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Innovation in pharmaceutical R&D: mapping the research landscape

• Open access
• Published: 10 October 2020
• Volume 125 , pages 1801–1832, ( 2020 )

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• Angelo Kenneth S. Romasanta   ORCID: orcid.org/0000-0002-4594-3243 1 , 2 ,
• Peter van der Sijde 1 &
• Jacqueline van Muijlwijk-Koezen 2  

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In response to the increasing number and breadth of innovation studies on the pharmaceutical industry, we mapped the literature to show the trends in recent research and to indicate areas for further research. In the first phase, we analyzed articles on the pharmaceutical industry published in innovation journals. We used these articles’ textual and citation data and applied hybrid cluster analysis. Three main clusters were produced based on the level of analysis innovation scholars had used to investigate the industry: macro, meso and micro. We describe the research topics within these clusters and show that, overall, innovation scholars increasingly focus on the meso-level, analyzing the relationships across different firms. This shift in interest toward the collaborative nature of drug discovery and development was also apparent in macro- and micro-level studies. To explore how this literature is used by scientists in the industry, our second phase involved analysis of the citing articles published in pharmaceutical journals. Using our findings, we propose research areas that can be further explored in order to create an engaged and better-integrated literature on pharmaceutical innovation.

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Introduction

Innovation is an important issue for the pharmaceutical industry, especially with regard to bringing new drugs to the market (e.g. Achilladelis and Antonakis 2001 ; Bianchi et al. 2011 ). Hundreds of studies on the industry are published each year, exploring the different trends and challenges in innovation in the industry (e.g. Bianchi et al. 2011 ; Bierly and Chakrabarti 1996 ; Malerba and Orsenigo 2015 ; Rafols et al. 2014 ; Tierney et al. 2013 ). Despite the increasing number of publications, there have been no efforts to map this literature. Without such overviews, it can be difficult to navigate the wide range of studies published on this industry and thus discern the direction the literature is taking. We therefore mapped the various innovation topics explored in the industry and identified areas for further research. Such a study is an important first step towards a coherent and better-engaged literature on pharmaceutical innovation.

In an earlier synthesis of the wider literature on innovation, Crossan and Apaydin ( 2010 ) found innovation to be both a process and an outcome related to the “production or adoption, assimilation, and exploitation of a value‐added novelty in economic and social spheres; renewal and enlargement of products, services, and markets; development of new methods of production; and establishment of new management systems.” While this is a useful starting point, such a broadly-defined construct overlooks issues that are of greater relevance to specific settings such as the pharmaceutical industry. By exploring innovation within a narrower scope, we hope we can identify the specific issues that matter to practitioners—i.e., scientists and managers in the industry—to help innovation scholars find priority research topics. In addition, as innovation scholars may narrowly focus on specific innovation processes, antecedents and drivers, our study may help show how their individual research fits with the other research carried out on the pharmaceutical industry.

Due to its various unique characteristics, the pharmaceutical industry remains a favored setting for innovation studies. The fast pace of technological advances in the life sciences allows exploration of how firms cope and adapt to change. Leading in research and development (R&D) investment (Demirel and Mazzucato 2012 ), the pharmaceutical industry also provides opportunities to study the dynamics between innovation and performance. At the same time, the complexity of drug development means that firms do not have all the capabilities in-house necessary to bringing a drug to the market on their own (Hagedoorn 1993 ; Powell et al. 2005 ). Such a dynamic environment enables innovation scholars to observe the flow of knowledge, capabilities, and technologies across different organizations.

The industry is also studied for practical reasons. Despite the variety of therapeutic targets and drug types, the need to ensure safety and treatment efficacy requires the same development cycle to be followed across the industry (Scannell et al. 2012 ). Such homogeneity makes it relatively easier to compare innovation processes between firms. Another reason is that the process also generates a large footprint of data from various regulatory and intellectual-property protection filings (Cohen et al. 2000 ). By piecing these together, innovation scholars can gain insight into innovation.

As well as contributing to the innovation literature overall, research on the pharmaceutical industry is important to help the industry address its unique challenges. For many years, the low productivity and return on investment in R&D have been a central issue for practitioners (Pammolli et al. 2011 ). At the same time, the cost of bringing a new drug to market has risen continually, the most recent estimate being USD 2.6B (DiMasi et al. 2016 ). The industry is also subject to increased scrutiny from regulators and the public on the value of different treatments (Scannell et al. 2012 ). If the innovation literature ensured that it understood the concerns and needs of the industry, its impact for practitioners would be maximized.

While this research is intended mainly for innovation scholars, we hope that our findings will also be of value to scientists and managers in the industry. Even if they are not interested in going into the nuances of innovation theories, they can use the framework we put forward to reflect on the social and economic forces affecting their practice.

We used bibliometric methods to analyze articles on innovation in the pharmaceutical industry. In the following, we detail our methodology, highlighting the hybrid cluster analysis we employed based on the combination of citation and textual data provided by the articles. Then, we examine the overall structure and content of the literature in innovation journals on the industry. As a starting point for ways in which the innovation literature might become engaged with issues facing the industry, we explore the diffusion of innovation topics in practitioner journals. We also identify processes in drug development that could benefit from further investigation by innovation scholars. Finally, we introduce creative ways for innovation scholars to gain new insights into innovation theory and the pharmaceutical industry.

Methodology

To explore both the innovation and practitioner literature, this study consisted of two phases. Data collection and analysis are summarized in Fig.  1 . Publication data were collected from the Web of Science in June 2019. Articles on innovation studies on the pharmaceutical industry were collected through a combination of keyword search and journal selection. The keywords “*pharmaceut*”, “drug discovery” and “drug development” were used to search for the relevant articles on the pharmaceutical industry.

Data collection and analysis of articles on innovation in the pharmaceutical industry

The main focus of this study is the literature on innovation in pharmaceutical research and development. As many studies of the industry focus on other business functions—such as manufacturing, marketing, finance and sales—we needed to filter out these domain-specific journals. Based on the ranking proposed by Thongpapanl ( 2012 ), we therefore narrowed our search to articles in the top-ranked innovation journals: Research Policy, Journal of Product Innovation Management, Research-Technology Management, Technovation, R&D Management, Industrial and Corporate Change, IEEE Transactions on Engineering Management, Journal of Technology Transfer, Technological Forecasting and Social Change, Journal of Engineering and Technology Management, International Journal of Technology Management, Science and Public Policy, Technology Analysis Strategic Management, Engineering Management Journal and Industry and Innovation .

We also included general management journals that, according to the same paper, were highly cited by the innovation literature. We chose the following : Strategic Management Journal, Management Science, Academy of Management Journal, Academy of Management Review, Organization Science, Journal of Business Venturing, Administrative Science Quarterly, Journal of Management and Journal of Management Studies.

It is important to note that other functions, such as marketing, manufacturing, supply chain and finance, are also key to innovation. These functions inform the R&D strategy, enabling companies to cope with the pressures from competition, the regulatory burden and increased public scrutiny. Nonetheless, to narrow the scope of this study, we left them aside, as they are vast, thriving and complex research areas that deserve their own analyses.

The initial dataset had 857 articles. The downloaded dataset was rechecked, and articles that lacked the keywords either in the abstract, title or author-selected keywords were removed. We also removed articles that did not have cited references, as these were crucial to our subsequent network analyses. The final dataset, which contained only articles and reviews, consisted of 677 documents. Figure  2 shows the distribution of these articles across journals.

Articles in the dataset by journal source and year

These articles were authored primarily by scholars based in Europe and the United States (see Fig.  3 ), following that most of the largest pharmaceutical companies are located in these regions. Nonetheless, emerging regions in the industry, such as China and India, are also represented in the dataset.

Articles in the dataset by authors’ country affiliations and by year

To analyze the clusters and research topics of the articles, we formed a hybrid network integrating the articles’ citation and textual data, following the methodology described by Glänzel and Thijs ( 2017 ). First, we constructed the bibliographic coupling network by counting per paper the number of references it shared with each other paper within the dataset. Cosine similarity was then calculated to establish the strength of the bibliographic coupling link between each pair of articles.

Because relying solely on citation data can be limiting—it takes no consideration of textual data, which can also be valuable in establishing relationships between articles—we integrated this information. First, we extracted text from the title, abstract and keywords of each article. This combined text was then preprocessed using the NLTK library in Python (Bird et al. 2009 ). First, we removed the stopwords (e.g. “I” , “a” and “the” ) in this text. Punctuation and digits were also removed. We used WordNetLemmatizer in NLTK to reduce different forms of each word to the same lemma. Next, to ensure that terms containing multiple words are analyzed properly (e.g. recognize “absorptive capacity” as one term instead of the words separated), we generated the n-grams in the text to combine words that very often occur in sequence. We combined such terms, setting the threshold that they occur at least 5 times in our dataset. Finally, we selected the terms that occurred most often across all documents and removed those we deemed to add noise to the analysis (e.g. “ case,” “analyze” and “study” ).

Once text processing had been completed, we used the Term Frequency Inverse Document Frequency (TF-IDF) vectorizer in NLTK. When TF-IDF is used to weigh each term for each article, less weight is placed on terms that occur very frequently throughout all articles (Ramos 2003 ). Finally, we calculated the cosine similarity for each pair of documents.

Once the textual and citation relationship between each paper had thus been measured, we combined these measures to create a hybrid network. Following Glänzel and Thijs ( 2017 ), we took the cosine of the linear combination of the underlying angles between the vectors representing the documents in the original networks. To calculate the similarity score r , we gave the equal weight of 0.5 to both the textual network (ξ) and the citation network (η) using the formula:

This value r was used as the link strength for each pair of articles, which was then imported to VosViewer for clustering and visualization (van Eck and Waltman 2010 ). Except for the visualization and clustering step, all preprocessing and analysis were implemented in Python (code is available on publication).

The clustering was done using the program VosViewer, where the generated clusters correspond directly to the generated layout of nodes (Waltman et al. 2010 ). We experimented with different numbers of clusters (3, 4 and 5) generated by tuning the clustering resolution parameter of VosViewer. For each generated clustering, we judged how coherent each cluster is by looking at the titles of articles in each cluster. In the end, we decided on having three clusters (from 0.85 clustering resolution) as we find that this clustering provided a clear delineation between the different levels of analysis used by innovation scholars in studying the pharmaceutical industry. These levels of analysis explored the environment (macro), interorganizational networks (meso) and organizations (micro), belonging to the main perspectives taken by management scholars in their studies (Hitt et al. 2007 ). To identify the underlying topics within these main clusters, we applied the clustering once again, now with a higher resulting cluster count of nine (from 1.2 resolution). These nine clusters corresponded to nine research topics distributed under the three main clusters. We labeled each topic by examining the articles and the top keywords according to the TF-IDF weights in each nine sub-cluster.

In our investigation of each topic, we looked at the following: (1) The number of articles in the cluster, (2) the number of citations within the innovation journals dataset; (3) the number of citations in pharmaceutical journals (explained in the following paragraph); (4) the median year of publication; (5) the average annual increase in the number of articles in the last 10 years, measured on the basis of the slope of the linear regression line where x is the year of publication and y is the number of articles published from 2009 to 2018; (6) the top keywords in the cluster extracted from the TF-IDF matrix; (7) the most-cited article within the dataset; and (8) the most-cited article by articles in the cluster in general.

In the second phase, to further explore the diffusion of these studies in practitioner-published journals, we conducted a short bibliometric analysis on the citing articles of the innovation articles collected in our original dataset. Using the Web of Science, we downloaded the citing articles in practitioner journals in the following categories: “ Pharmacology & Pharmacy,” “Biotechnology & Applied Microbiology,” “Medicinal Chemistry,” “Medicine, Research and Experimental,” and “Medicine, Internal and General.” As some of these journals occasionally cover other fields outside pharmaceutical R&D—two examples being agriculture and pharmacy—we narrowed the citing articles further by restricting them to have to contain “drug*”, “medicin*” and “*pharmaceut*” in their text. After further filtering, we thus obtained 220 articles distributed across the five Web of Knowledge categories (Fig.  4 ).

Distribution of Articles among Practitioner Journal Categories

By far, the journal Drug Discovery Today had the most publications with 35 articles, followed by Expert Opinion on Drug Discovery with 10 articles (Fig.  5 ). However, many of the most cited articles came from Nature Reviews Drug Discovery.

Top 10 Practitioner Journals by the Number of Articles

These articles were then pre-processed and analyzed using the same steps as before involving the hybrid cluster analysis using their textual and citation data. Clustering the articles then produced 5 main research topics exploring different processes in drug development. To understand the themes of interest to practitioners, each cluster was analyzed.

Finally, we created a Sankey diagram (Bogart 2018 ) to show how practitioner articles cite innovation journal articles, by respective topics. We then constructed a framework containing the topics from practitioner journals and innovation journals, showing areas that can be explored for further research.

Innovation in pharmaceutical R&D

As in Fig.  6 , the steady increase in the number of publications over time reflects innovation scholars’ increasing interest in the pharmaceutical industry. More importantly, the increasing number of articles citing these innovation studies in pharmaceutical journals reflects the greater interest among practitioners since approximately 2010.

Number of publications studying the pharmaceutical industry in innovation journals and citing articles in practitioner journals per year

Figure  7 shows the hybrid network from these innovation articles’ textual and citation data. It shows that the articles are distributed relatively equally across three clusters, each with at least 200 articles. The importance of all the perspectives on the study of pharmaceutical R&D is suggested by the fact that the most-cited articles are distributed across the various clusters. We identify these three main clusters as Macro, Meso and Micro. These correspond to the level of analysis used by innovation scholars in their study of the pharmaceutical industry.

Hybrid network of articles published in the top innovation journals on the pharmaceutical industry. Each node represents one article, labelled by the last name of the first author and the year of publication. The color of each node was generated from VosViewer clustering, corresponding to the level of analysis used. The size of each node shows the internal citation count of each article by other articles within the same network

We summarize the data for each cluster in Table 1 . As shown, cluster 1 pertains to the Macro level, which consists of studies examining the large-scale forces influencing innovation in the industry. The most relevant keywords at this level capture themes related to policy (e.g. “policy,” “market” and “patent” ) and geography (e.g. “japan”, “country” ). Cluster 2 refers to the Meso level, which explores the relationships among the different organizations in the industry. This emphasis on collaboration is reflected in keywords such as “alliance , ” “partner” and “network.” Finally, cluster 3 on Micro explores the innovation processes within firms, with keywords reflecting themes such as strategy (e.g. “management , ” “strategy” and “risk” ); and product development (e.g. “product , ” “process” and “project” ). The most relevant journals in terms of the number of publications were Research Policy for Macro , Strategic Management Journal for Meso and R&D Management for Micro , reflecting our intuition on the content of each cluster.

It is interesting to note that, although the literature could be structured in the many ways, the principal dimensions into which the pharmaceutical innovation literature is divided by our clustering are those denoting the level of analysis. For instance, if the typologies followed other studies of innovation (e.g. Crossan and Apaydin 2010 ), this literature could have been divided into other dimensions, such as processes (e.g. adoption, production, assimilation or exploitation) or nature (e.g. drug vs. service vs. enabling technologies).

To determine the amount of activity and the relative importance of each cluster to the literature as a whole, we tallied the number of articles and citations per cluster over time (Fig.  8 ). The  Macro cluster has received steady interest from both innovation scholars and practitioners as shown by the increase in publications and citations over time. Looking at the trends however, the Meso cluster has received the largest growth in activity from innovation scholars. In terms of interest from practitioners, it has been catching up only recently. In contrast, despite a decrease in the number of publications, the high number of citations among pharmaceutical journals shows that the Micro cluster is still the most important topic for practitioners.

Number of publications (left), cumulative citations within innovation journals (center) and cumulative citations in practitioner journals (right) per cluster over time

Under each of these 3 clusters, we find 9 research topics distributed among them: Innovation System and Knowledge Transfer in the Macro cluster; Strategic Alliances, Collaborative Networks, Competitive Advantage and Open Innovation in the Meso cluster; and Organizational Learning, Strategic Groups and Product Management in the Micro cluster. These topics are organized in Fig.  9 :

A framework integrating innovation research on the pharmaceutical industry. The research topics are grouped by their level of analysis. The size of the box reflects the number of articles. The colors in the bar for Median Year indicate how the focus of articles shifted between 2000 and 2014

In the following section, we provide an overview of each research topic, summarizing its major themes and exploring how they are situated within the innovation landscape. While these clusters were categorized based on the level they focus on, we note that there is a high degree of overlap across them. Nonetheless, despite the lack of hard boundaries, these clusters are still instructive, as they show the main themes according to which we can structure the literature.

Under the macro cluster, we find two research topics: Innovation System and Knowledge Transfer (Table 2 ).

Innovation Systems, the larger of the two topics in the number of publications, refers to networks of actors and institutions that create, diffuse and use innovations (Carlsson et al. 2002 ). As implied by the occurrence of keywords such as “ japan , ” “india” and “country , ” this topic covers the geography-dependent factors that affect the innovation processes within the pharmaceutical industry such as research policy, cultural norms, scientific institutions and the regulatory environment. In many studies of national innovation systems (Lundvall 1993 ; Nelson 1993 ), countries with developed pharmaceutical industries, typically in the West, are compared in terms of their competencies, policies and regulatory environments. Recently, the increasing involvement of countries such as China, India and Brazil in drug development has made innovation scholars study their catch-up with regard to building capabilities in the pharmaceutical sciences (e.g. Athreye et al. 2009 ; Guennif and Ramani 2012 ).

The second Macro topic, Knowledge Transfer , contains two important sub-themes: the intellectual property regime (“ patent , ” “intellectual” and “right” ) and basic research ( “university” and “science” ). The industry has one of the highest patenting rates, with an estimated 80% of its product innovations being patented (Arundel and Kabla 1998 ). Due to its reliance on intellectual property protection to appropriate value from knowledge (Mansfield 1986 ), a wide range of studies on them have emerged, for example on the creation of models for their proper valuation, on their roles in signaling firms’ R&D activities to shareholders, and on managing patent portfolios as part of the strategy. There has also been increased interest in studying how industry practices are affected by policies on intellectual property and technology transfer such as Trade-Related Aspects of Intellectual Property Rights, the global agreement known by the abbreviation TRIPS (e.g. Lemus and Marshall 2018 ; Vakili and McGahan 2016 ).

The second sub-theme under Knowledge Transfer investigates the role of basic research in bringing new drugs to the market (e.g. Gambardella 1992 ; McMillan et al. 2000 ). Such exploratory research, which aims to uncover the biological mechanism of diseases, is mainly conducted by universities and public research institutes as the high uncertainty and lack of a clear path towards commercialization make it unappealing for industry (Rafols et al. 2014 ). Consequently, innovation research primarily focused on how scientific knowledge can be effectively transferred from academia to industry (e.g. Gambardella 1992 ). Recently, however, academia has expanded their involvement in the drug development process through initiating research collaborations, creating spin-offs and even performing drug discovery itself (Tralau-Stewart et al. 2009 ). To keep up with such trends, it is thus crucial for innovation scholars to study this evolving nature of the relationship between academia and industry.

Under the Meso cluster, we identify the following research topics: Strategic Alliances, Collaborative Networks, Open Innovation and Resources and Capabilities (Table 3 ). The topic Strategic Alliances study various aspects of alliances in the industry including the various processes, stages, antecedents and characteristics of participating actors. These alliances can be mapped to show the structure by which the various pharmaceutical firms are connected in the topic Collaborative Networks . In contrast to this large-scale analysis, alliances can also be examined from the point of view of the firms that engage in them such as in the topics Open Innovation and Resources and Capabilities . Below we discuss each of these topics, exploring their differences in focus.

Of all research topics, Strategic Alliances has grown most in the last ten years, not only in terms of size, but also in its impact to the innovation literature as measured on the basis of citations. Examination of the keywords under this topic such as “alliance , ” “partner , ” “experience” and “collaborate” reveals a focus on the activity itself. Due to the increasing complexity of the R&D process, firms are required to collaborate (Powell et al. 1996 ; Rothaermel and Boeker 2008 ). Accordingly, many articles in this cluster analyze each phase of an alliance, from its initiation to its management and the evaluation of its performance and investigate the factors for their success (e.g. Hoang and Rothaermel 2005 ; Lane and Lubatkin 1998 ). In the pharmaceutical industry, there are different actors with different specialized competencies: universities in basic research, small companies in early drug discovery and large pharmaceutical firms in late drug development and marketing (Bianchi et al. 2011 ; Stuart et al. 2007 ). Aside from enabling drug development to progress forward, firms engaging in these alliances also have a secondary goal to extract crucial knowledge from their partners (Powell 1998 ). With the variety of modes of collaborations possible across the industry, the different actors have to consider carefully when to engage in such alliances and how such collaborations can help them in their route to bringing a new drug to the market. Correspondingly, innovation scholars have explored drivers of partnership initiation and partner selection. This has even prompted a debate in the management community whether smaller biotechnology startups or the larger pharmaceutical firms have the upper hand in initiating such collaborations (Diestre and Rajagopalan 2012 ; Mason and Drakeman 2014 ). With firms engaging in many such alliances at any given time, there has been an increasing scholarly interest in understanding how to effectively manage the firms’ portfolio of alliances (e.g. Van de Vrande 2013 ).

In the Meso cluster, the topic of Collaborative Networks is also growing. Unlike the previous topic, Strategic Alliances , which focuses on the collaborations themselves, Collaborative Networks situates collaborations against the broad context of the other collaborations occurring across the industry (Gulati 1998 ). Rather than looking at an alliance in isolation, networks use a wider perspective that is reflected in keywords such as “network , ” “structure” and “centrality.” To study these collaborations in aggregate, scholars use various network-analysis tools, mapping individuals or organizations as nodes linked by sets of social relationships. These networks can be recreated from various data sources, most commonly patents, publications and mergers and acquisitions. One area that is much explored is how networks facilitate the diffusion of technology (e.g. Gilsing and Nooteboom 2006 ; Orsenigo et al. 2001 ). Such studies typically raise the classic debate on the importance of structural holes (Burt 1992 ) and redundant network structures (Coleman 1988 ) in innovation. In any case, there is a greater appreciation of the active role that individuals and firms could take in situating themselves to have a central location within such networks (e.g. Dong and Yang 2016 ). Moreover, scholars have also examined other networks apart from interorganizational networks, including the network formed from the collaborations between scientists within an individual firm (e.g. Grigoriou and Rothaermel 2017 ).

Over the last 20 years, Open Innovation, which refers broadly to the opening of a firm’s boundaries to external innovation (Chesbrough 2003 ), has gained great interest in the pharmaceutical industry. As a concept that has considerable overlaps with the topics discussed above, open innovation has also received some criticism. First, since firms in the industry have always relied on collaboration, it has been labelled as old wine in a new bottle (Trott and Hartmann 2009 ). Believing mistakenly that it means open-source and thus a lack of intellectual property protections, many drug development practitioners also remain hesitant to use the term (Hunter and Stephens 2010 ). But despite these criticisms, open innovation has been an important construct that reflects the increasing relevance of externally derived innovation for pharmaceutical firms (Bianchi et al. 2011 ). While this is a broad multi-level research topic, our analysis of its application in the pharmaceutical industry suggests that it is studied often with the firm as the focal point. It has a particular focus on the different modes of relationship (such as “outsourcing” and “licensing” ) that these firms can use to engage with various knowledge sources such as academic institutions, biotech startups and large pharmaceutical companies. With this firm-centered view, a major construct relevant to the topic of open innovation has been absorptive capacity, which is defined as a firm’s “ability to recognize the value of external knowledge, assimilate it, and apply it to commercial ends” (Cohen and Levinthal 1990 ). In fact, due to its importance, Cohen and Levinthal's ( 1990 ) landmark paper is the most cited article on our dataset. With the evolving nature of how new knowledge is absorbed by firms in the industry, absorptive capacity has been revisited many times by innovation scholars—who, for instance, have traced its industry-specific process (Patterson and Ambrosini 2015 ). As the industry continues to shift towards collaborations, we see a greater convergence and integration of the Meso level with other levels of study.

While it is much cited in the dataset, the topic of Resources and Capabilities has not grown as much in recent years. Unlike the Meso topics above, which focus on the collaborative relationships between the firms in the industry, Resources and Capabilities focuses on the competition between firms. To gain competitive advantage, firms differentiate themselves from other firms (e.g. Henderson and Cockburn 1994 ; Thomke and Kuemmerle 2002 ). A central aspect of this topic is the resource-based view that traces firms’ competitive advantage to their control of valuable, rare and novel resources (Barney 1991 ). Correspondingly, keywords in this topic include “asset , ” “knowledge” and “product.” However, the increasing reliance on the outside world is also reflected in this topic—which explains the occurrence of the keywords “acquire” and “complementary. ” The importance of assimilating capabilities from external sources explains why Resources and Capabilities was allocated to the Meso level in our analysis, even though it seemed to fit better with the Micro level. This paradox highlights the need for further research on how, as part of their strategy, firms can manage the tension between competing and collaborating across different technological fields and therapeutic areas within the industry.

The Micro cluster contains three research topics that explore different parts of firm innovation: Product Development , Organizational Learning and Strategic Groups (Table 4 ).

In terms of the number of publications, the largest research topic found is Product Development . Keywords in this topic—such as “product , ” “project” and “process”— all relate to the management of the product development process, especially with regard to bringing new drugs to the market. Exploring the intricacies of the drug development process, this broad topic examines a variety of themes. One of these themes is the management of projects within firms, paying particular attention to managing the high failure rates in the drug development process (e.g. Grabowski and Vernon 1990 ). Due to the number of R&D projects in which a firm is engaged at any given time, investigating methods to manage effectively this portfolio is also important (e.g. Blau et al. 2004 ; Jacob and Kwak 2003 ). While Product Development previously had a high priority for innovation scholars examining the pharmaceutical industry, the low growth in the number of publications suggests that this focus has now been diminished. However, this does not mean that Product Development is no longer of interest to innovation scholars. Instead, we can argue that the framing or focus when analyzing product development has shifted. Previously, articles explored the process itself, looking at the specific practices firms apply internally. Research thus focused on various ways of optimizing the different steps involved in product development. However, as we saw in the previous discussions, innovation scholars have, as the pharmaceutical industry embraces external innovation, become more interested in the Meso level. Rather than analyzing research directly within the firm, innovation scholars have been more interested in a meta-level exploration of the ways in which in-company product development interacts with the external world. Thus, while Product Development remains a crucial issue, innovation scholars’ focus has shifted to capturing how drug development is influenced by various external developments such as in technology (e.g. Hopkins et al. 2007 ), regulation (e.g. Rzakhanov 2008 ) and marketing (e.g. Liu et al. 2016 ).

The next research topic, Organizational Learning , has grown steadily in recent years. It is related to the systems and routines firms have in place for generating, retaining, managing, utilizing and transferring knowledge (March 1991 ; Pisano 1994 ; Santos 2003 ), demonstrated by keywords such as “knowledge , ” “learning” and “process.” Scholars are thus interested in studying how various features of organizations enable them or impede them in coping with new knowledge. Scholars also explore how these organizational routines can change (Anand et al. 2012 ; Bresman 2013 ). To differentiate this topic from the previous Meso topics which are concerned with knowledge outside the firm, Organizational Learning focuses on the stage where knowledge is already within the boundaries of the firm. Especially for large firms with multiple laboratories worldwide, it is important that valuable knowledge diffuses across its various departments and are thus, integrated and applied towards various projects.

Strategic Groups , the final research topic in the Micro cluster, is the oldest of all research topics, and has not attracted much interest among innovation scholars in recent years. This topic focuses on classifying firms into various categories, and dissecting the similarities and differences in strategies and performance within and across groups (Bogner et al. 1996 ; Cool and Dierickx 1993 ). However, as innovation scholars now study the industry as a whole rather than intending to segment it further, this topic has gone out of fashion in the innovation literature.

Overall, in the last 10 years, there has been a relative stagnation in studies at the Micro level, partly because of the emphasis on innovation at the interorganizational level. Deeper levels of analysis are possible, such as the study of units, teams and individuals. Unfortunately, we found that such deeper-level analyses are not so common in the pharmaceutical industry. Unlike the synthesis by Crossan and Apaydin ( 2010 ), which shows leadership to be an important determinant of innovation, the pharmaceutical R&D literature seems to have placed less focus on this. However, due to the importance of highly skilled individuals (Cowlrick et al. 2011 ; D’Este and Perkmann 2011 ; Liu et al. 2015 ) and teams (Ben-Menahem et al. 2016 ; Bresman and Zellmer-Bruhn 2013 ; Stryker et al. 2012 ; Tang 2016 ) in innovation, this is an important area for further exploration.

Innovation topics in practitioner journals

In the second phase of this study, we identified the research topics valued by practitioners by examining the citing articles of the innovation studies we have presented above. Based on this new set of articles, we formed a similar hybrid network, which is shown in Fig.  10 . This identified the following five innovation topics published in practitioner journals: Research Productivity, Technology Transfer, Process Management, Clinical Development and Healthcare Marketing.

The hybrid network of citing articles in practitioner journals. Articles are labelled by first author and year. Colors show different clusters of study in VosViewer clustering. The size of each node indicates the internal citation count of each article by other articles within the same network

These research topics are summarized in Table 5 . Research Productivity and Process Management are concerned with the management of the entire drug discovery and development process. In contrast, certain topics are much more relevant in specific phases, such as Technology Transfer in early drug discovery, Clinical Development in the drug development stages, and Healthcare Marketing after a drug receives approval.

As the industry depends on R&D to bring new drugs to the market, Research Productivity is the largest topic of interest to practitioners. It involves measuring and diagnosing factors that affect R&D productivity. In the years soon after 2010, there was a focus on the decline in productivity brought to the industry by the increasing cost of developing new drugs (Paul et al. 2010 ; Scannell et al. 2012 ). Various factors were associated with this productivity decline, including the discovery of treatments for so-called low hanging fruits, the increased standard needed for new drugs compared to current drugs in the market, the overinvestment in basic research despite the many biological uncertainties (Pammolli et al. 2011 ; Scannell et al. 2012 ). To address such issues, studies have also explored new R&D models that can be embraced by companies to improve their R&D return such as embracing open innovation (Moors et al. 2014 ; Schuhmacher et al. 2016 ). In response to practitioners’ interest, innovation scholars could help to further ideate new models and evaluate these models’ potential in revitalizing the drug pipeline of firms.

While the topic of Process Management also concerns the entire drug discovery and development process in firms, it focuses more on the technical implementation of each step. Compared to the other research topics covered by practitioner journals, we find this topic to require deep domain knowledge, covering specific scientific problems such as chemical synthesis, drug formulation and quantitative analyses of portfolios. In other words, rather than assessing R&D from a meta-level, articles under Process Management aim directly to identify the best practices in conducting drug development. However, it is important to note that this also has evolved, placing much more emphasis on the role of external innovation. For instance, they explore options such as outsourcing for certain processes (e.g. Festel 2013 ). As this is a domain-specific topic, innovation scholars might not be as interested in exploring such niche matters. Despite this, we still recommend scholars to peek into the black box of such processes, as fresh sets of eyes may generate new insights for practitioners.

The topic of Technology Transfer has grown the most across the practitioner topics. Parallel to the topic Knowledge Transfer in the innovation literature, it is also concerned with the scientific advances surrounding the industry (e.g. Mittra et al. 2011 ) and the role of intellectual property protection to exploit such advances (e.g. Saotome et al. 2016 ). This topic has received mutual interest from both practitioners and innovation scholars as seen by the number of publications. Due to the value of generating basic research and transferring the knowledge this generates to the industry, we expect this research topic to attract continued interest.

The topic of Clinical Development is related to the latter parts of drug development, in which a lead candidate is tested for efficacy and safety. Eventually, only one drug in ten that enter this phase gets approval (Hay et al. 2014 ). Due to the high regulatory burden in this respect, the industry is concerned about how to navigate complex institutional requirements to bring a drug to the market. At the same time, this topic also explores how such institutional factors influence the types of R&D projects that firms engage in. For instance, countries’ favorable policies concerning orphan drugs have made it possible for new firms to participate in such areas (Heemstra et al. 2008 ; Kneller 2010 ). As this a relatively young research area in practitioner journals, there are many areas for further research. For example, innovation scholars might find the feedback loop between regulations and the therapeutic areas that firms invest in their R&D to be an interesting subject for further studies.

Healthcare Marketing focuses mainly on the interaction between the pharmaceutical industry and the healthcare system in different countries. Overviews of drug sales across various therapeutic areas are common in this topic (Cohen 2005 ). They also explore the marketing tactics that firms use to reach patients that need these treatments. Due to greater scrutiny of industry practices, this topic also includes discussions on the innovativeness and cost efficiency of drugs. Although our analysis suggests that this topic is the oldest, this impression is due largely to the statistical effect of many older analyses of drug introductions. In fact, there have been recent studies of this topic, demonstrating the interest of R&D practitioners in these matters. Thus, innovation scholars then can explore further how the two—marketing and R&D— can interact in order to improve the drug development process within firms.

Towards highly engaged innovation research

As implied by the previous sections, the emphasis on pharmaceutical R&D innovation differs between innovation journals and pharmaceutical journals. While innovation scholars are interested mainly in gaining potentially generalizable insights on innovation by exploring the industry, practitioners in the pharmaceutical industry are concerned primarily with improving their research practice. In turn, the topics that interest them are directly related to the specific processes in the drug development process—from target identification to the approval of drugs.

Despite such differences, the number of citations (see Fig.  6 ) shows that practitioners are increasingly interested in innovation research. However, as we show in the mapping of citations in Fig.  11 , this interest is compartmentalized within certain topics. As one would expect, most practitioner topics cite the innovation topic Product Development , since this delves most into the complexities of the pharmaceutical development process. Other topics in the Macro cluster, including Innovation System and Knowledge Transfer , have also attracted interest in specific practitioner topics. On the other hand, whereas the Meso cluster has been receiving increased attention in the innovation literature, such interest has not been apparent in the practitioner literature. This low diffusion shows that there is potential for more engaged and relevant studies for innovation scholars on the pharmaceutical industry.

Mapping research articles according to their distribution among the major innovation clusters and research topics (on the left) and distribution of citations per research topic in practitioner journals (on the right)

To increase the relevance of innovation research in pharmaceutical R&D, we propose the matrix in Fig.  12 . This shows how specific processes in the pharmaceutical industry could be further explored by innovation scholars to gain new insights on their respective topics of interest while maintaining relevance for practitioners. The matrix has two axes, the areas in the pharmaceutical industry studied (horizontal axis) and the levels of analysis in innovation research (vertical axis).

A framework integrating innovation topics (left) and pharmaceutical industry processes (top) for future research avenues

The R&D process to bring a new drug to the market is mired with high complexity and high failure rates. To illustrate the long path a drug has to take to reach the market, we briefly describe the drug discovery and development process (Paul et al. 2010 ). The first step involves the identification of a target disease and its underlying mechanisms. Once a protein has been identified as a suitable target for a drug, teams of scientists then work together to design a lead compound that can act on the target. Once there is a promising lead, it can then be tested for efficacy and safety in cell and animal models. Once a lead has been identified as a potential drug, it then enters the drug development process, where it undergoes clinical testing in humans. This development phase consists of three phases and is strongly regulated by the authorities (Vinet and Zhedanov 2011 ). Phase I consists of clinical trials in which toxicity is tested in a small number of healthy volunteers. Phase II consists of treating patients with the disease to test the drug’s safety, and, preliminarily, its efficacy. Finally, to establish the drug’s efficacy and discover possible side effects, it is tested in phase III trials in a large sample of patients. Once approved, the firm then has to manufacture and market the drug to ensure that it reaches its patients.

The first avenue for further research would be to have a detailed exploration of a particular process or technology in drug discovery and development through an innovation topic of interest (Fig.  12 , Label 1). Since most innovation studies treat the R&D process as a black box, such an investigation of a specific process could provide new insights that would help practitioners and potentially enrich their understanding of innovation. While this has already happened in a few case studies on specific technologies such as RNA interference (Natsukawa et al. 2013 ), lab-on-a-chip (van Merkerk and Robinson 2006 ) and gene therapy (Kapoor and Klueter 2015 ), such in-depth analyses are not as common. Yet, from our analysis, we show that practitioners value such engaged research as these enable the practitioners to view their innovation activities in a new light. Since we show that the meso-level has seldom been cited by practitioners, innovation scholars can examine various facets of collaborations occurring in specific processes in the pharmaceutical industry to increase the relevance of such meso-level studies to practitioners.

Apart from studying a particular pharmaceutical area from one innovation perspective, innovation scholars can analyze it from a multilevel perspective (Fig.  12 , Label 2). By exploring the environment, interorganizational relationships and organizational practices affecting a particular research area in the pharmaceutical sciences, innovation scholars can better inform scientists and managers on the issues they have to consider to improve their practice. Moreover, such multilevel analysis may also enable innovation scholars to gain a better understanding of the big-picture dynamics and microfoundations of a certain innovation topic. Exploring how these different levels interact for a specific process could also provide new insights (Fig.  12 , Label 3A and 3B). As knowing which pharmaceutical area to explore requires insider knowledge, innovation scholars should look into engaging in research collaborations with industry practitioners. Increasing the extent of such collaborations would help innovation scholars to gain richer insight on specific processes by gaining access to relevant data from companies.

Finally, with the individual steps of the drug development process being highly interlinked, valuable insights might thus be produced by studying how such processes interact with one another. Innovation scholars can explore how practitioners involved in the early stages of drug discovery adjust their practice as they anticipate the requirements by their peers involved in the later stages (Fig.  12 , Label 4A). They might also examine how downstream practitioners cope with the output of previous processes (Fig.  12 , Label 4B). This can also be further extended by following the entire drug development cycle through a single innovation theory (Fig.  12 , Label 5). Aside from gaining a complete picture of the drug development process, scholars can gain new insights on their innovation topic of interest by checking how robustly their theory applies through the different phases of drug development.

Conclusions

Innovation scholars continue to subject the pharmaceutical industry to extensive scrutiny. Given the continuous growth in the number of articles published per year, we conducted a bibliometric analysis that would map the structure of the literature and identify trends in research. We identified three main levels— Macro, Meso and Micro —at which the literature has assessed innovation over recent years. Between them, these levels comprise 9 research topics with different theoretical foundations and phenomena of interest. To trace the diffusion of ideas to practitioners, we analyzed the citing articles in journals published by practitioners in the industry.

We focus mainly on giving readers a top-down overview of the diverse range of research topics concerning R&D in the industry. We trust that by exposing scholars to the pertinent issues in other fields, cross-fertilization of ideas can be facilitated for new insights. Due to the extent of the innovation literature on the industry, this study could only survey each research area at a surface level. Nonetheless, by highlighting the main issues and the most relevant papers in each area, the hope is that we give readers a head start should they decide to dig deeper into a particular topic.

Overall, the focus of recent literature has tended to highlight the interactions between different firms as they absorb knowledge from the outside. For instance, network analysis has become a routine method for examining alliances in aggregate. At a more micro level, firms examine their different systems and strategies for learning from the outside world.

This high degree of emphasis on external innovation may also inform the innovation literature at large. Currently, most definitions of innovation focus implicitly on internally generated innovation. For instance, another top-cited study by Baregheh et al. ( 2009 ) defines it as “the multi‐stage process whereby organizations transform ideas into new/improved products, service or processes, in order to advance, compete and differentiate themselves in their marketplace.” Unlike such firm-centered views of innovation, most of the topics we studied in the pharmaceutical industry had an explicit emphasis on the role of the outside world in enriching organizational innovation. With such relationships playing a central role in the industry, our understanding of the nature of innovation may be advanced by incorporating this aspect.

We found that practitioners are increasingly interested in understanding the organizational forces that influence their innovation. To further increase the diffusion of such knowledge, we recommended ways to increase the relevance of the innovation literature with practitioner needs. Primarily, as we see great value in peeking into the black box of the R&D process, we hope to stimulate collaboration between practitioners and innovation scholars. By considering these issues that interest scientists and managers in the pharmaceutical industry, innovation scholars can help find solutions to the unique challenges facing the industry in bringing new drugs to the market.

This study mapped the landscape of innovation research on the pharmaceutical industry. As we focused on innovation issues, further analyses are needed to enumerate the specific technologies and scientific advances of concern to practitioners. For instance, recent drug discovery journals and pharmaceutical patents can be mapped to elucidate such trends. Moreover, as this analysis mainly focuses on the pharmaceutical industry, we leave out relevant adjacent areas including biotechnology, medical devices and healthcare. Nonetheless, by mapping the research landscape, this study provides the first steps towards an engaged and better-integrated literature on pharmaceutical innovation.

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Romasanta, A.S., van der Sijde, P. & van Muijlwijk-Koezen, J. Innovation in pharmaceutical R&D: mapping the research landscape. Scientometrics 125 , 1801–1832 (2020). https://doi.org/10.1007/s11192-020-03707-y

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Emerging of artificial intelligence and technology in pharmaceuticals: review

• Ayesha Sultana 1 ,
• Rahath Maseera 2 ,
• Abdul Rahamanulla 3 &
• Alima Misiriya   ORCID: orcid.org/0009-0007-0273-6782 1  

Future Journal of Pharmaceutical Sciences volume  9 , Article number:  65 ( 2023 ) Cite this article

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The review covers a variety of Artificial intelligence (AI) related topics in medication development. Additionally, it gives a quick account of the recent advances made in drug development by the pharmaceutical industry in cooperation with various AI. All facts of science have been impacted by advances in computing and technology. In all fields of science and technology, from fundamental engineering to medicine, AI has become a crucial component. AI has so influenced pharmaceutical chemistry and health care.

The use of computers to assist in drug creation has overtaken more conventional approaches in recent years. AI is frequently utilised to reduce the amount of time and improve drug design processes. The success rate of the developed medicine is further increased by the ease with which the target proteins may be discovered utilising AI. Every step of the medication design process involves the use of AI technology, which lowers the cost and greatly lowers the health hazards related to preclinical studies. AI is a powerful data mining technique that is based on vast amounts of pharmaceutical data and the machine learning process.

The use of AI in de novo drug design, activity scoring, virtual screening, and In silico evaluation of drug molecule characteristics is the consequence (absorption, distribution, metabolism, excretion, and toxicity). To speed up drug research and the healthcare system, pharmaceutical companies have joined with AI firms.

A key component of intelligence is the capacity for logical reasoning, which has long been the main area of AI study. A theorem-proving program created in 1955–1956 by Allen Newell, J. Clifford Shaw, and Herbert Simon of the RAND Corporation and Carnegie Mellon University is regarded as a significant milestone in this field. The ability of a digital computer or robot controlled by a computer to carry out tasks typically accomplished by intelligent beings is known as AI [ 1 ]. The phrase is frequently used in the quest to develop AI systems with cognitive capabilities resembling those of humans, such as the ability to reason, find meaning, generalise, and learn from experience. It has been established since the 1940s, when the first digital computers were developed, that computers can be programmed to carry out incredibly challenging jobs [ 2 ].

Today’s pharmaceutical market accepts medications after a lengthy and costly drug development process. The majority of drugs cost billions of dollars and take 10 years or more to reach the pharmaceutical market. This requires more money and more time for drug development. The concept of AI appears to be more promising in overcoming these drawbacks and should result in successful drug development programs [ 3 ]. AI is being used in new technologies such as drugs, prosthetics, and evolving and advanced robotics. Additional benefits of AI in the drug development process include identifying drug targets, suggesting molecules from data libraries with chemical modifications, and sometimes repurposing the drug [ 4 ].

As it is evident, current developments in AI have led to the development of a number of technologies that could be used in the creation of pharmaceutical products. Those who can take and utilise this technology as a tactical weapon will be the “winners” in a period of escalating rivalry. Turning potential into action is difficult because there is a chance to boost productivity while also enhancing consistency and quality where applications have succeeded [ 5 ] (Fig.  1 ).

Trends in AI

Expert system development : It involves creating automated systems that operate intelligently and counsel people on how to proceed.

Human intelligence in computers : It will assist in the development of analogous cognitive patterns in computers, enabling them to behave like people and take the necessary measures to resolve challenging situations.

Multi-domain applications : AI will aid in the implementation of multiple domains such as psychology, medical science, ethics, natural sciences, healthcare, and more [ 6 ].

Literature review

In a study, model trees, a form of AI, were used to model a database of formulations for immediate-release tablets. Artificial neural networks, which are well-known and frequently used in the disciplines of pharmaceutical product formulation, were utilised to compare the model’s performance to those networks. The correlation coefficient ( R 2 ) was used to evaluate the derived models predictability. Similar in quality to neural network models, multivariate linear equations derived from model tree studies were able to predict tablet tensile strength, disintegration time, and drug dissolution profiles. However, these equations uncovered additional, important data that had been kept secret in the formulation database. Model trees, as a transparent technology, are concluded to be useful tools for formulators [ 7 ].

By analysing the chemical structures of kinases, the author was able to determine their polypharmacology using KinomeX, an AI-based online platform based on Deep neural neutrons (DNNs). The DNN used by this platform was trained on 14,000 bioactivity data points obtained from more than 300 kinases. Therefore, it has real-world implications in determining a drug’s general selectivity towards the kinase family and particular kinase subfamilies, aiding in the development of various modifiers. One notable example is Ligand Express, Cyclica’s cloud-based proteome-screening AI technology. Finding receptors that can have both onand off-target interactions with a certain small chemical is done using this method. This facilitates comprehension of the medication’s potential negative effects [ 8 ].

AI’s ability to predict drug-target interactions has also been used to avoid polypharmacology and aid in the repurposing of medications. A medication that has been repurposed is eligible for Phase II clinical trials right away. This lowers spending as well because re-launching an existing medicine is less expensive ($8.4 million) than introducing a brand-new pharmacological entity ($41.3 million). It is possible to predict the novel relationship between a drug and an illness using the “guilt by association” technique, which is either knowledge-based or computationally driven. The Machine learning (ML) approach which makes use of methods like Support vector Machine (SVM), Neural network (NN), logistic regression, and Deep learning (DL) is popular in networks that are computationally driven. When repurposing a medicine, ML techniques like PREDICT, SPACE and logistic regression platforms take a drug’s gene expression profile into account [ 9 ].

The basic concept of AI in pharmaceutics

There are two major categories for AI developments. The first group includes technological methods and software, such as expert systems, that imitate human experience and draw conclusions from a set of rules. Devices that mimic how the brain operates, such as artificial neural networks, fall under the second group artificial neural network (ANNs). The ability of artificial neural networks to generalize is one of their most advantageous traits. These characteristics make them excellent for dealing with issues relating to formulation optimization in the development of pharmaceutical products [ 10 ] (Table 1 ).

How does AI works

Building an AI system involves carefully replicating human characteristics and skills in a machine and using that machine's processing power to outperform our talents. Understanding the different sub-domains of AI and how they could be applied to different fields of the industry requires a deep dive into the subject.

Machine learning (ML) : ML teaches a computer how to draw conclusions and make decisions based on prior knowledge. Without relying on human experience, it recognizes patterns and examines historical data to deduce the significance of these data points and arrive at a potential conclusion [ 20 ].

Deep learning (DL) : A machine learning technique is deep learning. DL rejuvenated neural network in 2000s which trained deeper networks. To classify, infer, and forecast the outcome, it teaches a machine to interpret inputs through layers. For example, it helps in knowing the complex internal representation which are necessary in understanding the difficult language or analyzing the objects by a way of going through in depth layers of activity vectors and finding the connection strengths that motivates these vectors by knowing stochastic gradient [ 21 ].

Neural networks (NN) These systems operate in a manner akin to human neural cells. They are a group of algorithms that mimic the way the human brain works by capturing the relationship between numerous underlying variables [ 17 ].

A machine's ability to read, comprehend, and interpret a language is known as Natural language processing (NLP). A computer will react appropriately once it recognizes the user's intended message [ 15 ].

Computer vision : By dissecting an image and examining various aspects of the item, computer vision algorithms attempt to comprehend an image. This aids the machine in classifying and learning from a collection of photos, enabling it to produce superior results based on prior observations [ 11 ].

Cognitive computing : By analyzing text, audio, images, and other inputs in the same way that humans do, cognitive computing algorithms attempt to simulate the functioning of the human brain and provide the desired results. Additionally, enrol in free courses on AI applications [ 16 ] (Fig.  2 ).

3 Types of AI

Applications of AI in pharmaceuticals and drug delivery

The power of long-term learning is often removed after training when an AI system is employed to regulate processes like manufacturing or clinical trials. The pharmaceutical industry has improved since the relatively recent adoption of Quality by design (QbD) methodologies, nevertheless, and the latest industry 4.0 initiatives seems to portray a sector in rapid development [ 22 ]. Therefore, there is a strong likelihood that if an early AI application is developed, it will be put into use. In contrast to other scientific fields, pharmaceutical sciences can cause delays in data codification and standardisation. Data accumulation and standardisation are essential for effectively training AI in the former [ 23 ] (Fig.  3 ).

Applications of AI in the pharma sector

The following are some examples of how AI is used in data processing:

Data searching and search engine optimization to produce the most pertinent results.

If–then logic chains that can be used to carry out a series of instructions dependent on parameters.

Pattern detection to find noteworthy patterns in vast data sets for original insights

Using probabilistic models to anticipate future results [ 24 ].

AI in health care

Administration : AI systems are assisting with daily administrative activities to minimize human mistakes and maximize productivity. Natural language processing (NLP) transcriptions of medical notes help organize patient information so that clinicians can read it more easily.

Telemedicine : In non-emergency scenarios, patients can contact a hospital’s AI system to analyze their symptoms, enter their vital signs, and determine whether they require medical assistance. Giving them only the most urgent situations lessen the workload of medical experts.

Assisted diagnosis : AI is now capable of reading MRI scans to look for tumors and other malignant growths at an exponentially faster rate than radiologists can, with a significantly narrower margin of error, thanks to computer vision and convolution neural networks.

Robotic surgery : Robotic surgery has a very small margin of error and can reliably execute surgeries 24 h a day without becoming fatigued. They are less intrusive than conventional techniques because of their high degree of precision, which can save the number of time patients need to recover in the hospital.

Vital stats monitoring : Continual evaluation of a person's health is necessary to assess how well they are doing. Wearable technology is increasingly widely used; however, this data is not readily accessible and needs to be analyzed to provide useful insights. Numerous applications could save lives because vital signs can forecast changes in health even before the patient is aware of them [ 25 ] (Fig.  4 ).

Advantages and disadvantages of AI

AI of next-generation 3D printed medicines

Pharmaceutical 3D printing (3DP) pipeline and AI can work together. The administration of individualized medications must replace the long-standing "one size fits all" concept in medicine. Pharmaceutical 3DP can deliver customized medications in the clinic, but now it necessitates the presence and skill of qualified 3DP practitioners. There are numerous standard process optimization tools, including Finite element analysis (FEA), and mechanistic modelling, but none are capable of thoroughly optimizing the various stages of pharmaceutical 3DP [ 26 ]. In contrast, ML can offer intelligent optimization of each step in the creation of 3DP pharmaceuticals. This will eventually eliminate the requirement for ongoing expert input into the development of 3DP medicines, hence removing obstacles to the technology's clinical implementation [ 27 ] (Table 2 ).

AI with nanotechnology

Due to current molecular commodities longer production times, greater costs, and reduced productivity, AI has grown in significance in the pharmaceutical business, pharmaceutics, and medication delivery [ 28 ]. However, even the development of current formulations is based on time-consuming, pricey, and unpredictably error-filled research [ 29 ]. Big data, AI, and multiscale modelling approaches are being integrated into pharmaceutics by a new system known as “computational pharmaceutics,” which is proposing a significant potential change to the drug delivery paradigm. This system has emerged in response to the exponential growth of computing power and algorithms over the past decade [ 30 ]. In vivo pharmacokinetic parameters, drug distribution, physical stability, in vitro-in vitro correlation, and pre-formulation of physical and chemical properties and activity prediction are all examples of activities being done to apply AI techniques to pharmaceutical product development [ 31 ] (Fig.  5 ).

AI in nanotechnology

AI to predict new treatment

Improvements in AI, revived interest in treatments for rare diseases. Over 7000 uncommon diseases currently affect more than 350 million people worldwide. To research novel medications for uncommon disorders, Heal, a biotech company with headquarters in the (United Kingdom) UK, has acquired $10 million funding. Thera chon, another Swiss biotech firm, has been given $60 million to create medications for uncommon genetic illnesses [ 32 ].

Adoption of AI by the pharma industry

Working with or acquiring tech companies and AI start-up many pharma companies make contact with specialised businesses and start-ups working on AI-powered drug discovery. This enables them to build promising therapeutic candidates based on known theories and experience using their knowledge and tools [ 33 ].

Interaction amongst academics: As pharma starts to accept AI, partnerships between business and academics are anticipated to expand.

Building internal expertise and supplying workers with the tools they need.

Open scientific initiatives and R&D challenges: This useful AI adoption approach for drug research carries a lower financial risk than previous approaches [ 34 ] (Table 3 ).

Challenges that pharma company’s face when attempting to adopt AI includes

The technology’s unfamiliarity, given its youth and esoteric nature, AI continues to be seen by many pharmaceutical businesses as a “black box.”

Inadequate IT infrastructure exists because the majority of IT programmes and systems in use today were not created or planned with artificial intelligence in mind. Even worse, pharma companies must shell out a sizable sum of money to improve their IT infrastructure.

Since a large portion of the data is in free text format, pharmaceutical companies must take extra steps to gather and arrange it in an analytically-friendly manner. Despite these limitations, there is no doubt that AI is already changing the biotech and pharmaceutical industries [ 35 ] (Fig.  6 ).

Challenges in AI

AI has demonstrated its utility in a variety of drug discovery fields. AI can help scientists in the design, planning, quality management, maintenance, and quality control of pharmaceutical development and delivery. It is not a panacea and will not bring about seismic changes overnight, but it has the potential to increase efficiency, provide useful insights, and highlight novel perspectives in the pharmaceutical discovery process. Pharmaceutical companies are currently undergoing a revolutionary shift, with the risk being carefully managed in the development of novel science and practice. AI's success in the innovative drug research and development process will be measured by its integration of many unfamiliar and new areas. Data management, drug discovery, diabetes treatment, digital consulting, and other uses of AI are available in this field. There is substantial proof that medical AI can help doctors and patients deliver healthcare in the twenty-first century much more effectively.

Availability of data and materials

The data that support the findings of this study are available from the corresponding author, upon reasonable request.

Abbreviations

Artificial Intelligence

Deep neural neutrons

Machine learning

Support vector machine

Neural network

Deep learning

• Artificial neural network

Natural language processing

• Quality by design

3D printing

Finite element analysis

Deubiquitinase

Convolutional neural network

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Sultana, A., Maseera, R., Rahamanulla, A. et al. Emerging of artificial intelligence and technology in pharmaceuticals: review. Futur J Pharm Sci 9 , 65 (2023). https://doi.org/10.1186/s43094-023-00517-w

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REVIEW article

Conceptual framework of the impact of health technology on healthcare system.

• Clinical Pharmacy Department, Faculty of Pharmacy, Cairo University, Cairo, Egypt

The World Health Organization (WHO) promotes health systems strengthening as a means of improving population health, especially in low- and middle-income countries. The United Nations Sustainable Development Goals highlight the importance of investing in workforce development to improve population health and economic well-being. In relation to pharmaceuticals, health systems face challenges in terms of i) guaranteeing access to needed drugs, ii) rationalizing medicines use, and iii) avoiding harm from adverse events. There is a pressing need to better understand the relationships between technology and pharmacy practice when strengthening pharmaceutical care systems. In response, this paper examines ways in which harnessing new technologies can change pharmacy practice and strengthen pharmaceutical systems for the benefit of patients. The paper will present a conceptual framework as well as exploring case studies.

Introduction

Every human has the right to the healthcare they need, to interventions that enable them to live healthy ( Porter, 2010 ). Provision of healthcare differs substantially between countries especially with the presence of different healthcare systems and insurance. More developed economies with larger resources usually have higher levels of health and well-being ( Cameron et al., 2009 ; Godman et al., 2017 ). Worldwide, the healthcare environment is changing. It is becoming increasingly obvious that affordable high-quality healthcare cannot be delivered without harnessing new ways of delivering care ( Kvedar et al., 2014 ; Godman et al., 2018 ). Using new technologies is a promising solution to help cope with current challenges and to improve healthcare and pharmacy practice ( Kvedar et al., 2014 ).

“Technology” may be defined as the “dynamic clustering of techniques, methods, skills and processes used in the production of goods or services or in the achievement of outcomes that deliver desired benefits for consumers” ( Baines et al., 2018 ). New technologies are having an impact on healthcare delivery. Health technology is defined by the WHO as “the application of organized knowledge and skills in the form of devices, medicines, vaccines, procedures and systems developed to solve a health problem, improve quality of lives” ( World Health Assembly, 2007 ).

For instance, the internet is allowing new healthcare solutions to emerge that improve communication and information sharing by patients and healthcare providers. Web-based electronic communications are proliferating in many ways including all sectors of civilization, for instance, e-mail, e-commerce, e-prescribing, e-health, etc. ( Mackert et al., 2014 ; Srivastava et al., 2015 ). Other new technologies are emerging. Electronic devices, telecommunications, wireless, and audio-visual systems are being constantly developed as information and communication technology (ICT) in health ( Henriquez-Camacho et al., 2014 ). The application of internet, web technology, and ICT in health is termed e-health ( Van De Belt et al., 2010; Mackert et al., 2014 ). Areas of e-health include electronic health records (EHR), e-prescribing, telemedicine, telehealth, consumer health informatics, and m-health ( Henriquez-Camacho et al., 2014 ).

E-health, telemedicine, telehealth, m-health, and digital health are all interchangeable terms included under the umbrella of “technology-enabled care” (TEC) ( Ryu, 2012b ). For instance, new technologies designed to support pharmacists have been called “technology-enabled pharmacy” ( Baines et al., 2018 ). Technology-enabled care involves using health technology, smartphones’ applications, voice and written messages, Bluetooth technology, digital media, automated robots, remote monitoring devices, and wearable technology for effective integration of care ( Ryu, 2012a ). Delivering care via those new technologies changes the ways by which information and health services are delivered which leads to patient-focused healthcare and personalization. The following are examples of how new technologies improve the healthcare delivery ( Kvedar et al., 2014 ):

● Getting access to specialty physicians, specialized knowledge, and time-place independent services enables us to overcome geographic boundaries.

● Remote diagnosis via interactive videoconferencing and remote consultation via counseling software or applications could overcome the space barrier and waiting time spent in clinics to meet physicians.

● Remote Intensive Care via Tele-ICU technologies can positively affect intensivist coverage over ICUs leading to decreased mortality and ICU lengths-of-stay.

● Medication adherence via smartphone reminder applications, internet-connected alarms for pill caps, or printed calendars on medication blister improves patients’ compliance leading to fewer clinical problems and less costly care over time.

● Reducing Referral Wait Times via e-referral service model could leverage specialist capacity.

However, no matter how promising that looks for patients’ drug adherence and healthcare, we have to consider patients’ adaptation to these ways of technologies stressing on uneducated or poorly educated populations; otherwise it all goes in vain ( Nashilongo et al., 2017 ; Rampamba et al., 2018 ).

The need for harnessing new technologies in healthcare and pharmacy practice is increasing. Their impact in enhancing patient–provider’s satisfaction, improving quality of life, participating patients’ on their own care and health directly, reducing cost, ensuring efficacy, enhancing the professional scope of pharmacy practice and extending care to wider population is significant ( Kvedar et al., 2014 ; Baines et al., 2018 ). Adopting new technologies could lead to a renaissance of pharmacy practice taking drug dispensing to a new level of appropriateness and overcoming the massive consequences of its misuse ( Baines, 2015 ; Markovic-Pekovic et al., 2017 ). Shedding light on the ways by which new technologies can impact healthcare and benefit the patient will allow health systems to strengthen their quality, releasing new innovations and paving the way to overcome any existing or potential challenges.

E-Health market becomes a huge business that offers a great potential in delivering care. The slow-paced field of healthcare is in need to join the global digital flow of technology. We can see the great impact of fast-paced digital technology on every detail of our life. Harmony of this fast-developing industry with the healthcare industry can dramatically change the healthcare delivering market ( The Rise of mHealth Apps: A Market Snapshot—Liquid State ).

The main issue that motivates this research article is poor availability of affordable and effective healthcare technologies in the developing countries. Although various technologies are available or under investigations, we still need technologies or to develop new ways to make them more accessible, affordable, effective, easy to use, and even capable of addressing problems of drug persistence and adherence in case of noncommunicable diseases (NCDs) ( Howitt et al., 2012 ; Masum et al., 2013 ).

Analysis of each project and developed e-health experiment in developed and developing world is a must to overcome challenges and try innovating new ways transforming healthcare. Therefore, this article overviews the impact of healthcare technologies on healthcare system in developed and developing world, discussing the pros and cons trying to find our pace in harnessing new ways for better healthcare delivery. Focusing on the situation in developing countries and in Egypt has a very crucial role in improving the structure of healthcare market in that world.

Health Technologies in Developed and Developing Countries

Electronic health record.

EHR is defined as a collective electronic record of the patients’ health information that includes the patient medical history, medication prescriptions, physical examinations, medical reports, and notes of the healthcare professionals ensuring the standardized readable complete orders ( Campanella et al., 2016 ). EHR as a growing healthcare innovation is associated with many benefits and challenges ( Chao et al., 2013 ). One of the major benefits of EHR is the storage of patients’ medical information in addition to the savings that could occur in the paper resources and the space needed for the documentation ( Chao et al., 2013 ; Kalogriopoulos et al., 2009 ).

EHR could also allow patients’ medical information sharing leading to more efficient workflow among the departments of the health institutes ( Shachak et al., 2009 ; O’Malley et al., 2010 ; Chao et al., 2013 ).

Another advantage that EHR could offer is the speeding up of the diagnosis and clinical decision process through making all the diagnostic results accessible once available. All of these advantages can be added to the medication repetition and drug allergies that could be avoided by the convenient proper access to patient medical records ( Chao et al., 2013 ). On the other hand, EHR might carry some challenges for the patients, physicians, and health institutions ( Chao et al., 2013 ). For patients, the main concern about EHR lies in the privacy issues in addition to the risk of less eye contact and face-to-face communication due to the physician putting information into the system during consultation ( Zhang et al., 2012 ; Chao et al., 2013 ).

Regarding the physicians, they might be faced with some challenges on using EHR as it might be a time-consuming system ( Abramson et al., 2012 ; Chao et al., 2013 ; Pollard et al., 2013 ). Also the risk of the health data loss, illegal information leakage, and the high cost associated with adopting the system are the three main drawbacks for the health institutes as stakeholders ( Kalogriopoulos et al., 2009 ; Chao et al., 2013 ; Syzdykova et al., 2017 ).

Because of the many advantages stated previously, many of the developed countries such as United States, Canada, and United Kingdom put a target of paperless hospitals via a stepwise transition from paper-based health records to EHR in their healthcare settings ( Al-Aswad et al., 2013 ). Some developing countries have also adopted EHR systems as a trial to improve their healthcare services, such as Kenya and Peru ( Kalogriopoulos et al., 2009 ). In 2001, an EHR system was developed in Kenya that serves about 60,000 patients. The beneficial impact of this system was the shortening in the length of patient visits, reduced provider time per patient, and less waiting time spent by the patients in the clinic ( Fraser et al., 2005 ).

Another example in Kenya is Kenyatta National Hospital. In this hospital, all patients’ data are entered electronically by the medical record staff into the health information system that could help in the data retrieval of any data in the future ( Kivoto et al., 2018 ).

Electronic Prescribing

E-prescribing is defined as an electronic computer-based transmission and filling of a prescription instead of paper prescriptions that assist healthcare providers to electronically submit or renew a prescription authorization to the community pharmacy that includes a two-way transmission between the care provider and the dispenser ( eHealth Initiative Foundation, 2008 ; Electronic prescription drug program., 2012 ). There are many pros and cons for the e-prescribing. Both the patient and the prescriber can benefit from such intervention ( eHealth Initiative Foundation, 2008 ; Klepser et al., 2016 ). For the patient, e-prescribing can improve the patient safety and quality of care through a number of ways such as elimination of illegible handwriting for paper-based prescriptions, avoiding oral miscommunications, presence of safety checkers, and accessibility to the patient medical and medication history. All of these can eventually reduce medication errors and the resultant adverse drug events ( Varkey et al., 2007 ; eHealth Initiative Foundation, 2008 ; Abramson et al., 2012 ).

For the prescribers, e-prescribing allows reducing the time spent on call backs and faxing for clarifications in addition to improving the prescriber mobility and convenience as it can be used through mobile devices ( eHealth Initiative Foundation 2008 ; Taylor et al., 2008 ; Klepser et al., 2016 ). For the medical governmental institutions, e-prescribing offers a great benefit as in case of drug recall as it allows finding all the patients with particular prescription ( eHealth Initiative Foundation, 2008 ). All of these benefits can be added to the valuable input of the e-prescribing in improving patients’ compliance through eliminating the effort of dropping off a paper prescription and reducing the obstacles in the prescription filling process in addition to providing the appropriate alternatives with lower cost that indeed can improve patient adherence to medications ( eHealth Initiative Foundation, 2008 ; Arlington, 2012 ).

In sum, the implementation of e-prescribing can overcome many paper-based prescription problems leading to cost savings, increased illegibility, reduced medication errors, the reduced need for redundant paperwork, and better access to the different drug information and patient medication history, eventually leading to improving therapy outcome ( Varkey et al., 2007 ; Taylor et al., 2008 ; Yu et al., 2009 ; Devine et al., 2010 ).

Despite of all the previously listed pros, there are challenges that have hindered the more widespread adoption of e-prescribing as high financial cost and the longer time it may take for the return of investment for small practices especially in rural areas ( eHealth Initiative Foundation, 2008 ; Lander et al., 2013 ; Porterfield et al., 2014 ). Since there is no absolute confidence of the completeness and the accuracy of patient medication history available in online records, this puts a burden on the prescriber to validate such information and to update the record accordingly ( eHealth Initiative Foundation, 2008 ). Another patient-related challenge regarding e-prescriptions is patient acceptance issues as some patients do not feel comfortable with e-prescriptions ( Greenberg et al., 2004 ; eHealth Initiative Foundation, 2008 ).

In an American study, Kaushal et al. assess the effectiveness of an e-prescribing system on medication errors with respect to prescribing errors ( Institute of Medicine (US) Committee on Quality of Health Care in America, 2000 ; Kaushal et al., 2010 ). This study showed the potential benefit of the e-prescribing system in improving patient safety through the (statistically significant) reduction in the prescribing errors.

Also in Sweden, many measures have been introduced within the last few years to enhance the quality and efficiency of prescribing. One of these measures was the implementation of e-prescribing that was agreed as one of the long-term strategies to ensure rational, safe, and cost-effective treatments ( Godman et al., 2009 ).

On assessing the acceptance of e-prescribing adoption in an Asian developing country (Pakistan), it was found that the main concern about its adoption is the associated high financial costs accompanying its implementation ( Kaushal et al., 2010 ).

Telehealth and Telemedicine

Telehealth or telecommunication enables the bi-directional transfer of information among the different partners of the healthcare system over considerable distances using text, voice, video, or images ( DA and Allen, 1995 ; Ryu, 2012a ; Goundrey-Smith, 2014 ). Telemedicine provides high-quality care despite socioeconomic and geographical barriers, and facilitates home monitoring and treatment ( Hjelm, 2005 ; Kahn, 2015 ). The patient can send and receive any modifications in his/her care plan through many mobile applications or even through remote nursing supplied by electronic devices and laptop ( Bellazzi et al., 2001 ; Vontetsianos et al., 2005 ). Through telemedicine, the healthcare provider can remind the patients to take the medications at the proper time improving medication adherence ( Ansari and Fong, 2006 ). Confidentiality and security issues are the most common challenges facing system developers. They have to ensure that transmitted information won’t be modified or accessed by unauthorized persons ( Hjelm, 2005 ; Ansari and Fong, 2006 ). Most available systems evaluating the impact of telemedicine focus on patient and physician perceptions and acceptance rather than short-term clinical outcomes ( Kahn, 2015 ). Another challenge facing physicians is that they can’t perform an entire physical examination through video call ( Hjelm, 2005 ). Telemedicine is integrated in healthcare systems since the early 1990s by both developed and developing countries through many ways. For example, integration of wireless oscillometric home blood pressure monitoring with smartphone technology in the United States demonstrated a greater improvement in blood pressure control ( Ciemins et al., 2018 ). In addition, BP readings are not uploaded by the patient but uploaded automatically at the time of measurement eliminating the probability that patients might alter or select the BP readings.

In Bangladesh, they assessed the application of a Portable Health Clinic. This system involves three phases: first the personnel has to register to get an ID; second, get examined by physician and the checkup data automatically sent and saved in a Portable Health Clinic server. Finally, the computer analyzes all the data and categorizes the individual health conditions into four categories (healthy, caution, affected, and emergency). The patient in need was connected audio-visually by a physician located in the clinic ( Nakashima et al., 2013 ).

Another example is the application of Telemedicine in the Eastern Province of Saudi Arabia, the e-Health Center established in the King Faisal Specialist Hospital & Research Centre (KFSH&RC) in Riyadh. The e-Health Center was implemented in order to facilitate access to medical consultations and to spread healthcare educational activities. The percentage of adopting telemedicine in Saudi Arabia is low. This was attributed to the lack of knowledge about the concept of telemedicine and its application and benefits. The most frequently cited challenges besides the lack of knowledge are the difficulty in applying telemedicine due to financial concern and weak infrastructure and the lack of time to adopt telemedicine ( El-Mahalli et al., 2012 ).

Automated Dispensing

An area of development in healthcare system is the application of automated dispensing in hospitals in order to improve the medication use process and to reduce medication errors. Automated dispensing involves the use of automated dispensing cabinets (ACDs). ACDs are computer-controlled devices providing secure storage units; access to the medication is restricted to holders of electronically readable keys or by fingerprint ( Pharmaceutical dispensing cabinet., 1979 ). Automated dispensing has been shown to reduce the incidence of dispensing errors, improve the efficiency and speed of the dispensing process, provide a more secure storage for narcotics, reduce the space occupied by drug storage, and reduce workload on pharmacists and nurses ( Fitzpatrick et al., 2005 ; Chapuis et al., 2010 ). However, nurse attitude toward automated medication dispensing may affect its effectiveness despite the implementation of a well-designed system ( Novek et al., 2000 ). Also, ACDs still account for 15% of dispensing errors, although ACD requires pharmacist review of drug orders before drug access; the devices have an “override system” option in case of emergency, which can be misused by inadequately trained persons. Alphabetic drug pick list feature may contribute to dispensing errors, with nurses confusing between two drugs with similar spelling, or similar appearance, if present in the same drawer. Also, concentration errors occur between adult and pediatric doses for the same medications stored in AC ( Paparella, 2006 ; Gaunt et al., 2007 ).

The following devices are examples of automated dispensing.

The Pyxis Medstation, an automated device operating like bank-teller machines placed in nursing units and linked to the hospital computers. All drug orders and patient file transferred on the device are readable by authorized persons. This system is designed to record all transactions and charges that are automatically loaded on patient bills ( Lee et al., 1992 ; Borel and Rascati, 1995 ).

The McLaughlin dispensing system, a bedside locked dispensing device, loaded with medications for every patient and programmed to unlock automatically at an appropriate time of administration ( Barker et al., 1984 ).

United Arab Emirates has launched robot auto-dispensing pharmacy at Al Fujairah Hospital in early 2017, Nadd Al Hamar Health Center, two smart pharmacies at Rashid Hospital in early 2018, another in Latifa Hospital, and one in Dubai Hospital. The robot at Al Fujairah Hospital can dispense up to 2,000 of pharmaceutical packs/hour and can store up to 45,000 medications at one time. Also, it can follow the expiry date of medications and give a clear image about monthly and annual needs and consumptions. The robot has saved patients’ waiting time and also saved more time for pharmacists for consultations and instructions rather than dispensing ( Dubai’s “Smart Pharmacy” robot auto-dispenses medicine in two minutes | News | Time Out Dubai, 2019 ; UAE Ministry of Health & Prevention Issues a Report on Robotic Pharmacy Project Achievements, 2019 ).

Our search didn’t retrieve any information about the impact of automated dispensing in LMICs, which may be due to the high cost needed to implement this kind of technology.

Barcode Medication Administration

Barcode medication administration consists of handheld device scanning patient identification wristbands and medication barcodes. This is connected to medication records allowing providers to verify appropriateness of medication before administration. If the scanned medication doesn’t correspond to the physician order, the device generates a warning sound or light ( Koppel et al., 2008 ; Morriss et al., 2009 ). Barcode medication administration ensures the five rights of medication administration: right patient, drug, dose, route, and time ( Morriss et al., 2009 ). Implementation of medication barcode scanning helps in reducing dispensing errors caused by ADCs ( Gaunt et al., 2007 ) without introducing new types of error ( Seibert et al., 2014 ). As the case with ADCs, the effectiveness of the technology depends on personnel use; also some technical issues, for example, non-readable torn or missed barcodes, malfunctioning scanners, or failing batteries hinder its appropriate use and delay drug administration ( Yang et al., 2012 ).

Integration of barcode administration system to the auto-dispensing pharmacy at United Arab Emirates’ hospitals had achieved zero dispensing errors in 2017 and 2018 ( Dubai’s “Smart Pharmacy” robot auto-dispenses medicine in two minutes | News | Time Out Dubai, 2019 ).

Unfortunately, similar to automated dispensing, no information was found about the impact of barcode medication application on healthcare system in LMICs.

Healthcare Service Status in Egypt

Several organizations and ministries share the organization and decision-making of healthcare systems in Egypt. These organizations include the Ministry of Health and population (MoHP), Health Insurance Organization (HIO), Curative Care Organization, and Educational Hospitals Organization ( Farid, 2017 ). Hospitals in Egypt are classified into public and private. The public hospitals are classified into three types: university hospitals, health insurance hospitals, and MoHP hospitals ( Eldin et al., 2013 ). Healthcare in Egypt faces many problems including the quality of healthcare services, the increase in out-of-pocket expenditure for those services (which has reached 55% of total spending), growing population, and the lack of updated infrastructure and trained personnel ( Farid, 2017 ). Only half of the Egyptians have health insurance. In 2014, the per capita expenditure on health sector reached $178 annually. The government is required to spend at least 3% of gross domestic product (GDP) on healthcare ( UPR Briefing, 2014 ). A new health insurance law is drafted to ensure a more comprehensive system of health services ( Farid, 2017 ).

Health Technology in Egypt

Egypt has made a progress in providing an ICT infrastructure and legal framework ( Hussein and Khalifa, 2012 ). The WHO eHealth profile of Egypt published in 2011 described the progress of eHealth applications in Egypt ( World Health Organization (WHO), 2010 ). The main eHealth foundation actions taken in Egypt include developing supportive eHealth policies and providing sufficient funding and developing the ICT infrastructure. The Egyptian government together with the MoHP and the Ministry of Communication and Information Technology (MCIT) worked on eHealth programs to provide better health services to the Egyptians. These programs include ( Hussein and Khalifa, 2012 ; Khedr and Alsheref, 2014 ):

● National Network for Citizen Health: It aimed to develop central treatment management, direct patients to different therapeutic units, develop information system and databases of the central department for citizen health treatment, and connect all peripheral departments by a virtual private network.

● Emergency Medical Call Center and Ambulance Center: The MoHP and MCIT cooperated to establish the Emergency Medical Service (EMS) in Greater Cairo and provide the ambulance service with a computerized ambulance dispatch system.

● Information System Units in public hospitals: It aimed to establish information system units in 700 hospitals nationwide. The units are used to facilitate patients’ registration, financial and administration operations, and training medical staff.

● National Healthcare Capacity Building Project: This project aimed to create a pool of competent healthcare professionals by providing training to MoHP staff. The training program included: Basic Information Technology (IT) skills, Biomedical Informatics Professional Training, and biomedical awareness. The project would also include a central unit that links hospitals and keeps medical records.

● National Cancer Registry Program: It utilizes state-of-the-art data mining technologies to find health indicators for investigating reasons of the cancer spreading. The city of Aswan was selected initially to be enlisted in the program.

● National Picture Archiving and Communications System (PACS) Project: Launched in 2010 and aimed to develop a centralized database PACS and Radiology Information System (RIS) that integrates clinical images and scanned documents into the patient’s EMRs. A local database was developed in Fum El Khalij (the main Center of Excellence) and the eight hospitals included in the project.

Other programs include the following: Pilot Project for Hospital Automation, IT Health Master Plan, Integrated Health Record System, and Regional Center for Women’s Health in Alexandria ( Hussein and Khalifa, 2012 ).

Other eHealth Applications in Egypt

Electronic health records.

Over the last years, the government tried to implement EHR nationwide. However, some of these trials failed for several reasons. These trials were as follows ( Abdelgaber et al., 2017 ):

a- First trial: An agreement was made between Data Management System Company and HIO to apply EHR and hospital administration systems. It was implemented initially in Suez hospital, but was stopped in 2010 for financial and political reasons.

b- Second trial: An agreement was made between Siemens Company and HIO to conduct EHR. It was implemented initially in Abu El-Rish children hospital. The system had some technical and administrative barriers and was stopped in 2010 for financial reasons.

c- Third trial: In this trial the Egyptian HealthCare Accreditation Program was made to make all hospitals accredited over time. Also medical records conduction was a condition for accreditation. However, it was not a condition to implement it electronically. But, it is still a good standardized start. Currently, four hospitals were accredited by MoHP and many others are working on it.

According to Khedr and Alsheref, the following public hospitals contain EHR systems: Ain Shams University Hospital, Kasr Al Ainy French Hospital, National Cancer Institute, and Nasser Institute Hospital; as well as the following private hospitals: Al Salam Al Dawly Hospital, Al Salam Hospital, and El Ganzouri Hospital. Those systems contained patient demographics, financial reports, appointments, lab data, and pharmacy data as databases. Those systems had the following limitations: lack of standardization, no system for nursing, physician examination, nor radiology; the lab systems and patient databases were not connected ( Khedr and Alsheref, 2014 ).

Limitations for Applying EHR in Egypt and Future Recommendations

A questionnaire-based study on 50 hospitals showed that only around 7% adopted EHR system ( Eldin et al., 2013 ). The main barriers for system adoption were costs (81%), lack of technical support (44%), and lack of standards (44%). In hospitals that adopted EHR, the main impacts were increasing quality of service (100%), improving performance (67%), and increasing physicians’ time efficiency (50%).

A Strengths, Weaknesses, Opportunities, and Threats (SWOT) analysis made by Abdelgaber et al. showed the main barriers for EHR implementation in Egypt. Those included high cost, lack of clear time plan, lack of IT infrastructure, technical ability and training, lack of standardization, privacy concerns, and resistance for new technology ( Abdelgaber et al., 2017 ).

Implementation of EHR in Egypt will improve quality of healthcare, decrease medication errors, facilitate the coordination between healthcare professionals and settings, and provide a fast access to patient information ( Thakkar and Davis, 2006 ). In order to apply an EHR nationwide network, we need to establish specialized EHR Management Board and EHR training authority, find a source of funding, set up the security criteria and accreditation procedures, and announce the benefits of EHR to the society ( Abdelgaber et al., 2017 ).

Telemedicine

According to the MCIT, by the end of February 2018, there were 100.24 million mobile subscribers (total Egyptian population is 99,414,614) while the mobile penetration rate was 110.19%, and 37.9 million internet users while the internet penetration rate was 44.3% ( Ministry of Communications and Information Technology – Egypt, 2018 ). Egypt has progressed in committing to intellectual property rights agreements, creating the National Telecommunications Regulatory Authority (NTRA) and issuing communications laws for liberalizing the communications sector in Egypt. The Egyptian Information Society worked on providing various applications for government, education, business, and healthcare ( Hussein and Khalifa, 2012 ).

In the past years, some telemedicine projects have been implemented. Those include:

● Arab-African Telemedicine Network Initiative: Launched in 2002 by International Telecommunication Union (ITU). It aimed to form a multi-country telemedicine network between participating countries to prevent and treat diseases. The expected participating countries included Egypt, Jordan, Libya, Morocco, Ethiopia, Tunisia, Sudan, Mali, and Uganda. The first phase aimed to connect two medical centers in US and Europe ( Hussein and Khalifa, 2012 ).

● Inter-hospital tele-consulting project between Palermo and Cairo: Launched in 2002 by ARNAS-Civic Hospital of Palermo and Italian Hospital Umberto I in Cairo. It aimed to establish a network for tele-consulting and exchanging data of complicated cases between the two hospitals ( Hussein and Khalifa, 2012 ).

● Egyptian Telemedicine Network (ETN): Launched by MoHP and MCIT in 2006. The first phase consisted of seven telemedicine units. Those units provided many healthcare services as radiology and ECG. In 2007, two mobile units were used for screening breast cancer. However, after 1 year, the project faced many financial, legal, and technological problems ( Sultan, 2006 ).

● Women HealthCare Mobile Unit Project: Launched in 2007 by the MoHP and MCIT to screen ladies over 45 years by mobile and fixed digital mammography imaging units for free. The RIS is used for registering the patients’ demographics. Then, the mammograms are sent to the national radiology center of excellence. The radiologists there use PACS and RIS for diagnosis and reporting to the units ( Eldin et al., 2013 ). By the end of 2010, the project consisted of 10 mobile units and 11 fixed units. Each unit was planned to receive 50–80 cases per day. Till today, more than 60,000 cases were checked.

● TeleMedic @ Egypt Project: Launched in 2009, by the Information Technology Institute (ITI)–MCIT. It was funded by the Holding Company for Biological Products and Vaccines—VACSERA (Egypt), the Research, Development and Innovation Program (RDI), and the Institute for Biomedical Engineering IBMT (Germany). It aimed to provide tele-consultation services for infectious diseases in unequipped areas. The project is conducted in the Family Care Unit of Mahsma in Ismailia and provides preventive, diagnostic, and public health services ( Hussein and Khalifa, 2011 ).

● Pan Africa Project: Launched in 2009 by the MCIT, Alexandria University, Telecommunications Consultants India Limited and the regional center for health development. Video-conference sessions were held between 12 hospitals in India and the healthcare organizations in Alexandria to provide tele-consultations. Also, the project provided eLearning programs ( Hussein and Khalifa, 2012 ).

● Saving children through tele-consultation in remote Egypt: This was a 1-year pilot (2009–2010) conducted by MCIT, the private sector in Egypt, the technical capacity of the Child and Adolescent Health Unit (CAH) of WHO-EMRO, and UNDP development knowledge of the country. It connected the Pediatric Department of the El Shatby Hospital–Alexandria and Siwa main hospital. It aimed to provide tele-consultation services and eLearning of physicians. Forty-five children were helped by this project ( El Tokali et al., 2009 ).

There have been other trials for launching telemedicine applications by different parties. For example:

● In 2008, a paper by Elgharably et al. presented the prototype of a system that allows physicians to monitor and diagnose patients especially the elderly over the Internet. A patient could measure his vital signs through a module connected wirelessly via Bluetooth to his computer and then send his data through the internet or through an SMS to be saved on a database in the healthcare organization. At any time the doctor could access the data of his patient, chat with him, or consult with another doctor. The system needs further work to improve its functionality and to be able to manufacture. This will necessitate making it integrable with HIS, PACS, and RIS ( Elgharably et al., 2008 ).

● In 2016, mDiabetes program was launched as a part of a larger mHealth program for chronic non-communicable diseases. It was run by a partnership between the MoHP, MCIT, and Ministry of Higher Education and Scientific Research (MHESR), WHO, and the ITU, known as Be He@lthy Be Mobile. This initiative aimed to use mobile technology to minimize illness and reduce the social and economic burden due to non-communicable diseases. Participants in the program would get regular SMS on lifestyle choices and steps about how to manage their diabetes. The first phase was planned to send 10,000 SMS. A second phase, later in 2016, was planned to cover a larger number of diabetics. It was planned in a later phase to send SMS on methods for diabetes prevention for the general population. Also, it was planned to provide a two-way interactive SMS service where recipients can select the information they need ( WHO.EMRO, 2016 ).

● In 2017, Heshmat et al. proposed a prototype mobile application called (YourClinic). It can be used through smartphones by patients, physicians, and nurses in outpatient clinics. Patients can use it to search for their preferred clinics and then select the preferred day and time. Also the patient can easily submit his medical complaints and an online consultation will be done for him. Physicians can use the application to know their future schedule for the next week. The nurse then coordinates with the physician and the patients. The expected impacts of the application upon patients, doctors, and nurses are decreasing waiting time, patients stress, and healthcare staff working hours. As a future work, a business model for this application will be developed ( Heshmat et al., 2017 ).

Currently, there is another mobile application used as a platform for reserving consultation appointments in outpatient private clinics called Vezeeta for Doctors. It was downloaded by more than 100,000 users ( Google play store, 2018 ).

Limitations for Applying Telemedicine in Egypt and Future Recommendations

Although Egypt has done many telemedicine projects, the majority of them face many challenges such as: lack of patients’ awareness and acceptance of receiving healthcare services via telemedicine applications, shortage of both financial and legalization frameworks, and lack of capacity building programs ( El Tokali et al., 2009 ).

Telemedicine can be useful in providing medical consultations, diagnoses, getting second medical opinion across distances, and providing e-learning for healthcare professionals. It can save patients and physicians travel, time, and money especially in rural areas ( Ouma and Herselman, 2008 ). Fortunately, there are many enabling factors for the provision of telemedicine in Egypt: the presence of an effective ICT infrastructure all over the country, the concept of telemedicine has been proven through MoHP/MCIT cooperation in different eHealth projects, the wide use of mobile technologies, and the availability of both ICT and healthcare capacities ( Hussein and Khalifa, 2012 ). Based on the ITU studies performed in 2008 ( International Telecommunication Union ITU, 2008 ) and WHO studies performed in 2010 ( World Health Organization, 2010 ), the recommendations to facilitate the telemedicine application in Egypt include:

1. developing the needed legislative and administrative frameworks,

2. designing long-term strategic plans to develop eHealth services,

3. adapting a national policy for eHealth,

4. fund research projects in telemedicine with international partners,

5. establish national programs for training healthcare professionals on eHealth solutions

Future Plans

No real development and progress can be achieved in the healthcare sector without a strong national ICT sector to lead the necessary change ( MCIT, 2018 ). Planning for a new ICT strategy in Egypt is a very promising step towards better healthcare status. New initiatives and development of communications on both national and international levels will be adopted by the new ICT 2030 strategy. Capacity building, designing electronics, and manufacturing are included in the new strategy aiming to maximize the contribution of ICT ( MCIT, 2018 ).

Health technologies can have a great role in solving the global challenges facing the healthcare systems. Worldwide, healthcare environment is changing due to shortage of healthcare professionals, the growth in chronic illness, and limited resources. Using eHealth applications will help us cope up with that change and will provide patient-focused healthcare and personalized medicine. In Egypt, various eHealth applications have been developed. However, they are still not optimally deployed due to many challenges as financial limitations and lack of ICT infrastructure and national eHealth policy. Therefore, action needs to be started by developing legislative frameworks, adapting a national policy for eHealth and funding research projects. This paper aims to shed the light for the Egyptian policy makers on how new technologies can impact healthcare so that we can start strengthening their quality, releasing new innovations, and paving the way to overcome any challenges.

Author Contributions

SF fulfills the criteria of authorship and she is the sole author of this paper. She was responsible for the research design, data collection and review, writing and revising the paper, and giving the final approval of the version to be submitted.

Conflict of Interest Statement

The author declares that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Abbreviations

ICT, Information and communication technology; NCDs, Noncommunicable diseases; HER, Health electronic records; ADCs, Automated dispensing cabinets; PACS, National Picture Archiving and Communications System project; RIS, Radiology Information System.

Acknowledgments

The author wishes to acknowledge the efforts made by Eglal Adel Bassiouny, MSc, and Hend Kamal Maamoun, MSc, Assistant Lecturers of Clinical Pharmacy, Faculty of Pharmacy, Cairo University, Egypt, and Mona Sobhy Gaber, BSc, and Sandra Nael Naguib, BSc, Teaching Assistants of Clinical Pharmacy, Faculty of Pharmacy, Cairo University, Egypt.

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Keywords: healthcare system, eHealth, health technologies, mHealth, electronic health records, telemedicine, e-prescribing, technology-enabled pharmacy

Citation: Farid SF (2019) Conceptual Framework of the Impact of Health Technology on Healthcare System. Front. Pharmacol. 10:933. doi: 10.3389/fphar.2019.00933

Received: 26 September 2018; Accepted: 22 July 2019; Published: 03 September 2019.

Reviewed by:

Copyright © 2019 Farid. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY) . The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

*Correspondence: Samar F. Farid, [email protected]

Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.

Top five research articles of 2020

Despite the significant challenges this year has posed, The Pharmaceutical Journal  has continued to publish high-quality peer-reviewed research.

Our researchers have made a range of investigations — from evaluating pharmacist interventions using the Simpler tool in Malaysia , to a pharmacist-led virtual thiopurine clinic to support people with inflammatory bowel disease and auto-immune hepatitis, here in the UK.

We have some exciting research coming up in 2021, but in case you missed them the first time around, here are the top five most popular research articles of 2020:

5. Misuse of prescription and over-the-counter drugs to obtain illicit highs: how pharmacists can prevent abuse

Use of prescription and over-the-counter drugs for recreational purposes is increasing, and this perspective article collates the existing literature to provide an in-depth overview of the misuse and diversion of a range of drugs with psychoactive potential, including gabapentinoids, antihistamine drugs and loperamide.

4. Effective detection and management of hypertension through community pharmacy in England

Community pharmacists can play a big role in managing hypertension — from the identification of medication-related problems, to providing lifestyle advice. Despite this, they are not routinely involved in structured hypertension management or screening programmes. So, this review summarises the evidence to recommend the roll-out of a community pharmacy-led hypertension management service.

3. Recent advances in the oral delivery of biologics

Oral administration of medicines is often preferred by patients for its convenience, but, for biologics, the gastrointestinal tract poses challenges for administering in this way. This review discusses the advantages and limitations of several novel drug delivery strategies, and highlights the work to be done to put this technology into clinical practice.

2. Immuno-oncology agents for cancer therapy

Immuno-oncology is a novel treatment that works by conditioning the body’s immune cells to recognise and kill cancer cells — combining this treatment with conventional therapies has led to promising improvements in patient outcomes. This review looks at the range of immuno-oncology agents, and how problems such as their toxicity and high cost can be overcome.

1. Investigational treatments for COVID-19

The emergence of COVID-19 resulted in a global research effort to find effective treatment options to relieve healthcare burdens and, ultimately, save lives. In June 2020, this rapid review summarised the clinical trials and treatment evidence at the time.

Check out The Pharmaceutical Journal’ s   ‘Everything you should know about the coronavirus outbreak’ for the latest on this continually evolving situation.

Find the full catalogue of articles in our research section .

Call for submissions

In 2021, The Pharmaceutical Journal will keep adding to the evidence base with review, perspective and research articles. If you have undertaken research into innovations and initiatives that can improve pharmacy services and administration, the pharmacological management of disease, or advances in drug development, please submit your article for consideration by email to: [email protected]

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111 Pharmacy Essay Topics

🏆 best essay topics on pharmacy, 🌶️ hot pharmacy essay topics, 👍 good pharmacy research topics & essay examples, 💡 simple pharmacy essay ideas, 🎓 most interesting pharmacy research titles.

• Pharmacy as a Professional Field and Its History
• Pharmacy: A Career Pathway
• Asthma: Pharmacology and Medicines Management
• The Competitiveness of Saudi Pharmaceutical Industry
• Pharmaceutical Companies and Utilitarian Ethics
• Reverse Logistics in Pharmaceutical Industry: Handling Products Back to the Manufacturer
• Virginia State Board of Pharmacy vs. Virginia Citizens Consumer Council
• Distribution Chain in the Pharmaceutical Industry International pharma trade is subject to government regulations, limiting the distribution of exported raw materials to ensure safety and prevent contamination.
• Database Design Proposal for Pharmaceutical Products Project is crucial for healthcare professionals to realize all the DDIs and avoid prescription errors. The database is useful for hospital managers to control substance use.
• CVS Pharmacy’s Strategic Plan of Expansion The strategic plan of CVS Pharmacy includes opening its stores worldwide, meaning going out of the USA borders.
• Pharmaceutical Advertising is Propaganda This paper has shown through a discussion that pharmaceutical advertising can be propaganda or informative and helpful to the life of a patient.
• CVS Pharmacy Inc. SWOT Analysis The paper is a SWOT analysis of CVS Company to understand the various factors, both internal and external, that affect its operations and how they could be used.
• Retail and Supply Chain in the Pharmaceutical Industry Retail is the result of work to optimize B2B strategy and logistics. The pharmaceutical industry has become a classic business that increases sales and revenue from customers.
• Pharmaceutical Industry: Drug Development Drug development is a lengthy process but rightly so since the result should be playing a curative role and not disease inducer.
• Ranitidine Medication’s Pharmaceutical Analysis Ranitidine has been shown to be an effective treatment for DUs and GUs, GERD, Zollinger-Ellison syndrome, and pyrosis when used at appropriate dosage levels and frequency.
• Pharmacology and Influence of Antibiotics Throughout the essay, both the fundamental conceptual concepts of science and the science-based properties of drugs are described.
• Pharmaceutical Supply Chain Management: Operational Plan Due to the specificity of its activity, a healthcare organization tends to require a pharmaceutical supplier which provides medicines to be vended in the facility.
• PharmaCARE: Product Safety & Intellectual Property Using the case of PharmaCARE, this paper discusses legal and ethical considerations in marketing and advertising, product safety, and intellectual property.
• Drug Testing in Pharmacology The aim of this paper is to analyze and review drug tests within the population of third-world countries and define whether these trials are ethical.
• Researching Retail Pharmacy Retail pharmacies are on the leading edge of rendering health care services to patients after the pharmacist has filled the consumer’s drug.
• Pfizer Pharmaceutical Company and Its Market Relations The main aim of the Pfizer Company is to make the medicine accessible to all, and it works in this direction.
• Animal Use in Pharmacology: Negative Effects on Humans and Animals The use of the animal to develop drugs for humans may result in the manufacture of harmful medicines. The substances used to manufacture the drugs have adverse impacts on animals.
• Pharmacology Transcribe: Explore More The take away for this teachable explore more is that I want you to remember medications that are on medication list, and what are they for.
• Good Manufacturing Practices for Pharmaceuticals Creating conditions for the safe production of pharmaceuticals is a practice that has evolved significantly due to the introduction of modern approaches to the manufacturing process.
• Outsourcing in the Pharmaceutical Industry Technology is the powerful force that now drives the world toward a single converging commonality. No place and nobody is insulated from the alluring attractions of modernity.
• Pharmacology: Drug Licensing Opportunity Obtaining a license for a new drug is a very costly and time consuming affair. Any pharmaceutical company would have to weigh all its options before embarking on such a process.
• Evidence-Based Pharmacology: Major Depression In this paper, a certain attention to different treatment approaches that can be offered to patients with depression will be paid, including the evaluation of age implications.
• Toxicological Evidence in Forensic Pharmacology Forensic toxicology entails the analysis of stains and drugs found in fluids and solid materials collected from a crime scene. Numerous methods are used in a toxicological analysis.
• Comparison of the Pharmacy Laws This paper aims to compare two laws related to the operation of pharmacies – the Drug Supply Chain Security Act (DSCSA) and the Texas Pharmacy Act.
• Importance of Compliance Procedures in Pharmacy Compliance procedures have been instituted in the pharmaceutical industry as a measure to foster professionalism in the practice of pharmacy and also to ensure public safety.
• New Pharmaceuticals and Their Path to the Market When a new pharmaceutical is invented, several steps need to be taken to bring it to the market. Effective marketing is preceded by primarily drug development and manufacturing.
• Pharmaceutical Industries: Changes and Challenges Pharmaceutical industries are responsible for the manufacture of drugs. Like any other industry that we know, they aim at making profits.
• The Environmental Condition of the Global Pharmaceutical Industry Pharmaceutical industry presently undergoes a sluggish growth with the intensification of pricing policies, sluggish growth of prescription drugs.
• Using Testing as a Learning Tool: Pharmaceutical Education The presentation about medication errors was introduced to new nurses and nursing students at the progressive care unit of the local hospital in Dallas.
• How Pharmaceutical Patents Create a Monopoly A breakdown of reasons why pharmaceutical corporations deserve patent rights, alongside potential negative effects associated with them, form the basis of this paper.
• Flagyl ER: Pharmacological Characteristics Flagyl ER is one of the medications that use metronidazole as an active component for the treatment of bacterial, parasitic, and protozoal infections.
• Metformin in Pharmaceuticals and Medicine Metformin stimulates glycogen formation and improves the transmission capacities of all varieties of membrane glucose carriers by interacting with glycogen synthase.
• Pharmacology Research: Cyclophosphamide The paper discusses Cyclophosphamide. It is a widely used chemotherapeutic prodrug that treats different types of cancer in a wide range of patient populations.
• Cloud Technology Innovation in Pharmaceutical Company Digital technology facilitates the storage of records and access to databases, but with each passing year, using physical hard drives becomes less efficient.
• Fibromyalgia Pharmacological Management Antidepressants and anti-seizure medications can be more effective for fibromyalgia treatment. It is vital to know about the side effects of medications to ensure patients’ safety.
• Why Pharmaceutical Industry Is High-Tech and Knowledge-Intensive Pharmaceuticals is one of the most high-tech and knowledge-intensive industries in the global economy, which is determined by three groups of interrelated factors.
• The Profound Knowledge of Pharmacology The successful absorption of the drug depends on various aspects. The profound knowledge of pharmacology allows for gaining more rapid treatment results.
• Acupuncture vs. Standard Pharmacological Therapy for Migraine Prevention The current paper aims to compare the efficiency of managing migraines by employing acupuncture and pharmacotherapy clinical processes.
• Merck’s Pharmaceutical Company Ethical Dillema The dilemma raised by Merck’s management is defined by two outcomes: the pharmaceutical company invest money in unsound project or have abandoned because of disproportionality.
• Unpatented Pharmaceuticals for American Public The pharmaceutical industry is greatly influenced by the registration of intellectual property rights for a product that has been manufactured.
• Acupuncture vs. Standard Pharmacological Therapy for Migraine Prevention “Systematic Review: Acupuncture vs. Standard Pharmacological Therapy for Migraine Prevention” is a study conducted by Zhang.
• Microeconomics Case Analyses in Pharmacology This paper examines the pharmaceutical industry using the theory and models of industry structure and Pfizer’s make-or-buy decision for developing and producing its COVID vaccine.
• Aspirin: Vascular Pharmacology Aspirin is one of the most used medications worldwide, with its history going back to 1897. It is a plant-based drug made out of salicylic acid.
• Interaction of the Pharmaceuticals with Alcohol Intake It is important to establish the key value of healthy living based on the interaction with the pharmaceuticals and alcohol intake to avoid developing a dependency on the elements
• Advanced Pharmacology: Arthritis Treatment Arthritis is more regular among aging adults, though it can be diagnosed in any other person irrespective of age, including children.
• Marriage and Family Therapy and Pharmacological Treatment The notion of marriage and family counseling presupposes a sophisticated process during which professionals are to adopt an integrative approach to the therapy.
• Major Depressive Disorder: Pharmacological Treatment SSRIs are effective first-line treatment for MDD. This class of medications includes many antidepressants with comparable effectiveness in treating this disorder.
• Post-Traumatic Stress Disorder: Pharmacological Treatment Approved medications can help treat PTSD symptoms and improve patient outcomes. SSRIs, such as sertraline, have been shown to reduce anxiety and increase concentration.
• Economics for Pharmaceutical Companies The paper discusses pharmaceuticals. They are an industry that is doing well financially due to the patents and exclusive rights they enjoy due to their developments.
• Pharmaceutical Science: Vicodin The aim of report on the drug known as Vicodin to highlight the truth in relation to its position and verify the truth behind claims made in reports such as Herper’s.
• Drug Release: Ethical Dilemma in Pharmaceutics A moral issue has emerged as to whether a pharmaceutical company has to release a new drug or not. This drug is thought to be an effective treatment of depression.
• Pharmacology: Uses of Albuterol and β2-Adrenergic Agonist This paper is aimed at reviewing research articles aimed at studying the use of albuterol and β2 adrenergic receptor agonist and defining the optimal frequency of its usage.
• National Pharmacy Technician Association One of the biggest global certified associations for pharmacy technicians is the National Pharmacy Technicians Association. This association was established in Houston, Texas.
• Accessing the Pharmacy Services: Safe Medication When receiving medication from a pharmacist, it is important to be aware of the extent of the pharmacist’s competency and their knowledge of the subject matter.
• Pharmaceuticals in the U.S.A. Analysis The purpose of the paper is to discuss the accessibility of medications to the population in the USA, their prices.
• Medical Pharmacology: Noradrenaline Effect on Vascular Rings Noradrenaline is a hormone produced as a catecholamine by the sympathetic neurons from the heart; it is mainly used as a neurotransmitter.
• Pharmacogenetics in Clinical Practice The improvements in the understanding of the effect of genetic differences on interpersonal variability in drug response contributed to the development of pharmacogenetics.
• Purdue Pharmaceutical Company’s OxyContin Opioid The adverse effects of OxyContin presuppose the development of addiction, deterioration of the overall state, and even death.
• Certification, Licensure, and Registration of Pharmacy Technicians The rules for certification, licensure, and registration of pharmacy technicians will be discussed in terms of the differences among these procedures with a focus on Texas laws.
• Genetics or New Pharmaceutical Article Within the Last Year Copy number variations (CNVs) have more impacts on DNA sequence within the human genome than single nucleotide polymorphisms (SNPs).
• Heath Care – Impact on Pharmaceutical Companies The signing of the Patient Protection and Affordable Care Act will demand that the Pharmaceutical industry align their practices within the guidelines of this legislation.
• Pharmaceutical Industry: Effective Market Strategy Effective market strategy greatly determines the successful performance of a business. A marketing strategy in the pharmaceutical industry.
• Ethics in Medical and Pharmaceutical Industry Ethics in the medical and pharmaceutical industry is a vital component of providing quality services and developing products that will benefit the patients.
• Disease Pathology, Management, and Pharmacological Impact for Tularemia and Hantavirus The purpose of this paper is to describe disease pathology, management, and pharmacological impact for Tularemia and Hantavirus.
• Veteran Pharmaceutical: Cause and Effect Due to the economic crunch being experienced all over America and the whole world at large, there has been a decline in profits for Veteran in the last few months.
• Cialis Production: Pharmaceutical Review The case relates to a firm that is in the process of innovating and launching a new drug with the brand name Cialis in the market. The drug is aimed at treating impotence in men.
• System Approach to Organizational Change: Pharmacy Automation As per the discussion and analysis in the paper, it will be clear that the automation and networking in a pharmacy enables to expand its customer base thus increasing the business.
• The Concept of Pharmacogenetics: Brief Analysis The present paper includes a brief analysis of the concept of pharmacogenetics, that is the study of people’s genetically determined responses to some drugs.
• Medical Pharmacology: The Langendorff Experiment The Langendorff experiment aimed at using an ex vivo isolated rat heart preparation to demonstrate the pharmacological effects of two unknown drugs.
• Pharmacogenetics Characteristics and Development Pharmacogenetics is a relatively young branch of medical science, but it is supposed to have significant potential when it comes to the effectiveness of treatment methods.
• Vapi Pharmaceutical Firms: Strategies for Toxic Waste Disposal Vapi (India) pharmaceutical companies’ strategies on toxic waste products don’t satisfy and comply with laws and legislation on toxic waste management.
• Computerized Provider Order Entry in Pharmacology Computerized provider order entry is an information system that provides a possibility to digitally enter the patient data and chart.
• PharmaCARE: Ethical and Legal Issues The case of PharmaCARE entails a scenario of manipulating the intellectual property rights responsible for safeguarding the production rights of PharmaCARE.
• National Pharmacy: Mobilising Creativity and Innovation This paper is focused on utilising innovation and creativity theoretical models to improve the work environment at the National Pharmacy L.L.C.
• Pharmacy and Policy: Inappropriate Prescription of Drugs It is essential to develop a policy that would enable to reduce the practice of multiple drug prescriptions and eliminate excess financial and health costs associated with it.
• The UK Pharmaceutical Industry: International Business This article will discuss the international business opportunities and risks faced by the pharmaceutical industry in the UK.
• Lack of Leadership in Pharmaceutical and Medical Companies This document concentrates on pharmaceutical and medical companies. It describes and expounds the unethical instances that these companies encountered in the course of their activities.
• CVS Company’s Pharmacy Fulfillment Process The current fulfillment process at CVS seems to be overly complicated of the entrepreneurship to function efficiently and make sure that the customers’ needs are met adequately.
• Employee Engagement in Pharmacy Services Employee engagement illustrates willingness and desire of employees to give their best and outperform themselves daily, motivated to contribute to organizational success.
• Caffeine Use in Medicine and Pharmacy Caffeine is used is increasingly becoming popular. The authors of the published research article are distinguished researchers in the field of medicine and pharmacy.
• Turing Pharmaceuticals’ Unethical Price Hikes Turing Pharmaceuticals received so much media attention due to an overnight increase in the price of the drug Daraprim from $13.50 per pill to $750.
• Pharmacy Technician Career: Programs That Can Help People to Become a Good Pharmacy Technician It is possible to outline some existing programs which can help a person to become a good Pharmacy Technician in Oklahoma.
• PharmaCare Company Ethical Issues This paper presents a case study of PharmaCare, which is one of those companies that have been victims of ethical issues. It will consider the emerging marketing strategy.
• PharmaCARE Company Analysis: Stakeholders and Practices PharmaCARE is one of the leading pharmaceutical companies in the world, which has made a significant contribution to the development of drugs and treatment of diseases.
• Pharmacare Company Ethic and Corporate Responsibility This paper evaluates the ethical and corporate responsibility issues that arise in the scenario presented involving Pharmacare: ethical treatment of employees and whistle blowing.
• Deregulating the Pharmacy Market: The Case of Iceland and Norway
• Margins and Market Shares: Pharmacy Incentives for Generic Substitution
• Mission and Target Market of Rite Aid Pharmacy
• Pharmacy: Where Serving Others Is the Key to Success
• Weighing, Measuring and Compounding in Pharmacy
• Bringing the Gap That Exists in Pharmacy Communications
• Why Is the Pharmacy Profession Not Just a Four-Year Commitment?
• Pharmacy: Medicine and Motivating Factors
• Hospital Pharmacy Decisions, Cost Containment, and the Use of Cost-Effectiveness Analysis
• Young Mothers and the Marketing of Pharmacy Services
• Overview of Intranet Pharmacy Information Systems
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• Strategic Marketing Management for Boots the Pharmacy
• Tools for Hospital Pharmacy Process Improvement
• Chemical Principles for Pharmacy Technicians
• Why Did Walgreens Eventually Become America’s Leading Pharmacy?
• Strategic Risk Management for Llyods Pharmacy

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Researchers detect a new molecule in space

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New research from the group of MIT Professor Brett McGuire has revealed the presence of a previously unknown molecule in space. The team's open-access paper, “ Rotational Spectrum and First Interstellar Detection of 2-Methoxyethanol Using ALMA Observations of NGC 6334I ,” appears in April 12 issue of The Astrophysical Journal Letters .

Zachary T.P. Fried , a graduate student in the McGuire group and the lead author of the publication, worked to assemble a puzzle comprised of pieces collected from across the globe, extending beyond MIT to France, Florida, Virginia, and Copenhagen, to achieve this exciting discovery. 

“Our group tries to understand what molecules are present in regions of space where stars and solar systems will eventually take shape,” explains Fried. “This allows us to piece together how chemistry evolves alongside the process of star and planet formation. We do this by looking at the rotational spectra of molecules, the unique patterns of light they give off as they tumble end-over-end in space. These patterns are fingerprints (barcodes) for molecules. To detect new molecules in space, we first must have an idea of what molecule we want to look for, then we can record its spectrum in the lab here on Earth, and then finally we look for that spectrum in space using telescopes.”

Searching for molecules in space

The McGuire Group has recently begun to utilize machine learning to suggest good target molecules to search for. In 2023, one of these machine learning models suggested the researchers target a molecule known as 2-methoxyethanol. 

“There are a number of 'methoxy' molecules in space, like dimethyl ether, methoxymethanol, ethyl methyl ether, and methyl formate, but 2-methoxyethanol would be the largest and most complex ever seen,” says Fried. To detect this molecule using radiotelescope observations, the group first needed to measure and analyze its rotational spectrum on Earth. The researchers combined experiments from the University of Lille (Lille, France), the New College of Florida (Sarasota, Florida), and the McGuire lab at MIT to measure this spectrum over a broadband region of frequencies ranging from the microwave to sub-millimeter wave regimes (approximately 8 to 500 gigahertz). 

The data gleaned from these measurements permitted a search for the molecule using Atacama Large Millimeter/submillimeter Array (ALMA) observations toward two separate star-forming regions: NGC 6334I and IRAS 16293-2422B. Members of the McGuire group analyzed these telescope observations alongside researchers at the National Radio Astronomy Observatory (Charlottesville, Virginia) and the University of Copenhagen, Denmark. 

“Ultimately, we observed 25 rotational lines of 2-methoxyethanol that lined up with the molecular signal observed toward NGC 6334I (the barcode matched!), thus resulting in a secure detection of 2-methoxyethanol in this source,” says Fried. “This allowed us to then derive physical parameters of the molecule toward NGC 6334I, such as its abundance and excitation temperature. It also enabled an investigation of the possible chemical formation pathways from known interstellar precursors.”

Looking forward

Molecular discoveries like this one help the researchers to better understand the development of molecular complexity in space during the star formation process. 2-methoxyethanol, which contains 13 atoms, is quite large for interstellar standards — as of 2021, only six species larger than 13 atoms were detected outside the solar system , many by McGuire’s group, and all of them existing as ringed structures.  

“Continued observations of large molecules and subsequent derivations of their abundances allows us to advance our knowledge of how efficiently large molecules can form and by which specific reactions they may be produced,” says Fried. “Additionally, since we detected this molecule in NGC 6334I but not in IRAS 16293-2422B, we were presented with a unique opportunity to look into how the differing physical conditions of these two sources may be affecting the chemistry that can occur.”

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Automated machine learning robot unlocks new potential for genetics research

This technology will save labs time and money while enabling large-scale experiments.

University of Minnesota Twin Cities researchers have constructed a robot that uses machine learning to fully automate a complicated microinjection process used in genetic research.

In their experiments, the researchers were able to use this automated robot to manipulate the genetics of multicellular organisms, including fruit fly and zebrafish embryos. The technology will save labs time and money while enabling them to more easily conduct new, large-scale genetic experiments that were not possible previously using manual techniques

The research is featured on the cover of the April 2024 issue of GENETICS , a peer-reviewed, open access, scientific journal. The work was co-led by two University of Minnesota mechanical engineering graduate students Andrew Alegria and Amey Joshi. The team is also working to commercialize this technology to make it widely available through the University of Minnesota start-up company, Objective Biotechnology.

Microinjection is a method for introducing cells, genetic material, or other agents directly into embryos, cells, or tissues using a very fine pipette. The researchers have trained the robot to detect embryos that are one-hundredth the size of a grain of rice. After detection, the machine can calculate a path and automate the process of the injections.

"This new process is more robust and reproducible than manual injections," said Suhasa Kodandaramaiah, a University of Minnesota mechanical engineering associate professor and senior author of the study. "With this model, individual laboratories will be able to think of new experiments that you couldn't do without this type of technology."

Typically, this type of research requires highly skilled technicians to perform the microinjection, which many laboratories do not have. This new technology could expand the ability to perform large experiments in labs, while reducing time and costs.

"This is very exciting for the world of genetics. Writing and reading DNA have drastically improved in recent years, but having this technology will increase our ability to perform large-scale genetic experiments in a wide range of organisms," said Daryl Gohl, a co-author of the study, the group leader of the University of Minnesota Genomics Center's Innovation Lab and research assistant professor in the Department of Genetics, Cell Biology, and Development.

Not only can this technology be used in genetic experiments, but it can also help to preserve endangered species through cryopreservation, a preservation technique conducted at ultra-low temperatures.

"You can use this robot to inject nanoparticles into cells and tissues that helps in cryopreservation and in the process of rewarming afterwards," Kodandaramaiah explained.

Other team members highlighted other applications for the technology that could have even more impact.

"We hope that this technology could eventually be used for in vitro fertilization, where you could detect those eggs on the microscale level," said Andrew Alegria, co-lead author on the paper and University of Minnesota mechanical engineering graduate research assistant in the Biosensing and Biorobotics Lab.

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Materials provided by University of Minnesota . Note: Content may be edited for style and length.

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• Andrew D Alegria, Amey S Joshi, Jorge Blanco Mendana, Kanav Khosla, Kieran T Smith, Benjamin Auch, Margaret Donovan, John Bischof, Daryl M Gohl, Suhasa B Kodandaramaiah. High-throughput genetic manipulation of multicellular organisms using a machine-vision guided embryonic microinjection robot . GENETICS , 2024; 226 (4) DOI: 10.1093/genetics/iyae025

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Research Designs and Methodologies Related to Pharmacy Practice

The need for evidence to inform policy and practice in pharmacy is becoming increasingly important. In parallel, clinical pharmacy and practice research is evolving. Research evidence should be used to identify new areas for improved health service delivery and rigorously evaluate new services in pharmacy. The generation of such evidence through practice-based research should be predicated on appropriate use of robust and rigorous methodologies. In addition to the quantitative and qualitative approaches used in pharmacy practice research, mixed methods and other novel approaches are increasingly being applied in pharmacy practice research. Approaches such as discrete choice experiments, Delphi techniques, and simulated client technique are now commonly used in pharmacy practice research. Therefore, pharmacy practice researchers need to be competent in the selection, application, and interpretation of these methodological and analytical approaches. This chapter focuses on introducing traditional and novel study designs and methodologies that are particularly pertinent to contemporary clinical pharmacy and practice research. This chapter will introduce the fundamentals and structures of these methodologies, but more details regarding the different approaches may be found within the Encyclopedia.

Learning Objectives

• • Discuss the value of pharmacy practice research to evidence-based practice and policy.
• • Describe the classifications and types of study designs commonly used in pharmacy practice research.
• • Discuss the concepts and structure of common study designs used in pharmacy practice research including experimental, quasi-experimental, observational, qualitative, and mixed method designs.
• • Discuss the important considerations for conducting pharmacy practice research in terms of study design, data collection, data analyses, and ethical considerations.

Introduction to Research Methodologies Used in Pharmacy Practice

The mission of pharmacy profession and the role of pharmacists in healthcare have evolved toward patient-centered care in the last few decades. Pharmacists with their expertise in drug therapy and accessibility to the public have unprecedented opportunities to assume increasing responsibility for direct patient care ( Bond, 2006 ). New cognitive pharmaceutical services and new roles for pharmacists continue to emerge.

In the era of evidence-based practice and health services, it is not just adequate to propose those new pharmacy services or new roles without evidence of their benefit ( Awaisu and Alsalimy, 2015 , Bond, 2006 ). New pharmacy services and new roles must be proven to be feasible, acceptable, cost-effective, and increase health outcomes. Pharmacy practice research provides such evidence and can confirm the value of a new service, inform policy, and result in practice changes ( Bond, 2006 , Chen and Hughes, 2016 ). Research evidence should be used to identify new areas for improved health service delivery and rigorously evaluate new services. The research used to generate such evidence should be grounded in robust and rigorous methodologies ( Chen and Hughes, 2016 ). Traditionally, common quantitative and qualitative methods such as randomized controlled trials, cohort study, case control study, questionnaire-based surveys, and phenomenology using qualitative interviews have been used in pharmacy. However, in recent years, novel and more complex methods are being developed and utilized. Pharmacy practice researchers need to know how these old and new methodological approaches should be selected, applied, and interpreted in addressing research problems.

Various study designs, including, but not limited to experimental, quasi-experimental, observational, qualitative, and mixed method designs, have been used in pharmacy practice research. Furthermore, different classification systems (e.g., quantitative vs. qualitative, experimental vs. observational, descriptive vs. analytical study designs) have been used in the literature. The choice of a study design to answer a research question in pharmacy practice research is driven by several factors, including the type of the research question or the research hypothesis, expertise of the investigator, availability of data, and funding opportunities. Pharmacy practice researchers need to be competent in the selection, design, application, and interpretation of these methodological and analytical approaches. Today, many of the research methods used in pharmacy practice research have been adapted from fields such as sociology, anthropology, psychology, economics, and other disciplines. This paradigm shift has led to a greater emphasis on the appropriate choice of a specific research design or method to answer a specific research question ( Chen and Hughes, 2016 ). Consequently, pharmacy practice researchers should place an emphasis on the reliability of the methods selected, the correct interpretation of their findings, the testing of a specific hypothesis, and the internal validity of their data, among other considerations. Novice and early career researchers should be familiar and have sound foundation in a variety of methods applied in pharmacy practice research, which will be covered in this chapter and other chapters in this Encyclopedia. We do believe that more experienced researchers should focus on certain methods in order to advance research in our discipline.

Core Quantitative and Qualitative Approaches Used in Pharmacy Practice Research

Traditionally, core quantitative approaches used in pharmacy practice research include nonexperiments, quasi-experimental designs, and true experimental designs such as prospective randomized controlled intervention trials. Nonexperiments also include observational study designs that are often described as pharmacoepidemiologic study designs such as case–control study, cohort study, nested case–control study, and cross-sectional study ( Etminan, 2004 , Etminan and Samii, 2004 ). In recent years, conventional qualitative approaches and their philosophical paradigms are increasingly been used in pharmacy. These include the five qualitative approaches to inquiry: narrative research, phenomenology, grounded theory, ethnography, and case study. These qualitative methods are often difficult for pharmacy practice researchers to comprehend, and researchers tend to describe the methods of data collection such as individual interviews and focus group discussions as qualitative methods of inquiry. These data collection methods are briefly described later in this chapter, among others. Furthermore, there is an increasing importance on the appropriate selection and use of mixed method approach ( Hadi et al., 2013 ; Hadi and Closs, 2016a , Hadi and Closs, 2016b ), which are often designed and applied wrongly. Finally, it is worthwhile to be familiar with novel research methodologies such as discrete choice experiments, Delphi techniques, simulated client technique, and nominal group techniques, which fall between quantitative and qualitative approaches, often with no clear differentiation on where they belong. Although called “novel” in the context of this chapter, these methods are not new in other relevant disciplines, but new and not commonly used in pharmacy practice research.

Research Question and Selection of Study Design

Pharmacy practice researchers begin by conception of a research idea or identifying a research question and defining a hypothesis based on the question. The researcher then selects a study design that will be suitable to answer the research question. The study design should be appropriately selected prior to initiation of any research investigation. Selecting an inappropriate study design may potentially undermine the validity of a study in its entirety. Investigators are encouraged to critically think about the possible study designs to ensure that the research question is adequately addressed and should be able to adequately justify their choice. These study designs have been variously classified and one common classification system is quantitative vs. qualitative study designs. Study designs play a major role in determining the scientific value of research studies. Inappropriate choice of a study design is impossible to correct after completion of the study. Therefore, thorough planning is required to avoid unconvincing results and invalid conclusions. Good understanding of basic study design concepts will aid researchers in conducting robust and rigorous practice-based research. This chapter introduces the structure and the fundamentals of common study designs used in pharmacy practice research and discusses the important considerations for conducting pharmacy practice research in terms of study design, data collection, data analyses, and ethical considerations.

Classification of Research Methodologies Used in Pharmacy Practice

Various classifications for research designs and methods used in pharmacy practice have been used in the literature. The following are some of the approaches for the classification of research designs:

Case example: Investigators were looking for the association between acute myocardial infarction and smoking status, type of tobacco, amount of smoke, etc. ( Teo et al., 2006 ). Another example of a case–control study from published literature is the study investigating the association between the use of phenylpropanolamine and the risk of hemorrhagic stroke ( Kernan et al., 2000 ).

Case example: Investigators were interested to determine the long-term effectiveness of influenza vaccines in elderly people; they recruited cohorts of vaccinated and unvaccinated community-dwelling elderly ( Nichol et al., 2007 ).

Case example: A case report was written by a physician who contracted Severe Acute Respiratory Syndrome (SARS) during an outbreak in Hong Kong ( Wu and Sung, 2003 ). Another example is an ecological study examining diet and sunlight as risks for prostate cancer mortality ( Colli and Colli, 2006 ). Chim et al. conducted a large population-based survey in Australia to determine what community members think about the factors that do and should influence government spending on prescribed medicines ( Chim et al., 2017 ).

Case example: A group of investigators carried out a study to establish an association between the use of traditional eye medicines (TEM) and corneal ulcers. In this case, both case–control and cohort study designs are applicable. In an example of a case control study, Archibugi et al. aimed to investigate the association between aspirin and statin exclusive and combined and pancreatic ductal adenocarcinoma occurrence ( Archibugi et al., 2017 ). Another example of a cohort study is a study carried out by Wei et al. in which they investigated whether or not acid-suppression medicines increased the risk of bacterial gastroenteritis ( Wei et al., 2017 ).

Case examples: Investigators conducted a study about the newer versus older antihypertensive agents in African hypertensive patients (NOAAH) trial (nct01030458) to compare the efficacy of single-pill combinations of newer versus older antihypertensive agents (i.e., a single-pill combination of newer drugs, not involving a diuretic, with a combination of older drugs including a diuretic) ( Odili et al., 2012 ). In a crossover design, a group of investigators evaluated the effect of spironolactone on nonresolving central serous chorioretinopathy ( Bousquet et al., 2015 ).

Case examples: Prashanth et al. aimed to understand if (and how) a package of interventions targeting primary health centers and community participation platforms affect utilization and access to generic medicines for people with noncommunicable diseases using quasi-experimental design approach ( Prashanth et al., 2016 ).

• c. Observational design—It involves only observation of natural phenomena and does not involve investigator intervention. Typically, this study design investigates associations and not causation. Examples include cohort study and case–control study. These studies can explore an association between a pharmacologic agent and a disease of interest. Case examples: Please see previous examples of these.

Case examples: Please see experimental studies, and case–control and cohort study designs.

Case examples: Investigators in Canada explored the lived experiences of youth who are prescribed antipsychotics by conducting interpretative phenomenology study ( Murphy et al., 2015 ).

Case examples: Shiyanbola et al. combined focus group discussion with a survey tool to investigate patients' perceived value and use of quality measures in evaluating and choosing community pharmacies ( Shiyanbola and Mort, 2015 ).

Below is a brief description of traditional and novel pharmacoepidemiologic study designs. Several examples of pharmacoepidemiologic study designs are provided above. Some descriptive studies including case reports, case series, and ecological studies will not be described in this chapter.

• a. Case–control studies—In this design, patients (those who develop the disease or outcome of interest) are identified and control patients (those who do not develop the disease or outcome of interest) are sampled at random from the original cohort that gives rise to the cases ( Etminan and Samii, 2004 , Newman et al., 2013 ). The distribution of exposure to certain risk factors between the cases and the controls is then explored, and an odds ratio (OR) is calculated.
• b. Cohort studies—This can be described as a study in which a group of exposed subjects and a group of unexposed subjects are followed over time and the incidence of the disease or outcome of interest in the exposed group is compared with that in the unexposed group ( Etminan and Samii, 2004 , Hulley et al., 2013 ).
• c. Case-crossover studies—The case-crossover may be considered comparable to a crossover randomized controlled trial in which the patients act as their own control ( Etminan and Samii, 2004 ). Pattern of exposure among the cases is compared between event time and control time. The between-patient confounding that occurs in a classic case-control study is circumvented in this design. Tubiana et al. evaluated the role of antibiotic prophylaxis and assessed the relation between invasive dental procedures and oral streptococcal infective endocarditis, using a nationwide population-based cohort and a case-crossover study design ( Tubiana et al., 2017 ).
• d. Case–time control studies—This design is an extension of the case-crossover design, but includes a control group ( Etminan and Samii, 2004 ). A group of researchers assessed medication-related hospitalization. They used the case–time control study design to investigate the associations between 12 high risk medication categories (e.g., antidiabetic agents, diuretics, benzodiazepine hypnotics) and unplanned hospitalizations ( Lin et al., 2017 ).
• e. Nested case–control studies—In this design, a cohort of individuals is followed during certain time periods until a certain outcome is reached and the analysis is conducted as a case–control study in which cases are matched to only a sample of control subjects ( Etminan, 2004 ). de Jong et al. examined the association between interferon-β (IFN-β) and potential adverse events using population-based health administrative data in Canada ( De Jong et al., 2017 ).
• f. Cross-sectional studies—In this type of study, the investigator measures the outcome of interest and the exposures among the study participants at the same time ( Hulley et al., 2013 , Setia, 2016b ). It provides a snapshot of a situation for a particular period.

Quantitative Research Designs in Pharmacy Practice

A wide range of quantitative methods are commonly applied in pharmacy practice research. These methods are widely used in published pharmacy practice literature to explore appropriateness of medicines use, appropriateness and quality of prescribing, and medication safety, through analyzing existing datasets, direct observation, or self-report ( Green and Norris, 2015 ). Pharmacy practice research questions also seek to determine the knowledge, behaviors, attitudes, and practices of pharmacists, other healthcare providers, patients, policy-makers, regulators, and the general public. Quantitative methods are also used in evaluating the effect of new pharmacy services and interventions to improve medicines use. These practice research projects provide valuable insights about how medicines are used, and how to maximize their benefits and minimize their harmful effects. In the context of this chapter, quantitative study designs will be broadly classified into three: (1) observational, (2) experimental and quasi experimental, and (3) other designs.

Observational Study Designs

Pharmacoepidemiology is a “relatively new science that explores drug efficacy or toxicity using large observational study designs” ( Etminan, 2004 , Etminan and Samii, 2004 ). These study designs explore drug use studies that usually cannot be answered using randomized controlled trials or other experimental designs. In several instances, experimental study designs may not be suitable or feasible; in such circumstances, observational study designs are applied ( Cummings et al., 2013 ). As the name implies, observational studies involve merely observing the subjects in a noncontrolled setting, without investigator intervention or manipulating other aspects of the study. Therefore, observational studies are nonexperimental. The observation of the variables of interest can be prospective, retrospective, or current depending on the type of the observational study.

In pharmacoepidemiology and other areas of pharmacy practice, researchers are often interested in measuring the relationships between exposure to a drug and its efficacy, toxicity, or other outcomes of interest using observational study designs. It is worthwhile to note that observational study designs investigate association, but, in most cases, not causation. Here, we provide descriptions of some commonly used study designs in pharmacoepidemiology and pharmacy practice research in general.

Case–Control Studies

Case–control study design is used to determine association between risk factors or exposures and outcomes. It is a useful design to study exposures in rare diseases or diseases that take long time to develop ( Newman et al., 2013 ). It investigates exposures in individuals with and those without the outcome of interest. Nevertheless, case–control studies can help to identify harmful or beneficial exposures. Furthermore, the outcome of interest can be undesirable (e.g., mortality) or desirable (e.g., microbiological cure). As the name suggests, in a case–control study design, there are two groups of subjects: (1) cases (individuals with the outcome of interest) and (2) controls (individuals without the outcome of interest) ( Newman et al., 2013 ). Cases are randomly selected based on prespecified eligibility criteria from a population of interest. Appropriate representative controls for the cases selected are then identified. The researchers then retrospectively investigate possible exposures to the risk factor. Fig. 1 represents a schematic diagram of a case–control study.

Case–control study design.

Case–control studies are relatively inexpensive, less time-consuming to conduct, allow investigation of several possible exposures or associations, and are suitable for rare diseases. Selection of the control group is a critical component of case–control studies. Case–control studies have several drawbacks: confounding must be controlled, subject to recall, observation, and selection biases.

OR is the measure of association used for the analysis of case–control studies. This is defined as the odds of exposure to a factor in those with a condition or disease compared with those who do not have the condition or disease.

Cohort Studies

Similar to case–control studies, cohort studies determine an association between exposures/factors and development of an outcome of interest. As previously described, a cohort study is a study in which a group of exposed subjects and a group of unexposed subjects are followed over time to measure and compare the rate of a disease or an outcome of interest in both groups ( Etminan and Samii, 2004 , Hulley et al., 2013 ). A cohort study can be prospective (most common) or retrospective. While a case–control study begins with patients with and those without the outcome of interest (e.g., diseased and nondiseased patients), a cohort study begins with exposed and unexposed patients (e.g., patients with and those without certain risk factor) ( Hulley et al., 2013 , Setia, 2016a ). In a cohort study, both the exposed and the unexposed subjects are members of a larger cohort in which subjects may enter and exit the cohort at different periods in time ( Etminan and Samii, 2004 , Hulley et al., 2013 ).

Typically, a cohort study should have a defined time zero, which is defined as the time of entry into the cohort ( Etminan and Samii, 2004 ). The cohort (a group of exposed and unexposed subjects, who are free of the outcome at time zero) is followed for a certain period until the outcome of interest occurs. In addition, information or data related to all potential confounders or covariates should also be collected as failure to account for these can bias the results and over- or underestimates the risk estimate. There are two types of cohort studies: retrospective cohort and prospective cohort studies.

Retrospective cohort study, also known as historical cohort study, begins and ends in the present, while looking backward to collect information about exposure that occurred in the past ( Fig. 2 ). Historical cohort studies are relatively less time-consuming and less expensive than prospective cohort studies ( Etminan and Samii, 2004 , Hulley et al., 2013 , Setia, 2016a ). In addition, there is no loss to follow-up and researchers can investigate issues not amenable to intervention study designs. However, these studies are only as good as the data available, the investigator has limited control of confounding variables, and it is prone to recall bias.

Retrospective (historical) cohort study design.

On the other hand, prospective cohort study, also known as longitudinal cohort study, begins in the present and progresses forward, collecting data from enrolled subjects whose outcomes fall in the future ( Etminan and Samii, 2004 , Hulley et al., 2013 , Setia, 2016a ) ( Fig. 3 ). Prospective cohort studies are easier to plan for data collection, have low recall bias, and the researcher has a better control of confounding factors. On the other hand, it is difficult to study rare conditions; they are more prone to selection bias, more time-consuming, expensive, and loss of subjects to follow-up is common.

Prospective (longitudinal) cohort study design.

Relative risk (RR) is the measure of association used for the analysis of a cohort study. This is defined as the risk of an event or development of an event relative to exposure (i.e., the risk of subjects developing a condition when exposed to a risk factor compared with subjects who have not been exposed to the risk factor).

Case-Crossover Studies

This is a relatively new design in the field of epidemiology in which the patients act as their own controls ( Maclure, 1991 ). In this design, there is a case and a control element both of which come from the same subject. In other words, each case serves as its own control. It can be considered equivalent to a crossover RCT with a washout period ( Etminan and Samii, 2004 ). Pattern of exposure to the risk factor is compared between the event time and the control time ( Etminan and Samii, 2004 ). Case-crossover study design is useful to investigate triggers within an individual. For instance, it is applicable when studying a transient exposure or risk factor. However, determination of the period of the control and case components is a crucial and challenging aspect of a case-crossover study design. Since the patients serve as their own controls, the interindividual variability that is inherent in classic case–control studies is eliminated. This is important in studies involving progressive disease states in which disease severity may differ between patients such as multiple sclerosis. OR is estimated using techniques such as Mantel–Haenszel statistics and logistic regression.

Cross-Sectional Studies

Cross-sectional studies also known as prevalence studies identify the prevalence or characteristics of a condition in a group of individuals. This design provides a snapshot of the prevalence or the characteristics of the study subjects in a single time point. The study investigator measures the outcomes and the exposures in the study subjects simultaneously ( Etminan and Samii, 2004 , Hulley et al., 2013 , Setia, 2016b ). Hence, cross-sectional studies do not follow up patients to observe outcomes or exposures of interest. Data are often collected through surveys. Cross-sectional design cannot provide cause and effect relationships between certain exposures and outcomes of interest.

Experimental and Quasi-Experimental Study Designs

In a typical experimental study design, the investigator assigns subjects to the intervention and control/comparison groups in an effort to determine the effects of the intervention ( Cummings et al., 2013 ). Since the investigator has the opportunity to control various aspects of the experiment, this allows the researcher to determine the causal link between exposure to the intervention and outcome of interest. The researcher either randomly or conveniently assigns the subjects to an experimental group and a control group. When the investigator performs randomization, the study is considered a true experiment (see Fig. 4 ). On the other hand, if subjects are assigned into groups without randomization, the study is considered a quasi-experiment (refer to Fig. 5 ). As with experimental designs, quasi-experimental designs also attempt to demonstrate a causal link between the intervention and the outcome of interest. Due to the challenges of conducting a true experimental design, the quasi-experimental study designs have been consistently used in pharmacist intervention research.

True experimental study design.

Quasi experimental study design.

RCTs are considered the gold standard of experimental study designs in pharmacy practice and evidence-based research ( Cummings et al., 2013 ). The investigator randomly assigns a representative sample of the study population into an experimental group and a control group ( Fig. 4 ). Randomization in RCT is to minimize confounding and selection bias; it enables attainment of similar experimental and control groups, thereby isolating the effect of the intervention. The experimental group receives the treatment or intervention (e.g., a new drug or pharmaceutical care for treatment of a certain disease), while the control group receives a placebo treatment, no treatment, or usual care treatment depending on the objective of the study ( Cummings et al., 2013 ). These groups are then followed prospectively over time to observe the outcomes of interest that are hypothesized to be affected by the treatment or intervention. The result of the study is considered to have high internal validity if significant changes on the outcome variable occur in the experimental group, but not the control group. The investigator can infer that the treatment or intervention is the most probable cause of the changes observed in the intervention group. The unit of randomization in RCTs is usually the patient, but can sometimes be clusters to circumvent the drawbacks of contamination.

RCTs are very challenging to undertake and pharmacy practice researchers should ensure design of robust experiments, while considering all essential elements and adhering to best practices. For instance, to determine the impact of a cognitive pharmaceutical service, the selection of a representative sample of the population is a prime consideration in an RCT. Moreover, RCTs are expensive, labor-intensive, and highly prone to attrition bias or loss to follow-up.

In pharmacy practice research, it is often difficult to comply with the stringent requirements of true experimental designs such as RCTs, due to logistic reasons and/or ethical considerations ( Grady et al., 2013 , Krass, 2016 ). Whenever true experimental models are not feasible to be applied in pharmacy practice research, the researcher should endeavor to use a more robust quasi-experimental design. For instance, when randomization is not feasible, the researcher can choose from a range of quasi-experimental designs that are non-randomized and often noncontrolled ( Grady et al., 2013 , Krass, 2016 ). Quasi-experimental studies used in pharmacy literature may be classified into five major categories: (1) quasi-experimental design without control groups (i.e., one group pre–posttest design); (2) quasi-experimental design that use control groups with no pretest; (3) quasi-experimental design that use control groups and pretests (i.e., nonequivalent control group design with dependent pretests and posttests) (see Fig. 5 ); (4) interrupted time series and; (5) stepped wedge designs ( Brown and Lilford, 2006 , Grady et al., 2013 , Harris et al., 2006 ).

The one group pretest posttest design and the nonequivalent control group design ( Fig. 5 ) are the most commonly applied quasi-experimental designs in practice-based research literature. These designs have been commonly used to evaluate the effect of pharmacist interventions in medications management in general and specific disease states management. The lack of randomization and/or the lack of control group is a major weakness and a threat to internal validity in quasi-experimental designs ( Grady et al., 2013 ). The observed changes could be due to some effects other than the treatment.

Other Quantitative Study Designs

In addition to the common observational, experimental, and quasi-experimental designs described above, there are other designs that are used in pharmacy. These research methods include, but are not limited to, simulated client technique, discrete choice experiments, and Delphi techniques. These methods, which are considered relatively new to pharmacy, are now commonly used in pharmacy practice research. In this chapter, we briefly describe these methods and their application in pharmacy. However, a more detailed description of their components and the nitty gritty of their application in pharmacy practice are available elsewhere within this textbook.

Simulated Client Method

The use of simulated client or simulated patient (mystery shopper) method to assess practices or behaviors in pharmacy practice has received much attention in recent times ( Watson et al., 2004 , Watson et al., 2006 ). “A simulated patient is an individual who is trained to visit a pharmacy (or drug store) to enact a scenario that tests a specific behavior of the pharmacist or pharmacy staff” ( Watson et al., 2006 ). A review by Watson et al. demonstrated the versatility and applicability of this method to pharmacy practice research in both developing and developed countries ( Watson et al., 2006 ). The investigators also identified some important characteristics that should be taken into consideration in designing studies that use this technique.

This method can be used to assess wide range of cognitive pharmacy services including counseling and advice provision, treatment of minor ailments, provision of nonprescription medicines, and public health pharmacy, among other things. This method can be a robust and rigorous method of assessing pharmacy practice if used appropriately ( Watson et al., 2006 , Xu et al., 2012 ). More recent developments have documented that the simulated patient methods have been used to provide formative feedback in addition to assessing practice behavior of pharmacists and their staff ( Xu et al., 2012 ).

In a case example, a group of investigators evaluated Qatari pharmacists' prescribing, labeling, dispensing, and counseling practices in response to acute community-acquired gastroenteritis ( Ibrahim et al., 2016 ). In another example, the investigators documented the state of insomnia management at community pharmacies in Pakistan ( Hussain et al., 2013 ).

Discrete Choice Experiments

Evidence in healthcare suggests that understanding consumers' preferences can help policy-makers to design services to match their views and preferences ( Ryan, 2004 ). Traditionally, studies to understand patients' and consumers' preferences for pharmaceutical services used opinion or satisfaction survey instruments. Nevertheless, such satisfaction surveys lack the ability to identify the drivers of satisfaction or the relative importance of the different characteristics of the service ( Vass et al., 2016 ). Discrete choice experiments are a novel survey-based method in pharmacy that are predicated on economic theories that allow systematic quantification of preferences to help identify which attributes of a good or service consumers like, the relative value of each attribute, and the balance between the different attributes ( Naik Panvelkar et al., 2010 , Ryan, 2004 , Vass et al., 2016 ). In-depth description of this method and its essential elements are described in another chapter in the Encyclopedia.

Qualitative Research Designs in Pharmacy Practice

Qualitative research methodology is applied to investigate a problem that has unmeasurable variables, to get a comprehensive understanding of the topic, through discussing it with the involved individuals, and to recognize the natural context in which the investigated issue takes place ( Creswell, 2013 ). The use of qualitative research methodology is becoming increasingly common across diverse health-related disciplines, including pharmacy practice. This is because of its ability to describe social processes and behaviors associated with patients or healthcare professionals, which strengthen the research impact ( McLaughlin et al., 2016 ). Therefore, pharmacy researchers and practitioners need to be better oriented to qualitative research methods ( Behar-Horenstein et al., 2018 ).

In the following section, interpretative frameworks and philosophical orientations, methodologies, data collection and analysis methods, approaches to ensure rigor, and ethical considerations in qualitative research are briefly discussed ( Cohen et al., 2013 , Creswell, 2013 ).

Interpretative Framework and Philosophical Assumptions of Qualitative Research

Interpretative frameworks.

Interpretative frameworks are the conceptual structures for comprehension, which form researcher's reasoning and views of truth and knowledge ( Babbie, 2015 ). Different scholars have categorized qualitative research paradigms or interpretative frameworks differently. The following are examples of interpretative framework categories that are used in health science research based on the categorization of Creswell (2013) : (1) social constructivism (interpretivism) framework; (2) post-positivism framework; (3) transformative, feminist, critical frameworks and disabilities theories; (4) postmodern frameworks; (5) pragmatism frameworks.

Philosophical Assumptions

Philosophical assumptions are theories and perspectives about ontology, epistemology, axiology, and methodology, which underpin the interpretative frameworks selected by qualitative researchers ( Cohen et al., 2013 ). As with interpretative framework, there are numerous means to categorize the philosophical assumptions that are folded within interpretative framework. The following are explanations of philosophical assumptions based on the categorization of Creswell (2013) :

• 1. Ontological assumptions, which define the nature of reality
• 2. Epistemological assumptions, which clarify means for knowing reality
• 3. Axiological assumptions, which explain the role and influence of researcher values
• 4. Methodological assumptions, which identify approaches to inquiry

It is important that a qualitative researcher understands how interpretative frameworks (e.g., social constructivism, post-positivism, and pragmatic interpretative frameworks) are differentiated because of their underpinning philosophical assumptions (i.e., ontological, epistemological, axiological, and methodological assumptions).

Approaches to Inquiry (Methodology)

It is important that qualitative researchers understand the differences between the characteristics of the five qualitative approaches to inquiry, in order to select an approach to inquiry and attain methodological congruence ( Creswell, 2013 ). The five approaches to qualitative research inquiry are:

• a. Narrative research: Describes participants' written and spoken stories about their experiences with a phenomenon being investigated, while considering the chronological connection of the phenomenon's series of events ( Anderson and Kirkpatrick, 2016 , Creswell, 2013 , Czarniawska, 2004 ).
• b. Phenomenological research: Describes the essence of participants' common experiences of a phenomenon, so that the description is a general essence rather than an individual experience ( Creswell, 2013 , Giorgi, 1997 , Moustakas, 1994 ).
• c. Grounded theory research: Aims to generate a theory grounded in participants' data that conceptually explain a social phenomenon, which could involve social processes, or actions or interactions ( Creswell, 2013 , Strauss and Corbin, 1990 , Woods et al., 2016 ).
• d. Ethnographic research: Involves describing the shared patterns of values, behaviors, and beliefs of culture-sharing participants ( Creswell, 2013 , Harris, 1968 , Rosenfeld et al., 2017 ).
• e. Case study research: Provides an in-depth examination of a real-life contemporary phenomenon that researchers cannot change over time, to illustrate the significance of another general topic ( Baker, 2011 , Creswell, 2013 , de León-Castañeda et al., 2018 , Mukhalalati, 2016 , Yin, 2014 ).

Data Collection and Analysis Methods in Qualitative Research

Data collection tools in qualitative research can be categorized into the following fundamental categories ( Creswell, 2013 ):

• a. Observation
• b. Documents
• c. Individual semi-structured interviews
• d. Focus groups (FGs)
• e. Audio-visual materials
• f. Emails chat rooms, weblogs, social media, and instant messaging.
• a. Topic guides: Topic guides guide the discussions in focus groups and individual interviews, and contain open-ended questions and probes, to enable the researcher to understand the complete picture, based on participant views and experiences. They are developed based on the literature review, aim and objectives, research questions, and propositions ( Kleiber, 2004 ).
• b. Audio recording of FGs and interviews: Audio recording of discussions that take place in interviews and FGs is essential for managing and analyzing data, and for increasing the accuracy of data collection and analysis, and ultimately enhancing the dependability and credibility of the research ( Rosenthal, 2016 , Tuckett, 2005 ).
• c. Transcription of FGs and interviews recording: Verbatim transcription refers to the word-for-word conversion of oral words from an audio-recorded format into a scripted text format. Transcribing data is considered as the first data reduction step because it generates texts that can be examined and rechecked ( Miles et al., 2014 , Grossoehme, 2014 ).

Data analysis comprises several fundamental steps, including reading the transcribed text, arranging data, coding data deductively based on prefigured themes or inductively to produce emergent themes, and then summarizing the codes into themes, and finally presenting the analyzed data as results ( Cohen et al., 2013 , Crabtree and Miller, 1999 , Pope et al., 2000 ).

The most commonly used data analysis methods in health science research are:

Thematic analysis is characterized by identifying, analyzing, and reporting themes that are available in the data ( Braun and Clarke, 2006 , Castleberry and Nolen, 2018 ).

Content analysis comprises systematic coding followed by quantification of the analyzed data in a logical and unbiased way ( Berelson, 1952 , Vaismoradi et al., 2013 ).

Discourse analysis emphasizes the core format and the structure of texts to examine the assumptions and concealed aspirations behind discourses ( Brown and Yule, 1983 , Gee, 2004 ).

Quality Perspectives in Qualitative Research

Qualitative research validation involves ensuring the rigor of the utilized data collection, management, and analysis methods, by utilizing approaches to ensure the quality. In pharmacy practice research, Hadi and Closs, 2016a , Hadi and Closs, 2016b argued that quality in qualitative research topic has not been discussed widely in the literature, and therefore Hadi and Closs, 2016a , Hadi and Closs, 2016b suggested using several trustworthiness criteria to ensure the rigor of qualitative study. The trustworthiness criteria for ensuring quality in qualitative research ( Lincoln and Guba, 1985 ) are:

This criterion aims to ensure that the results are true and increases the possibility that the conclusions are credible ( Cohen and Crabtree, 2008 ).

This criterion aims to indicate that the research results are repeatable and consistent, in order to support the conclusions of the research ( Cohen and Crabtree, 2008 ).

This criterion aims to confirm the neutrality in interpretation by ensuring that the perspectives of participants, not the bias of researchers, influence the results ( Krefting, 1991 ).

This criterion involves identifying the contexts to which the study results can be generalized, and indicating if the study conclusions can be applied in similar setting ( Yin, 2014 ).

Reflexivity implies revealing and evaluating the effect and biases that researchers can possibly bring to research process, by explaining the researcher's opinion, feelings, and experience with the phenomenon in question, and explaining the influence of this experience on research methods, findings, and write-ups ( Creswell, 2013 , Krefting, 1991 , Lincoln and Guba, 1985 ).

Ethical Considerations

Obtaining an ethical approval from the Institutional Review Board (IRB) is required before conducting the qualitative research ( Creswell, 2013 ). The key ethical issues that need to be considered are:

Informed consent refers to the decision taken by a competent individual to voluntarily participate in a research, after adequately understanding the research. Participant information leaflet is usually distributed to participants before they consent to participate in the research to clarify them the voluntary nature of research participation, the aim and objectives of the research, the rights of the respondents and the potential risks and harms, the data collection, management and storage conditions, and the right of participants to withdraw from the research ( Jefford and Moore, 2008 ).

The anonymity is usually ensured by not disclosing names of participants and by utilizing a code system to identify them during data collection, management, analysis, and in the writing up of the research. The confidentiality of participants and data is ensured by using a code system to identify participants, and by storing all data in a locked cabinet and a password-protected computer for a specified period of time ( Creswell, 2013 ).

Power imbalance is caused by the fact that participants have the experience about the investigated phenomenon, and researchers need to obtain information about these experiences. The power imbalance is usually associated with interaction between the researcher and participants during recruitment stage, and during data collection, analysis, interpretation, and validation stages. Hence, researchers should take suitable measures at each stage to decrease the influence of possible power imbalance, and should enhance trust with participants ( Karnieli-Miller et al., 2009 , Yardley, 2000 ).

Mixed Methods in Pharmacy Practice Research

Research studies in pharmacy practice usually utilize single-method research designs. However, often these report numerous limitations and may not adequately answer the research question. Therefore, the combination of more than one research method to answer certain research questions has become increasingly common in pharmacy practice research ( Ryan et al., 2015 ). Mixed methods research design is now a popular and widely used research paradigm in pharmacy practice research fields ( Hadi et al., 2013 , Hadi et al., 2014 ; Hadi and Closs, 2016a , Hadi and Closs, 2016b , Ryan et al., 2015 ). Mixed methods research allows the expansion of the scope of research to offset the weaknesses of using either quantitative or qualitative approach alone ( Creswell et al., 2004 , Hadi et al., 2013 ; Hadi and Closs, 2016a , Hadi and Closs, 2016b , Pluye and Hong, 2014 ). Typically, qualitative and quantitative data are collected concurrently or sequentially in order to increase the validity and the comprehensiveness of the study findings ( Creswell et al., 2004 , Hadi et al., 2013 ; Hadi and Closs, 2016a , Hadi and Closs, 2016b , Pluye and Hong, 2014 , Ryan et al., 2015 ). The mixed method approach provides an expanded understanding of phenomenon under investigation through the comparison between qualitative and quantitative data ( Hadi et al., 2013 ; Hadi and Closs, 2016a , Hadi and Closs, 2016b , Pluye and Hong, 2014 ).

This section provides an overview and application of mixed method research in pharmacy practice. However, considerations in selecting, designing, and analyzing mixed methods research studies as well as the various typologies of mixed methods research are discussed elsewhere. Johnson et al. (2007) proposed the following definition for mixed methods research: “The type of research in which a researcher or team of researchers combines elements of qualitative and quantitative research approaches (e.g., use of qualitative and quantitative viewpoints, data collection, analysis, inference techniques) for the broad purpose of breadth and depth of understanding and corroboration.”

Mixed methods design allows the viewpoints of participants to be reflected, enables methodological flexibility, and promotes multidisciplinary teamwork ( Ryan et al., 2015 ). Furthermore, the approach allows a more holistic understanding of the research question. However, its major limitations include: need for wide range of research expertise across the research team members, highly labor-intensive, and the complexity of data integration.

Scholars believe that it is challenging to provide researchers with a step-by-step guide on how to undertake a mixed methods study and that this is driven by the specific research question ( Ryan et al., 2015 ). Nevertheless, the investigator should precisely determine the type of qualitative and quantitative methods to be employed, the order of data collection to be undertaken, the data collection instruments to be used, and the method of data analysis ( Ryan et al., 2015 ). This approach encompasses a synthesis of findings from both quantitative and qualitative components, which is achieved through integration of the findings from each approach ( Hadi et al., 2013 ; Hadi and Closs, 2016a , Hadi and Closs, 2016b , Pluye and Hong, 2014 ).

Different models or typologies for mixed methods research have been described in the literature. The most common typologies used in pharmacy practice and health services research include: concurrent or convergent parallel design, exploratory sequential design, explanatory sequential design, and the embedded design ( Hadi et al., 2013 , Pluye and Hong, 2014 ). Scholars believe that there are several factors to consider when selecting the typology or model of mixed methods research to use. These factors include: the order of qualitative and quantitative data collection (concurrent vs. sequential); priority of data (i.e., which type of data has priority between quantitative and qualitative data); purpose of integration of the data (e.g., triangulation); and number of data strands ( Hadi et al., 2013 , Pluye and Hong, 2014 ). In mixed methods research, integration of qualitative and quantitative findings is critical, and this research approach does not simply involve the collection of these data ( Ryan et al., 2015 ).

Summary and Take-Home Messages

• • In the era of evidence-based practice, it is not sufficient to propose new pharmacy services or roles without evidence of their benefit.
• • New pharmacy services and new roles must be proven to be feasible, acceptable, beneficial, and cost-effective.
• • Practice-based research provides such evidence and can inform policy, confirm the value of the new service, and change practice.
• • Various study designs, including, but not limited to experimental, quasi-experimental, observational, qualitative, and mixed-methods designs, have been used in pharmacy practice research.
• • Pharmacy practice researchers need to be competent in the selection, design, application, and interpretation of these methodological and analytical approaches.
• • The choice of any study design in pharmacy practice research is driven by the expertise of the investigator, type of research question or hypothesis, data availability, time orientation, ethical issues, and availability of funding.

There is a great demand for innovation and quality in pharmacy practice. These can be achieved partly through robust and well-designed pharmacy practice research. Pharmacy students, practitioners, educators, and policy-makers are exposed to a variety of research designs and methods. We need to have the best evidence (e.g., in policy, regulation, practice) for making decisions about the optimal research design that ensures delivering an ultimate pharmacy practice and a quality patient care.

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What the data says about crime in the U.S.

A growing share of Americans say reducing crime should be a top priority for the president and Congress to address this year. Around six-in-ten U.S. adults (58%) hold that view today, up from 47% at the beginning of Joe Biden’s presidency in 2021.

We conducted this analysis to learn more about U.S. crime patterns and how those patterns have changed over time.

The analysis relies on statistics published by the FBI, which we accessed through the Crime Data Explorer , and the Bureau of Justice Statistics (BJS), which we accessed through the  National Crime Victimization Survey data analysis tool .

To measure public attitudes about crime in the U.S., we relied on survey data from Pew Research Center and Gallup.

Additional details about each data source, including survey methodologies, are available by following the links in the text of this analysis.

With the issue likely to come up in this year’s presidential election, here’s what we know about crime in the United States, based on the latest available data from the federal government and other sources.

How much crime is there in the U.S.?

It’s difficult to say for certain. The  two primary sources of government crime statistics  – the Federal Bureau of Investigation (FBI) and the Bureau of Justice Statistics (BJS) – paint an incomplete picture.

The FBI publishes  annual data  on crimes that have been reported to law enforcement, but not crimes that haven’t been reported. Historically, the FBI has also only published statistics about a handful of specific violent and property crimes, but not many other types of crime, such as drug crime. And while the FBI’s data is based on information from thousands of federal, state, county, city and other police departments, not all law enforcement agencies participate every year. In 2022, the most recent full year with available statistics, the FBI received data from 83% of participating agencies .

BJS, for its part, tracks crime by fielding a  large annual survey of Americans ages 12 and older and asking them whether they were the victim of certain types of crime in the past six months. One advantage of this approach is that it captures both reported and unreported crimes. But the BJS survey has limitations of its own. Like the FBI, it focuses mainly on a handful of violent and property crimes. And since the BJS data is based on after-the-fact interviews with crime victims, it cannot provide information about one especially high-profile type of offense: murder.

All those caveats aside, looking at the FBI and BJS statistics side-by-side  does  give researchers a good picture of U.S. violent and property crime rates and how they have changed over time. In addition, the FBI is transitioning to a new data collection system – known as the National Incident-Based Reporting System – that eventually will provide national information on a much larger set of crimes , as well as details such as the time and place they occur and the types of weapons involved, if applicable.

Which kinds of crime are most and least common?

Property crime in the U.S. is much more common than violent crime. In 2022, the FBI reported a total of 1,954.4 property crimes per 100,000 people, compared with 380.7 violent crimes per 100,000 people.  

By far the most common form of property crime in 2022 was larceny/theft, followed by motor vehicle theft and burglary. Among violent crimes, aggravated assault was the most common offense, followed by robbery, rape, and murder/nonnegligent manslaughter.

BJS tracks a slightly different set of offenses from the FBI, but it finds the same overall patterns, with theft the most common form of property crime in 2022 and assault the most common form of violent crime.

How have crime rates in the U.S. changed over time?

Both the FBI and BJS data show dramatic declines in U.S. violent and property crime rates since the early 1990s, when crime spiked across much of the nation.

Using the FBI data, the violent crime rate fell 49% between 1993 and 2022, with large decreases in the rates of robbery (-74%), aggravated assault (-39%) and murder/nonnegligent manslaughter (-34%). It’s not possible to calculate the change in the rape rate during this period because the FBI  revised its definition of the offense in 2013 .

The FBI data also shows a 59% reduction in the U.S. property crime rate between 1993 and 2022, with big declines in the rates of burglary (-75%), larceny/theft (-54%) and motor vehicle theft (-53%).

Using the BJS statistics, the declines in the violent and property crime rates are even steeper than those captured in the FBI data. Per BJS, the U.S. violent and property crime rates each fell 71% between 1993 and 2022.

While crime rates have fallen sharply over the long term, the decline hasn’t always been steady. There have been notable increases in certain kinds of crime in some years, including recently.

In 2020, for example, the U.S. murder rate saw its largest single-year increase on record – and by 2022, it remained considerably higher than before the coronavirus pandemic. Preliminary data for 2023, however, suggests that the murder rate fell substantially last year .

How do Americans perceive crime in their country?

Americans tend to believe crime is up, even when official data shows it is down.

In 23 of 27 Gallup surveys conducted since 1993 , at least 60% of U.S. adults have said there is more crime nationally than there was the year before, despite the downward trend in crime rates during most of that period.

While perceptions of rising crime at the national level are common, fewer Americans believe crime is up in their own communities. In every Gallup crime survey since the 1990s, Americans have been much less likely to say crime is up in their area than to say the same about crime nationally.

Public attitudes about crime differ widely by Americans’ party affiliation, race and ethnicity, and other factors . For example, Republicans and Republican-leaning independents are much more likely than Democrats and Democratic leaners to say reducing crime should be a top priority for the president and Congress this year (68% vs. 47%), according to a recent Pew Research Center survey.

How does crime in the U.S. differ by demographic characteristics?

Some groups of Americans are more likely than others to be victims of crime. In the  2022 BJS survey , for example, younger people and those with lower incomes were far more likely to report being the victim of a violent crime than older and higher-income people.

There were no major differences in violent crime victimization rates between male and female respondents or between those who identified as White, Black or Hispanic. But the victimization rate among Asian Americans (a category that includes Native Hawaiians and other Pacific Islanders) was substantially lower than among other racial and ethnic groups.

The same BJS survey asks victims about the demographic characteristics of the offenders in the incidents they experienced.

In 2022, those who are male, younger people and those who are Black accounted for considerably larger shares of perceived offenders in violent incidents than their respective shares of the U.S. population. Men, for instance, accounted for 79% of perceived offenders in violent incidents, compared with 49% of the nation’s 12-and-older population that year. Black Americans accounted for 25% of perceived offenders in violent incidents, about twice their share of the 12-and-older population (12%).

As with all surveys, however, there are several potential sources of error, including the possibility that crime victims’ perceptions about offenders are incorrect.

How does crime in the U.S. differ geographically?

There are big geographic differences in violent and property crime rates.

For example, in 2022, there were more than 700 violent crimes per 100,000 residents in New Mexico and Alaska. That compares with fewer than 200 per 100,000 people in Rhode Island, Connecticut, New Hampshire and Maine, according to the FBI.

The FBI notes that various factors might influence an area’s crime rate, including its population density and economic conditions.

What percentage of crimes are reported to police? What percentage are solved?

Most violent and property crimes in the U.S. are not reported to police, and most of the crimes that  are  reported are not solved.

In its annual survey, BJS asks crime victims whether they reported their crime to police. It found that in 2022, only 41.5% of violent crimes and 31.8% of household property crimes were reported to authorities. BJS notes that there are many reasons why crime might not be reported, including fear of reprisal or of “getting the offender in trouble,” a feeling that police “would not or could not do anything to help,” or a belief that the crime is “a personal issue or too trivial to report.”

Most of the crimes that are reported to police, meanwhile,  are not solved , at least based on an FBI measure known as the clearance rate . That’s the share of cases each year that are closed, or “cleared,” through the arrest, charging and referral of a suspect for prosecution, or due to “exceptional” circumstances such as the death of a suspect or a victim’s refusal to cooperate with a prosecution. In 2022, police nationwide cleared 36.7% of violent crimes that were reported to them and 12.1% of the property crimes that came to their attention.

Which crimes are most likely to be reported to police? Which are most likely to be solved?

Around eight-in-ten motor vehicle thefts (80.9%) were reported to police in 2022, making them by far the most commonly reported property crime tracked by BJS. Household burglaries and trespassing offenses were reported to police at much lower rates (44.9% and 41.2%, respectively), while personal theft/larceny and other types of theft were only reported around a quarter of the time.

Among violent crimes – excluding homicide, which BJS doesn’t track – robbery was the most likely to be reported to law enforcement in 2022 (64.0%). It was followed by aggravated assault (49.9%), simple assault (36.8%) and rape/sexual assault (21.4%).

The list of crimes  cleared  by police in 2022 looks different from the list of crimes reported. Law enforcement officers were generally much more likely to solve violent crimes than property crimes, according to the FBI.

The most frequently solved violent crime tends to be homicide. Police cleared around half of murders and nonnegligent manslaughters (52.3%) in 2022. The clearance rates were lower for aggravated assault (41.4%), rape (26.1%) and robbery (23.2%).

When it comes to property crime, law enforcement agencies cleared 13.0% of burglaries, 12.4% of larcenies/thefts and 9.3% of motor vehicle thefts in 2022.

Are police solving more or fewer crimes than they used to?

Nationwide clearance rates for both violent and property crime are at their lowest levels since at least 1993, the FBI data shows.

Police cleared a little over a third (36.7%) of the violent crimes that came to their attention in 2022, down from nearly half (48.1%) as recently as 2013. During the same period, there were decreases for each of the four types of violent crime the FBI tracks:

• Police cleared 52.3% of reported murders and nonnegligent homicides in 2022, down from 64.1% in 2013.
• They cleared 41.4% of aggravated assaults, down from 57.7%.
• They cleared 26.1% of rapes, down from 40.6%.
• They cleared 23.2% of robberies, down from 29.4%.

The pattern is less pronounced for property crime. Overall, law enforcement agencies cleared 12.1% of reported property crimes in 2022, down from 19.7% in 2013. The clearance rate for burglary didn’t change much, but it fell for larceny/theft (to 12.4% in 2022 from 22.4% in 2013) and motor vehicle theft (to 9.3% from 14.2%).

Note: This is an update of a post originally published on Nov. 20, 2020.

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John Gramlich is an associate director at Pew Research Center

8 facts about Black Lives Matter

#blacklivesmatter turns 10, support for the black lives matter movement has dropped considerably from its peak in 2020, fewer than 1% of federal criminal defendants were acquitted in 2022, before release of video showing tyre nichols’ beating, public views of police conduct had improved modestly, most popular.

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