motives for ill-conceived policies whose results belied the stated objectives. Their entry point in the complex socio-cultural and political determinants of health policies was political economics because of the huge weight of health expenditures in the global economy (up to 17% of U.S. GDP and 11.3% of Germany’s GDP) and the political leverage acquired by the economic players. The economic determinism of health care policies was so powerful that these players did not even need to be coordinated to gear health systems towards care markets [ 65 ].

From corruption [ 66 ], political leverage, and lobbying to trade, it takes time for relationships between commercial organisations and public institutions to result in health systems’ structures and new professional practice. Some studies thus adopted an historical viewpoint [ 12 , 65 , 67 , 68 ] to probe the care commoditisation mechanisms. Even in non-profit organisations, the main determinant of poor healthcare accessibility proved to be the business mission of health financing, management, and medical practice.

However, correlations between events, sequences, sociological observations, and relationships between historical times enabled us to identify professional, cultural, and geostrategic determinants of health policies alongside economic ones. The prevailing order was reflected in professional culture thanks to education, information, scientific ideology, and advertising. The resulting personal characteristics, identity, and knowledge of physicians and professionals were the conditions of health systems’ reproducibility. Bourdieu calls these internal features “habitus,” i.e., ways of doing and being, and “representations”.

Since 1985, the trend has been towards the privatisation of health financing, public subsidies for private health care providers, commercial management of health services, and for-profit medical practice, in spite of the wealth of evidence pointing to the risks of large-scale mortality and morbidity and threats to professional ethics associated with the commoditisation of care.

Governments and multilateral agencies ought to be held accountable when health policies cause avoidable mortality and suffering and thus human rights violations, or at least “be shamed”, as Sir Michael Marmot once said. Therefore, with States being fields “structured according to oppositions linked to specific forms of capital” [ 69 ], health system and policy research should not so much address the knowledge needs of policy makers directly as those of physicians, socially-minded professionals, and patients’ organisations that could leverage them. Political indictments on the impact of health policies require these organisations to access the relevant scientific and professional information in order to question and challenge public policies in the health sector.

The studies analysed here stemmed from the human right to access professional care in universal health systems and the knowledge they produced was directed at physicians, health professionals, and patients’ organisations sharing moral values and interested in lobbying health policies. The present meta-analysis sheds light on the requirements of this type of research:

Inductive, multidisciplinary policy research is time-consuming but often a condition to study health policies independently:

International health policies assessments benefit from analysing national healthcare policies and disease control programmes.

National health policies should be studied with political economy and medical concepts, and through the lenses of political science and history, but importantly on the grounds of health systems and services productivity assessment.

Medical concepts, public health models, and indicators of professional care delivery and non-profit health management make it possible to evaluate health systems from a professionally- and socially-driven, problem-based perspective.

Health systems and policy researchers need scientific and professional knowledge. Academics should engage in medical, managerial, and policy-making work alongside their research and teaching activities. Therefore, medical and public health schools should learn to assess the academic’s professional proficiency and ability to derive validated theory from their practice.

Professional ethics should be a criterion for evaluating care quality:

Although values are an obstacle to Weberian axiological neutrality in medical, public health, and education policy studies, they are indispensable to assess care quality, health services, and healthcare systems. From a phenomenological perspective, they ought to be made available to the reader.

Health systems have evolved rapidly over the last three decades. Long-term reliance on the same set of explicit ethical and technical criteria applicable to medical practice and health services organisation is what allows valid conclusions to be drawn from time series and comparative or historical studies of health systems that belong to different eras.

Such studies ought not to be only descriptive and critical but also designed as proposals to improve health systems and policies. Those analysed here reveal many nationwide experiences to improve access to professional care. Some countries (Costa Rica, Cuba, Spain, Sri Lanka, Thailand, and Italy), states (e.g. , Kerala), regions or cities (e.g. Rosario, Argentina), and health systems (Chilean public services) acquired collective knowledge to develop non-commercial care delivery and promote ethical, medical practice. There is no doubt that decades of neoliberal policy have compromised their professional achievements, to the point that they are often no longer perceptible.

Medical journals ought to be devoted to professional practice and not only to science, and be independent and publicly financed. Given the undeclared conflict of interest created by the presence of insurance banks in the shareholding of top impact-factor medical journals, physicians’ and patients’ organisations should lobby public universities to stop relying on the researcher’s bibliometrics and the impact factor to decide on scientists’ careers.

The hypothesis that the authors formulated in 1983 can reasonably be accepted. Health markets most likely undermine patients’ health, physicians and professionals’ status and morale, and taxpayers’ interests. The key function of health sector reforms is not public health but economic: they aim to privatise the profitable part of health care financing; maximise the return on health care with commercial healthcare management of services and for-profit care delivery; prevent public services from being involved in a competition with the private sector for health care delivery, management, and financing; and open markets in LMICs with public aid funds to medical and pharmaceutical goods preferably manufactured in industrialised countries.

The studies analysed here show physicians and their organisations that commercial healthcare financing is incompatible with ethical, medical practice because, with or without vertical integration (in HMOs or PPOs), whether through contracts or wages, it imposes the goal of maximising shareholders’ profits on physicians and health professionals, whereas this commercial mission goes against the grain of Hippocratic ethics.

To patients’ organisations, the studies analysed here prove worldwide that care commercialisation prevents solidarity in healthcare financing and obstructs equal access to care. Markets segment health systems, they foment competition between physicians, whilst cooperation among them is essential to peoples’ health [ 13 ]. Moreover, they use public expenditure on healthcare inefficiently.

This research thus opens avenues for joint political action by patients’ and physicians’ organisations to defend and promote social protection in health because it shows that both doctors and patients benefit from professional care delivery and publicly-oriented care financing and management; the major contemporary threat to care accessibility and quality, namely, the privatisation of health care financing, also jeopardises the physicians’ autonomy, ethics, and incomes.

Finally, this research shows that competition prevails between not only commercial entities but also sectors. The interests of insurance banks investing in health and those of all the other economic actors are contradictory: Inter-country comparisons of total health expenditures reveal that the commodisation of care is accompanied by broad inter-sectoral, macro-economic redistribution. Economic agents that do not invest in health insurances would do better to learn from this.

Availability of data and materials

Data sharing is not applicable to this article as no datasets were generated or analysed during the current study.

Whilst these schemes had been forced upon employers, they unexpectedly proved to be highly favourable to economic growth.

Referring to health services and systems, we use the terms “publicly oriented”, “publicly minded”, “socially driven”, “non commercial”, and “not for profit” interchangeably

Abbreviations

Disability-adjusted life years

European Commission

Global health initiatives

General practitioner

High income countries

Infant mortality rate

Low and middle income countries

Medical doctor

Millennium development goals

Maternal mortality rate

Quality-adjusted life years

Sustainable development goals

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Acknowledgements

We are indebted to Professors Charlene Harrington (Department of Social & Behavioral Sciences, University of California San Francisco), Antonio Ugalde (University of Texas at Austin, College of Liberal Arts), and Matt Anderson (Albert Einstein College of Medicine, New York) for their indispensable comments. Gaby Leyden edited the manuscript thoroughly. No error can be attributed to them.

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Publication of the supplement has been funded by the Institute of Tropical Medicine, Antwerp, Belgium.

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Unger, JP., Morales, I. & De Paepe, P. Objectives, methods, and results in critical health systems and policy research: evaluating the healthcare market. BMC Health Serv Res 20 (Suppl 2), 1072 (2020). https://doi.org/10.1186/s12913-020-05889-w

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Research Article

Assessing the impact of healthcare research: A systematic review of methodological frameworks

Roles Conceptualization, Data curation, Formal analysis, Funding acquisition, Investigation, Methodology, Validation, Writing – original draft, Writing – review & editing

Affiliation Centre for Patient Reported Outcomes Research, Institute of Applied Health Research, College of Medical and Dental Sciences, University of Birmingham, Birmingham, United Kingdom

Roles Conceptualization, Formal analysis, Funding acquisition, Methodology, Project administration, Supervision, Validation, Writing – review & editing

* E-mail: [email protected]

Roles Data curation, Formal analysis, Methodology, Validation, Writing – review & editing

Roles Formal analysis, Methodology, Supervision, Validation, Writing – review & editing

• Samantha Cruz Rivera, 
• Derek G. Kyte, 
• Olalekan Lee Aiyegbusi, 
• Thomas J. Keeley, 
• Melanie J. Calvert

• Published: August 9, 2017
• https://doi.org/10.1371/journal.pmed.1002370
• Reader Comments

Increasingly, researchers need to demonstrate the impact of their research to their sponsors, funders, and fellow academics. However, the most appropriate way of measuring the impact of healthcare research is subject to debate. We aimed to identify the existing methodological frameworks used to measure healthcare research impact and to summarise the common themes and metrics in an impact matrix.

Methods and findings

Two independent investigators systematically searched the Medical Literature Analysis and Retrieval System Online (MEDLINE), the Excerpta Medica Database (EMBASE), the Cumulative Index to Nursing and Allied Health Literature (CINAHL+), the Health Management Information Consortium, and the Journal of Research Evaluation from inception until May 2017 for publications that presented a methodological framework for research impact. We then summarised the common concepts and themes across methodological frameworks and identified the metrics used to evaluate differing forms of impact. Twenty-four unique methodological frameworks were identified, addressing 5 broad categories of impact: (1) ‘primary research-related impact’, (2) ‘influence on policy making’, (3) ‘health and health systems impact’, (4) ‘health-related and societal impact’, and (5) ‘broader economic impact’. These categories were subdivided into 16 common impact subgroups. Authors of the included publications proposed 80 different metrics aimed at measuring impact in these areas. The main limitation of the study was the potential exclusion of relevant articles, as a consequence of the poor indexing of the databases searched.

Conclusions

The measurement of research impact is an essential exercise to help direct the allocation of limited research resources, to maximise research benefit, and to help minimise research waste. This review provides a collective summary of existing methodological frameworks for research impact, which funders may use to inform the measurement of research impact and researchers may use to inform study design decisions aimed at maximising the short-, medium-, and long-term impact of their research.

Author summary

Why was this study done.

• There is a growing interest in demonstrating the impact of research in order to minimise research waste, allocate resources efficiently, and maximise the benefit of research. However, there is no consensus on which is the most appropriate tool to measure the impact of research.
• To our knowledge, this review is the first to synthesise existing methodological frameworks for healthcare research impact, and the associated impact metrics by which various authors have proposed impact should be measured, into a unified matrix.

What did the researchers do and find?

• We conducted a systematic review identifying 24 existing methodological research impact frameworks.
• We scrutinised the sample, identifying and summarising 5 proposed impact categories, 16 impact subcategories, and over 80 metrics into an impact matrix and methodological framework.

What do these findings mean?

• This simplified consolidated methodological framework will help researchers to understand how a research study may give rise to differing forms of impact, as well as in what ways and at which time points these potential impacts might be measured.
• Incorporating these insights into the design of a study could enhance impact, optimizing the use of research resources.

Citation: Cruz Rivera S, Kyte DG, Aiyegbusi OL, Keeley TJ, Calvert MJ (2017) Assessing the impact of healthcare research: A systematic review of methodological frameworks. PLoS Med 14(8): e1002370. https://doi.org/10.1371/journal.pmed.1002370

Academic Editor: Mike Clarke, Queens University Belfast, UNITED KINGDOM

Received: February 28, 2017; Accepted: July 7, 2017; Published: August 9, 2017

Copyright: © 2017 Cruz Rivera et al. This is an open access article distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Data Availability: All relevant data are within the paper and supporting files.

Funding: Funding was received from Consejo Nacional de Ciencia y Tecnología (CONACYT). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript ( http://www.conacyt.mx/ ).

Competing interests: I have read the journal's policy and the authors of this manuscript have the following competing interests: MJC has received consultancy fees from Astellas and Ferring pharma and travel fees from the European Society of Cardiology outside the submitted work. TJK is in full-time paid employment for PAREXEL International.

Abbreviations: AIHS, Alberta Innovates—Health Solutions; CAHS, Canadian Academy of Health Sciences; CIHR, Canadian Institutes of Health Research; CINAHL+, Cumulative Index to Nursing and Allied Health Literature; EMBASE, Excerpta Medica Database; ERA, Excellence in Research for Australia; HEFCE, Higher Education Funding Council for England; HMIC, Health Management Information Consortium; HTA, Health Technology Assessment; IOM, Impact Oriented Monitoring; MDG, Millennium Development Goal; NHS, National Health Service; MEDLINE, Medical Literature Analysis and Retrieval System Online; PHC RIS, Primary Health Care Research & Information Service; PRISMA, Preferred Reporting Items for Systematic Reviews and Meta-Analyses; PROM, patient-reported outcome measures; QALY, quality-adjusted life year; R&D, research and development; RAE, Research Assessment Exercise; REF, Research Excellence Framework; RIF, Research Impact Framework; RQF, Research Quality Framework; SDG, Sustainable Development Goal; SIAMPI, Social Impact Assessment Methods for research and funding instruments through the study of Productive Interactions between science and society

Introduction

In 2010, approximately US$240 billion was invested in healthcare research worldwide [ 1 ]. Such research is utilised by policy makers, healthcare providers, and clinicians to make important evidence-based decisions aimed at maximising patient benefit, whilst ensuring that limited healthcare resources are used as efficiently as possible to facilitate effective and sustainable service delivery. It is therefore essential that this research is of high quality and that it is impactful—i.e., it delivers demonstrable benefits to society and the wider economy whilst minimising research waste [ 1 , 2 ]. Research impact can be defined as ‘any identifiable ‘benefit to, or positive influence on the economy, society, public policy or services, health, the environment, quality of life or academia’ (p. 26) [ 3 ].

There are many purported benefits associated with the measurement of research impact, including the ability to (1) assess the quality of the research and its subsequent benefits to society; (2) inform and influence optimal policy and funding allocation; (3) demonstrate accountability, the value of research in terms of efficiency and effectiveness to the government, stakeholders, and society; and (4) maximise impact through better understanding the concept and pathways to impact [ 4 – 7 ].

Measuring and monitoring the impact of healthcare research has become increasingly common in the United Kingdom [ 5 ], Australia [ 5 ], and Canada [ 8 ], as governments, organisations, and higher education institutions seek a framework to allocate funds to projects that are more likely to bring the most benefit to society and the economy [ 5 ]. For example, in the UK, the 2014 Research Excellence Framework (REF) has recently been used to assess the quality and impact of research in higher education institutions, through the assessment of impact cases studies and selected qualitative impact metrics [ 9 ]. This is the first initiative to allocate research funding based on the economic, societal, and cultural impact of research, although it should be noted that research impact only drives a proportion of this allocation (approximately 20%) [ 9 ].

In the UK REF, the measurement of research impact is seen as increasingly important. However, the impact element of the REF has been criticised in some quarters [ 10 , 11 ]. Critics deride the fact that REF impact is determined in a relatively simplistic way, utilising researcher-generated case studies, which commonly attempt to link a particular research outcome to an associated policy or health improvement despite the fact that the wider literature highlights great diversity in the way research impact may be demonstrated [ 12 , 13 ]. This led to the current debate about the optimal method of measuring impact in the future REF [ 10 , 14 ]. The Stern review suggested that research impact should not only focus on socioeconomic impact but should also include impact on government policy, public engagement, academic impacts outside the field, and teaching to showcase interdisciplinary collaborative impact [ 10 , 11 ]. The Higher Education Funding Council for England (HEFCE) has recently set out the proposals for the REF 2021 exercise, confirming that the measurement of such impact will continue to form an important part of the process [ 15 ].

With increasing pressure for healthcare research to lead to demonstrable health, economic, and societal impact, there is a need for researchers to understand existing methodological impact frameworks and the means by which impact may be quantified (i.e., impact metrics; see Box 1 , 'Definitions’) to better inform research activities and funding decisions. From a researcher’s perspective, understanding the optimal pathways to impact can help inform study design aimed at maximising the impact of the project. At the same time, funders need to understand which aspects of impact they should focus on when allocating awards so they can make the most of their investment and bring the greatest benefit to patients and society [ 2 , 4 , 5 , 16 , 17 ].

Box 1. Definitions

• Research impact: ‘any identifiable benefit to, or positive influence on, the economy, society, public policy or services, health, the environment, quality of life, or academia’ (p. 26) [ 3 ].
• Methodological framework: ‘a body of methods, rules and postulates employed by a particular procedure or set of procedures (i.e., framework characteristics and development)’ [ 18 ].
• Pathway: ‘a way of achieving a specified result; a course of action’ [ 19 ].
• Quantitative metrics: ‘a system or standard of [quantitative] measurement’ [ 20 ].
• Narrative metrics: ‘a spoken or written account of connected events; a story’ [ 21 ].

Whilst previous researchers have summarised existing methodological frameworks and impact case studies [ 4 , 22 – 27 ], they have not summarised the metrics for use by researchers, funders, and policy makers. The aim of this review was therefore to (1) identify the methodological frameworks used to measure healthcare research impact using systematic methods, (2) summarise common impact themes and metrics in an impact matrix, and (3) provide a simplified consolidated resource for use by funders, researchers, and policy makers.

Search strategy and selection criteria

Initially, a search strategy was developed to identify the available literature regarding the different methods to measure research impact. The following keywords: ‘Impact’, ‘Framework’, and ‘Research’, and their synonyms, were used during the search of the Medical Literature Analysis and Retrieval System Online (MEDLINE; Ovid) database, the Excerpta Medica Database (EMBASE), the Health Management Information Consortium (HMIC) database, and the Cumulative Index to Nursing and Allied Health Literature (CINAHL+) database (inception to May 2017; see S1 Appendix for the full search strategy). Additionally, the nonindexed Journal of Research Evaluation was hand searched during the same timeframe using the keyword ‘Impact’. Other relevant articles were identified through 3 Internet search engines (Google, Google Scholar, and Google Images) using the keywords ‘Impact’, ‘Framework’, and ‘Research’, with the first 50 results screened. Google Images was searched because different methodological frameworks are summarised in a single image and can easily be identified through this search engine. Finally, additional publications were sought through communication with experts.

Following Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines (see S1 PRISMA Checklist ), 2 independent investigators systematically screened for publications describing, evaluating, or utilising a methodological research impact framework within the context of healthcare research [ 28 ]. Papers were eligible if they included full or partial methodological frameworks or pathways to research impact; both primary research and systematic reviews fitting these criteria were included. We included any methodological framework identified (original or modified versions) at the point of first occurrence. In addition, methodological frameworks were included if they were applicable to the healthcare discipline with no need of modification within their structure. We defined ‘methodological framework’ as ‘a body of methods, rules and postulates employed by a particular procedure or set of procedures (i.e., framework characteristics and development)’ [ 18 ], whereas we defined ‘pathway’ as ‘a way of achieving a specified result; a course of action’ [ 19 ]. Studies were excluded if they presented an existing (unmodified) methodological framework previously available elsewhere, did not explicitly describe a methodological framework but rather focused on a single metric (e.g., bibliometric analysis), focused on the impact or effectiveness of interventions rather than that of the research, or presented case study data only. There were no language restrictions.

Data screening

Records were downloaded into Endnote (version X7.3.1), and duplicates were removed. Two independent investigators (SCR and OLA) conducted all screening following a pilot aimed at refining the process. The records were screened by title and abstract before full-text articles of potentially eligible publications were retrieved for evaluation. A full-text screening identified the publications included for data extraction. Discrepancies were resolved through discussion, with the involvement of a third reviewer (MJC, DGK, and TJK) when necessary.

Data extraction and analysis

Data extraction occurred after the final selection of included articles. SCR and OLA independently extracted details of impact methodological frameworks, the country of origin, and the year of publication, as well as the source, the framework description, and the methodology used to develop the framework. Information regarding the methodology used to develop each methodological framework was also extracted from framework webpages where available. Investigators also extracted details regarding each framework’s impact categories and subgroups, along with their proposed time to impact (‘short-term’, ‘mid-term’, or ‘long-term’) and the details of any metrics that had been proposed to measure impact, which are depicted in an impact matrix. The structure of the matrix was informed by the work of M. Buxton and S. Hanney [ 2 ], P. Buykx et al. [ 5 ], S. Kuruvila et al. [ 29 ], and A. Weiss [ 30 ], with the intention of mapping metrics presented in previous methodological frameworks in a concise way. A consensus meeting with MJC, DGK, and TJK was held to solve disagreements and finalise the data extraction process.

Included studies

Our original search strategy identified 359 citations from MEDLINE (Ovid), EMBASE, CINAHL+, HMIC, and the Journal of Research Evaluation, and 101 citations were returned using other sources (Google, Google Images, Google Scholar, and expert communication) (see Fig 1 ) [ 28 ]. In total, we retrieved 54 full-text articles for review. At this stage, 39 articles were excluded, as they did not propose new or modified methodological frameworks. An additional 15 articles were included following the backward and forward citation method. A total of 31 relevant articles were included in the final analysis, of which 24 were articles presenting unique frameworks and the remaining 7 were systematic reviews [ 4 , 22 – 27 ]. The search strategy was rerun on 15 May 2017. A further 19 publications were screened, and 2 were taken forward to full-text screening but were ineligible for inclusion.

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https://doi.org/10.1371/journal.pmed.1002370.g001

Methodological framework characteristics

The characteristics of the 24 included methodological frameworks are summarised in Table 1 , 'Methodological framework characteristics’. Fourteen publications proposed academic-orientated frameworks, which focused on measuring academic, societal, economic, and cultural impact using narrative and quantitative metrics [ 2 , 3 , 5 , 8 , 29 , 31 – 39 ]. Five publications focused on assessing the impact of research by focusing on the interaction process between stakeholders and researchers (‘productive interactions’), which is a requirement to achieve research impact. This approach tries to address the issue of attributing research impact to metrics [ 7 , 40 – 43 ]. Two frameworks focused on the importance of partnerships between researchers and policy makers, as a core element to accomplish research impact [ 44 , 45 ]. An additional 2 frameworks focused on evaluating the pathways to impact, i.e., linking processes between research and impact [ 30 , 46 ]. One framework assessed the ability of health technology to influence efficiency of healthcare systems [ 47 ]. Eight frameworks were developed in the UK [ 2 , 3 , 29 , 37 , 39 , 42 , 43 , 45 ], 6 in Canada [ 8 , 33 , 34 , 44 , 46 , 47 ], 4 in Australia [ 5 , 31 , 35 , 38 ], 3 in the Netherlands [ 7 , 40 , 41 ], and 2 in the United States [ 30 , 36 ], with 1 model developed with input from various countries [ 32 ].

https://doi.org/10.1371/journal.pmed.1002370.t001

Methodological framework development

The included methodological frameworks varied in their development process, but there were some common approaches employed. Most included a literature review [ 2 , 5 , 7 , 8 , 31 , 33 , 36 , 37 , 40 – 46 ], although none of them used a recognised systematic method. Most also consulted with various stakeholders [ 3 , 8 , 29 , 31 , 33 , 35 – 38 , 43 , 44 , 46 , 47 ] but used differing methods to incorporate their views, including quantitative surveys [ 32 , 35 , 43 , 46 ], face-to-face interviews [ 7 , 29 , 33 , 35 , 37 , 42 , 43 ], telephone interviews [ 31 , 46 ], consultation [ 3 , 7 , 36 ], and focus groups [ 39 , 43 ]. A range of stakeholder groups were approached across the sample, including principal investigators [ 7 , 29 , 43 ], research end users [ 7 , 42 , 43 ], academics [ 3 , 8 , 39 , 40 , 43 , 46 ], award holders [ 43 ], experts [ 33 , 38 , 39 ], sponsors [ 33 , 39 ], project coordinators [ 32 , 42 ], and chief investigators [ 31 , 35 ]. However, some authors failed to identify the stakeholders involved in the development of their frameworks [ 2 , 5 , 34 , 41 , 45 ], making it difficult to assess their appropriateness. In addition, only 4 of the included papers reported using formal analytic methods to interpret stakeholder responses. These included the Canadian Academy of Health Sciences framework, which used conceptual cluster analysis [ 33 ]. The Research Contribution [ 42 ], Research Impact [ 29 ], and Primary Health Care & Information Service [ 31 ] used a thematic analysis approach. Finally, some authors went on to pilot their framework, which shaped refinements on the methodological frameworks until approval. Methods used to pilot the frameworks included a case study approach [ 2 , 3 , 30 , 32 , 33 , 36 , 40 , 42 , 44 , 45 ], contrasting results against available literature [ 29 ], the use of stakeholders’ feedback [ 7 ], and assessment tools [ 35 , 46 ].

Major impact categories

1. primary research-related impact..

A number of methodological frameworks advocated the evaluation of ‘research-related impact’. This encompassed content related to the generation of new knowledge, knowledge dissemination, capacity building, training, leadership, and the development of research networks. These outcomes were considered the direct or primary impacts of a research project, as these are often the first evidenced returns [ 30 , 62 ].

A number of subgroups were identified within this category, with frameworks supporting the collection of impact data across the following constructs: ‘research and innovation outcomes’; ‘dissemination and knowledge transfer’; ‘capacity building, training, and leadership’; and ‘academic collaborations, research networks, and data sharing’.

1 . 1 . Research and innovation outcomes . Twenty of the 24 frameworks advocated the evaluation of ‘research and innovation outcomes’ [ 2 , 3 , 5 , 7 , 8 , 29 – 39 , 41 , 43 , 44 , 46 ]. This subgroup included the following metrics: number of publications; number of peer-reviewed articles (including journal impact factor); citation rates; requests for reprints, number of reviews, and meta-analysis; and new or changes in existing products (interventions or technology), patents, and research. Additionally, some frameworks also sought to gather information regarding ‘methods/methodological contributions’. These advocated the collection of systematic reviews and appraisals in order to identify gaps in knowledge and determine whether the knowledge generated had been assessed before being put into practice [ 29 ].

1 . 2 . Dissemination and knowledge transfer . Nineteen of the 24 frameworks advocated the assessment of ‘dissemination and knowledge transfer’ [ 2 , 3 , 5 , 7 , 29 – 32 , 34 – 43 , 46 ]. This comprised collection of the following information: number of conferences, seminars, workshops, and presentations; teaching output (i.e., number of lectures given to disseminate the research findings); number of reads for published articles; article download rate and number of journal webpage visits; and citations rates in nonjournal media such as newspapers and mass and social media (i.e., Twitter and blogs). Furthermore, this impact subgroup considered the measurement of research uptake and translatability and the adoption of research findings in technological and clinical applications and by different fields. These can be measured through patents, clinical trials, and partnerships between industry and business, government and nongovernmental organisations, and university research units and researchers [ 29 ].

1 . 3 . Capacity building , training , and leadership . Fourteen of 24 frameworks suggested the evaluation of ‘capacity building, training, and leadership’ [ 2 , 3 , 5 , 8 , 29 , 31 – 35 , 39 – 41 , 43 ]. This involved collecting information regarding the number of doctoral and postdoctoral studentships (including those generated as a result of the research findings and those appointed to conduct the research), as well as the number of researchers and research-related staff involved in the research projects. In addition, authors advocated the collection of ‘leadership’ metrics, including the number of research projects managed and coordinated and the membership of boards and funding bodies, journal editorial boards, and advisory committees [ 29 ]. Additional metrics in this category included public recognition (number of fellowships and awards for significant research achievements), academic career advancement, and subsequent grants received. Lastly, the impact metric ‘research system management’ comprised the collection of information that can lead to preserving the health of the population, such as modifying research priorities, resource allocation strategies, and linking health research to other disciplines to maximise benefits [ 29 ].

1 . 4 . Academic collaborations , research networks , and data sharing . Lastly, 10 of the 24 frameworks advocated the collection of impact data regarding ‘academic collaborations (internal and external collaborations to complete a research project), research networks, and data sharing’ [ 2 , 3 , 5 , 7 , 29 , 34 , 37 , 39 , 41 , 43 ].

2. Influence on policy making.

Methodological frameworks addressing this major impact category focused on measurable improvements within a given knowledge base and on interactions between academics and policy makers, which may influence policy-making development and implementation. The returns generated in this impact category are generally considered as intermediate or midterm (1 to 3 years). These represent an important interim stage in the process towards the final expected impacts, such as quantifiable health improvements and economic benefits, without which policy change may not occur [ 30 , 62 ]. The following impact subgroups were identified within this category: ‘type and nature of policy impact’, ‘level of policy making’, and ‘policy networks’.

2 . 1 . Type and nature of policy impact . The most common impact subgroup, mentioned in 18 of the 24 frameworks, was ‘type and nature of policy impact’ [ 2 , 7 , 29 – 38 , 41 – 43 , 45 – 47 ]. Methodological frameworks addressing this subgroup stressed the importance of collecting information regarding the influence of research on policy (i.e., changes in practice or terminology). For instance, a project looking at trafficked adolescents and women (2003) influenced the WHO guidelines (2003) on ethics regarding this particular group [ 17 , 21 , 63 ].

2 . 2 . Level of policy impact . Thirteen of 24 frameworks addressed aspects surrounding the need to record the ‘level of policy impact’ (international, national, or local) and the organisations within a level that were influenced (local policy makers, clinical commissioning groups, and health and wellbeing trusts) [ 2 , 5 , 8 , 29 , 31 , 34 , 38 , 41 , 43 – 47 ]. Authors considered it important to measure the ‘level of policy impact’ to provide evidence of collaboration, coordination, and efficiency within health organisations and between researchers and health organisations [ 29 , 31 ].

2 . 3 . Policy networks . Five methodological frameworks highlighted the need to collect information regarding collaborative research with industry and staff movement between academia and industry [ 5 , 7 , 29 , 41 , 43 ]. A policy network emphasises the relationship between policy communities, researchers, and policy makers. This relationship can influence and lead to incremental changes in policy processes [ 62 ].

3. Health and health systems impact.

A number of methodological frameworks advocated the measurement of impacts on health and healthcare systems across the following impact subgroups: ‘quality of care and service delivering’, ‘evidence-based practice’, ‘improved information and health information management’, ‘cost containment and effectiveness’, ‘resource allocation’, and ‘health workforce’.

3 . 1 . Quality of care and service delivery . Twelve of the 24 frameworks highlighted the importance of evaluating ‘quality of care and service delivery’ [ 2 , 5 , 8 , 29 – 31 , 33 – 36 , 41 , 47 ]. There were a number of suggested metrics that could be potentially used for this purpose, including health outcomes such as quality-adjusted life years (QALYs), patient-reported outcome measures (PROMs), patient satisfaction and experience surveys, and qualitative data on waiting times and service accessibility.

3 . 2 . Evidence-based practice . ‘Evidence-based practice’, mentioned in 5 of the 24 frameworks, refers to making changes in clinical diagnosis, clinical practice, treatment decisions, or decision making based on research evidence [ 5 , 8 , 29 , 31 , 33 ]. The suggested metrics to demonstrate evidence-based practice were adoption of health technologies and research outcomes to improve the healthcare systems and inform policies and guidelines [ 29 ].

3 . 3 . Improved information and health information management . This impact subcategory, mentioned in 5 of the 24 frameworks, refers to the influence of research on the provision of health services and management of the health system to prevent additional costs [ 5 , 29 , 33 , 34 , 38 ]. Methodological frameworks advocated the collection of health system financial, nonfinancial (i.e., transport and sociopolitical implications), and insurance information in order to determine constraints within a health system.

3 . 4 . Cost containment and cost-effectiveness . Six of the 24 frameworks advocated the subcategory ‘cost containment and cost-effectiveness’ [ 2 , 5 , 8 , 17 , 33 , 36 ]. ‘Cost containment’ comprised the collection of information regarding how research has influenced the provision and management of health services and its implication in healthcare resource allocation and use [ 29 ]. ‘Cost-effectiveness’ refers to information concerning economic evaluations to assess improvements in effectiveness and health outcomes—for instance, the cost-effectiveness (cost and health outcome benefits) assessment of introducing a new health technology to replace an older one [ 29 , 31 , 64 ].

3 . 5 . Resource allocation . ‘Resource allocation’, mentioned in 6frameworks, can be measured through 2 impact metrics: new funding attributed to the intervention in question and equity while allocating resources, such as improved allocation of resources at an area level; better targeting, accessibility, and utilisation; and coverage of health services [ 2 , 5 , 29 , 31 , 45 , 47 ]. The allocation of resources and targeting can be measured through health services research reports, with the utilisation of health services measured by the probability of providing an intervention when needed, the probability of requiring it again in the future, and the probability of receiving an intervention based on previous experience [ 29 , 31 ].

3 . 6 . Health workforce . Lastly, ‘health workforce’, present in 3 methodological frameworks, refers to the reduction in the days of work lost because of a particular illness [ 2 , 5 , 31 ].

4. Health-related and societal impact.

Three subgroups were included in this category: ‘health literacy’; ‘health knowledge, attitudes, and behaviours’; and ‘improved social equity, inclusion, or cohesion’.

4 . 1 . Health knowledge , attitudes , and behaviours . Eight of the 24 frameworks suggested the assessment of ‘health knowledge, attitudes, behaviours, and outcomes’, which could be measured through the evaluation of levels of public engagement with science and research (e.g., National Health Service (NHS) Choices end-user visit rate) or by using focus groups to analyse changes in knowledge, attitudes, and behaviour among society [ 2 , 5 , 29 , 33 – 35 , 38 , 43 ].

4 . 2 . Improved equity , inclusion , or cohesion and human rights . Other methodological frameworks, 4 of the 24, suggested capturing improvements in equity, inclusion, or cohesion and human rights. Authors suggested these could be using a resource like the United Nations Millennium Development Goals (MDGs) (superseded by Sustainable Development Goals [SDGs] in 2015) and human rights [ 29 , 33 , 34 , 38 ]. For instance, a cluster-randomised controlled trial in Nepal, which had female participants, has demonstrated the reduction of neonatal mortality through the introduction of maternity health care, distribution of delivery kits, and home visits. This illustrates how research can target vulnerable and disadvantaged groups. Additionally, this research has been introduced by the World Health Organisation to achieve the MDG ‘improve maternal health’ [ 16 , 29 , 65 ].

4 . 3 . Health literacy . Some methodological frameworks, 3 of the 24, focused on tracking changes in the ability of patients to make informed healthcare decisions, reduce health risks, and improve quality of life, which were demonstrably linked to a particular programme of research [ 5 , 29 , 43 ]. For example, a systematic review showed that when HIV health literacy/knowledge is spread among people living with the condition, antiretroviral adherence and quality of life improve [ 66 ].

5. Broader economic impacts.

Some methodological frameworks, 9 of 24, included aspects related to the broader economic impacts of health research—for example, the economic benefits emerging from the commercialisation of research outputs [ 2 , 5 , 29 , 31 , 33 , 35 , 36 , 38 , 67 ]. Suggested metrics included the amount of funding for research and development (R&D) that was competitively awarded by the NHS, medical charities, and overseas companies. Additional metrics were income from intellectual property, spillover effects (any secondary benefit gained as a repercussion of investing directly in a primary activity, i.e., the social and economic returns of investing on R&D) [ 33 ], patents granted, licences awarded and brought to the market, the development and sales of spinout companies, research contracts, and income from industry.

The benefits contained within the categories ‘health and health systems impact’, ‘health-related and societal impact’, and ‘broader economic impacts’ are considered the expected and final returns of the resources allocated in healthcare research [ 30 , 62 ]. These benefits commonly arise in the long term, beyond 5 years according to some authors, but there was a recognition that this could differ depending on the project and its associated research area [ 4 ].

Data synthesis

Five major impact categories were identified across the 24 included methodological frameworks: (1) ‘primary research-related impact’, (2) ‘influence on policy making’, (3) ‘health and health systems impact’, (4) ‘health-related and societal impact’, and (5) ‘broader economic impact’. These major impact categories were further subdivided into 16 impact subgroups. The included publications proposed 80 different metrics to measure research impact. This impact typology synthesis is depicted in ‘the impact matrix’ ( Fig 2 and Fig 3 ).

CIHR, Canadian Institutes of Health Research; HTA, Health Technology Assessment; PHC RIS, Primary Health Care Research & Information Service; RAE, Research Assessment Exercise; RQF, Research Quality Framework.

https://doi.org/10.1371/journal.pmed.1002370.g002

AIHS, Alberta Innovates—Health Solutions; CAHS, Canadian Institutes of Health Research; IOM, Impact Oriented Monitoring; REF, Research Excellence Framework; SIAMPI, Social Impact Assessment Methods for research and funding instruments through the study of Productive Interactions between science and society.

https://doi.org/10.1371/journal.pmed.1002370.g003

Commonality and differences across frameworks

The ‘Research Impact Framework’ and the ‘Health Services Research Impact Framework’ were the models that encompassed the largest number of the metrics extracted. The most dominant methodological framework was the Payback Framework; 7 other methodological framework models used the Payback Framework as a starting point for development [ 8 , 29 , 31 – 35 ]. Additional methodological frameworks that were commonly incorporated into other tools included the CIHR framework, the CAHS model, the AIHS framework, and the Exchange model [ 8 , 33 , 34 , 44 ]. The capture of ‘research-related impact’ was the most widely advocated concept across methodological frameworks, illustrating the importance with which primary short-term impact outcomes were viewed by the included papers. Thus, measurement of impact via number of publications, citations, and peer-reviewed articles was the most common. ‘Influence on policy making’ was the predominant midterm impact category, specifically the subgroup ‘type and nature of policy impact’, in which frameworks advocated the measurement of (i) changes to legislation, regulations, and government policy; (ii) influence and involvement in decision-making processes; and (iii) changes to clinical or healthcare training, practice, or guidelines. Within more long-term impact measurement, the evaluations of changes in the ‘quality of care and service delivery’ were commonly advocated.

In light of the commonalities and differences among the methodological frameworks, the ‘pathways to research impact’ diagram ( Fig 4 ) was developed to provide researchers, funders, and policy makers a more comprehensive and exhaustive way to measure healthcare research impact. The diagram has the advantage of assorting all the impact metrics proposed by previous frameworks and grouping them into different impact subgroups and categories. Prospectively, this global picture will help researchers, funders, and policy makers plan strategies to achieve multiple pathways to impact before carrying the research out. The analysis of the data extraction and construction of the impact matrix led to the development of the ‘pathways to research impact’ diagram ( Fig 4 ). The diagram aims to provide an exhaustive and comprehensive way of tracing research impact by combining all the impact metrics presented by the different 24 frameworks, grouping those metrics into different impact subgroups, and grouping these into broader impact categories.

NHS, National Health Service; PROM, patient-reported outcome measure; QALY, quality-adjusted life year; R&D, research and development.

https://doi.org/10.1371/journal.pmed.1002370.g004

This review has summarised existing methodological impact frameworks together for the first time using systematic methods ( Fig 4 ). It allows researchers and funders to consider pathways to impact at the design stage of a study and to understand the elements and metrics that need to be considered to facilitate prospective assessment of impact. Users do not necessarily need to cover all the aspects of the methodological framework, as every research project can impact on different categories and subgroups. This review provides information that can assist researchers to better demonstrate impact, potentially increasing the likelihood of conducting impactful research and reducing research waste. Existing reviews have not presented a methodological framework that includes different pathways to impact, health impact categories, subgroups, and metrics in a single methodological framework.

Academic-orientated frameworks included in this review advocated the measurement of impact predominantly using so-called ‘quantitative’ metrics—for example, the number of peer-reviewed articles, journal impact factor, and citation rates. This may be because they are well-established measures, relatively easy to capture and objective, and are supported by research funding systems. However, these metrics primarily measure the dissemination of research finding rather than its impact [ 30 , 68 ]. Whilst it is true that wider dissemination, especially when delivered via world-leading international journals, may well lead eventually to changes in healthcare, this is by no means certain. For instance, case studies evaluated by Flinders University of Australia demonstrated that some research projects with non-peer-reviewed publications led to significant changes in health policy, whilst the studies with peer-reviewed publications did not result in any type of impact [ 68 ]. As a result, contemporary literature has tended to advocate the collection of information regarding a variety of different potential forms of impact alongside publication/citations metrics [ 2 , 3 , 5 , 7 , 8 , 29 – 47 ], as outlined in this review.

The 2014 REF exercise adjusted UK university research funding allocation based on evidence of the wider impact of research (through case narrative studies and quantitative metrics), rather than simply according to the quality of research [ 12 ]. The intention was to ensure funds were directed to high-quality research that could demonstrate actual realised benefit. The inclusion of a mixed-method approach to the measurement of impact in the REF (narrative and quantitative metrics) reflects a widespread belief—expressed by the majority of authors of the included methodological frameworks in the review—that individual quantitative impact metrics (e.g., number of citations and publications) do not necessary capture the complexity of the relationships involved in a research project and may exclude measurement of specific aspects of the research pathway [ 10 , 12 ].

Many of the frameworks included in this review advocated the collection of a range of academic, societal, economic, and cultural impact metrics; this is consistent with recent recommendations from the Stern review [ 10 ]. However, a number of these metrics encounter research ‘lag’: i.e., the time between the point at which the research is conducted and when the actual benefits arise [ 69 ]. For instance, some cardiovascular research has taken up to 25 years to generate impact [ 70 ]. Likewise, the impact may not arise exclusively from a single piece of research. Different processes (such as networking interactions and knowledge and research translation) and multiple individuals and organisations are often involved [ 4 , 71 ]. Therefore, attributing the contribution made by each of the different actors involved in the process can be a challenge [ 4 ]. An additional problem associated to attribution is the lack of evidence to link research and impact. The outcomes of research may emerge slowly and be absorbed gradually. Consequently, it is difficult to determine the influence of research in the development of a new policy, practice, or guidelines [ 4 , 23 ].

A further problem is that impact evaluation is conducted ‘ex post’, after the research has concluded. Collecting information retrospectively can be an issue, as the data required might not be available. ‘ex ante’ assessment is vital for funding allocation, as it is necessary to determine the potential forthcoming impact before research is carried out [ 69 ]. Additionally, ex ante evaluation of potential benefit can overcome the issues regarding identifying and capturing evidence, which can be used in the future [ 4 ]. In order to conduct ex ante evaluation of potential benefit, some authors suggest the early involvement of policy makers in a research project coupled with a well-designed strategy of dissemination [ 40 , 69 ].

Providing an alternate view, the authors of methodological frameworks such as the SIAMPI, Contribution Mapping, Research Contribution, and the Exchange model suggest that the problems of attribution are a consequence of assigning the impact of research to a particular impact metric [ 7 , 40 , 42 , 44 ]. To address these issues, these authors propose focusing on the contribution of research through assessing the processes and interactions between stakeholders and researchers, which arguably take into consideration all the processes and actors involved in a research project [ 7 , 40 , 42 , 43 ]. Additionally, contributions highlight the importance of the interactions between stakeholders and researchers from an early stage in the research process, leading to a successful ex ante and ex post evaluation by setting expected impacts and determining how the research outcomes have been utilised, respectively [ 7 , 40 , 42 , 43 ]. However, contribution metrics are generally harder to measure in comparison to academic-orientated indicators [ 72 ].

Currently, there is a debate surrounding the optimal methodological impact framework, and no tool has proven superior to another. The most appropriate methodological framework for a given study will likely depend on stakeholder needs, as each employs different methodologies to assess research impact [ 4 , 37 , 41 ]. This review allows researchers to select individual existing methodological framework components to create a bespoke tool with which to facilitate optimal study design and maximise the potential for impact depending on the characteristic of their study ( Fig 2 and Fig 3 ). For instance, if researchers are interested in assessing how influential their research is on policy making, perhaps considering a suite of the appropriate metrics drawn from multiple methodological frameworks may provide a more comprehensive method than adopting a single methodological framework. In addition, research teams may wish to use a multidimensional approach to methodological framework development, adopting existing narratives and quantitative metrics, as well as elements from contribution frameworks. This approach would arguably present a more comprehensive method of impact assessment; however, further research is warranted to determine its effectiveness [ 4 , 69 , 72 , 73 ].

Finally, it became clear during this review that the included methodological frameworks had been constructed using varied methodological processes. At present, there are no guidelines or consensus around the optimal pathway that should be followed to develop a robust methodological framework. The authors believe this is an area that should be addressed by the research community, to ensure future frameworks are developed using best-practice methodology.

For instance, the Payback Framework drew upon a literature review and was refined through a case study approach. Arguably, this approach could be considered inferior to other methods that involved extensive stakeholder involvement, such as the CIHR framework [ 8 ]. Nonetheless, 7 methodological frameworks were developed based upon the Payback Framework [ 8 , 29 , 31 – 35 ].

Limitations

The present review is the first to summarise systematically existing impact methodological frameworks and metrics. The main limitation is that 50% of the included publications were found through methods other than bibliographic databases searching, indicating poor indexing. Therefore, some relevant articles may not have been included in this review if they failed to indicate the inclusion of a methodological impact framework in their title/abstract. We did, however, make every effort to try to find these potentially hard-to-reach publications, e.g., through forwards/backwards citation searching, hand searching reference lists, and expert communication. Additionally, this review only extracted information regarding the methodology followed to develop each framework from the main publication source or framework webpage. Therefore, further evaluations may not have been included, as they are beyond the scope of the current paper. A further limitation was that although our search strategy did not include language restrictions, we did not specifically search non-English language databases. Thus, we may have failed to identify potentially relevant methodological frameworks that were developed in a non-English language setting.

In conclusion, the measurement of research impact is an essential exercise to help direct the allocation of limited research resources, to maximise benefit, and to help minimise research waste. This review provides a collective summary of existing methodological impact frameworks and metrics, which funders may use to inform the measurement of research impact and researchers may use to inform study design decisions aimed at maximising the short-, medium-, and long-term impact of their research.

Supporting information

S1 appendix. search strategy..

https://doi.org/10.1371/journal.pmed.1002370.s001

S1 PRISMA Checklist. Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) checklist.

https://doi.org/10.1371/journal.pmed.1002370.s002

Acknowledgments

We would also like to thank Mrs Susan Bayliss, Information Specialist, University of Birmingham, and Mrs Karen Biddle, Research Secretary, University of Birmingham.

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• Published: 12 May 2023

Global trends in the scientific research of the health economics: a bibliometric analysis from 1975 to 2022

• Liliana Barbu   ORCID: orcid.org/0000-0003-0641-7483 1  

Health Economics Review volume  13 , Article number:  31 ( 2023 ) Cite this article

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Health science is evolving extremely rapidly at worldwide level. There is a large volume of articles about health economics that are published each year. The main purpose of this research is to explore health economics in the world's scholarly literature based on a scient metric analysis to outline the evolution of research in the field.

The Web of Science repository was used to get the data (1975–2022). The study explores 1620 documents from health economics. CiteSpace software was used to provide network visualisations. Four thousand ninety-six authors, 1723 institutions, 847 journals and 82 countries were involved in the sample. The current research contains a descriptive analysis, a co-authorship analysis, a co-citation analysis, and a co-occurrence analysis in health economics.

Drummond M.F (author), the USA (country), University of London (institution) and Value Health (journal) are among the most important contributors to the health economics literature. Co-authorship analysis highlights that cooperation between authors, institutions and countries is weak. However, Drummond M.F. is the most collaborative author, the USA is the most collaborative country, and University of York is the most collaborative institution. The study offers an image about the most co-cited references (Arrow K.J., 1963), authors (Margolis H.) and journals (British Medical Journal). The current research hotspots in health economics are “behavioural economics” and “economic evaluation”. The main findings should be interpreted in accordance with the selection strategy used in this paper.

All in all, the paper maps the literature on health economics and may be used for future research.

Introduction

The health economy is a branch of the economy that deals with concerns of the production and consumption of health services and healthcare that relate to efficiency, effectiveness, value, and behaviour. Applying economic ideas, concepts, and methods to institutions, actors, and activities that have an impact on people's health is known as health economics [ 1 ]. The health economy is studying how to allocate limited resources to meet human desires in the medical industry and disease care. The health economy often tries to meet the most pressing challenges facing the health system. Studies in health economics provide to decision-makers precious information about the effective use of resources that are available to maximize health benefits.

The health economics is a component of public health, a component that It can be used to examine health issues and medical treatment. Health economists consider the origin of their discipline to Petty W. (1623–1687) [ 2 ] who propose valuation of human life based on a person’s contribution to national production. Arrow K. is credited with creating the field of health economics in a work where he conceptually distinguished between health and other goods [ 3 ]. Since Arrow K.'s fundamental publication on health economics from 1963, the scale of the healthcare sector, the share of public budgets allocated to healthcare, and the body of research on health economics have all increased significantly [ 4 ].

The current pandemic context has proved the need for a functioning public health system capable of meeting any challenges. The World Health Organization report for 2020 presents an examination of 190 nations' global health spending from 2000 to 2018. The report shows that global health spending has increased consistently between 2000 and 2018, reaching $ 8.3 trillion, or 10% of world GDP [ 5 ]. At the level of OECD Member States, the latest estimates show an average increase in health spending of about 3.3% in 2019, whereas health spending as a percentage of GDP stayed about where it had been in prior years, at 8.8% [ 6 ]. These indicators rose sharply in 2020, as economies faced a pandemic. The increases were driven by an increase in the level of allocation of government resources for health, while private spending on health tended to decline. At EU level, the public sector plays a major role in funding health services. In 2/3 of Member States, more than 70% of health spending is funded by the public sector [ 7 ]. In 2020, the EU's overall public health spending was €1.073 billion, or 8.0% of GDP ( https://ec.europa.eu/eurostat/statistics-explained/index.php?title=Government_expenditure_on_health ). For governments, public spending on health is one of the spending categories with the quickest growth.

Health economics is the application of economic theory, models, and empirical techniques to the analysis of decision-making by individuals, health care providers, and governments regarding health and health care. Even though the methodologies are distinct in terms of health care, health economics aims to apply the same analytical tools that would be applied to any good or service that the economy provides [ 8 ]. By offering a clear framework for decision-making based on the efficiency principle, health economics seeks to simplify decision-making [ 9 ]. Extensive government interference, insoluble uncertainty in many dimensions, asymmetric knowledge, barriers to entry, externality, and the presence of a third-party agent are all characteristics that set health economics apart from other fields [ 10 ].

Health economics is the field were interdisciplinarity bring additional value for society. Health economics development has not been without controversy. Health economics refers to a variety of elements that interact to affect the expenses and spending of the healthcare sector. Its controversy rises from the roles of people, healthcare providers, insurers, governmental bodies, and private companies in influencing the healthcare sector expenses. The parties that interact in this field have some conflicting goals. On the one hand, health care policymakers and public hospitals have as objective to provide real value to the patients, to balance public interest and economic restrictions. On the other hand, private hospitals, insurance companies aim to obtain profit for their shareholders. There are several weaknesses that should be rectified in the future. Among weaknesses it can be found deficiencies in the supply of health economists [ 11 ], a lack of financial resource independence between the local and central levels, the key macroeconomic variables' unfavourable behaviour, and the difficulty in developing new financing alternatives [ 12 ]. In addition to having too close relationships to national institutions and sponsors of health economics research, health economics also has excessively loose connections with general economic theory [ 13 ]. Considering increased demands in healthcare services and limited health care budgets, health economics faces real challenges in providing decision making frameworks and there will always be challenging healthcare decisions. Although it has not always been an impartial instrument, health economics does give useful information for policy [ 14 ]. Regarding how well economics integrates with promoting health, there is scepticism, and public health has mixed feelings on the subject. Health economics has been accused of focusing more on the consumption of healthcare services than the creation of healthcare [ 15 ]. Despite several methodological limitations, health economics can provide helpful concepts and principles that aid in comprehending the effects of resource allocation decisions [ 9 ]. All practitioners must have a elementary comprehension of some economic concepts to both understand the helpful ideas the field may provide and recognize its inadequacies.

The main purpose of the research is to examine the health economics literature published worldwide based on a scient metric analysis to outline the development of the field's research. The existence of a multitude of articles published on health economics determines the need to address and measure it quantitatively. Such an analysis is justified by the need must be aware of the current trends and future directions of research in the field of health economics. Health science is evolving extremely rapidly at worldwide level. There is a large volume of articles about health economics that are published each year. Another argument is that there several computer programs which allows for scient metric analysis of health economics publications. This article contributes to the bibliometric literature on health economics by offering answers to the subsequent research inquiries: How scientific production has evolved in health economics? Who are the most important authors and publications in health economics? What are the geographical and institutional hubs of knowledge production in health economics? What kind of collaboration between authors, organizations, and nations are there in the field of health economics research? Which are the most cited authors and the most cited papers, and which are the most attractive journals for publishing research results in health economics? What are the most debated conceptual approaches in health economics?

The remainder of the paper is structured as follows. The second section introduces a short literature review. Research methodology and data collection are presented in Sect. 3. Section 4 contains the quantitative and qualitative scient metric analysis on health economics by using CiteSpace software (descriptive analysis, collaboration analysis, co-citation analysis and keywords co-occurrence analysis). The last part concludes the analysis, presents the research limitations, and describes future directions of research.

Literature background

Although there are thousands of articles published on health economics, very few articles aim for bibliometric analysis of the field and use computer programs. A first article published by Rubin, R. M. and Chang, C. F. (2003) aims at the study of 5,545 indexed articles, in the period 1991–2000, in the EconLit database, in the Health Economics section [ 16 ]. The second study is published by Wagstaff, A. and Culyer, A. J. in 2012 and extends the previous bibliometric research done by Rubin and Chang also based on the articles indexed in EconLit on health, over 40 years [ 17 ]. The third study, published by Moral-Munoz J.A et all in 2020, focuses on articles indexed in the Web of Science, between 2010 and 2019, which have the word "health" and do not use scientometric software [ 18 ].

It would be worth mentioning a descriptive analysis of the field conducted by Jakovljevic M. and Pejcic A. in 2017, but without the use of bibliometric indicators. The authors quantitatively analyze health economics publications by querying the PubMed, Scopus, WoS and NHS economic evaluation Database between 2000 and 2016 and conclude with the existence of an upward flow of health economics publications [ 19 ]. In this context, the proposed research is characterized by focusing on WoS articles that refer strictly to "health economics" and their computer processing to obtain maps and connections between studies.

Research methodology

Research methods.

In the current paper two research methods were used: bibliometric analysis and knowledge mapping. Regarding the first one, it should be mentioned that bibliometric research methods are used delivering quantitative analysis of textual works, in this case publications about health economics. This method allows bibliographic overviews of scientific production in the field. In the scientific community, the technique is increasingly employed to provide details regarding relationships between various groups [ 20 ]. Bibliometric analysis uses statistical tools and different metrics as part of the analysis (frequency/ count, co-citation, co-authorship, co-occurrence, betweenness centrality, citation burst, modularity, centrality, sigma, Silhouette etc.). Bibliometric analysis naturally presents itself as a tool to qualify, then quantify, the study conducted [ 21 ].

Regarding the second one, bibliometric analysis uses a large quantity of information that should be transformed in knowledge. This is done by using data visualization and knowledge maps. An enormous and complex collection of knowledge resources can be more easily accessed and navigated by using knowledge mapping strategies [ 22 ]. Knowledge mapping is the process of making knowledge maps, it makes explicit knowledge graphic and visual. Knowledge maps are static, they are a “snapshot in time” that aids in understanding and organizing knowledge flow for researchers [ 23 ]. A process, method, or instrument called “knowledge mapping” is used to analyse knowledge to find traits or meanings and perceive knowledge in an understandable and transparent way [ 24 ]. One of the advantages of knowledge mapping includes the freedom to combine without restriction, i.e., without restrictions on the number of connections and concepts that can be established [ 25 ].

Data source and search strategy

For this analysis we decided to use one of the most reliable databases: Web of Science (WoS) because it contains a data for large period. The data was retrieved from the Web of Science Core Collection by using title search tool TI = (health economics). The primary literature data were downloaded on 7th of October 2022. The query objective was to integrate in this analysis all research papers related to health economics. We did not introduce any restrictions regarding the topic or time span for searching documents. We intend to have a comprehensive view of the research area and to see its evolution over time. As a result, 2340 documents were retrieved. Among publications about health economics, the most numerous documents are the articles (37.6%), followed by editor materials (19.8%), meeting abstracts (13.9%) and book reviews (13.3%). There are also review articles on the subject, proceeding papers, letters, books, and book chapters which were kept in the sample. The other types of documents were removed resulting a sample of 2305 publications. The language of almost all publications is English (91.4%), followed by German (4.3%). The percentage of publications produced in other languages, such as French, Spanish, Portuguese, Russian etc. is less than 1.5% for each of them. Publications in other languages than English were eliminated, remaining 2108 documents in the sample.

The next step is to identify and remove duplicates by using Excel function (Conditional Formatting – Highlight Duplicate Values), therefore 8 duplicates were removed. In the sample under analysis, a multitude of types of documents indexed in WoS and referring to the concept of health economics can be observed. During the step of checking for duplications, it was found that there are too many duplicates of documents’ title, most of them due to editorial materials or book reviews. This led to a thorough analysis of publication by type of document (eg there are more than 10 reviews for one book or more than 10 editorial materials signed by the same editor). We identified some publications which are irrelevant for the purpose of our analysis. One hundred eighty-six editorial materials without citations and all 286 book reviews were removed resulting 1628 publications. We kept the editorial materials with citation because some of them have more than 100 citations. We searched for anonymous publications, more exactly we looked for incomplete data (author’s name is missing) and we removed 8 documents.

For the remaining documents the "Full Record and Cited References" was downloaded on 13th of October 2022 (txt files) and used as original data for the proposed bibliometrics analysis and science mapping. The final data collection, which consists of 1620 publications, is supported by 16,755 citing articles (excluding self-citations) and has been cited 18,504 times (excluding self-citations), giving it an H-index of 59. The data are statistical analysed by using annual distribution of publications, authors, journals. Co-authorship analysis focuses on collaboration between authors, institutions, and countries. Cited references, cited authors, and cited journal are used in co-citation analyses, and finally, the co-occurrence will integrate keyword in this research.

The graphical representation of selection procedure can be seen in Fig.  1 .

Selection procedure flow chart. Source: Authors

Visualization tools

Bibliometric method needs a certain amount of data to be statistically credible. This is the reason for that computerized data treatment is needed. Moreover, databases contain hundreds or thousands of entries which are analysed by using computer software. There is many bibliometric software, each of them has particularities and weaknesses. CiteSpace was chosen in this study because it is very user friendly, intuitively, and easy to use. CiteSpace 6.1.R2. available for free download at https://citespace.podia.com . A variety of networks created from scientific publications, such as collaboration networks, author co-citation networks, and document co-citation networks, are supported by structural and temporal analysis in CiteSpace. CiteSpace can produce knowledge domain X-rays. The CiteSpace parameters for this investigation were as follows: time-slicing was from 1975 to 2022, years per slice was 1 year, Look Back Years (LBY) = -1, Link Retaining Factor (LRF) = -1. For text processing and links, we preserved the default settings. We used several nodes (authors, institutions, journal, references, keywords) and metrics (such as citation burstiness, Sigma, Silhouette, rad Q, betweenness centrality) depending on the study that was done. Top N% is set to be equal to 100%, Top N is set to be 50, and g-index is set to be 25.

Statistical analysis

The first step to follow in the scient metric analysis is to analyse the evolution of publications’ number in the researched field. The way in which they are distributed over the years indicates the attention that the field of health economics has benefited from and the speed at which its conceptual development took place. The first 3 papers about health economics were published in 1975, indicating the lowest number of annual publications, but also a concept that has existed for over 4 decades. From Fig.  2 , a general upward trend of health economics publications can be observed, but with numerous upward and downward fluctuations, generating sinusoidal cycles with an average duration of 3–4 years. The period 1975 – 1986 is characterized by a very low number of publications, 98 publications written by 110 authors in 12 years, representing 6% of the total sample. The next two decades (1987 – 2006) are characterized by a slightly increasing trend in the number of publications, with an annual average of approximately 23 publications on health economics, reaching a total of 454 publications written by 826 authors and representing 28% of the total number of analysed publications. Cyclical evolution is highlighted by booms in 1987, 1990, 1995, 1999, 2001.

Literature production related to health economics between 1975 and 2022. Source: Authors

The following period, 2007 – 2022 (16 years) is characterized by an upward evolution of the number of health economics publications, 1068 publications with an annual average of 67 articles (3261 authors involved), meaning 2.3 times more numerous as in the previous two decades and representing 66% of the total sample. In 2017, 86 studies on health economics were published, reaching the highest value in the analysed period. The quantitative evolution of publications in health economics it is explained by a higher interest of the researchers and policymakers to explore the benefits of health economics. The need to identify the ways in which health economics contributes to the healthcare system development represent a solid motivation to continue intensive research in the field.

The evolution of the citations’ number follows, like a shadow, the evolution of publications’ number. The upward trend is maintained, also respecting the previously presented temporal distribution, but without cyclical and sinusoidal fluctuations. The evolution of the citations’ number indicates the growing interest of specialists in researching the field, especially after 2000 when a constant and galloping annual increase in citations begins. The last 5 years show a very high interest of researchers and academics in health economics research, with a maximum point in 2021, with over 2000 citations, an evolution argued by the emergence of the global pandemic. All the figures and observations indicate a constant interest in the conceptualization of health economics and foresee a deeper development in the future.

Geographical analysis allows a better understanding of the field. The 1620 publications involved the work of authors from 82 countries. Among them, the first 10 states with significant contributions in the field of health economics stand out: the USA (605 papers), England (400), Canada (115), Australia (103), Netherlands (75), Scotland (64), Germany (59), Switzerland (57), France (47) and Italy (43). 96.8% of all publications were produced by top-10 countries. According to statistics, the USA is the top nation. 37% of all analysed documents are written by American authors, which is 1.5 times more than values recorded by England (rank 2) and 5.2 times more than Canada, rank 3. There are 49 nations where there are fewer than or equal to 5 publications during entire period.

In our study, a sum of 4096 different authors were identified, and they individually published between one and 16 papers, but only 170 persons are co-authors of more than 3 papers. Table 1 lists the top 10 authors with publications about health economics. Drummond M.F. is the leader, even if he published Essentials of Health Economics with his co-author, Mooney G.H., in 1982. He is affiliated to University of Yor (the UK). The top ten most productive authors published 107 articles, which represents 6.6% of the total publications. The most authors (95.8% of all authors) contributed to the health economics research with less than two papers. It should be noted that the number of authors is 2.5 times over the number of papers., which means that publications are made by cooperation between researchers.

From the point of view of affiliation, the 4096 authors belong to 1723 institutions. The top 10 organizations with many health economics articles are University of London (91 publications), University of California System (54), University of York (51), Harvard University (45), University of Birmingham (41), University of Pennsylvania (34), University of Oxford (30), University of Aberdeen (28), University of California Los Angeles (28) and University of Washington (28). The list is dominated by institutions from the UK and the USA. The top-10 institutions contributed to health economics research field by 230 papers which represents 26.5% of total publications.

It is very important to see which journals have published the most articles about health economics. Regarding the publication’s titles, 847 distinct journals published all 1620 documents related to health economics. It should be mentioned that 782 journals (92.3%) published from one to three articles on health economics during 1975 – 2022. Table 2 lists the top 10 most prolific journals, and together they have published 364 articles, which means 43% of all publications in the sample. The leading journal is the Value in Health (Impact Factor = 5.156) with 160 papers meaning 9.8% of all publications from the sample.

Co-authorship analysis

Co-authorship networks and social network analysis are becoming more and more effective techniques for evaluating collaboration patterns and locating top scientists and institutions [ 26 ]. The author collaboration network can help identify authors with high contributions and reveal the co-operative relationships between the authors. By using CiteSpace, the co-authorship network was created without pruning the sliced networks. Co-authors network has 1028 nodes and 1166 links. Figure  3 presents the network between the most collaborative authors in health economics, all of them published 4 or more publications as co-authors. As indicated by the node name, each node represents a different author, and the font size corresponds to the number of publications for each author. The connections made by the co-authorship of researchers are represented by the interconnections between each pair of nodes. The degree of cooperation between the two authors is indicated by the thickness of the link.

The network of authors’ collaboration in health economics. Source: Authors

Co-authors’ map shows that there are not strong collaboration relationships between authors, the network density level is 0.0022. Moreover, they are divided in small research groups and cooperation for research in health economics is insignificant. Top five collaborative authors are Drummond M. (20 publications), Mooney G. (16), Trosch R. (8), Marchese D. (8) and Fuchs V. (8). They are followed by Basu A. (7), Edwards R. (7), Coast J. (7), Peeples P. (7) and Comella C. (6).

In Fig.  3 it can be seen the cooperation between two research teams. These research teams are formed around key authors in health economics and integrated as most collaborative ones. First research team is created around Drummond M. and Mooney G. They published in 1982 and 1983, in British Medical Journal, 9 papers about different aspects of health economics [ 27 , 28 ]. The second research team is created around Trosch R. and Marchese D., who participated between 2012 and 2015 at several annual meeting, conferences, and congresses to present their work about clinical and health economics outcomes registry in cervical dystonia [ 29 , 30 ]. There are 72 scholars as co-authors in at least 3 publications showing a weak cooperation in health economics. From the perspective of citation burst, there are 5 bursting authors with a burst duration between 2 and 8 years: Drummond M. 1981–1999, Mooney G. 1982–1986, Marchese D. 2012–2015, Trosch R. 2012–2015, and Peeples P. 2018–2020. Bust analysis confirms the existence of the two research teams and their period of activity.

We continue exploring the co-authorship analysis by studying the level of cooperation between institutions. For this purpose, we generated a network where the nodes are the institutions, and we did not used pruning methods. The level of cooperation is revealed by the thickness between institutions’ nodes. The network contains 751 nodes, 944 links, and a density of 0.0034. In Fig.  4 are labelled the institutions with more than 4 collaborative papers, the label size is depending on the number of collaborative publications. No institution has a large value of centrality, meaning that cooperation among the analysed institutions is weak, the links are very transparent because of an insignificant number of publications written by collaboration between organizations or universities.

The network of institutions’ collaboration in health economics. Source: Authors

As seen in Fig.  4 , the top-10 most collaborative institutions in health economics area are: University of York (28 publications), University of Oxford (23), University of Pennsylvania (21), University of Washington (20), University of Birmingham (17), Erasmus University (16), Harvard University (16), Bangor University (15), University of California Los Angeles (13) and University of Toronto (12). There are six institutions for which there was identified citation burst as follows: University of Oxford 2016–2020, University of Pennsylvania 2017–2022, University California Los Angeles 2013–2016, King’s College London 2006–2011, London School of Hygiene & Tropical Medicine 2008–2010, University of Washington 2015–2018. Cooperation among institutions is depending on cooperation among authors. It is understood that poor collaboration at the individual level is followed by an identical one at the organizational level.

Progress in any field can be achieved only by communication. Analysing country co-authorship may lead to identification of leading states in health economics research. The visualisation map for country collaboration reveals a network of 202 nodes, 710 links and 0.035 density. It should be noted that country co-authorship network has a density 10 times larger than institutions co-authorship network. The map was generated in CiteSpace without pruning parameter. In Fig.  5 are displayed the countries having more than 5 collaborative health economics-related publications.

The network of countries’ collaboration in health economics. Source: Authors

As can be observed, the biggest nodes correspond to the most prominent and cooperative nations. The collaboration between institutions from these nations is shown by the links between the nodes. The discrepancies between the first two countries and the other states are obvious. The network of the most collaborative country, the USA, consist in 521 publications. It is followed by England with 344 publications. It is obvious that these two nations played a crucial part in worldwide academic exchanges in health economics area. The third and the fourth most collaborative countries are Canada (105 publications) and Australia (100 publications), which shows a degree of cooperation 5 times lower than that of the leading country. The top-10 most collaborative countries continue with the following nations: Netherlands (74 publications), Germany (58), Switzerland (56), Scotland (48), France (46) and Italy (43). Citation burst was identified for 4 countries: the USA 1975–1981, Scotland 1982–2003, Switzerland 1999–2006, and China 2020–2022. Citation burst analysis reveals that China, which stated to published research in health economics in 2006, faces an upward trend in the last two years.

Co-citations analysis

The following step of our current analysis is to find the most frequently cited publications in health economics sector. Co-citation reference analysis help to identification of the most important references in health economics. 16,755 references are linked to our sample. We obtain a co-citation network of 1550 nodes and 7240 links with a density of 0.0060. The network map was obtained without pruning parameter. In Fig.  6 are labelled the papers with more than 5 co-citations. Table 3 lists the top 10 articles in the field of health economics by the number of citations.

Visualization of reference co-citation networks for health economics research. Source: Authors

As we expected, the most influential paper is published by Arrow K.J. in 1963. In his paper, the author investigates and studies the unique distinctions between medical care and other goods and services in normative economics. He focuses on medical-care industry and its efficacy by rethinking the industry from economics perspective. This publication is the basic brick in the conceptualization of health economics. Unfortunately, this part of analysis reveals some basic limitation in bibliometric analysis: incomplete and compromised database because of incorrect data filled by authors. As it can be seen in Fig.  6 , the second most influential paper belongs to an anonymous author who wrote in 1996 a paper about cost effectiveness. A manual search in references database revealed the possibility to correlate the anonymous publications to a book written by Gold M.R., Siegel J.E., Russell L.B. and Weinstein M.C. The authors published in 1996 a book about cost effectiveness in health and medicine and there are several book reviews about it. The third and the fourth most co-cited publications are signed by Drummond M.F. and his co-authors. In fact, it is about a book entitled “Methods for the Economic Evaluation of Health Care Programmes”, first published in 1987 at and then renewed in the following editions: 1997 (2nd), 2005 (3rd) and 2015 (4th). Regardless the edition number, the book is a worldwide bestseller and it very cited in health economics research. It should be mentioned that the 2nd edition of the book appears twice in the database because some authors incorrectly cited Drummond. There are many book reviews for this book because it describes techniques and tools for evaluation of health care programs. It provides syntheses of new and emerging methodologies, and it is less concerned with the theoretical and ethical foundations of the methodologies (Drummond M.F et all, 2005). The book promotes basic health economic concepts and theories.

The citation burst was checked to see the period when a document citation increases sharply in frequency. There are 12 cited papers with citation burst fluctuating from 3.95 for Volpp K.G (2008) and 9.58 for Arrow K.J. (1963). Ten of twelve papers with citation burst are the ones from Table 3 , the most co-cited documents in health economics. The top-10 papers by burst are Arrow K.J. 1963 (period 2012–2018, citation burst 9.58), Drummond M.F. 1997 (2000–2008, 8.76), Anonymous 1996 (1999–2011, 8.86), Drummond M.F 2005 (2008 – 2019, 8.42), Kahneman D. 2011 (2013–2022, 5.03), Williams A. 1985 (1986–1998, 4.44), Lakdawalla (2018–2022, 4.44), Kahneman D. 1979 (2019–2022, 4.38) and Grossman M. 1972 (2016–2019, 4.35).

Two of Kahneman D.’s works stands out. One of them is represented by a book, another worldwide bestseller, entitled “Thinking, Fast and Slow” published in 2011 in London. His psychological book is appreciated because it aids in the public understanding of issues related to engineering, medicine, and behavioural science. The second paper is written by Kahneman D. and Tversky A. in 1979 and presents opponents of the anticipated utility theory as a framework for risky decision-making and introduces an alternative model called prospect theory.

We can find highly cited authors whose work is well known in the health economics research community by using author co-citation networks. CiteSpace configurations are the same. The network of co-cited writers has 1422 nodes, 12,462 linkages, with a density of 0.0123. The node size reflects the number of co-citations by author. In Fig.  7 the nodes with co-citations over 14 are labelled by the corresponding first author. Once again there are incomplete data in the database. We face with an anonymous person as the most cited author in health economics research. This author without name was 300 time co-cited. We manually checked the database to find additional information about this anonymous author. According to the findings we assume it is about Margolis H. who published in 1982 a book about selfishness, altruism, and rationality. Margolis H. is a professor at the University of Chicago and in his book about social choice propose and argue a distinction between self-interest and group-interest for a person, and he also develop an equilibrium model for his theory [ 41 ].

Visualization of authors co-citation networks for health economics research. Source: Authors

Drummond M.F. is on the second position, positioning himself with two publications in the top-10 most co-cited authors. Once again it is about his publication with Mooney G.H. about Essentials in Health Economics which was already mentioned in the paper. Williams A. is the third co-cited author, followed by Culyer A.J and Arrow K.J. It should be noted that World Health Organization’s (WHO) publications are ones of the most co-cited document in health economics research. Unfortunately, it is hard to identify the titles of WHO’s publications from 1993 and 2009 (see Table 4 ) because there is more than one publication per year for this international organization. However, we assume that it is about an anonymous publication focused on tuberculosis as a worldwide problem [ 42 ] (published in 1993) and a publication about health risk at the global level [ 43 ] (published in 2009).

There are no scholars who have a betweenness centrality greater than zero. This indicates that there is no author more influential than other scholars, and no one exert a significant influence on the evolution of health economics research. The evolution of health economics theory was influenced by all the authors discussed in this paper.

In terms of burstiness, there are 35 cited authors with citation burst between 9.26 and 3.90. It means that their papers were intensively cited during a specific period. The top-10 cited authors by bursts is Drummond M. 1988 (bursts of 9.26, period 1995–1999), Maynard A, 1982 (8.60, 1998–2003), WHO 2009 (8.09, 2009–2015), OECD 2013 (7.77, 2013–2022), Williams A. 1982 (7.63, 1986–2003), Johannesson M. 1996 (7.59, 1996–2003), Kahneman D. 2000 (7.55, 2016–2022), WHO 1993 (7.02, 2011–2022), Cutler D.M. 2007 (6.97, 2012–2016) and Donaldson C. 1995 (6.94, 1995–2003). Even if they are not included in the previous ranking, the following cited authors should be mentioned because their burstiness periods exceeds 10 years: Fuchs V.R. 21 years (bursts of 4.54, period 1977–1998), Williams A. 17 years (7.63, 1986–2003), Mooney G. 14 years (5.29, 1995–2009), Dolan P. 14 years (4.84, 2003–2017) and Weinstein M.C. 13 years (4.14, 1999–2011).

The same way as previous maps, the cited journal visualization map for health economics research (Fig.  8 ) was created in CiteSpace, but this network has 1273 nodes (cited journals), 25,008 linkages, and a density of 0.0309. The cited journals with more than 38 citations are labelled in the network.

Journal co-citation network visualization for health economics research. Source: Authors

The top ten journals by citations in health economics are presented in Table 5 . The BMJ – British Medical Journal (381 citations) is the journal published by British Medical Association and the most prominent cited journal in health economics area. It is followed by the New England Journal of Medicine (306 citations) and The Lancet (257 citations). The journal published by American Medicinal Association ranks on the fourth place. A journal that receives a lot of citations and has a high citation burstiness score has garnered the interest of academics recently.

The citation surge affects 70 cited journals. The cited journal with the strongest citation bursts is Plos One (21.79, 2014–2022), which is not the most cited one. It is followed by British Medical Journal (20.22, 1982–2006), Value Health (13.15, 2018–2022), BMJ Open (12.48, 2017–2022), Applied Health Economics and Health Policy (10.38, 2017–2022), BMC Health Services Research (10.16, 2019–2022), Frontiers in Public Health (9.99, 2020–2022), Cost Effectiveness and Resource Allocation (9.66, 1998–2005), JAMA Internal Medicine (9.24, 2019–2022) and BMC Public Health (8.71, 2016–2022). It should be noted that 8 cited journals of the ranking are bursting to the present. British Medical Journal (24 years), American Journal of Psychiatry (15 years), The Journal of Health Services Research and Policy (14 years), The New England Journal of Medicine (13 years) and Medical Care (12 years) are the cited journals with the longest periods of bursting, even if the interest in these journals is currently low. It must be added that four of the most cited journals in health economics research are on a top-10 list of journals with the highest JIF in 2021. All these journals are one of the most influential journals in health research.

Co-occurrence analysis

In this section of the analysis, we can pinpoint the key ideas and areas of interest in health economics research. To discover the primary study subjects in many scientific research domains, keywords are generally regarded as one of the most crucial elements of any research paper [ 44 ]. Co-occurrence analysis is used to identify the conceptual structure of the field. Without any pruning, the network of related keywords is shown in Fig.  9 . The network of co-occurred keyword has 694 nodes (keywords), 2823 links (connections), and a density of 0.0117. One percent of all keywords, those with a frequency greater than or equal to five, are labelled.

Keywords co-occurrence network for health economics research. Source: Authors

Table 6 presents the top 30 keywords which are used and connected in the 1620 analysed papers. “Health economics” and “cost effectiveness” are the most co-occurred items in health economics research, they have been connected for 121 times. “Care” follows them as the second high-count keyword with a frequency of 115. One crucial statistic used in the analysis of the keyword co-occurrence network is centrality. Centrality shows a keyword's strength, influence, or other specific characteristics. In this analysis all the keywords have a null betweenness centrality.

By using bursts detection, we tried to identify research hotspots in health economics. Surprisingly, there are only two keywords with citation bursts during 1975–2022: “behavioural economics” and “economic evaluation”. The keyword with the strongest bursts is “behavioural economics” (5.57) and it caught scholars’ attention between 2019 and 2022. The second keyword by citation bursts is “economic evaluation” (4.62). This item is bursting from 2020 to 2022. It can be observed that both research themes have short periods of bursts, and they continue bursting to present.

CiteSpace allows a cluster analysis of keywords to identify topics that have captured the attention of researchers. By applying clustering tool, the keywords network has been divided in 14 clusters, labelled by keywords. Table 7 presents the top 10 keywords clusters, in descending order of their size, and the most used keywords in the analysed sample of publications. There are 14 clusters with different sizes, from 80 research topics in health economics to 4 research topics. Their Silhouette values varies from 0.757 to 0.995 which means that keywords match well to their own cluster. Figure  10 show that the clustering configuration is appropriate.

Keywords clusters. Source: Authors

The largest cluster (#0) is labelled “Health economics” and has 80 components. It contains publications about health economics, cost effectiveness, quality of life, and management. Cost effectiveness analysis and health technology assessment are subjects in the second largest cluster (#1). It is labelled “Value framework” and has 78 topics. The third cluster (#2) “Economic evaluation” contains 75 topics and the most important are care, economic evaluation, outcome, and benefits. Other research topics refer to behavioural economics, demand, cost, quality of life, risk, cancer, public heath, financial incentives, therapy, etc.

The evolution over time of the keywords can be seen in Fig.  11 , structured by cluster. CiteSpace restricts the time pane analyses to the period 1990 – 2022. Figures  11 and 12 present how interest of researchers in health economics has evolved over time. In Fig.  12 are labelled the keywords with a frequency larger than 10. In the 1990s the hot topics of research in health economics were “care”, “impact”, “health economics”, “cost”, “cost effectiveness”, “quality of life”, “outcome”, “economic evaluation”. The most debated research topics in the 2000s were “children”, “air pollution”, “patient”, “management”, “people”, “public health”, “choice”, “therapy and “risk”. In the 2010s focus is on “behavioural economics”, “population”, “obesity”, “uncertainty”, “ technology”, “health policy”, “health system”. How future research in health economics looks? It cannot be estimated with certainty, but some directions are drawn as follows: “inequality”, “care expenditure”, “health technologies”, “analysis plan”, “adaptative design”, “transparency”, “biodiversity”. These topics may shape the future literature in health economics.

Timeline view of keywords clusters in health economics between 1990 and 2022. Source: Authors

Time zone view of keywords clusters in health economics between 1990 and 2022. Source: Authors

The performed literature analysis enables us to respond to the research queries that were addressed in the paper's introduction, as follows:

How scientific production has evolved in health economics?

It can be observed a general upward trend of health economics publications, but with numerous upward and downward fluctuations, generating sinusoidal cycles with an average duration of 3–4 years. The period 1975 – 1986 is characterized by a very low number of publications. The next two decades (1987 – 2006) are characterized by a slightly increasing trend in the number of publications, with an annual average of approximately 23 publications on health economics. The following period, 2007 – 2022 is characterized by an upward evolution of the number of health economics publications, 1068 publications with an annual average of 67 articles. The evolution of the citations’ number indicates the growing interest of specialists in researching the field, especially after 2000 when a constant and galloping annual increase in citations begins. The last 5 years show a very high interest of researchers and academics in health economics research, which is justified by the existence of worldwide Covid pandemic period.

Who are the most important authors and publications in health economics?

In our study, 4096 different authors were identified, and they individually published between one and 16 papers. Among the most important authors in health economics are Drummond M.F., Jonsson B., Coast J., Donaldson C. and Edwards R.T. Regarding the publication’s titles, 847 distinct journals published all 1620 documents related to health economics. Value Health, Health Economics, British Medical Journal, Pharmacoeconomics and Health Policy are among journals with high interest in health economics publications.

What are the geographical and institutional hubs of knowledge production in health economics?

The analysed publications involved the work of authors from 82 countries. The states with significant contributions in the field of health economics are the USA, England, Canada, Australia, and Netherlands. From the point of view of affiliation, the authors belong to 1723 institutions. The institutions with a high number of publications about health economics are University of London, University of California System, University of York, Harvard University and University of Birmingham.

What kind of collaboration between authors, organizations, and nations are there in the field of health economics research?

There are not strong collaboration relationships between authors. They are divided in small research groups and cooperation for research in health economics is insignificant. The most collaborative authors are Drummond M., Mooney G., Trosch R., Marchese D., and Fuchs V. There are two research teams created around Drummond M. and Mooney G., on the one hand, and around Trosch R. and Marchese D., on the other hand. Cooperation among institutions is depending on cooperation among authors. It is understood that poor collaboration at the individual level is followed by an identical one at the organizational level. The most collaborative institutions in health economics area are University of York, University of Oxford, University of Pennsylvania, University of Washington, and University of Birmingham. Regarding collaboration between countries, the USA and England played a key role in worldwide academic exchanges in health economics area, followed by Canada, Australia, and Netherlands.

Which are the most cited authors and the most cited papers, and which are the most attractive journals for publishing research results in health economics?

The most influential paper is published by Arrow K.J. in 1963, entitled “Uncertainty and the Welfare Economics of Medical Care”. The second most influential paper belongs to an anonymous author who wrote in 1996 a paper about cost effectiveness. We assume that is a book written by Gold M.R., Siegel J.E., Russell L.B. and Weinstein M.C., entitled “Cost-Effectiveness in Health and Medicine”. The third and the fourth most cited publications are signed by Drummond M.F. and his co-authors. In fact, it is about a book entitled “Methods for the Economic Evaluation of Health Care Programmes”, first published in 1987 at and then renewed in several editions. Another influential book was written by Kahneman D., entitled “Thinking, Fast and Slow” and published in 2011. The most cited author is Margolis H., who published in 1982 a book about “Selfishness, Altruism, and Rationality”. Drummond M.F. is on the second position with the publications about “Essentials in Health Economics”. Williams A. is the third cited author, followed by Culyer A.J and Arrow K.J. It should be noted that World Health Organization’s (WHO) publications are ones of the most cited document in health economics research. The most cited journals in health economics are The BMJ – British Medical Journal, The New England Journal of Medicine, The Lancet, Journal of American Medicinal Association and Health Economics. Beside them, other very influential journals are Plos One, Value Health, BMJ Open, Applied Health Economics and Health Policy and BMC Health Services Research.

What are the most debated conceptual approaches in health economics?

“Health economics”, “cost effectiveness” and “care” are the most debated concepts in health economics. But the current research hotspots in health economics are “behavioural economics” and “economic evaluation”.

Discussions and conclusions

The current bibliographic analysis was done for a specialized literature: health economics. This analysis contributes to the evaluation of the progress of the global knowledge in health economics and to the evaluation of the interest in health economics research. Moreover, the research allows the identification of the authors who contributed to the theoretical conceptualization of health economics, but also the identification of the most cited works in the field. A bibliometric analysis of the health economics research topic was produced, based on 1620 papers that were published between 1975 and 2021 and indexed in WoS. According to the tables and figures above, we have identified the important authors, publications, nations, organizations, keywords, and references.

By giving information on the current state of the art and identifying trends and research possibilities through the selection and analysis of the most pertinent publications published in the subject of health economics, the current study completes the body of existing research.

Through an extensive field mapping, the study increases the added value for the study of health economics theory. The development patterns of health economics are described by identifying trends in research production in that field and the most productive nations. The identification of top contributors’ points to possible collaborators (universities and researchers) for additional research projects. Finding the most appealing source names reveals publishing prospects for health economics-related articles. Leading thematic areas and developing research areas can be found to help academics identify research gaps in health economics.

Limitations and future research directions

Even though the bibliometric analysis and mapping visualization on articles relevant to health economics in the current research have produced numerous fascinating results, this methodology has several drawbacks. These limitations are due to the bibliometric analysis and quality of database. A quantitative analysis reduces the influence of subjective judgments. In several parts of the analysis, we were forces to use manual search because of inadequate or incomplete data. Maybe, manual analysis is required to learn additional specifics about different aspects of health economics theory by using a systematic review analysis.

The following limitations of the current study should be considered. First, the search strategy leads to a lost in publications which do not contain the query word in the publication title. Therefore, the main findings should be interpreted in accordance with the selection strategy used in this paper. The dataset is downloaded only from WoS, maybe multi-source searching is more convincing. Publications in other languages were not analysed. For some publications the name of author was missing. Some journals change their title in time, and they appear twice as being different journals. In this analysis it was used an inhomogeneous sample due to the type of publications.

Therefore, these restrictions remain issues that need to be resolved in additional research. To sum up, our analysis cannot cover every crucial publication concerning health economics, but we believe that the results allow us to have reliable insight into the knowledge domain. This study could be carried out in the future utilizing new search criteria, time periods, or bibliometric analytic parameters.

Availability of data and materials

The data can be extracted from Web of Science. All data are available upon application.

Abbreviations

European Union

Gross Domestic Product

Journal Impact Factor

Organisation for Economic Co-operation and Development

Science Citation Index Expanded

Social Science Citation Index

The United Kingdom

The United States of America

World Health Organization

Web of Science

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Acknowledgements

Project financed by Lucian Blaga University of Sibiu through the research grant LBUS-IRG-2022-08.

This study was fund by Lucian Blaga University of Sibiu through the research grant LBUS-IRG-2022–08.

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Barbu, L. Global trends in the scientific research of the health economics: a bibliometric analysis from 1975 to 2022. Health Econ Rev 13 , 31 (2023). https://doi.org/10.1186/s13561-023-00446-7

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• Published: 27 July 2006

The importance of human resources management in health care: a global context

• Stefane M Kabene 1 , 3 ,
• Carole Orchard 3 ,
• John M Howard 2 ,
• Mark A Soriano 1 &
• Raymond Leduc 1  

Human Resources for Health volume  4 , Article number:  20 ( 2006 ) Cite this article

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This paper addresses the health care system from a global perspective and the importance of human resources management (HRM) in improving overall patient health outcomes and delivery of health care services.

We explored the published literature and collected data through secondary sources.

Various key success factors emerge that clearly affect health care practices and human resources management. This paper will reveal how human resources management is essential to any health care system and how it can improve health care models. Challenges in the health care systems in Canada, the United States of America and various developing countries are examined, with suggestions for ways to overcome these problems through the proper implementation of human resources management practices. Comparing and contrasting selected countries allowed a deeper understanding of the practical and crucial role of human resources management in health care.

Proper management of human resources is critical in providing a high quality of health care. A refocus on human resources management in health care and more research are needed to develop new policies. Effective human resources management strategies are greatly needed to achieve better outcomes from and access to health care around the world.

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Defining human resources in health care

Within many health care systems worldwide, increased attention is being focused on human resources management (HRM). Specifically, human resources are one of three principle health system inputs, with the other two major inputs being physical capital and consumables [ 1 ]. Figure 1 depicts the relationship between health system inputs, budget elements and expenditure categories.

Relationship between health system inputs, budget elements and expenditure categories . Source: World Health Report 2000 Figure 4.1 pg.75. http://www.who.int.proxy.lib.uwo.ca:2048/whr/2000/en/whr00_ch4_en.pdf Figure 1 identifies three principal health system inputs: human resources, physical capital and consumables. It also shows how the financial resources to purchase these inputs are of both a capital investment and a recurrent character. As in other industries, investment decisions in health are critical because they are generally irreversible: they commit large amounts of money to places and activities that are difficult, even impossible, to cancel, close or scale down [1].

Human resources, when pertaining to health care, can be defined as the different kinds of clinical and non-clinical staff responsible for public and individual health intervention [ 1 ]. As arguably the most important of the health system inputs, the performance and the benefits the system can deliver depend largely upon the knowledge, skills and motivation of those individuals responsible for delivering health services [ 1 ].

As well as the balance between the human and physical resources, it is also essential to maintain an appropriate mix between the different types of health promoters and caregivers to ensure the system's success [ 1 ]. Due to their obvious and important differences, it is imperative that human capital is handled and managed very differently from physical capital [ 1 ]. The relationship between human resources and health care is very complex, and it merits further examination and study.

Both the number and cost of health care consumables (drugs, prostheses and disposable equipment) are rising astronomically, which in turn can drastically increase the costs of health care. In publicly-funded systems, expenditures in this area can affect the ability to hire and sustain effective practitioners. In both government-funded and employer-paid systems, HRM practices must be developed in order to find the appropriate balance of workforce supply and the ability of those practitioners to practise effectively and efficiently. A practitioner without adequate tools is as inefficient as having the tools without the practitioner.

Key questions and issues pertaining to human resources in health care

When examining health care systems in a global context, many general human resources issues and questions arise. Some of the issues of greatest relevance that will be discussed in further detail include the size, composition and distribution of the health care workforce, workforce training issues, the migration of health workers, the level of economic development in a particular country and sociodemographic, geographical and cultural factors.

The variation of size, distribution and composition within a county's health care workforce is of great concern. For example, the number of health workers available in a country is a key indicator of that country's capacity to provide delivery and interventions [ 2 ]. Factors to consider when determining the demand for health services in a particular country include cultural characteristics, sociodemographic characteristics and economic factors [ 3 ].

Workforce training is another important issue. It is essential that human resources personnel consider the composition of the health workforce in terms of both skill categories and training levels [ 2 ]. New options for the education and in-service training of health care workers are required to ensure that the workforce is aware of and prepared to meet a particular country's present and future needs [ 2 ]. A properly trained and competent workforce is essential to any successful health care system.

The migration of health care workers is an issue that arises when examining global health care systems. Research suggests that the movement of health care professionals closely follows the migration pattern of all professionals in that the internal movement of the workforce to urban areas is common to all countries [ 2 ]. Workforce mobility can create additional imbalances that require better workforce planning, attention to issues of pay and other rewards and improved overall management of the workforce [ 2 ]. In addition to salary incentives, developing countries use other strategies such as housing, infrastructure and opportunities for job rotation to recruit and retain health professionals [ 2 ], since many health workers in developing countries are underpaid, poorly motivated and very dissatisfied [ 3 ]. The migration of health workers is an important human resources issue that must be carefully measured and monitored.

Another issue that arises when examining global health care systems is a country's level of economic development. There is evidence of a significant positive correlation between the level of economic development in a country and its number of human resources for health [ 3 ]. Countries with higher gross domestic product (GDP) per capita spend more on health care than countries with lower GDP and they tend to have larger health workforces [ 3 ]. This is an important factor to consider when examining and attempting to implement solutions to problems in health care systems in developing countries.

Socio-demographic elements such as age distribution of the population also play a key role in a country's health care system. An ageing population leads to an increase in demand for health services and health personnel [ 3 ]. An ageing population within the health care system itself also has important implications: additional training of younger workers will be required to fill the positions of the large number of health care workers that will be retiring.

It is also essential that cultural and geographical factors be considered when examining global health care systems. Geographical factors such as climate or topography influence the ability to deliver health services; the cultural and political values of a particular nation can also affect the demand and supply of human resources for health [ 3 ]. The above are just some of the many issues that must be addressed when examining global health care and human resources that merit further consideration and study.

The impact of human resources on health sector reform

When examining global health care systems, it is both useful and important to explore the impact of human resources on health sector reform. While the specific health care reform process varies by country, some trends can be identified. Three of the main trends include efficiency, equity and quality objectives [ 3 ].

Various human resources initiatives have been employed in an attempt to increase efficiency. Outsourcing of services has been used to convert fixed labor expenditures into variable costs as a means of improving efficiency. Contracting-out, performance contracts and internal contracting are also examples of measures employed [ 3 ].

Many human resources initiatives for health sector reform also include attempts to increase equity or fairness. Strategies aimed at promoting equity in relation to needs require more systematic planning of health services [ 3 ]. Some of these strategies include the introduction of financial protection mechanisms, the targeting of specific needs and groups, and re-deployment services [ 3 ]. One of the goals of human resource professionals must be to use these and other measures to increase equity in their countries.

Human resources in health sector reform also seek to improve the quality of services and patients' satisfaction. Health care quality is generally defined in two ways: technical quality and sociocultural quality. Technical quality refers to the impact that the health services available can have on the health conditions of a population [ 3 ]. Sociocultural quality measures the degree of acceptability of services and the ability to satisfy patients' expectations [ 3 ].

Human resource professionals face many obstacles in their attempt to deliver high-quality health care to citizens. Some of these constraints include budgets, lack of congruence between different stakeholders' values, absenteeism rates, high rates of turnover and low morale of health personnel [ 3 ].

Better use of the spectrum of health care providers and better coordination of patient services through interdisciplinary teamwork have been recommended as part of health sector reform [ 4 ]. Since all health care is ultimately delivered by people, effective human resources management will play a vital role in the success of health sector reform.

In order to have a more global context, we examined the health care systems of Canada, the United States of America, Germany and various developing countries. The data collection was achieved through secondary sources such as the Canadian Health Coalition, the National Coalition on Health Care and the World Health Organization Regional Office for Europe. We were able to examine the main human resources issues and questions, along with the analysis of the impact of human resources on the health care system, as well as the identification of the trends in health sector reform. These trends include efficiency, equity and quality objectives.

Health care systems

The Canadian health care system is publicly funded and consists of five general groups: the provincial and territorial governments, the federal government, physicians, nurses and allied health care professionals. The roles of these groups differ in numerous aspects. See Figure 2 for an overview of the major stakeholders in the Canadian health care system.

Overview of the major stakeholders in the Canadian health care system . Figure 2 depicts the major stakeholders in the Canadian health care system and how they relate.

Provincial and territorial governments are responsible for managing and delivering health services, including some aspects of prescription care, as well as planning, financing, and evaluating hospital care provision and health care services [ 5 ]. For example, British Columbia has shown its commitment to its health care program by implementing an increase in funding of CAD 6.7 million in September 2003, in order to strengthen recruitment, retention and education of nurses province-wide [ 6 ]. In May 2003, it was also announced that 30 new seats would be funded to prepared nurse practitioners at the University of British Columbia and at the University of Victoria [ 6 ]. Recently the Ontario Ministry of Health and Long Term Care announced funding for additional nurse practitioner positions within communities. Furthermore, most provinces and territories in Canada have moved the academic entry requirement for registered nurses to the baccalaureate level, while increasing the length of programmes for Licensed Practice Nurses to meet the increasing complexity of patient-care needs. Several provinces and territories have also increased seats in medical schools aimed towards those students wishing to become family physicians [ 7 ].

The federal government has other responsibilities, including setting national health care standards and ensuring that standards are enforced by legislative acts such as the Canada Health Act (CHA) [ 5 ]. Constitutionally the provinces are responsible for the delivery of health care under the British North America (BNA) Act; the provinces and territories must abide by these standards if they wish to receive federal funding for their health care programs [ 8 ]. The federal government also provides direct care to certain groups, including veterans and First Nation's peoples, through the First Nationals and Inuit Health Branch (FNIHB). Another role of the federal government is to ensure disease protection and to promote health issues [ 5 ].

The federal government demonstrates its financial commitment to Canada's human resources in health care by pledging transfer funds to the provinces and direct funding for various areas. For example, in the 2003 Health Care Renewal Accord, the federal government provided provinces and territories with a three-year CAD 1.5 billion Diagnostic/Medical Equipment Fund. This was used to support specialized staff training and equipment that improved access to publicly funded services [ 6 ].

The third group – private physicians – is generally not employed by the government, but rather is self-employed and works in a private practice. They deliver publicly-funded care to Canadian citizens. Physicians will negotiate fee schedules for their services with their provincial governments and then submit their claims to the provincial health insurance plan in order to receive their reimbursement [ 5 ].

The roles of nurses consist of providing care to individuals, groups, families, communities and populations in a variety of settings. Their roles require strong, consistent and knowledgeable leaders, who inspire others and support professional nursing practice. Leadership is an essential element for high-quality professional practice environments in which nurses can provide high-quality nursing care [ 9 ].

In most Canadian health care organizations, nurses manage both patient care and patient care units within the organization. Nurses have long been recognized as the mediators between the patient and the health care organization [ 10 ]. In care situations, they generally perform a coordinating role for all services needed by patients. They must be able to manage and process nursing data, information and knowledge to support patient care delivery in diverse care-delivery settings [ 10 ]. Workplace factors most valued by nurses include autonomy and control over the work environment, ability to initiate and sustain a therapeutic relationship with patients and a collaborative relationship with physicians at the unit level [ 11 ].

In addition to doctors and nurses, there are many more professionals involved in the health care process. Allied health care professionals can consist of pharmacists, dietitians, social workers and case managers, just to name a few. While much of the focus is on doctors and nurses, there are numerous issues that affect other health care providers as well, including workplace issues, scopes of practice and the impact of changing ways of delivering services [ 12 ]. Furthermore, with health care becoming so technologically advanced, the health care system needs an increasing supply of highly specialized and skilled technicians [ 12 ]. Thus we can see the various roles played by these five groups and how they work together to form the Canadian health care system.

Canada differs from other nations such as the United States of America for numerous reasons, one of the most important being the CHA. As previously mentioned, the CHA sets national standards for health care in Canada. The CHA ensures that all Canadian citizens, regardless of their ability to pay, will have access to health care services in Canada. "The aim of the CHA is to ensure that all eligible residents of Canada have reasonable access to insured health services on a prepaid basis, without direct charges at the point of service" [ 6 ].

Two of the most significant stipulations of the CHA read: "reasonable access to medically necessary hospital and physician services by insured persons must be unimpeded by financial or other barriers" and "health services may not be withheld on the basis of income, age, health status, or gender" [ 5 ]. These two statements identify the notable differences between the Canadian and American health care systems. That is, coverage for the Canadian population is much more extensive.

Furthermore in Canada, there has been a push towards a more collaborative, interdisciplinary team approach to delivering health care; this raises many new issues, one of which will involve successful knowledge transfer within these teams [ 13 ]. Effective knowledge management, which includes knowledge transfer, is increasingly being recognized as a crucial aspect of an organization's basis for long-term, sustainable, competitive advantage [ 34 ]. Even though health care in Canada is largely not for profit, there will still be the need for effective knowledge management practices to be developed and instituted. The introduction of interdisciplinary health teams in Canadian hospitals is a relatively new phenomenon and their connection to the knowledge management policies and agendas of governments and hospital administrations raises important questions about how such teams will work and to what extent they can succeed in dealing with the more difficult aspects of knowledge management, such as the transfer of tacit knowledge.

The multidisciplinary approach tends to be focused around specific professional disciplines, with health care planning being mainly top-down and dominated by medical professionals. Typically there is a lead professional (usually a physician) who determines the care and, if necessary, directs the patient to other health care specialists and allied professionals (aides, support workers). There is generally little involvement by the patient in the direction and nature of the care. Interdisciplinary health care is a patient-centred approach in which all those involved, including the patient, have input into the decisions being made.

The literature on teamwork and research on the practices in hospitals relating to multidisciplinary teams suggests that interdisciplinary teams face enormous challenges [ 13 ], therefore multidisciplinary teamwork will continue to be a vital part of the health care system. However, the goal of this teamwork should not be to displace one health care provider with another, but rather to look at the unique skills each one brings to the team and to coordinate the deployment of these skills. Clients need to see the health worker most appropriate to deal with their problem [ 14 ].

Some of the issues regarding the Canadian public system of health have been identified in the Mazankowski Report, which was initiated by Alberta's Premier Ralph Klein in 2000. Many issues have arisen since this time and have been debated among Canadians. One of the most contentious, for example, is the possibility of introducing a two-tier medical system. One tier of the proposed new system would be entirely government-funded through tax dollars and would serve the same purpose as the current publicly-funded system. The second tier would be a private system and funded by consumers [ 5 ].

However, the CHA and the Canadian Nurses Association (CNA) are critical of any reforms that pose a threat to the public health care system. It should be noted that although Canada purports to have a one-tier system, the close proximity of private, fee-for-service health care in the United States really creates a pay-as-you-go second tier for wealthy Canadians. In addition, many health care services such as most prescriptions and dental work are largely funded by individuals and/or private or employer paid insurance plans.

It is important to realize the differences between the proposed two-tier system and the current health care system. Presently, the public health care system covers all medically necessary procedures and the private sector provides 30% for areas such as dental care. With the new system, both public and private care would offer all services and Canadians would have the option of choosing between the two.

The proposal of the two-tier system is important because it highlights several important issues that concern many Canadians, mainly access to the system and cost reduction. Many Canadians believe the current public system is not sustainable and that a two-tiered system would force the public system to become more efficient and effective, given the competition of the private sector. However, the two-tiered system is not within the realm of consideration, since the majority of Canadians are opposed to the idea of a privatized system [ 5 ]. No proposals have come forward that show how a privately funded system would provide an equal quality of services for the same cost as the current publicly funded system.

United States of America

The health care system in the United States is currently plagued by three major challenges. These include: rapidly escalating health care costs, a large and growing number of Americans without health coverage and an epidemic of substandard care [ 15 ].

Health insurance premiums in the United States have been rising at accelerating rates. The premiums themselves, as well as the rate of increase in premiums, have increased every year since 1998; independent studies and surveys indicate that this trend is likely to continue over the next several years [ 15 ]. As a result of these increases, it is more difficult for businesses to provide health coverage to employees, with individuals and families finding it more difficult to pay their share of the cost of employer-sponsored coverage [ 15 ]. The rising trend in the cost of employer-sponsored family health coverage is illustrated in Figure 3 .

The trend of the cost of employer-sponsored family health care coverage in the United States . Source: National Coalition on Health Care 2004 pg.9. http://www.nchc.org/materials/studies/reform.pdf . Figure 3 illustrates the increase in health insurance premiums since 2001. These increases are making it more difficult for businesses to continue to provide health coverage for their employees and retirees [15].

To help resolve this problem, health maintenance organizations (HMO) have been introduced, with the goal of focusing on keeping people well and out of hospitals in the hope of decreasing employer costs. HMOs are popular alternatives to traditional health care plans offered by insurance companies because they can cover a wide variety of services, usually at a significantly lower cost [ 16 ]. HMOs use "networks" of selected doctors, hospitals, clinics and other health care providers that together provide comprehensive health services to the HMOs members [ 16 ]. The overall trade-off with an HMO is reduced choice in exchange for increased affordability.

Another problem to address regarding the American health care system is the considerable and increasing number of Americans without health coverage. Health care coverage programs such as Medicare offer a fee-for-service plan that covers many health care services and certain drugs. It also provides access to any doctor or hospital that accepts Medicare [ 17 ]. Patients with limited income and resources may qualify for Medicaid, which provide extra help paying for prescription drug costs [ 17 ]. However, according to figures from the United States Census Bureau, the number of Americans without health coverage grew to 43.6 million in 2002; it is predicted that the number of uninsured Americans will increase to between 51.2 and 53.7 million in 2006 [ 15 ].

Those Americans without health care insurance receive less care, receive care later and are, on average, less healthy and less able to function in their daily lives than those who have health care insurance. Additionally, the risk of mortality is 25% higher for the uninsured than for the insured [ 15 ].

Despite excellent care in some areas, the American health care system is experiencing an epidemic of substandard care; the system is not consistently providing high-quality care to its patients [ 15 ]. There appears to be a large discrepancy between the care patients should be receiving and the care they are actually getting. The Institute of Medicine has estimated that between 44 000 and 98 000 Americans die each year from preventable medical errors in hospitals [ 15 ].

It is also useful to examine the demographic characteristics of those Americans more likely to receive substandard care. Research shows that those Americans with little education and low income receive a lower standard of care [ 18 ]. This finding may be explained by the fact that patients who have lower education levels tend to have more difficulty explaining their concerns to physicians, as well as eliciting a response for those concerns because health professionals often do not value their opinions [ 18 ].

Case studies

As shown by the extensive literature, statistics and public opinion, there is a growing need for health care reform in the United States of America. There is a duty and responsibility of human resources professionals to attempt to elicit change and implement policies that will improve the health care system.

It is informative to examine case studies in which human resources professionals have enacted positive change in a health care setting. One such case from 1995 is that of a mid-sized, private hospital in the New York metropolitan area. This case presents a model of how human resources can be an agent for change and can partner with management to build an adaptive culture to maintain strong organizational growth [ 19 ].

One of the initiatives made by human resources professionals in an attempt to improve the overall standard of care in the hospital was to examine and shape the organization's corporate culture. Steps were taken to define the values, behaviors and competences that characterized the current culture, and analyze these against the desired culture [ 19 ]. A climate survey was conducted in the organization; it became the goal of the human resources professionals to empower employees to be more creative and innovative [ 19 ]. To achieve this, a new model of care was designed that emphasized a decentralized nursing staff and a team-based approach to patient care. Nursing stations were redesigned to make them more accessible and approachable [ 19 ].

Human resources management also played an important role in investing in employee development. This was achieved by assisting employees to prepare and market themselves for internal positions and if desired, helping them pursue employment opportunities outside the organization [ 19 ]. This case makes obvious the important roles that human resources management can play in orchestrating organizational change.

Another case study that illustrates the importance of human resources management to the health care system is that of The University of Nebraska Medical Center in 1995. During this period, the hospital administrative staff recognized a variety of new challenges that were necessitating organizational change. Some of these challenges included intense price competition and payment reform in health care, reduced state and federal funding for education and research, and changing workforce and population demographics [ 20 ]. The organizational administrators recognized that a cultural reformation was needed to meet these new challenges. A repositioning process was enacted, resulting in a human resources strategy that supported the organization's continued success [ 20 ]. This strategy consisted of five major objectives, each with a vision statement and series of action steps.

Staffing: Here, the vision was to integrate a series of organization-wide staffing strategies that would anticipate and meet changing workforce requirements pertaining to staff, faculty and students. To achieve this vision, corporate profiles were developed for each position to articulate the core competences and skills required [ 20 ].

Performance management: The vision was to hold all faculty and staff accountable and to reward individual and team performance. With this strategy, managers would be able to provide feedback and coaching to employees in a more effective and timely manner [ 20 ].

Development and learning: The vision was to have all individuals actively engaged in the learning process and responsible for their own development. Various unit-based training functions were merged into a single unit, which defined critical technical and behavioral competencies [ 20 ].

Valuing people: The vision was to have the hospital considered as a favored employer and to be able to attract and retain the best talent. To facilitate this vision, employee services such as child care and wellness were expanded [ 20 ].

Organizational effectiveness. The vision was to create an organization that is flexible, innovative and responsive [ 20 ]. The developments of these human resources strategies were essential to the effectiveness of the organization and to demonstrate the importance of human resources in the health care industry.

Both these case studies illustrate that effective human resources management is crucial to health care in a practical setting and that additional human resources initiatives are required if solutions are to be found for the major problems in the United States health care system.

Approximately 92% of Germany's population receives health care through the country's statutory health care insurance program, Gesetzliche Krankenversicherung (GKV). GKV designed an organizational framework for health care in Germany and has identified and constructed the roles of payers, providers and hospitals. Private, for-profit companies cover slightly less than 8% of the population. This group would include, for example, civil servants and the self-employed. It is estimated that approximately 0.2% of the population does not have health care insurance [ 21 ]. This small fragment may be divided into two categories: either the very rich, who do not require it, or the very poor, who obtain their coverage through social insurance. All Germans, regardless of their coverage, use the same health care facilities. With these policies nearly all citizens are guaranteed access to high-quality medical care [ 22 ].

While the federal government plays a major part in setting the standards for national health care policies, the system is actually run by national and regional autonomous organizations. Rather than being financed solely through taxes, the system is covered mostly by health care premiums [ 22 ]. In 2003, about 11.1% of Germany's gross domestic product (GDP) went into the health care system [ 23 ] versus the United States, with 15% [ 24 ] and Canada at 9.9% [ 25 ]. However, Germany still put about one third of its social budget towards health care [ 22 ].

The supply of physicians in Germany is high, especially compared to the United States, and this is attributed largely to the education system. If one meets the academic requirements in Germany, the possibility to study medicine is legally guaranteed [ 26 ]. This has led to a surplus of physicians and unemployment for physicians has become a serious problem. In 2001, the unemployment rate for German physicians of 2.1% led many German doctors to leave for countries such as Norway, Sweden and the United Kingdom, all of which actively recruit from Germany [ 27 ].

Germany's strong and inexpensive academic system has led the country to educate far more physicians than the United States and Canada. In 2003, Germany had 3.4 practicing physicians per 1000 inhabitants [ 23 ], versus the United States, which had 2.3 practicing physicians per 1000 inhabitants in 2002 [ 24 ] and Canada, which had 2.1 practicing physicians per 1000 inhabitants in 2003 [ 25 ]. It is also remarkable that health spending per capita in Germany (USD 2996) [ 23 ] amounted to about half of health spending per capita in the United States (USD 5635) [ 24 ], and slightly less than Canada's health spending (USD 3003) [ 25 ]. This clearly demonstrates the Germans' strength regarding cost containment.

There are several issues that physicians face in the German health care system. In a 1999 poll, 49.9% of respondents said they were very or fairly satisfied with their health care system, while 47.7% replied they were very or fairly dissatisfied with it [ 28 ]. Furthermore, the degree of competition between physicians is very high in Germany and this could lead to a reduction in physician earnings. Due to this competition, many younger physicians currently face unemployment. The German law also limits the number of specialists in certain geographical areas where there are issues of overrepresentation [ 22 ]. Thus, the oversupply of physicians in Germany leads to many challenges, including human resources management in the health care system.

In Germany a distinction is made between office-based physicians and hospital-based physicians. The income of office-based physicians is based on the number and types of services they provide, while hospital-based physicians are compensated on a salary basis. This division has created a separated workforce that German legislation is now working to eliminate by encouraging the two parties to work together, with the aim of reducing overall medical costs [ 22 ].

Developing countries

Accessing good-quality health care services can be incredibly arduous for those living in developing countries, and more specifically, for those residing in rural areas. For many reasons, medical personnel and resources may not be available or accessible for such residents. As well, the issue of migrant health care workers is critical. Migrant health workers can be defined as professionals who have a desire and the ability to leave the country in which they were educated and migrate to another country. The workers are generally enticed to leave their birth country by generous incentive offers from the recruiting countries [ 29 ].

Developing countries struggle to find means to improve living conditions for their residents; countries such as Ghana, Kenya, South Africa and Zimbabwe are seeking human resources solutions to address their lack of medically trained professionals. Shortages in these countries are prevalent due to the migration of their highly educated and medically trained personnel.

Professionals tend to migrate to areas where they believe their work will be more thoroughly rewarded. The International Journal for Equity in Health (2003) suggested that those who work in the health care profession tend to migrate to areas that are more densely populated and where their services may be better compensated. Health care professionals look to areas that will provide their families with an abundance of amenities, including schools for their children, safe neighborhoods and relatives in close proximity. For medical professionals, the appeal of promotions also serves as an incentive for educating oneself further [ 30 ]. As one becomes more educated, the ability and opportunity to migrate increases and this can lead to a further exodus of needed health care professionals.

These compelling reasons tend to cause medical professionals to leave their less-affluent and less-developed areas and migrate to areas that can provide them with better opportunities. This has caused a surplus in some areas and a huge deficit in others. This epidemic can be seen in nations such as Nicaragua. Its capital city, Managua, holds only one fifth of the country's population, yet it employs almost 50% of the medically trained health care workers. The same situation can be found in other countries, such as Bangladesh, where almost one third of the available health personnel are employed "in four metropolitan districts where less than 15% of the population lives" [ 30 ]. Clearly this presents a problem for those living outside these metropolitan districts.

Other possible explanations put forth by Dussault and Franceschini, both of the Human Development Division of the World Bank Institute, include "management style, incentive and career structures, salary scales, recruitment, posting and retention practices" [ 31 ]. Salary scales can differ quite drastically between originating and destination countries, which are shown in Figures 4 and 5 . They also state that in developing countries the earning potential one would see in more affluent or populated urban areas is much higher than one would expect to earn in rural areas.

Ratio of nurse wages (PPP USD), destination country to source country . Source: Vujicic M, Zurn P, Diallo K, Orvill A, Dal Poz MR 2004. http://www.human-resources-health.com/content/2/1/3 . Figure 4 shows the difference between the wage in the source country and destination country for nurses. This difference is also known as the "wage premium" [29].

Ratio of physician wages (PPP USD), destination country to source country . Source: Vujicic M, Zurn P, Diallo K, Orvill A, Dal Poz MR 2004. http://www.human-resources-health.com/content/2/1/3 . Figure 5 shows the difference between the wage in the source country and destination country for physicians [29].

As more health professionals emigrate to urban areas, the workloads for those in the rural areas greatly increase. This leads to a domino effect, in that those in such dire situations look for areas where they may be able to find more satisfactory and less demanding working conditions [ 31 ]. Vujicic et al. (2004) summarizes numerous variables that influence the migration pattern and has created a formula to express their impact. It is possible to quantify the factors, and human resources professionals need to look at the costs and benefits of altering the factors so that the migration pattern is more favorable. This formula is expressed as the results shown in Table 1 , which shows the different reasons for one to migrate in terms of the popularity of a given reason.

There is a tendency for developed countries faced with decreasing numbers of nationally trained medical personnel to recruit already-trained individuals from other nations by enticing them with incentives. Zimbabwe has been particularly affected by this problem. In 2001, out of approximately 730 nursing graduates, more than one third (237) of them relocated to the United Kingdom [ 29 ]. This was a dramatic increase from 1997, when only 26 (approximately 6.2%) of the 422 nursing program graduates migrated to the United Kingdom [ 29 ]. This leads to the loss of skilled workers in developing countries and can be very damaging, since the education systems in developing countries are training individuals for occupations in the medical profession, yet are not able to retain them [ 29 ].

Countries that have the capacity to educate more people than necessary in order to meet their domestic demand have tried to counterbalance this problem by increasing their training quota. Vujicic et al. (2004) identify that "the Philippines has for many years trained more nurses than are required to replenish the domestic stock, in an effort to encourage migration and increase the level of remittance flowing back into the country" [ 29 ].

Developed countries attract internationally trained medical professionals for many reasons. To begin with, "political factors, concerns for security, domestic birth rates, the state of the economy and war (both at home and abroad)" [ 26 ] influence the number of people that will be allowed or recruited into a country. Also, due to the conditions of the labor market compared to the demand in developed countries, governments may make allowances to their strict policies regarding the type of and number of professionals they will allow into their country [ 29 ]. This can be seen in a Canadian example:

Canada maintains] a list of occupations within which employment vacancies [are] evident. Potential immigrants working in one of these [listed] occupations would have a much higher chance of being granted entry than if they worked in a non-listed occupation [ 29 ].

Though Canada attracts internationally trained medical professionals, those employment vacancies may not always be open. Although there may be up to 10 000 international medical graduates (IMG) in Canada, many are not legally allowed to practice. Many immigrants cannot afford the costs of retraining and may be forced to find a new job in a completely unrelated field, leaving their skills to go to waste [ 32 ]. In 2004, Ontario had between 2000 and 4000 IMGs looking for work in medical fields related to their training and background [ 33 ]. That year, IMG Ontario accepted 165 IMGs into assessment and training positions, which was a 50% increase over the last year, and a 600% increase from the 24 positions in 1999 [ 33 ].

Another appeal for developed countries with regard to foreign trained health care professionals is that they may be less of a financial burden to the host country than those trained domestically. This is because educational costs and the resources necessary for training are already taken care of by the international medical schools and governments [ 29 ]. Though these reasons may make recruiting foreign medical professionals seem appealing, there are still ongoing debates as to whether those trained outside the host country are equally qualified and culturally sensitive to the country to which they migrate. Developing countries are addressing these concerns by establishing health professional training programs similar to those in developed countries [ 29 ]. These practices can be seen in, "the majority of nursing programs in Bangladesh, the Philippines and South Africa [which] are based on curricula from United Kingdom or USA nursing schools" [ 29 ]. Because of these actions, those who are trained may be more likely to leave and use their skills where they will be recognized and more highly rewarded.

There are also ethical considerations when examining the practice of recruiting health care professionals, particularly if they are recruited from regions or countries where health care shortages already exist. The rights of individuals to move as they see fit may need to be balanced against the idea of the greater good of those left behind.

Due to the shortages, it has been found the level of health service in rural or poor areas has decreased, leading to lower quality and productivity of health services, closure of hospital wards, increased waiting times, reduced numbers of available beds for inpatients, diversion of emergency department patients and underuse of remaining personnel or substitution with persons lacking the required skills for performing critical interventions [ 30 ].

The article "Not enough here, too many there: understanding geographical imbalances in the distribution of the health workforce" (2003), states that a reduced number of health care workers in a given area has a direct effect on the life expectancy of its residents. For example, in the rural areas of Mexico, life expectancy is 55 years, compared to 71 years in the urban areas. Additionally, in "the wealthier, northern part of the country, infant mortality is 20/1000 as compared to more than 50/1000 in the poorer southern states" [ 31 ].

Globalization – a common thread

While the issues raised in this article are common to many countries, the approaches taken to address them may not be the same in each country. Factors affecting the approaches that can be taken, some of which have been raised, include demographics, resources and philosophical and political perspectives. However, an overarching issue that affects not only health care but many other areas is that of globalization itself.

Different countries have traditionally had different perspectives on health care that have influenced their approaches to health care delivery. In Canada for example, health care is considered a right; its delivery is defined by the five main principles of the Canada Health Act, which officially precludes a significant role for private delivery of essential services. In the United States, health care is treated more as another service that, while it should be accessible, is not considered a right. Therefore there is a much larger private presence in health care delivery the United States than there is in Canada. In other parts of the world, the approach to health care falls between these perspectives.

As the move towards globalization for many goods and services increases, countries will have to consider how this will affect their approaches to health care delivery. As mentioned earlier, there is already a degree of labor mobility within a country that affects the quality and availability of health care services. There is also already a degree of international mobility of health care workers, as shown by the number of workers recruited developed countries.

While the international mobility of labor is generally not as unencumbered as that for goods and capital, that may be changing as more and more regional free trade agreements are considered. Canada, the United States and Mexico have NAFTA (North American Free Trade Agreement), Europe has the EU (European Union) and talks are under way to consider expanding the NAFTA agreement to include Central and South America, to expand EU membership and to consider an Asian trading bloc including China and India.

If health care becomes a part of these new trade agreements, countries will be obliged to treat health care delivery according to the rules of the agreement. Using the NAFTA as an example, if health care is included, governments could not treat domestic providers more favorably than foreign firms wanting to deliver services. In Canada the concern is that it would mean the end of the Canada Health Act, since NAFTA would allow private, for-profit American or Mexican firms to open.

All five issues raised in this research would be affected by the increase in international trade agreements that included health care. Therefore, governments, health care providers and human resources professionals cannot ignore this important consideration and trend when examining solutions to the issues. Depending upon their relative negotiation strengths and positions, some countries may not benefit as much as others with these agreements.

For example, it is more likely that countries with well-developed private, for-profit, health care expertise, such as the United States, would expand into developing countries rather than the other way around. If there is an increased ability for labor mobility, then it is likely that health care professionals in the poorer, developing countries would move to where the opportunities are better. We already see this internally in the move from rural to urban centers; this would likely continue if the health care professionals had the opportunity to move out of country to where they could have greater financial rewards for their expertise.

When considering the countries examined in this paper, it is likely that Canada and the United States would initially be the two most likely to move towards a more integrated approach to health care delivery. There is already a trade agreement in place, many of the factors influencing health care are similar (demographics, training, level of economic development, geography, cultural factors) and they are currently each other's largest trading partners. While the current agreement, which includes Mexico, does not cover health care, there is pressure to broaden the agreement to include areas not currently covered. If this happens, human resources professionals will have to increase their understanding of what the new health care delivery realities could be. For example, if the move is more towards the Canadian example of a largely not-for-profit, mainly publicly-funded health care delivery system, then it will be more of an adjustment for the American professionals.

However, the likelihood of the Canadian approach to health care's being adopted in the United States is very slim. During the presidency of Bill Clinton, the government attempted to introduce a more universal health care delivery system, which failed completely. Even though there are over 40 million Americans with no health care coverage, the idea of a universal, publicly-funded system went nowhere. Also, within Canada there is increasing pressure to consider a more active role for private health care delivery. Therefore, it is more likely that Canadian health care and human resource professionals will have to adapt to a style more like the American, privately delivered, for-profit approach.

If this is the direction of change, human resources professionals in Canada will need to adjust how they approach the challenges and new realities. For instance, there would likely be an increased role for insurance companies and health maintenance organizations (HMO) as they move towards the managed care model of the United States. With an HMO approach, financial as well as health needs of the patients are considered when making medical decisions. An insured patient would select from the range of services and providers that his/her policy covers and approves. Human resources professionals would need to work with a new level of administration, the HMO, which currently does not exist to any significant degree in Canada.

As mentioned earlier, it is likely that developing countries would be receiving health care models and approaches from developed countries rather than the other way around. In particular, a country such as the United States that has a strong, private, for-profit approach already in place would likely be the source from which the health care models would be drawn. Therefore, health care, as well as human resources professionals in those countries, would also need to adapt to these new realities.

In Germany, where there is currently an oversupply of physicians, a move towards a more global approach to health care delivery, through increased trade agreements, could result in even more German health care professionals' leaving the country. The challenge to be addressed by human resource professionals within the German health care system in this situation would be to prevent, or slow, the loss of the best professionals to other countries. Spending public resources in educating professionals only to have significant numbers of them leave the country is not a financially desirable or sustainable situation for a country.

While examining health care systems in various countries, we have found significant differences pertaining to human resources management and health care practices. It is evident that in Canada, CHA legislation influences human resources management within the health care sector. Furthermore, the result of the debate on Canada's one-tier versus two-tier system may have drastic impacts on the management of human resources in health care. Additionally, due to a lack of Canadian trained health professionals, we have found that Canada and the United States have a tendency to recruit from developing countries such as South Africa and Ghana, in order to meet demand.

Examination of the relationship between health care in the United States and human resources management reveals three major problems: rapidly escalating health care costs, a growing number of Americans without health care coverage and an epidemic regarding the standard of care. These problems each have significant consequences for the well-being of individual Americans and will have devastating affects on the physical and psychological health and well-being of the nation as a whole.

The physical health of many Americans is compromised because these factors make it difficult for individuals to receive proper consultation and treatment from physicians. This can have detrimental effects on the mental state of the patient and can lead to large amounts of undue stress, which may further aggravate the physical situation.

Examining case studies makes it evident that human resources management can and does play an essential role in the health care system. The practices, policies and philosophies of human resources professionals are imperative in developing and improving American health care. The implication is that further research and studies must be conducted in order to determine additional resource practices that can be beneficial to all organizations and patients.

Compared to the United States, Canada and developing countries, Germany is in a special situation, given its surplus of trained physicians. Due to this surplus, the nation has found itself with a high unemployment rate in the physician population group. This is a human resources issue that can be resolved through legislation. Through imposing greater restrictive admissions criteria for medical schools in Germany, they can reduce the number of physicians trained. Accompanying the surplus problem is the legislative restriction limiting the number of specialists allowed to practice in geographical areas. These are two issues that are pushing German-trained physicians out of the country and thus not allowing the country to take full advantage of its national investment in training these professionals.

Developing countries also face the problem of investing in the training of health care professionals, thus using precious national resources, but losing many of their trained professionals to other areas of the world that are able to provide them with more opportunities and benefits. Human resources professionals face the task of attempting to find and/or retain workers in areas that are most severely affected by the loss of valuable workers.

Human resources management plays a significant role in the distribution of health care workers. With those in more developed countries offering amenities otherwise unavailable, chances are that professionals will be more enticed to relocate, thus increasing shortages in all areas of health care. Due to an increase in globalization, resources are now being shared more than ever, though not always distributed equally.

Human resources implications of the factors

While collectively the five main areas addressed in the article represent health care issues affecting and affected by human resources practices, they are not all equal in terms of their influence in each country. For instance, in Canada there are fewer health care issues surrounding the level of economic development or migration of health workers, whereas these issues are much more significant in developing countries. In the United States, the level of economic development is not a significant issue, but the accessibility of health care based upon an individual's financial situation certainly is, as evidenced by the more than 40 million Americans who have no health care coverage. Germany's issues with the size of its health care worker base have to do with too many physicians, whereas in Canada one of the issues is having too few physicians. Table 2 summarizes some of the implications for health care professionals with regard to the five main issues raised in the article. One of the main implications of this paper, as shown in Table 2 , is that HRP will have a vital role in addressing all the factors identified. Solutions to health care issues are not just medical in nature.

Policy approaches in a global approach to health care delivery

As mentioned at the start of this paper, there are three main health system inputs: human resources, physical capital and consumables. Given that with sufficient resources any country can obtain the same physical capital and consumables, it is clear that the main differentiating input is the human resources. This is the input that is the most difficult to develop, manage, motivate, maintain and retain, and this is why the role of the human resources professional is so critical.

The case studies described earlier showed how human resources initiatives aimed at improving organizational culture had a significant and positive effect on the efficiency and effectiveness of the hospitals studied. Ultimately all health care is delivered by people, so health care management can really be considered people management; this is where human resources professionals must make a positive contribution.

Human resource professionals understand the importance of developing a culture that can enable an organization to meet its challenges. They understand how communities of practice can form around common goals and interests, and the importance of aligning these to the goals and interests of the organization.

Given the significant changes that globalization of health care can introduce, it is important that human resources professionals be involved at the highest level of strategic planning, and not merely be positioned at the more functional, managerial levels. By being actively involved at the strategic levels, they can ensure that the HR issues are raised, considered and properly addressed.

Therefore, human resources professionals will also need to have an understanding not only of the HR area, but of all areas of an organization, including strategy, finance, operations, etc. This need will have an impact on the educational preparation as well as the possible need to have work experience in these other functional areas.

We have found that the relationship between human resources management and health care is extremely complex, particularly when examined from a global perspective. Our research and analysis have indicated that several key questions must be addressed and that human resources management can and must play an essential role in health care sector reform.

The various functions of human resources management in health care systems of Canada, the United States of America, Germany and various developing countries have been briefly examined. The goals and motivations of the main stakeholders in the Canadian health care system, including provincial governments, the federal government, physicians, nurses and allied health care professionals, have been reviewed. The possibility of a major change in the structure of Canadian health care was also explored, specifically with regard to the creation of a two-tier system. The American health care system is currently challenged by several issues; various American case studies were examined that displayed the role of human resources management in a practical setting. In Germany, the health care situation also has issues due to a surplus of physicians; some of the human resources implications of this issue were addressed. In developing countries, the migration of health workers to more affluent regions and/or countries is a major problem, resulting in citizens in rural areas of developing countries experiencing difficulties receiving adequate medical care.

Since all health care is ultimately delivered by and to people, a strong understanding of the human resources management issues is required to ensure the success of any health care program. Further human resources initiatives are required in many health care systems, and more extensive research must be conducted to bring about new human resources policies and practices that will benefit individuals around the world.

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Acknowledgements

The authors are grateful to Valerie Sloby from PCHealthcare for her editorial assistance and helping in reviewing the manuscript.

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SK conceived the paper, worked on research design, did data analysis and led the writing of the paper. CO, JH, MS and RL all actively participated in data analysis, manuscript writing and review. All authors read and approved the final manuscript.

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Kabene, S.M., Orchard, C., Howard, J.M. et al. The importance of human resources management in health care: a global context. Hum Resour Health 4 , 20 (2006). https://doi.org/10.1186/1478-4491-4-20

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Health insurance sector in India: an analysis of its performance

Vilakshan - XIMB Journal of Management

ISSN : 0973-1954

Article publication date: 30 November 2020

Issue publication date: 16 December 2020

Health insurance is one of the major contributors of growth of general insurance industry in India. It alone accounts for around 29% of total general insurance premium income earned in India. The growth of this sector is important from the perspective of overall growth of general insurance Industry. At the same time, problems in this sector are also many which are affecting its performance.

Design/methodology/approach

The paper provides an understanding on performance of health insurance sector in India. This study attempts to find out how much claims and commission and management expenses it has to incur to earn certain amount of premium. Methodology used for the study is regression analysis to establish relationship between dependent variable (Profit/Loss) and independent variable (Health Insurance Premium earned).

Findings of the study indicate that there is significant relationship between earned premium and underwriting loss. There has been increase of premium earnings which instead of increasing profit for the sector in fact has increased underwriting loss over the years. The earnings of the sector is growing at compounded annual growth rate of 27% still it is unable to earn underwriting profit.

Originality/value

This study is self-driven based on secondary data obtained from insurance regulatory and development authority site.

• Health insurance premium
• Management expenses
• Insurance regulatory and development authority
• Underwriting loss
• Compound annual growth rate

Dutta, M.M. (2020), "Health insurance sector in India: an analysis of its performance", Vilakshan - XIMB Journal of Management , Vol. 17 No. 1/2, pp. 97-109. https://doi.org/10.1108/XJM-07-2020-0021

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Copyright © 2020, Madan Mohan Dutta.

Published in Vilakshan - XIMB Journal of Management . Published by Emerald Publishing Limited. This article is published under the Creative Commons Attribution (CC BY 4.0) licence. Anyone may reproduce, distribute, translate and create derivative works of this article (for both commercial and non-commercial purposes), subject to full attribution to the original publication and authors. The full terms of this licence maybe seen at http://creativecommons.org/licences/by/4.0/legalcode

1. Introduction

1.1 meaning of insurance.

Insurance is a contract between two parties where by one party agrees to undertake the risk of the other in exchange for consideration known as premium and promises to indemnify the party on happening of an uncertain event. The great advantage of insurance is that it spreads the risk of a few people over a large group of people exposed to risk of similar type.

Insurance has been identified as a sunrise sector by the financial planners of India. The insurance industry has lot of potential to grow, penetrate and service the masses of India. Insurance is all about protection. An insured needs two types of protection life and non-life. General insurance industry deals with non-life protection of the insured of which health insurance is a part.

1.2 Meaning of health insurance

Health insurance is a part of general insurance which contributes about 29% of premium amongst all other sectors of general insurance. But problems in this sector are many which is the driving force behind this study. This study will help the insurance companies to understand their performance and the quantum of losses that this sector is making over the years.

A plan that covers or shares the expenses associated with health care can be described as health insurance. These plans fall into commercial health insurance, which is provided by government, private and stand-alone health insurance companies.

Health insurance in India typically pays for only inpatient hospitalization and for treatment at hospitals in India. Outpatient services are not payable under health policies in India. The first health policy in India was Mediclaim Policy. In 2000, the Government of India liberalized insurance and allowed private players into the insurance sector. The advent of private insurers in India saw the introduction of many innovative products like family floater plans, critical illness plans, hospital cash and top-up policies.

Health insurance in India is an emerging insurance sector after life and automobile insurance sector. Rise in middle class, higher hospitalization cost, expensive health care, digitization and increase in awareness level are some important drivers for the growth of health insurance market in India.

Lifestyle diseases are on the rise. A sedentary lifestyle has pervaded our being. There is lower physical labour today than earlier and there is no reason why this would not be the trend going forward. The implication is the advent of lifestyle chronic diseases such as cardiac problems and diabetes.

In the context of the Indian health insurance industry, one could look at it both ways. Mired by low penetration and negative consumer perception about its utility are affecting the prospect of this industry. The flipside though is that we have hardly scratched the surface of the opportunity that lies in the future. It is as if the glass is half full. Much remains to be conquered and even more remains to be accomplished.

Health insurance companies needs to be optimistic and have courage to bring in innovation in the areas of product, services and distribution system. Bring it to the fold as the safety net that smartly covers and craft a health insurance plan befitting the need of the customers.

1.3 Background of health insurance sector in India

India’s tryst with health insurance programme goes back to the late 1940s and early 1950s when the civil servants (Central Government Health Scheme) and formal sector workers (Employees’ State Insurance Scheme) were enrolled into a contributory but heavily subsidized health insurance programmes. As a consequence of liberalization of the economy since the early 1990s, the government opened up private sector (including health insurance) in 1999. This development threw open the possibility for higher income groups to access quality care from private tertiary care facilities. However, India in the past five years (since 2007) has witnessed a plethora of new initiatives, both by the central government and a host of state governments also entering the bandwagon of health insurance. One of the reasons for initiating such programs may be traced to the commitment of the governments in India to scale up public spending in health care.

1.4 The need for health insurance in India

1.4.1 lifestyles have changed..

Indians today suffer from high levels of stress. Long hours at work, little exercise, disregard for a healthy balanced diet and a consequent dependence on junk food have weakened our immune systems and put us at an increased risk of contracting illnesses.

1.4.2 Rare non-communicable diseases are now common.

Obesity, high blood pressure, strokes and heart attacks, which were earlier considered rare, now affect an increasing number of urban Indians.

1.4.3 Medical care is unbelievably expensive.

Medical breakthroughs have resulted in cures for dreaded diseases. These cures however are available only to a select few. This is because of high operating and treatment expenses.

1.4.4 Indirect costs add to the financial burden.

Indirect sources of expense like travel, boarding and lodging, and even temporary loss of income account for as much as 35% of the overall cost of treatment. These facts are overlooked when planning for medical expenses.

1.4.5 Incomplete financial planning.

Most of us have insured our home, vehicle, child’s education and even our retirement years. Ironically however we have not insured our health. We ignore the fact that illnesses strike without warning and seriously impact our finances and eat into our savings in the absence of a good health insurance or medical insurance plan.

1.5 Classification of health insurance plans in India

Health insurance plans in India today can be broadly classified into the following categories:

1.5.1 Hospitalization.

Hospitalization plans are indemnity plans that pay cost of hospitalization and medical costs of the insured subject to the sum insured. There is another type of hospitalization policy called a top-up policy . Top-up policies have a high deductible typically set a level of existing cover.

1.5.2 Family floater health insurance.

Family health insurance plan covers entire family in one health insurance plan. It works under assumption that not all member of a family will suffer from illness in one time.

1.5.3 Pre-existing disease cover plans.

It offers covers against disease that policyholder had before buying health policy. Pre-existing disease cover plans offers cover against pre-existing disease, e.g. diabetes, kidney failure and many more. After waiting for two to four years, it gives covers to the insured.

1.5.4 Senior citizen health insurance.

This type of health insurance plan is for older people in the family. It provides covers and protection from health issues during old age.

1.5.5 Maternity Health insurance.

Maternity health insurance ensures coverage for maternity and other additional expenses.

1.5.6 Hospital daily cash benefit plans.

Daily cash benefits are a defined benefit policy that pays a defined sum of money for every day of hospitalization.

1.5.7 Critical illness plans.

These are benefit-based policies which pay a lump sum amount on certain critical illnesses, e.g. heart attack, cancer and stroke.

1.5.8 Disease-specific special plans.

Some companies offer specially designed disease-specific plans such as Dengue Care and Corona Kavach policy.

1.6 Strength, weakness, opportunity and threat analysis of health insurance sector (SWOT analysis)

The strengths, weaknesses, opportunities and threats (SWOT) is a study undertaken to identify internal strengths and weaknesses as well as external opportunities and threats of the health insurance sector.

1.6.1 Strengths.

The growth trend of the health insurance sector is likely to be high due to rise in per capita income and emerging middle-income group in India. New products are being launched in this sector by different insurance companies which will help to satisfy customers need. Customers will be hugely benefited when cash less facility will be provided to all across the country by all the insurance companies.

1.6.2 Weaknesses.

The financial condition of this sector is weak due to low investment in this sector. The public sector insurance companies are still dominating this industry due to their greater infrastructure facilities. This sector is prone to high claim ratio and many false claims are also made.

1.6.3 Opportunities.

The possibility of future growth of this sector is high, as penetration in the rural sector is low. The improvement of technology and the use of internet facility are helping this sector to grow in magnitude and move towards environment-friendly paperless regime.

1.6.4 Threats.

The biggest threat of this sector lies in the change in the government regulations. The profitability of this sector is affected due to increasing expenses and claims. The economic slowdown and recession in the economy can affect growth of this sector adversely. The increasing losses and need for insurance might reach a point of no return where insurance companies may be compelled to decline an insurance policy.

1.7 Political economic socio cultural and technological analysis of health insurance sector (PEST analysis)

This analysis describes a framework of macro-environmental factors used as strategic tool for understanding business position, growth potential and direction for operations.

1.7.1 Political factors.

Service tax on premium on insurance policies is being increased by the government for past few years during budget. Government monopoly in this sector came to an end after insurance companies were opened up for private participation in the year 2000. Foreign players were allowed to enter into joint venture with their Indian counterpart with 26% holding and which was further increased to 49% in the year 2015.

1.7.2 Economic factors.

The gross savings of people in India have increased significantly thereby encouraging people to buy insurance policy to cover their risks. Insurance companies are fast becoming prominent players in the security market. As these companies have huge disposable income which they are investing in the security market.

1.7.3 Socio-cultural factors.

Increase in insurance knowledge is helping people to increase their awareness about the risk to be covered through insurance. Change in lifestyle is leading to increase in risk thereby giving an opportunity to insurance companies to innovate newer products. Societal benefit is derived by transfer of risk through insurance due to improved socio-cultural environment.

1.7.4 Technological factors.

Insurance companies deals in large database and maintaining it by the application of latest technology is huge gain for this sector. Technological advancement has helped insurance companies to sale their products through their electronic portals. This has made their task of providing service to the customers easier and faster.

2. Review of literature

After opening up of the insurance industry health insurance sector has become significant both from economic and social point of view and researchers have explored and probed these aspects.

Ellis et al. (2000) reviewed a variety of health insurance systems in India. It was revealed that there is a need for a competitive environment which can only happen with the opening up of the insurance sector. Aubu (2014) conducted a comparative study on public and private companies towards marketing of health insurance policies. Study revealed that private sector services evoked better response than that of public sector because of new strategies and technologies adopted by them. Nair (2019) has made a comparative study of the satisfaction level of health insurance claimants of public and private sector general insurance companies. It was revealed that majority of the respondents had claim of reimbursement nature through third party administrator. Satisfaction with respect to settlement of claim was found relatively higher for public sector than private sector. Devadasan et al. (2004) studied community health insurance to be an important intermediate step in the evolution of an equitable health financing mechanism in Europe and Japan. It was concluded that community health insurance programmes in India offer valuable lessons for its policy makers. Kumar (2009) examined the role of insurance in financing health care in India. It was found that insurance can be an important means of mobilizing resources, providing risk protection and health insurance facilities. But for this to happen, it will require systemic reforms of this sector from the end of the Government of India. Dror et al. (2006) studied about willingness among rural and poor persons in India to pay for their health insurance. Study revealed that insured persons were more willing to pay for their insurance than the uninsured persons. Jayaprakash (2007) examined to understand the hurdles preventing the people to purchase health insurance policies in the country and methods to reduce claims ratio in this sector. Yadav and Sudhakar (2017) studied personal factors influencing purchase decision of health insurance policies in India. It was found that factors such as awareness, tax benefit, financial security and risk coverage has significant influence on purchase decision of health insurance policy holders. Thomas (2017) examined health insurance in India from the perspective of consumer insights. It was found that consumers consider various aspects before choosing a health insurer like presence of a good hospital network, policy coverage and firm with wide product choice and responsive employees. Savita (2014) studied the reason for the decline of membership of micro health insurance in Karnataka. Major reason for this decline was lack of money, lack of clarity on the scheme and intra house-hold factors. However designing the scheme according to the need of the customer is the main challenge of the micro insurance sector. Shah (2017) analysed health insurance sector post liberalization in India. It was found that significant relationship exists between premiums collected and claims paid and demographic variables impacted policy holding status of the respondents. Binny and Gupta (2017) examined opportunities and challenges of health insurance in India. These opportunities are facilitating market players to expand their business and competitiveness in the market. But there are some structural problems faced by the companies such as high claim ratio and changing need of the customers which entails companies to innovate products for the satisfaction of the customers. Chatterjee et al. (2018) have studied health insurance sector in India. The premise of this paper was to study the current situation of the health-care insurance industry in India. It was observed that India is focusing more on short-term care of its citizens and must move from short-term to long-term care. Gambhir et al. (2019) studied out-patient coverage of private sector insurance in India. It was revealed that the share of the private health insurance companies has increased considerably, despite of the fact that health insurance is not a good deal. Chauhan (2019) examined medical underwriting and rating modalities in health insurance sector. It was revealed that while underwriting a health policy one has to keep in mind the various aspects of insured including lifestyle, occupation, health condition and habits. There have been substantial studies on health insurance done in India and abroad. But there has not been any work on performance of health insurance sector based on underwriting profit or loss.

3. Research gap

After extensive review of literature it is understood that there has not been substantial study on the performance of health insurance sector taking underwriting profit or loss into consideration. In spite of high rate of growth of earned premium, this sector is unable to make underwriting profit. This is mainly because growth of premium is more than compensated by claims incurred and commission and other expenses paid. Thereby leading to growth of underwriting loss over the years across the different insurance companies covered under both public and private sector. This unique feature of negative performance of this sector has not been studied so far in India.

4. Objectives

review health insurance scenario in India; and

study the performance of health insurance sector in India with respect to underwriting profit or loss by the application of regression analysis.

5. Research methodology

The study is based on secondary data sourced from the annual reports of Insurance Regulatory Development Authority (IRDA), various journals, research articles and websites. An attempt has been made to evaluate the performance of the health insurance sector in India. Appropriate research tools have been used as per the need and type of the study. The information so collected has been classified, tabulated and analysed as per the objectives of the study.

The data is based on a time period of 12 years ranging from 2006–2007 to 2018–2019.

Secondary data analysis has been done using regression of the form: Y =   a   +   b X

The research has used SPSS statistics software package for carrying out regression and for the various graphs Microsoft Excel software has been used.

5.1 The problem statement

It is taken to be a general assumption that whenever the premium increases the profit also increases. This determines that profits are actually dependent on the premium income. Hence, whenever the premium tends to increase, the profit made also supposed to increase.

The aim of the study is to find out whether the underwriting profit of the health insurance sector is increasing or there is an underwriting loss.

The problem statement is resolved by applying regression analysis between the premium earned and underwriting profit or loss incurred. It is assumed that if the underwriting profit increases along with the premium received, then the pattern forms a normal distribution and alternate hypothesis can be accepted and if this pattern of dependability is not found then the null hypothesis will be accepted stating that there is no relation between the premium and the underwriting loss or the underwriting profit by the sector. But what is happening in this sector is the increase in premium is leading to increase in underwriting loss. So premium is negatively impacting underwriting profit which is astonishing thing to happen and is the crux of the problem of this sector.

5.1.1 Underwriting profit/loss = net premium earned – (claim settled + commission and management expenses incurred).

Underwriting profit is a term used in the insurance industry to indicate earned premium remaining after claims have been settled and commission and administrative expenses have been paid. It excludes income from investment earned on premium held by the company. It is the profit generated by the insurance company in the normal course of its business.

5.2 Data analysis

Table 1 shows that health insurance premium increased from Rs.1910 crores in 2006–2007 to Rs. 33011 crores in 2018–2019. But claims incurred together with commission and management expenses have grown from Rs. 3349 crores to Rs. 40076 crores during the same period. So the claims and management expenses incurred together is more than the health insurance premium earned in all the years of our study thereby leading to underwriting loss.

Claim incurred shown above is the outcome of the risk covered against which premium is received and commission and management expenses are incurred to obtain contract of insurance. Both these expenses are important for insurance companies to generate new business as stiff competition exists in this sector since it was opened up in the year 2000.

Figure 1 depicts the relationship between health insurance premium earned and claims and management expenses incurred by the insurance companies of the health insurance sector for the period 2006–2007 to 2018–2019.

Bar chart between premiums earned and claims and management expenses incurred show that claims and management expenses together is higher than premium earned in all the years of the study thereby leading to losses. Claims, commission and management expenses are important factors leading to the sale of insurance policies thereby earning revenue for the insurance companies in the form of premium. But proper management of claims and commission and management expenses will help this sector to improve its performance.

Table 2 provides insight into the performance of health insurance sector in India. The growth of health insurance in India has been from Rs.1909 crores for the financial year 2006–2007 to Rs. 33011crores for the financial year 2018–2019. The growth percentage is 1629% i.e. growing at an average rate of 135% per annum. Compounded Annual Growth Rate (CAGR) is working out to be 27%.

From the same table, it can be inferred that health insurance sector is making underwriting loss in all the financial years. There is no specific trend can be seen, it has increased in some years and decreased in some other years. Here underwriting loss is calculated by deducting claims and commission and management expenses incurred from health insurance premium earned during these periods.

With every unit of increase in premium income the claims incurred together with commission and management expenses paid increased more than a unit. Thereby up setting the bottom line. So instead of earning profit due to better business through higher premium income, it has incurred losses.

Underwriting principles needs to be streamlined so that proper scrutiny of each policy is carried out so that performance of this sector improves.

It is seen from Figure 2 that there is stiff rise in premium earned over the years but claims and commission and management expenses incurred have also grown equally and together surpassed earned premium. So the net impact resulted in loss to this sector which can also be seen in the figure. It is also seen that loss is increasing over the years. So, increase in earnings of revenue in the form of premium is leading to increase in losses in this sector which is normally not seen in any other sectors.

But a time will come when commission and management expenses will stabilize through market forces to minimize underwriting losses. On the other hand, it will also require proper management of claims so that health insurance sector can come of this unprofitable period.

5.3 Interpretation of regression analysis

5.3.1 regression model..

Where Y = Dependent variable

X = Independent variablea = Intercept of the lineb = Slope of the line

5.3.2 Regression fit.

Here, Y is dependent variable (Underwriting Profit or Loss) which is to be predicted, X is the known independent variable (Health Insurance Premium earned) on which predictions are to be based and a and b are parameters, the value of which are to be determined ( Table 3 ). Y =   − 1028.737 − 0.226   X

5.3.3 Predictive ability of the model.

The value of R 2 = 0.866 which explains 86.6% relationship between health insurance premium earned and loss made by this sector ( Table 4 ). In other words, 13.4% of the total variation of the relationship has remained unexplained.

4.1 Regression coefficients ( Table 5 ).

H1.1 : β = 0 (No influence of Health Insurance Premium earned on Underwriting Profit or Loss made)

5.4.1.2 Alternative hypothesis.

H1.2 : β ≠ 0 (Health Insurance Premium earned influences underwriting Profit or Loss made by this sector)

The computed p -value at 95% confidence level is 0.000 which is less than 0.05. This is the confidence with which the alternative hypothesis is accepted and the null hypothesis is rejected. Thus regression equation shows that there is influence of health insurance premium earned on loss incurred by this sector.

The outcome obtained in this analysis is not what happens normally in the industry. With the increase of revenue income in the form of premium, it may lead to either profit or loss. But what is happening surprisingly here is that increase of revenue income is leading to increase of losses. So growth of premium income instead of influencing profit is actually influencing growth of losses.

6.1 Findings

The finding from the analysis is listed below:

The average growth of net premium for the health insurance has been around 135% per annum even then this sector is unable to earn underwriting profit.

The CAGR works out to around 27%. CAGR of 27% for insurance sector is considered to be very good rate of growth by any standard.

Along with high growth of premium, claims and commission and management expenses incurred in this sector have also grown substantially and together it surpassed in all the years of the study.

Thus, growth of claims and commission and management expenses incurred has more than compensated high rate of growth of health insurance premium earned. This resulted into underwriting loss that this sector is consistently making.

Astonishing findings has been higher rate of increase of premium earnings leading to higher rate of underwriting loss incurred over the years. Even though the sector is showing promise in terms of its revenue collection, but it is not enough to earn underwriting profit.

6.2 Recommendations

COVID 19 outbreak in India has led to a spike in health-care costs in the country. So, upward revision of premium charges must be considered to see bottom line improvement in this sector.

Immediate investigation of the claim is required. This will enable the insurers to curb unfair practice and dishonest means of making a claim which is rampant in this sector.

Health insurance market is not able to attract younger generation of the society. So entry age-based pricing might attract this group of customers. An individual insured at the age 30 and after 10 years of continuous coverage the premium will be less than the other individual buying a policy at the age of 40 for the first time.

6.3 Limitations and scope of future studies

The analysis of performance of health insurance sector in India taking underwriting profit into consideration is the only study of its kind in this sector. As a result, adequate literature on the subject was not available.

Health insurance and health care are part of medical care industry and are inter dependent with each other. So performance of health insurance sector can be better understood by taking health-care industry into consideration which is beyond the scope of the study.

This sector is consistently incurring losses. So, new ideas need to be incorporated to reduce losses if not making profits.

Opportunity of the insurance companies in this sector lies in establishing innovative product, services and distribution channels. So, continuous modification by the application of research is required to be undertaken.

Health insurance sector will take a massive hit, as tax benefit is going to be optional from this financial year. This can be a subject of study for the future.

6.4 Conclusion

This sector is prone to claims and its bottom line is always under tremendous pressure. In recent times, IRDA has taken bold step by increasing the premium rate of health insurance products. This will help in the growth of this sector.

With better technological expertise coming in from the foreign partners and involvement by the IRDA the health insurance sector in India must turn around and start to earn profit.

The COVID-19 pandemic is a challenge for the health insurance industry on various fronts at the same time it provides an opportunity to the insurers to fetch in new customers.

The main reason for high commission and management expense being cut-throat competition brought in after opening up of the insurance sector in the year 2000. So, new companies are offering higher incentives to the agents and brokers to penetrate into the market. This trend needs to be arrested as indirectly it is affecting profitability of this sector.

The study will richly contribute to the existing literature and help insurance companies to know about their performance and take necessary measures to rectify the situation.

Chart on health insurance premium earned and claims and management expenses paid

Chart on performance of health insurance sector in India

Data showing health insurance premium earned and claims and management expenses paid

. Dependent variable: Underwriting profit or loss;

. Predictors: (Constant), Health insurance premium earned

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Yadav , S.C. and Sudhakar , A. ( 2017 ), “ Personal factors influencing purchase decision making: a study of health insurance sector in India ”, BIMAQUEST , Vol. 17 , pp. 48 - 59 .

Further reading

Beri , G.C. ( 2010 ), Marketing Research , TATA McGraw Hill Education Private , New Delhi, ND .

Dutta , M.M. and Mitra , G. ( 2017 ), “ Performance of Indian automobile insurance sector ”, KINDLER , Vol. 17 , pp. 160 - 168 .

Majumdar , P.I. and Diwan , M.G. ( 2001 ), Principals of Insurance , Insurance Institute of India , Mumbai, MM .

Pai , V.A. and Diwan , M.G. ( 2001 ), “ Practice of general insurance ”, Insurance Institute of India , Mumbai, MM .

Shahi , A.K. and Gill , H.S. ( 2013 ), “ Origin, growth, pattern and trends: a study of Indian health insurance sector ”, IOSR Journal of Humanities and Social Science , Vol. 12 , pp. 1 - 9 .

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