who developed the case study research method

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The History of the Case Study at Harvard Business School

• 28 Feb 2017

Many first-time HBS Online participants are surprised to learn that, often, the professor is not at the center of their learning experience. Instead of long faculty lectures, the HBS Online learning model centers on smaller, more digestible pieces of content that require participants to interact with each other, test concepts, and learn from real-world examples.

Often, the professor fades into the background and lets the focus shift to interviews with executives, industry leaders, and small business owners. Some students might be left thinking, "Wait, where did that professor go? Why am I learning about a grocery store in Harvard Square?"

In the words of The Hitchhiker's Guide to the Galaxy , “Don’t panic.” These interviews, or cases, feature leaders at companies of all sizes and provide valuable examples of business concepts in action. This case study method forms the backbone of the Harvard Business School curriculum.

Back in the 1920s, HBS professors decided to develop and experiment with innovative and unique business instruction methods. As the first school in the world to design a signature, distinctive program in business, later to be called the MBA, there was a need for a teaching method that would benefit this novel approach.

HBS professors selected and took a few pages to summarize recent events, momentous challenges, strategic planning, and important decisions undertaken by major companies and organizations. The idea was, and remains to this day, that through direct contact with a real-world case, students will think independently about those facts, discuss and compare their perspectives and findings with their peers, and eventually discover a new concept on their own.

Central to the case method is the idea that students are not provided the "answer" or resolution to the problem at hand. Instead, just like a board member, CEO, or manager, the student is forced to analyze a situation and find solutions without full knowledge of all methods and facts. Without excluding more traditional aspects, such as interaction with professors and textbooks, the case method provides the student with the opportunity to think and act like managers.

Since 1924, the case method has been the most widely applied and successful teaching instrument to come out of HBS, and it is used today in almost all MBA and Executive Education courses there, as well as in hundreds of other top business schools around the world. The application of the case method is so extensive that HBS students will often choose to rely on cases, instead of textbooks or other material, for their research. Large corporations use the case method as well to approach their own challenges, while competing universities create their own versions for their students.

This is what the case method does—it puts students straight into the game, and ensures they acquire not just skills and abstract knowledge, but also a solid understanding of the outside world.

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Home » Case Study – Methods, Examples and Guide

Case Study – Methods, Examples and Guide

Table of Contents

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

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

Types of Case Study

Types and Methods of Case Study are as follows:

Single-Case Study

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

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

Multiple-Case Study

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

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

Exploratory Case Study

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

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

Descriptive Case Study

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

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

Instrumental Case Study

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

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

Case Study Data Collection Methods

Here are some common data collection methods for case studies:

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

Observations

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

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

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

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

How to conduct Case Study Research

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

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

Examples of Case Study

Here are some examples of case study research:

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

Application of Case Study

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

Business and Management

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

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

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

Social Sciences

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

Law and Ethics

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

Purpose of Case Study

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

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

Case studies can also serve other purposes, including:

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

Advantages of Case Study Research

There are several advantages of case study research, including:

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

Limitations of Case Study Research

There are several limitations of case study research, including:

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

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The Case Study as Research Method: A Practical Handbook

Qualitative Research in Accounting & Management

ISSN : 1176-6093

Article publication date: 21 June 2011

Scapens, R.W. (2011), "The Case Study as Research Method: A Practical Handbook", Qualitative Research in Accounting & Management , Vol. 8 No. 2, pp. 201-204. https://doi.org/10.1108/11766091111137582

Emerald Group Publishing Limited

Copyright © 2011, Emerald Group Publishing Limited

This book aims to provide case‐study researchers with a step‐by‐step practical guide to “help them conduct the study with the required degree of rigour” (p. xi).

It seeks to “demonstrate that the case study is indeed a scientific method” (p. 104) and to show “the usefulness of the case method as one tool in the researcher's methodological arsenal” (p. 105). The individual chapters cover the various stages in conducting case‐study research, and each chapter sets out a number of practical steps which have to be taken by the researcher. The following are the eight stages/chapters and, in brackets, the number of steps in each stages:

Assessing appropriateness and usefulness (4).

Ensuring accuracy of results (21).

Preparation (6).

Selecting cases (4).

Collecting data (7).

Analyzing data (4).

Interpreting data (3).

Reporting results (4).

It is particularly noticeable that ensuring accuracy of results has by far the largest number of number of steps – 21 steps compared to seven or fewer steps in the other stages. This reflects Gagnon's concern to demonstrate the scientific rigour of case‐study research. In the forward, he explains that the book draws on his experience in conducting his own PhD research, which was closely supervised by three professors, one of whom was inclined towards quantitative research. Consequently, his research was underpinned by the principles and philosophy of quantitative research. This is clearly reflected in the approach taken in this book, which seeks to show that case‐study research is just as rigorous and scientific as quantitative research, and it can produce an objective and accurate representation of the observed reality.

There is no discussion of the methodological issues relating to the use of case‐study research methods. This is acknowledged in the forward, although Gagnon refers to them as philosophical or epistemological issues (p. xii), as he tends to use the terms methodology and method interchangeably – as is common in quantitative research. Although he starts (step 1.1) by trying to distance case and other qualitative research from the work of positivists, arguing that society is socially constructed, he nevertheless sees social reality as objective and independent of the researcher. So for Gagnon, the aim of case research is to accurately reflect that reality. At various points in the book the notion of interpretation is used – evidence is interpreted and the (objective) case findings have to be interpreted.

So although there is a distancing from positivist research (p. 1), the approach taken in this book retains an objective view of the social reality which is being researched; a view which is rather different to the subjective view of reality taken by many interpretive case researchers. This distinction between an objective and a subjective view of the social reality being researched – and especially its use in contrasting positivist and interpretive research – has its origins the taxonomy of Burrell and Morgan (1979) . Although there have been various developments in the so‐called “objective‐subjective debate”, and recently some discussion in relation to management accounting research ( Kakkuri‐Knuuttila et al. , 2008 ; Ahrens, 2008 ), this debate is not mentioned in the book. Nevertheless, it is clear that Gagnon is firmly in the objective camp. In a recent paper, Johnson et al. (2006, p. 138) provide a more contemporary classification of the different types of qualitative research. In their terms, the approach taken in this book could be described as neo‐empiricist – an approach which they characterise as “qualitative positivists”.

The approach taken in this handbook leaves case studies open to the criticisms that they are a small sample, and consequently difficult to generalise, and to arguments that case studies are most appropriate for exploratory research which can subsequently be generalised though quantitative research. Gagnon explains that this was the approach he used after completing his thesis (p. xi). The handbook only seems to recognise two types of case studies, namely exploratory and raw empirical case studies – the latter being used where “the researcher is interested in a subject without having formed any preconceived ideas about it” (p. 15) – which has echoes of Glaser and Strauss (1967) . However, limiting case studies to these two types ignores other potential types; in particular, explanatory case studies which are where interpretive case‐study research can make important contributions ( Ryan et al. , 2002 ).

This limited approach to case studies comes through in the practical steps which are recommended in the handbook, and especially in the discussion of reliability and validity. The suggested steps seem to be designed to keep very close to the notions of reliability and validity used in quantitative research. There is no mention of the recent discussion of “validity” in interpretive accounting research, which emphasises the importance of authenticity and credibility and their implications for writing up qualitative and case‐study research ( Lukka and Modell, 2010 ). Although the final stage of Gagnon's handbook makes some very general comments about reporting the results, it does not mention, for example, Baxter and Chua's (2008) paper in QRAM which discusses the importance of demonstrating authenticity, credibility and transferability in writing qualitative research.

Despite Gagnon's emphasis on traditional notions of reliability and validity the handbook provides some useful practical advice for all case‐study researchers. For example, case‐study research needs a very good research design; case‐study researchers must work hard to gain access to and acceptance in the research settings; a clear strategy is needed for data collection; the case researcher should create field notes (in a field notebook, or otherwise) to record all the thoughts, ideas, observations, etc. that would not otherwise be collected; and the vast amount of data that case‐study research can generate needs to be carefully managed. Furthermore, because of what Gagnon calls the “risk of mortality” (p. 54) (i.e. the risk that access to a research site may be lost – for instance, if the organisation goes bankrupt) it is crucial for some additional site(s) to be selected at the outset to ensure that the planned research can be completed. This is what I call “insurance cases” when talking to my own PhD students. Interestingly, Gagnon recognises the ethical issues involved in doing case studies – something which is not always mentioned by the more objectivist type of case‐study researchers. He emphasises that it is crucial to honour confidentiality agreements, to ensure data are stored securely and that commitments are met and promises kept.

There is an interesting discussion of the advantages and disadvantages of using computer methods in analysing data (in stage 6). However, the discussion of coding appears to be heavily influenced by grounded theory, and is clearly concerned with producing an accurate reflection of an objective reality. In addition, Gagnon's depiction of case analysis is overly focussed on content analysis – possibly because it is a quantitative type of technique. There is no reference to the other approaches available to qualitative researchers. For example, there is no mention of the various visualisation techniques set out in Miles and Huberman (1994) .

To summarise, Gagnon's book is particularly useful for case‐study researchers who see the reality they are researching as objective and researcher independent. However, this is a sub‐set of case‐study researchers. Although some of the practical guidance offered is relevant for other types of case‐study researchers, those who see multiple realities in the social actors and/or recognise the subjectivity of the research process might have difficulty with some of the steps in this handbook. Gagnon's aim to show that the case study is a scientific method, gives the handbook a focus on traditional (quantitatively inspired) notions rigour and validity, and a tendency to ignore (or at least marginalise) other types of case study research. For example, the focus on exploratory cases, which need to be supplemented by broad based quantitative research, overlooks the real potential of case study research which lies in explanatory cases. Furthermore, Gagnon is rather worried about participant research, as the researcher may play a role which is “not consistent with scientific method” (p. 42), and which may introduce researcher bias and thereby damage “the impartiality of the study” (p. 53). Leaving aside the philosophical question about whether any social science research, including quantitative research, can be impartial, this stance could severely limit the potential of case‐study research and it would rule out both the early work on the sociology of mass production and the recent calls for interventionist research. Clearly, there could be a problem where a researcher is trying to sell consulting services, but there is a long tradition of social researchers working within organisations that they are studying. Furthermore, if interpretive research is to be relevant for practice, researchers may have to work with organisations to introduce new ideas and new ways of analysing problems. Gagnon would seem to want to avoid all such research – as it would not be “impartial”.

Consequently, although there is some good practical advice for case study researchers in this handbook, some of the recommendations have to be treated cautiously, as it is a book which sees case‐study research in a very specific way. As mentioned earlier, in the Forward Gagnon explicitly recognises that the book does not take a position on the methodological debates surrounding the use of case studies as a research method, and he says that “The reader should therefore use and judge this handbook with these considerations in mind” (p. xii). This is very good advice – caveat emptor .

Ahrens , T. ( 2008 ), “ A comment on Marja‐Liisa Kakkuri‐Knuuttila ”, Accounting, Organizations and Society , Vol. 33 Nos 2/3 , pp. 291 ‐ 7 , Kari Lukka and Jaakko Kuorikoski.

Baxter , J. and Chua , W.F. ( 2008 ), “ The field researcher as author‐writer ”, Qualitative Research in Accounting & Management , Vol. 5 No. 2 , pp. 101 ‐ 21 .

Burrell , G. and Morgan , G. ( 1979 ), Sociological Paradigms and Organizational Analysis , Heinneman , London .

Glaser , B.G. and Strauss , A.L. ( 1967 ), The Discovery of Grounded Theory: Strategies for Qualitative Research , Aldine , New York, NY .

Johnson , P. , Buehring , A. , Cassell , C. and Symon , G. ( 2006 ), “ Evaluating qualitative management research: towards a contingent critieriology ”, International Journal of Management Reviews , Vol. 8 No. 3 , pp. 131 ‐ 56 .

Kakkuri‐Knuuttila , M.‐L. , Lukka , K. and Kuorikoski , J. ( 2008 ), “ Straddling between paradigms: a naturalistic philosophical case study on interpretive research in management accounting ”, Accounting, Organizations and Society , Vol. 33 Nos 2/3 , pp. 267 ‐ 91 .

Lukka , K. and Modell , S. ( 2010 ), “ Validation in interpretive management accounting research ”, Accounting, Organizations and Society , Vol. 35 , pp. 462 ‐ 77 .

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Ryan , R.J. , Scapens , R.W. and Theobald , M. ( 2002 ), Research Methods and Methodology in Finance and Accounting , 2nd ed. , Thomson Learning , London .

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• R. M. Channaveer 4 &
• Rajendra Baikady 5  

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This chapter reviews the strengths and limitations of case study as a research method in social sciences. It provides an account of an evidence base to justify why a case study is best suitable for some research questions and why not for some other research questions. Case study designing around the research context, defining the structure and modality, conducting the study, collecting the data through triangulation mode, analysing the data, and interpreting the data and theory building at the end give a holistic view of it. In addition, the chapter also focuses on the types of case study and when and where to use case study as a research method in social science research.

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What the Case Study Method Really Teaches

• Nitin Nohria

Seven meta-skills that stick even if the cases fade from memory.

It’s been 100 years since Harvard Business School began using the case study method. Beyond teaching specific subject matter, the case study method excels in instilling meta-skills in students. This article explains the importance of seven such skills: preparation, discernment, bias recognition, judgement, collaboration, curiosity, and self-confidence.

During my decade as dean of Harvard Business School, I spent hundreds of hours talking with our alumni. To enliven these conversations, I relied on a favorite question: “What was the most important thing you learned from your time in our MBA program?”

• Nitin Nohria is the George F. Baker Professor of Business Administration, Distinguished University Service Professor, and former dean of Harvard Business School.

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• Case Study | Definition, Examples & Methods

Case Study | Definition, Examples & Methods

Published on 5 May 2022 by Shona McCombes . Revised on 30 January 2023.

A case study is a detailed study of a specific subject, such as a person, group, place, event, organisation, or phenomenon. Case studies are commonly used in social, educational, clinical, and business research.

A case study research design usually involves qualitative methods , but quantitative methods are sometimes also used. Case studies are good for describing , comparing, evaluating, and understanding different aspects of a research problem .

Table of contents

When to do a case study, step 1: select a case, step 2: build a theoretical framework, step 3: collect your data, step 4: describe and analyse the case.

A case study is an appropriate research design when you want to gain concrete, contextual, in-depth knowledge about a specific real-world subject. It allows you to explore the key characteristics, meanings, and implications of the case.

Case studies are often a good choice in a thesis or dissertation . They keep your project focused and manageable when you don’t have the time or resources to do large-scale research.

You might use just one complex case study where you explore a single subject in depth, or conduct multiple case studies to compare and illuminate different aspects of your research problem.

Prevent plagiarism, run a free check.

Once you have developed your problem statement and research questions , you should be ready to choose the specific case that you want to focus on. A good case study should have the potential to:

• Provide new or unexpected insights into the subject
• Challenge or complicate existing assumptions and theories
• Propose practical courses of action to resolve a problem
• Open up new directions for future research

Unlike quantitative or experimental research, a strong case study does not require a random or representative sample. In fact, case studies often deliberately focus on unusual, neglected, or outlying cases which may shed new light on the research problem.

If you find yourself aiming to simultaneously investigate and solve an issue, consider conducting action research . As its name suggests, action research conducts research and takes action at the same time, and is highly iterative and flexible. 

However, you can also choose a more common or representative case to exemplify a particular category, experience, or phenomenon.

While case studies focus more on concrete details than general theories, they should usually have some connection with theory in the field. This way the case study is not just an isolated description, but is integrated into existing knowledge about the topic. It might aim to:

• Exemplify a theory by showing how it explains the case under investigation
• Expand on a theory by uncovering new concepts and ideas that need to be incorporated
• Challenge a theory by exploring an outlier case that doesn’t fit with established assumptions

To ensure that your analysis of the case has a solid academic grounding, you should conduct a literature review of sources related to the topic and develop a theoretical framework . This means identifying key concepts and theories to guide your analysis and interpretation.

There are many different research methods you can use to collect data on your subject. Case studies tend to focus on qualitative data using methods such as interviews, observations, and analysis of primary and secondary sources (e.g., newspaper articles, photographs, official records). Sometimes a case study will also collect quantitative data .

The aim is to gain as thorough an understanding as possible of the case and its context.

In writing up the case study, you need to bring together all the relevant aspects to give as complete a picture as possible of the subject.

How you report your findings depends on the type of research you are doing. Some case studies are structured like a standard scientific paper or thesis, with separate sections or chapters for the methods , results , and discussion .

Others are written in a more narrative style, aiming to explore the case from various angles and analyse its meanings and implications (for example, by using textual analysis or discourse analysis ).

In all cases, though, make sure to give contextual details about the case, connect it back to the literature and theory, and discuss how it fits into wider patterns or debates.

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Chapter 11 Case Research

Case research, also called case study, is a method of intensively studying a phenomenon over time within its natural setting in one or a few sites. Multiple methods of data collection, such as interviews, observations, prerecorded documents, and secondary data, may be employed and inferences about the phenomenon of interest tend to be rich, detailed, and contextualized. Case research can be employed in a positivist manner for the purpose of theory testing or in an interpretive manner for theory building. This method is more popular in business research than in other social science disciplines.

Case research has several unique strengths over competing research methods such as experiments and survey research. First, case research can be used for either theory building or theory testing, while positivist methods can be used for theory testing only. In interpretive case research, the constructs of interest need not be known in advance, but may emerge from the data as the research progresses. Second, the research questions can be modified during the research process if the original questions are found to be less relevant or salient. This is not possible in any positivist method after the data is collected. Third, case research can help derive richer, more contextualized, and more authentic interpretation of the phenomenon of interest than most other research methods by virtue of its ability to capture a rich array of contextual data. Fourth, the phenomenon of interest can be studied from the perspectives of multiple participants and using multiple levels of analysis (e.g., individual and organizational).

At the same time, case research also has some inherent weaknesses. Because it involves no experimental control, internal validity of inferences remain weak. Of course, this is a common problem for all research methods except experiments. However, as described later, the problem of controls may be addressed in case research using “natural controls”. Second, the quality of inferences derived from case research depends heavily on the integrative powers of the researcher. An experienced researcher may see concepts and patterns in case data that a novice researcher may miss. Hence, the findings are sometimes criticized as being subjective. Finally, because the inferences are heavily contextualized, it may be difficult to generalize inferences from case research to other contexts or other organizations.

It is important to recognize that case research is different from case descriptions such as Harvard case studies discussed in business classes. While case descriptions typically describe an organizational problem in rich detail with the goal of stimulating classroom discussion and critical thinking among students, or analyzing how well an organization handled a specific problem, case research is a formal research technique that involves a scientific method to derive explanations of organizational phenomena.

Case research is a difficult research method that requires advanced research skills on the part of the researcher, and is therefore, often prone to error. Benbasat et al. (1987) [8] describe five problems frequently encountered in case research studies. First, many case research studies start without specific research questions, and therefore end up without having any specific answers or insightful inferences. Second, case sites are often chosen based on access and convenience, rather than based on the fit with the research questions, and are therefore cannot adequately address the research questions of interest. Third, researchers often do not validate or triangulate data collected using multiple means, which may lead to biased interpretation based on responses from biased interviewees. Fourth, many studies provide very little details on how data was collected (e.g., what interview questions were used, which documents were examined, what are the organizational positions of each interviewee, etc.) or analyzed, which may raise doubts about the reliability of the inferences. Finally, despite its strength as a longitudinal research method, many case research studies do not follow through a phenomenon in a longitudinal manner, and hence present only a cross-sectional and limited view of organizational processes and phenomena that are temporal in nature.

Key Decisions in Case Research

Several key decisions must be made by a researcher when considering a case research method. First, is this the right method for the research questions being studied? The case research method is particularly appropriate for exploratory studies for discovering relevant constructs in areas where theory building at the formative stages, for studies where the experiences of participants and context of actions are critical, and for studies aimed at understanding complex, temporal processes (why and how of a phenomenon) rather than factors or causes (what). This method is well-suited for studying complex organizational processes that involve multiple participants and interacting sequences of events, such as organizational change and large-scale technology implementation projects.

Second, what is the appropriate unit of analysis for a case research study? Since case research can simultaneously examine multiple units of analyses, the researcher must decide whether she wishes to study a phenomenon at the individual, group, and organizational level or at multiple levels. For instance, a study of group decision making or group work may combine individual-level constructs such as individual participation in group activities with group-level constructs, such as group cohesion and group leadership, to derive richer understanding than that can be achieved from a single level of analysis.

Third, should the researcher employ a single-case or multiple-case design? The single case design is more appropriate at the outset of theory generation, if the situation is unique or extreme, if it is revelatory (i.e., the situation was previously inaccessible for scientific investigation), or if it represents a critical or contrary case for testing a well-formulated theory. The multiple case design is more appropriate for theory testing, for establishing generalizability of inferences, and for developing richer and more nuanced interpretations of a phenomenon. Yin (1984) [9] recommends the use of multiple case sites with replication logic, viewing each case site as similar to one experimental study, and following rules of scientific rigor similar to that used in positivist research.

Fourth, what sites should be chosen for case research? Given the contextualized nature of inferences derived from case research, site selection is a particularly critical issue because selecting the wrong site may lead to the wrong inferences. If the goal of the research is to test theories or examine generalizability of inferences, then dissimilar case sites should be selected to increase variance in observations. For instance, if the goal of the research is to understand the process of technology implementation in firms, a mix of large, mid-sized, and small firms should be selected to examine whether the technology implementation process differs with firm size. Site selection should not be opportunistic or based on convenience, but rather based on the fit with research questions through a process called “theoretical sampling.”

Fifth, what techniques of data collection should be used in case research? Although interview (either open-ended/unstructured or focused/structured) is by far the most popular data collection technique for case research, interview data can be supplemented or corroborated with other techniques such as direct observation (e.g., attending executive meetings, briefings, and planning sessions), documentation (e.g., internal reports, presentations, and memoranda, as well as external accounts such as newspaper reports), archival records (e.g., organization charts, financial records, etc.), and physical artifacts (e.g., devices, outputs, tools). Furthermore, the researcher should triangulate or validate observed data by comparing responses between interviewees.

Conducting Case Research

Most case research studies tend to be interpretive in nature. Interpretive case research is an inductive technique where evidence collected from one or more case sites is systematically analyzed and synthesized to allow concepts and patterns to emerge for the purpose of building new theories or expanding existing ones. Eisenhardt (1989) [10] propose a “roadmap” for building theories from case research, a slightly modified version of which is described below. For positivist case research, some of the following stages may need to be rearranged or modified; however sampling, data collection, and data analytic techniques should generally remain the same.

Define research questions. Like any other scientific research, case research must also start with defining research questions that are theoretically and practically interesting, and identifying some intuitive expectations about possible answers to those research questions or preliminary constructs to guide initial case design. In positivist case research, the preliminary constructs are based on theory, while no such theory or hypotheses should be considered ex ante in interpretive research. These research questions and constructs may be changed in interpretive case research later on, if needed, but not in positivist case research.

Select case sites. The researcher should use a process of “theoretical sampling” (not random sampling) to identify case sites. In this approach, case sites are chosen based on theoretical, rather than statistical, considerations, for instance, to replicate previous cases, to extend preliminary theories, or to fill theoretical categories or polar types. Care should be taken to ensure that the selected sites fit the nature of research questions, minimize extraneous variance or noise due to firm size, industry effects, and so forth, and maximize variance in the dependent variables of interest. For instance, if the goal of the research is to examine how some firms innovate better than others, the researcher should select firms of similar size within the same industry to reduce industry or size effects, and select some more innovative and some less innovative firms to increase variation in firm innovation. Instead of cold-calling or writing to a potential site, it is better to contact someone at executive level inside each firm who has the authority to approve the project or someone who can identify a person of authority. During initial conversations, the researcher should describe the nature and purpose of the project, any potential benefits to the case site, how the collected data will be used, the people involved in data collection (other researchers, research assistants, etc.), desired interviewees, and the amount of time, effort, and expense required of the sponsoring organization. The researcher must also assure confidentiality, privacy, and anonymity of both the firm and the individual respondents.

Create instruments and protocols. Since the primary mode of data collection in case research is interviews, an interview protocol should be designed to guide the interview process. This is essentially a list of questions to be asked. Questions may be open-ended (unstructured) or closed-ended (structured) or a combination of both. The interview protocol must be strictly followed, and the interviewer must not change the order of questions or skip any question during the interview process, although some deviations are allowed to probe further into respondent’s comments that are ambiguous or interesting. The interviewer must maintain a neutral tone, not lead respondents in any specific direction, say by agreeing or disagreeing with any response. More detailed interviewing techniques are discussed in the chapter on surveys. In addition, additional sources of data, such as internal documents and memorandums, annual reports, financial statements, newspaper articles, and direct observations should be sought to supplement and validate interview data.

Select respondents. Select interview respondents at different organizational levels, departments, and positions to obtain divergent perspectives on the phenomenon of interest. A random sampling of interviewees is most preferable; however a snowball sample is acceptable, as long as a diversity of perspectives is represented in the sample. Interviewees must be selected based on their personal involvement with the phenomenon under investigation and their ability and willingness to answer the researcher’s questions accurately and adequately, and not based on convenience or access.

Start data collection . It is usually a good idea to electronically record interviews for future reference. However, such recording must only be done with the interviewee’s consent. Even when interviews are being recorded, the interviewer should take notes to capture important comments or critical observations, behavioral responses (e.g., respondent’s body language), and the researcher’s personal impressions about the respondent and his/her comments. After each interview is completed, the entire interview should be transcribed verbatim into a text document for analysis.

Conduct within-case data analysis. Data analysis may follow or overlap with data collection. Overlapping data collection and analysis has the advantage of adjusting the data collection process based on themes emerging from data analysis, or to further probe into these themes. Data analysis is done in two stages. In the first stage (within-case analysis), the researcher should examine emergent concepts separately at each case site and patterns between these concepts to generate an initial theory of the problem of interest. The researcher can interview data subjectively to “make sense” of the research problem in conjunction with using her personal observations or experience at the case site. Alternatively, a coding strategy such as Glasser and Strauss’ (1967) grounded theory approach, using techniques such as open coding, axial coding, and selective coding, may be used to derive a chain of evidence and inferences. These techniques are discussed in detail in a later chapter. Homegrown techniques, such as graphical representation of data (e.g., network diagram) or sequence analysis (for longitudinal data) may also be used. Note that there is no predefined way of analyzing the various types of case data, and the data analytic techniques can be modified to fit the nature of the research project.

Conduct cross-case analysis. Multi-site case research requires cross-case analysis as the second stage of data analysis. In such analysis, the researcher should look for similar concepts and patterns between different case sites, ignoring contextual differences that may lead to idiosyncratic conclusions. Such patterns may be used for validating the initial theory, or for refining it (by adding or dropping concepts and relationships) to develop a more inclusive and generalizable theory. This analysis may take several forms. For instance, the researcher may select categories (e.g., firm size, industry, etc.) and look for within-group similarities and between-group differences (e.g., high versus low performers, innovators versus laggards). Alternatively, she can compare firms in a pair-wise manner listing similarities and differences across pairs of firms.

Build and test hypotheses. Based on emergent concepts and themes that are generalizable across case sites, tentative hypotheses are constructed. These hypotheses should be compared iteratively with observed evidence to see if they fit the observed data, and if not, the constructs or relationships should be refined. Also the researcher should compare the emergent constructs and hypotheses with those reported in the prior literature to make a case for their internal validity and generalizability. Conflicting findings must not be rejected, but rather reconciled using creative thinking to generate greater insight into the emergent theory. When further iterations between theory and data yield no new insights or changes in the existing theory, “theoretical saturation” is reached and the theory building process is complete.

Write case research report. In writing the report, the researcher should describe very clearly the detailed process used for sampling, data collection, data analysis, and hypotheses development, so that readers can independently assess the reasonableness, strength, and consistency of the reported inferences. A high level of clarity in research methods is needed to ensure that the findings are not biased by the researcher’s preconceptions.

Interpretive Case Research Exemplar

Perhaps the best way to learn about interpretive case research is to examine an illustrative example. One such example is Eisenhardt’s (1989) [11] study of how executives make decisions in high-velocity environments (HVE). Readers are advised to read the original paper published in Academy of Management Journal before reading the synopsis in this chapter. In this study, Eisenhardt examined how executive teams in some HVE firms make fast decisions, while those in other firms cannot, and whether faster decisions improve or worsen firm performance in such environments. HVE was defined as one where demand, competition, and technology changes so rapidly and discontinuously that the information available is often inaccurate, unavailable or obsolete. The implicit assumptions were that (1) it is hard to make fast decisions with inadequate information in HVE, and (2) fast decisions may not be efficient and may result in poor firm performance.

Reviewing the prior literature on executive decision -making, Eisenhardt found several patterns, although none of these patterns were specific to high-velocity environments. The literature suggested that in the interest of expediency, firms that make faster decisions obtain input from fewer sources, consider fewer alternatives, make limited analysis, restrict user participation in decision-making, centralize decision-making authority, and has limited internal conflicts. However, Eisenhardt contended that these views may not necessarily explain how decision makers make decisions in high-velocity environments, where decisions must be made quickly and with incomplete information, while maintaining high decision quality.

To examine this phenomenon, Eisenhardt conducted an inductive study of eight firms in the personal computing industry. The personal computing industry was undergoing dramatic changes in technology with the introduction of the UNIX operating system, RISC architecture, and 64KB random access memory in the 1980’s, increased competition with the entry of IBM into the personal computing business, and growing customer demand with double-digit demand growth, and therefore fit the profile of the high-velocity environment. This was a multiple case design with replication logic, where each case was expected to confirm or disconfirm inferences from other cases. Case sites were selected based on their access and proximity to the researcher; however, all of these firms operated in the high-velocity personal computing industry in California’s Silicon Valley area. The collocation of firms in the same industry and the same area ruled out any “noise” or variance in dependent variables (decision speed or performance) attributable to industry or geographic differences.

The study employed an embedded design with multiple levels of analysis: decision (comparing multiple strategic decisions within each firm), executive teams (comparing different teams responsible for strategic decisions), and the firm (overall firm performance). Data was collected from five sources:

• Initial interviews with Chief Executive Officers: CEOs were asked questions about their firm’s competitive strategy, distinctive competencies, major competitors, performance, and recent/ongoing major strategic decisions. Based on these interviews, several strategic decisions were selected in each firm for further investigation. Four criteria were used to select decisions: (1) the decisions involved the firm’s strategic positioning,

(2) the decisions had high stakes, (3) the decisions involved multiple functions, and (4) the decisions were representative of strategic decision-making process in that firm.

• Interviews with divisional heads: Each divisional head was asked sixteen open-ended questions, ranging from their firm’s competitive strategy, functional strategy, top management team members, frequency and nature of interaction with team, typical decision making processes, how each of the previously identified decision was made, and how long it took them to make those decisions. Interviews lasted between 1.5 and 2 hours, and sometimes extended to 4 hours. To focus on facts and actual events rather than respondents’ perceptions or interpretations, a “courtroom” style questioning was employed, such as when did this happen, what did you do, etc. Interviews were conducted by two people, and the data was validated by cross-checking facts and impressions made by the interviewer and note-taker. All interview data was recorded, however notes were also taken during each interview, which ended with the interviewer’s overall impressions. Using a “24-hour rule”, detailed field notes were completed within 24 hours of the interview, so that some data or impressions were not lost to recall.
• Questionnaires: Executive team members at each firm were completed a survey questionnaire that captured quantitative data on the extent of conflict and power distribution in their firm.
• Secondary data: Industry reports and internal documents such as demographics of the executive teams (responsible for strategic decisions), financial performance of firms, and so forth, were examined.
• Personal observation: Lastly, the researcher attended a 1-day strategy session and a weekly executive meeting at two firms in her sample.

Data analysis involved a combination of quantitative and qualitative techniques. Quantitative data on conflict and power were analyzed for patterns across firms/decisions. Qualitative interview data was combined into decision climate profiles, using profile traits (e.g., impatience) mentioned by more than one executive. For within-case analysis, decision stories were created for each strategic decision by combining executive accounts of the key decision events into a timeline. For cross-case analysis, pairs of firms were compared for similarities and differences, categorized along variables of interest such as decision speed and firm performance. Based on these analyses, tentative constructs and propositions were derived inductively from each decision story within firm categories. Each decision case was revisited to confirm the proposed relationships. The inferred propositions were compared with findings from the existing literature to reconcile examine differences with the extant literature and to generate new insights from the case findings. Finally, the validated propositions were synthesized into an inductive theory of strategic decision-making by firms in high-velocity environments.

Inferences derived from this multiple case research contradicted several decision-making patterns expected from the existing literature. First, fast decision makers in high-velocity environments used more information, and not less information as suggested by the previous literature. However, these decision makers used more real-time information (an insight not available from prior research), which helped them identify and respond to problems, opportunities, and changing circumstances faster. Second, fast decision makers examined more (not fewer) alternatives. However, they considered these multiple alternatives in a simultaneous manner, while slower decision makers examined fewer alternatives in a sequential manner. Third, fast decision makers did not centralize decision making or restrict inputs from others, as the literature suggested. Rather, these firms used a two-tiered decision process in which experienced counselors were asked for inputs in the first stage, following by a rapid comparison and decision selection in the second stage. Fourth, fast decision makers did not have less conflict, as expected from the literature, but employed better conflict resolution techniques to reduce conflict and improve decision-making speed. Finally, fast decision makers exhibited superior firm performance by virtue of their built-in cognitive, emotional, and political processes that led to rapid closure of major decisions.

Positivist Case Research Exemplar

Case research can also be used in a positivist manner to test theories or hypotheses. Such studies are rare, but Markus (1983) [12] provides an exemplary illustration in her study of technology implementation at the Golden Triangle Company (a pseudonym). The goal of this study was to understand why a newly implemented financial information system (FIS), intended to improve the productivity and performance of accountants at GTC was supported by accountants at GTC’s corporate headquarters but resisted by divisional accountants at GTC branches. Given the uniqueness of the phenomenon of interest, this was a single-case research study.

To explore the reasons behind user resistance of FIS, Markus posited three alternative explanations: (1) system-determined theory: resistance was caused by factors related to an inadequate system, such as its technical deficiencies, poor ergonomic design, or lack of user friendliness, (2) people-determined theory: resistance was caused by factors internal to users, such as the accountants’ cognitive styles or personality traits that were incompatible with using the system, and (3) interaction theory: resistance was not caused not by factors intrinsic to the system or the people, but by the interaction between the two set of factors. Specifically, interaction theory suggested that the FIS engendered a redistribution of intra-organizational power, and accountants who lost organizational status, relevance, or power as a result of FIS implementation resisted the system while those gaining power favored it.

In order to test the three theories, Markus predicted alternative outcomes expected from each theoretical explanation and analyzed the extent to which those predictions matched with her observations at GTC. For instance, the system-determined theory suggested that since user resistance was caused by an inadequate system, fixing the technical problems of the system would eliminate resistance. The computer running the FIS system was subsequently upgraded with a more powerful operating system, online processing (from initial batch processing, which delayed immediate processing of accounting information), and a simplified software for new account creation by managers. One year after these changes were made, the resistant users were still resisting the system and felt that it should be replaced. Hence, the system-determined theory was rejected.

The people-determined theory predicted that replacing individual resistors or co-opting them with less resistant users would reduce their resistance toward the FIS. Subsequently, GTC started a job rotation and mobility policy, moving accountants in and out of the resistant divisions, but resistance not only persisted, but in some cases increased! In one specific instance, one accountant, who was one of the system’s designers and advocates when he worked for corporate accounting, started resisting the system after he was moved to the divisional controller’s office. Failure to realize the predictions of the people-determined theory led to the rejection of this theory.

Finally, the interaction theory predicted that neither changing the system or the people (i.e., user education or job rotation policies) will reduce resistance as long as the power imbalance and redistribution from the pre-implementation phase were not addressed. Before FIS implementation, divisional accountants at GTC felt that they owned all accounting data related to their divisional operations. They maintained this data in thick, manual ledger books, controlled others’ access to the data, and could reconcile unusual accounting events before releasing those reports. Corporate accountants relied heavily on divisional accountants for access to the divisional data for corporate reporting and consolidation. Because the FIS system automatically collected all data at source and consolidated them into a single corporate database, it obviated the need for divisional accountants, loosened their control and autonomy over their division’s accounting data, and making their job somewhat irrelevant. Corporate accountants could now query the database and access divisional data directly without going through the divisional accountants, analyze and compare the performance of individual divisions, and report unusual patterns and activities to the executive committee, resulting in further erosion of the divisions’ power. Though Markus did not empirically test this theory, her observations about the redistribution of organizational power, coupled with the rejection of the two alternative theories, led to the justification of interaction theory.

Comparisons with Traditional Research

Positivist case research, aimed at hypotheses testing, is often criticized by natural science researchers as lacking in controlled observations, controlled deductions, replicability, and generalizability of findings – the traditional principles of positivist research. However, these criticisms can be overcome through appropriate case research designs. For instance, the problem of controlled observations refers to the difficulty of obtaining experimental or statistical control in case research. However, case researchers can compensate for such lack of controls by employing “natural controls.” This natural control in Markus’ (1983) study was the corporate accountant who was one of the system advocates initially, but started resisting it once he moved to controlling division. In this instance, the change in his behavior may be attributed to his new divisional position. However, such natural controls cannot be anticipated in advance, and case researchers may overlook then unless they are proactively looking for such controls. Incidentally, natural controls are also used in natural science disciplines such as astronomy, geology, and human biology, such as wait for comets to pass close enough to the earth in order to make inferences about comets and their composition.

The problem of controlled deduction refers to the lack of adequate quantitative evidence to support inferences, given the mostly qualitative nature of case research data. Despite the lack of quantitative data for hypotheses testing (e.g., t-tests), controlled deductions can still be obtained in case research by generating behavioral predictions based on theoretical considerations and testing those predictions over time. Markus employed this strategy in her study by generating three alternative theoretical hypotheses for user resistance, and rejecting two of those predictions when they did not match with actual observed behavior. In this case, the hypotheses were tested using logical propositions rather than using mathematical tests, which are just as valid as statistical inferences since mathematics is a subset of logic.

Third, the problem of replicability refers to the difficulty of observing the same phenomenon given the uniqueness and idiosyncrasy of a given case site. However, using Markus’ three theories as an illustration, a different researcher can test the same theories at a different case site, where three different predictions may emerge based on the idiosyncratic nature of the new case site, and the three resulting predictions may be tested accordingly. In other words, it is possible to replicate the inferences of case research, even if the case research site or context may not be replicable.

Fourth, case research tends to examine unique and non-replicable phenomena that may not be generalized to other settings. Generalizability in natural sciences is established through additional studies. Likewise, additional case studies conducted in different contexts with different predictions can establish generalizability of findings if such findings are observed to be consistent across studies.

Lastly, British philosopher Karl Popper described four requirements of scientific theories: (1) theories should be falsifiable, (2) they should be logically consistent, (3) they should have adequate predictive ability, and (4) they should provide better explanation than rival theories. In case research, the first three requirements can be increased by increasing the degrees of freedom of observed findings, such as by increasing the number of case sites, the number of alternative predictions, and the number of levels of analysis examined. This was accomplished in Markus’ study by examining the behavior of multiple groups (divisional accountants and corporate accountants) and providing multiple (three) rival explanations.

Popper’s fourth condition was accomplished in this study when one hypothesis was found to match observed evidence better than the two rival hypotheses.

[8] Benbasat, I., Goldstein, D. K., and Mead, M. (1987). “The Case Research Strategy in Studies of Information Systems,” MIS Quarterly (11:3), 369-386.

[9] Yin, R. K. (2002), Case Study Research: Design and Methods . Thousand Oaks, CA: Sage Publications.

[10] Eisenhardt, K. M. (1989). “Building Theories from Case Research,” Academy of Management Review

(14:4), 532-550.

[11] Eisenhardt, K. M. (1989). “Making Fast Strategic Decisions in High-Velocity Environments,” Academy of Management Journal (32:3), 543-576.

[12] Markus, M. L. (1983). “Power, Politics, and MIS Implementation,” Communications of the ACM (26:6), 430-444.

• Social Science Research: Principles, Methods, and Practices. Authored by : Anol Bhattacherjee. Provided by : University of South Florida. Located at : http://scholarcommons.usf.edu/oa_textbooks/3/ . License : CC BY-NC-SA: Attribution-NonCommercial-ShareAlike

• Open access
• Published: 22 April 2024

Optimizing evidence-based practice implementation: a case study on simulated patient protocols in long-term opioid therapy

• Ellen Green   ORCID: orcid.org/0000-0003-2643-984X 1 ,
• Megan Hamm 2 ,
• Catherine Gowl 2 ,
• Reed Van Deusen 2 ,
• Jane M. Liebschutz 2 ,
• J. Deanna Wilson 3 &
• Jessica Merlin 2  

Implementation Science Communications volume  5 , Article number:  44 ( 2024 ) Cite this article

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Metrics details

Substantial work has been done to update or create evidence-based practices (EBPs) in the changing health care landscape. However, the success of these EBPs is limited by low levels of clinician implementation.

The goal of this study is to describe the use of standardized/simulated patient/person (SP) methodology as a framework to develop implementation bundles to increase the effectiveness, sustainability, and reproducibility of EBPs across health care clinicians.

We observed 12 clinicians’ first-time experiences with six unique decision-making algorithms, developed previously using rigorous Delphi methods, for use with patients exhibiting concerning behaviors associated with long-term opioid therapy (LTOT) for chronic pain. Clinicians were paired with two SPs trained to portray individuals with one of the concerning behaviors addressed by the algorithms in a telehealth environment. The SP evaluations were followed by individual interviews, guided by the Consolidated Framework for Implementation Research (CFIR), with each of the clinician participants.

Participants

Twelve primary care clinicians and 24 SPs in Western Pennsylvania.

Main measurement

The primary outcome was identifying likely facilitators for the successful implementation of the EBP using the SP methodology. Our secondary outcome was to assess the feasibility of using SPs to illuminate likely implementation barriers and facilitators.

The SP portrayal illuminated factors that were pertinent to address in the implementation bundle. SPs were realistic in their portrayal of patients with concerning behaviors associated with LTOT for chronic pain, but clinicians also noted that their patients in practice may have been more aggressive about their treatment plan.

Conclusions

SP simulation provides unique opportunities for obtaining crucial feedback to identify best practices in the adoption of new EBPs for high-risk patients.

Zoom simulated patient evaluations.

Peer Review reports

Contributions to the literature

This study uses simulated patients (SPs) in implementation science planning, offering insights into identifying gaps and tailoring implementation strategies effectively.

Focusing on long-term opioid therapy, our research exemplifies SPs’ practical role in implementing evidence-based practices, addressing a critical gap in substance use therapy.

Beyond training, our findings provide insights into SPs as facilitators for professionals dealing with high-risk patients, acknowledging both the potential benefits and limitations of the SP methodology.

Introduction

Timely adoption of current evidence-based practices (EBPs) is key to ensuring high-quality care in our changing health care environment. Creating EBPs alone is insufficient to ensure their implementation. Without well-designed implementation strategies, the adoption of these practices can take decades [ 1 ]. This is because clinicians often face barriers to implementing EBPs, including limited awareness, resistance to change, and resource constraints. Organizational culture, patient factors, and the complexity of implementation further contribute to the challenges. Evaluation of implementation strategies outside of an active practice setting can address these barriers and increase the likelihood of dissemination, long-term adoption, and appropriate use of EBPs by providing a controlled environment for assessment, feedback, and identification of facilitators for a successful implementation [ 2 , 3 , 4 ]. We argue that the standardized/simulated patient/person (SP) methodology serves as a valuable tool for formulating implementation strategies for EBPs before their application in practice.

SPs are people trained to portray complex behaviors and react as an actual patient would to a clinician in real time creating a fully interactive patient-clinician experience outside of a real-world practice [ 5 ]. SPs can be trained to consistently exhibit specific emotions (e.g., anger [ 6 ]), desires (e.g., prescriptions), and/or patient needs (e.g., language barriers [ 7 ]) across clinicians. The flexible nature of simulation can be leveraged to reflect either a single patient encounter or multiple patient visits portraying the passage of time depending on the application (e.g., teach providers how to perform a physical exam or re-evaluate patients after a new prescription). While SP methodology is commonly used to train and test clinicians on new techniques [ 5 , 8 , 9 , 10 , 11 , 12 , 13 , 14 , 15 ], its application to the planning phases of implementation science remains limited. Our work specifically leverages SP methodology within the planning phases of an implementation bundle for an EBP - a novel approach that has been underutilized in existing literature.

There are several advantages to using SP methodology as a part of implementation strategy. First, the consistent portrayal of a patient case can help identify gaps in EBP implementation and facilitate targeted solutions for future implementation. Second, recruiting clinicians from multiple and diverse practices to use the EBPs with SPs can provide insight into how the EBP would be best implemented in their unique practice setting after the provider has first-hand experience with the EBP. This can provide richer and more diverse insight for implementation scientists relative to feedback from directly implementing an EBP into a singular practice that may not generalize to other clinics.

Likewise, evaluating an EBP outside of the daily activities of a typical clinical practice provides clinicians with immediate and protected time for debriefing. Without dedicated time for good feedback, it is difficult to identify areas of improvement for implementation. Also, developing implementation strategies for EBPs in practice can be high risk for patients. The use of SPs provides a safe environment to develop implementation strategies and gain active experience with EBPs without putting patients at risk [ 16 ]. Lastly, SPs can provide insight into events that may be uncommon or take a long time to occur in practice, which can expedite necessary adaptation of implementation strategies for EBPs. Overall, SPs may provide a critical step in increasing the likelihood of a successful adoption of an EBP by identifying the barriers and facilitators prior to implementation in the field.

For these reasons, we adopted the SP methodology for a research project implementing an evidence-based approach to addressing concerning behaviors in patients on long-term opioid therapy (LTOT), such as diversion, use of other substances, or non-adherence to pain therapy. Although the evidence for the effectiveness of LTOT is limited [ 17 , 18 , 19 ], there are millions of Americans prescribed opioid analgesics yearly, with more than 17% of Americans receiving an opioid prescription in 2017, with an average of 3.4 opioid prescriptions dispensed per patient [ 20 ]. Multiple efforts to improve opioid prescribing have occurred on the broader policy level (e.g., prescription drug monitoring programs), the insurance level (limits on doses or length of time), and through education (the RDA risk evaluation and mitigation strategy program [ 21 ] and most recently, the drug enforcement agency requirement for training on addiction and opioids) [ 22 ]. While opioid prescribing has decreased overall [ 23 ], none of these broader measures address concerning behaviors among patients taking LTOT. To augment non-specific recommendations in the CDC guide to prescribing opioids (“weigh the risks and benefits” [ 24 ]) and other broader prescribing policy, our team previously developed a set of evidence-based clinical decision-making algorithms using Delphi process to address concerning behaviors among patients prescribed opioids. The lack of uptake of most clinical guidelines [ 25 , 26 ] led the team to look for effective ways to implement these EBP. Because the concerning behaviors of patients on LTOT may occur sporadically among primary care physicians (PCPs), using the SP methodology would allow for rapid feedback, making it attractive for developing and testing potential implementation methods of the EBP.

In this article, we describe the SP methodology for developing an implementation bundle for a new EBP to address concerning behaviors among patients on LTOT. In conjunction with the SP methodology, we used observation and discussion from one-on-one structured interviews to develop an implementation bundle to increase the likelihood of effective, sustainable, and reproducible adoption in practice. Our approach was guided by the Consolidated Framework for Implementation Research (CFIR), a commonly used tool to guide qualitative inquiry about how clinicians would implement EBPs in practice [ 27 ].

We demonstrate the important and practical use of the SP methodology for developing implementation strategies for a new EBP: 6 treatment algorithms designed to address common and challenging behaviors associated with long-term opioid therapy (LTOT) developed by Merlin and colleagues and published in 2016 [ 28 ]. As previously described, these algorithms were developed using a modified Delphi process [ 29 , 30 ], a rigorous methodology that uses several rounds of questionnaires sent to a panel of experts to find consensus on how to respond to behaviors such as missing appointments with clinicians prescribing the opioid, taking more opioid than prescribed, and substance use. One of the algorithms is included as an example of the new EBP in Fig. 1 . In the present study, we conducted SP sessions with providers using 6 SP cases, one for each algorithm. These SP sessions were followed by one-on-one structured interviews with questions mapping onto domains from the CFIR to assist in the development of an implementation bundle for the new EBP.

SEQ figure \* ARABIC 1: “Other Substance Use” Algorithm

Case development

We developed 6 SP cases. Each case simulated a patient exhibiting a unique concerning behavior addressed by the algorithms (see Table 1 outlining the behaviors portrayed). The SP cases were written with unfolding steps to represent three visits with a provider, because the algorithms guide decision points that would normally occur in subsequent follow-up visits in real-life practice (Fig. 1 ). The unfolding nature of the scenarios was piloted early in the SP case development process to ensure feasibility.

SP cases were next reviewed by a Patient-Provider Advisory Board (PPAB) consisting of 3 patients with lived experience with opioids, 4 researchers (among whom are PCPs familiar with caring for patients with opioid misuse disorder), and a primary care provider with familiarity with providing care for patients with opioid misuse. SP cases were edited based on feedback from the PPAB. In concert with the review of the 6 cases, the PPAB reviewed the instructions which provided context, expectations for SP-clinician interactions, and training on the algorithms (see Appendix ). Finally, cases and instructions were piloted with an SP and a provider outside of the panel. During this pilot, a physician with topical expertise was recruited to interact with SPs portraying two SP cases over three subsequent visits on a remote/telehealth platform (Zoom). This pilot helped to further develop the other five SP cases in structuring how clinicians would be oriented, updated, and guided through the simulations.

Training and description of organization for SPs

Four experienced SPs were recruited from the University of Pittsburgh SP program to portray the patients exhibiting misuse behaviors. The SPs in the University of Pittsburgh School of Medicine SP Program received foundational training in case portrayal, providing feedback, supported physical exam training, and checklist scoring. This 16-h onboarding combines both active training and also guided observation of SP activities. It prepares SPs to identify, recognize, and reward learner skill in portrayal, and to record it faithfully in assessments.

To allow rotation, redundancy and information sharing, the SPs worked in pairs for each case, alternating the role of moderator and patient. When not portraying the patient, the SP acted as a moderator by providing clinicians with inter-visit updates in accordance with what the clinicians ordered in the first session and noted the passage of time between visits. A fifth experienced SP was recruited to proctor the event—orienting the clinicians as they arrived, running the Zoom sessions, and serving as a backup should one of the other SPs not be able to participate. They also were given an overview of case content, portrayal, and event structure. SPs were provided with case materials a week in advance of the portrayal date, were able to ask questions over email, and completed a case-specific training to align portrayal with parameters provided in the inter-visit updates with SP staff in the 45 min preceding the simulation. The SP program follows the Association for Standardized Patient Educators (ASPE) Standards of Best Practice, which “were written to ensure the growth, integrity, and safe application of SP-based education practices.” [ 9 ]

Description of session for clinicians

Clinicians were emailed information and instructions about the event prior to participating in the session (see Appendix ). All sessions were held virtually via the Zoom interface due to the COVID pandemic. During the sessions, there was a brief orientation for participants. The orientation included (1) a brief training in how to use the algorithms; (2) an overview of how to approach the simulated interaction (i.e., as close to real practice as possible); and (3) an overview of the one-on-one interview that would follow to discuss the approaches to implement the management algorithms.

Clinicians then moved into Zoom breakout rooms to begin their patient encounters. Clinicians were given up to 60 min to have their 3 distinct visits per patient. There was a 15-min break, and then another 60 min for the second patient scenario.

For each of the 60-min SP scenarios, clinicians were told that they were about to see a patient who was being seen by one of their partners (Dr. Williams) who recently left the practice. Dr. Williams had started the patient on opioid therapy and had an opioid agreement with the patient. Participants were given a copy of Dr. Williams’ last progress note and the opioid agreement prior to meeting the patient. After reviewing this information, the clinicians joined a Zoom breakout room with the SP portraying their patient. Once the provider ended the first encounter, the portraying SP turned off their camera, and, to reflect the passage of time between visits, the moderator gave the clinicians the results of any testing they ordered and any information about the patient that had changed between the last and next visit. The provider indicated when they were ready to start the next encounter. This process was repeated between the second and third encounter.

Data collection: semi-structured interviews

Immediately after they interacted with the SPs, each participant completed a one-on-one interview to reflect on and assess the experience, as well as to provide feedback on how the algorithms should ultimately be integrated into practices like theirs. Interviews were conducted by three experienced qualitative data specialists who work at Qualitative, Evaluation and Stakeholder Engagement Research Services (Qual EASE) at the University of Pittsburgh. Multiple interviewers conducted the interviews, because multiple interviews needed to be conducted at the same time following each SP session. Interviewers used a semi-structured interview guide developed by the research team that covered the following domains: (1) Assessment of their orientation to the algorithms, including training; (2) Assessment of their interaction with the SPs; (3) Assessment of and opinions on the algorithms; and (4) Description of how they thought the algorithms would operate in their practices, and how they could best be implemented there. Interviews were conducted on Zoom and recorded.

Questions and further probing were used to best assess how the algorithms could be implemented in their practices, which map onto several CFIR domains and constructs as shown in Table 2 .

Within one week of their completion, the qualitative methodologist associated with the project wrote a summary of each interview, which was forwarded to the study team so that they could begin to plan for implementation. Following that initial summary, interviews were transcribed verbatim with identifying details redacted. Under the supervision of the qualitative methodologist, experienced analysts at Qual EASE inductively developed a codebook reflecting the content of the interviews, with coding categories reflecting the four areas of the interview guide mentioned above. Use of the codebook was practiced on two transcripts by 2 Qual EASE coders, following which they both applied the codebook to the remaining 10 transcripts. Cohen’s Kappa statistics were used to assess intercoder reliability; the average kappa score was 0.8565, indicating “near perfect” agreement. The primary coder for the project then conducted a conventional content [ 31 ] and thematic analysis [ 32 , 33 ], which was reviewed by the qualitative methodologist, and shared with the study team to better facilitate implementation planning.

Data collection: development of implementation bundle

The final step to developing the implementation bundle—which included materials for initial training, an online algorithm interface, e-consultation support, and electronic health record (EHR) integration for the 6 algorithms—was to review notes from the structured interviews. The bundle was then drafted and reviewed by the PPABs and co-Is.

Recruitment and study sample

Recruitment emails were sent to Community Medical Inc. (CMI). CMI is a network of 400 primary care and specialty physicians who practice throughout western and central Pennsylvania and provide care for over 495,000 patients. The practices cover a large geographic area; however, the network is predominantly in Allegheny County. Participants were required to be primary care clinicians at CMI practices and at least 18 years of age. Each of the clinicians were recruited to participate in two virtual patient evaluations followed by one-on-one interviews. The experience lasted approximately 4 h and clinicians were paid $1000 for their participation. We ultimately recruited 12 PCPs to participate in the virtual experience, which provided two perspectives for each of the 6 SP cases.

Table 3 summarizes the demographic characteristics of the clinicians participating in our study. All of our participants (100%) were trained as physicians with 33% specializing in Internal Medicine, while 66% specialized in Family Medicine during their residency. There was a prevalence of urban practitioners (58%), followed by those in suburban areas (42%), with an absence of participants from rural locales. We had 42% male and 58% female participants. The racial and ethnic composition of our study cohort is diverse, with White participants comprising the majority at 50%, followed by 33% of participants identifying as Asian. Additional categories encompass Hispanic, Latino, or Spanish origin of any race (17%), and two or more races (17%), with a nuanced representation of other racial and ethnic identities.

Implementation support strategies

When asked about how algorithms should be implemented in practices like theirs, clinicians indicated that the orientation they had received to the algorithms would be a useful implementation support strategy. Other themes illustrating helpful implementation support strategies included (1) the importance of having the algorithm use endorsed by practice leadership, and of having a local “champion” who promoted their use; (2) integration of the algorithm workflow into practice EHRs; (3) practice and location-specific inputs into the algorithms, such that a suggestion to refer to a specialist come with a list of who, specifically, to refer to, or a suggestion to call security provide the practice-specific number for security; (4) access to specialists who could help interpret unclear or difficult-to read drug screens or suggest a particular course of action with a tricky patient.

Representative quotes supporting these themes, as well as the CFIR domains that they map to, are provided in Table 4 . These findings were integrated into an implementation toolkit that included an initial training session followed by a suite of supports, including EHR integration, algorithm guidance hosted on a separate website with links to useful tools, and support for clinician participants via e-consultation.

Simulation feedback from clinicians

We identified two themes related to the physicians’ encounters with the SPs: (1) clinicians found it useful to practice the algorithms with the SPs; (2) while clinicians applauded the skill of the SPs, they noted that not all actual patient counters go so smoothly. Each is presented in more detail below.

Clinicians found it useful to practice the algorithms with the SPs

Clinicians interviewed found it useful to practice the algorithms with the SPs. As will be discussed below, not all clinicians found the scenarios or SP reactions to be fully realistic. However, they did find practicing the algorithms in this way to be a useful way of learning the algorithms. As one provider put it:

It was a good chance to sort of get to look through the algorithm while I’m talking to them and sort of follow along. So, that was good to get familiar with the algorithm itself in a situation where you don’t feel like you’re with a real patient who you’re, like, ignoring to read through the algorithm.

Another provider similarly reflected:

So, that was really helpful, because this is sort of cut and dry of the way it’s written. And not until you’re in an actual patient scenario do you see some of the gray nuances. For example, one of the cases, the patient was having trouble sleeping secondary to pain. So, she was using her oxycodone in the evening to help with sleep, but it was related to pain. So, it wasn’t this clear-cut ‘I’m just using this to fall asleep at night.’ It was ‘I’m using this because at night my pain is worse which is affecting my sleep, so that’s why I’m using it.’ Which is a gray space. So, having the algorithm to sort of follow through and use as a guide let me make sure I’m asking all the right questions, let me make sure I’m offering all the other alternative things, was definitely beneficial.

While clinicians applauded the skill of the SPs, they noted that not all actual patient counters go so smoothly

Many clinicians described the practice session with SPs as being realistic or very similar to encounters with real patients. One provider described themselves as “shocked” at how realistic the SPs were, adding that “I felt very engaged in each of the scenarios. Like, they knew their background, they kind of were living the patient. I was really impressed... the scenarios were spot-on.” Other clinicians described the scenarios as “realistic situations that you can see in the office every day,” and “totally realistic.”

However, some clinicians described pointed differences with real life patient visits. For example, the following provider described that some of their actual patients would simply never agree to the treatment plans presented in the algorithms:

In the back of my mind I’m thinking of my actual patients who I’ve run into these instances and how this would go, and I don’t think it would’ve – it won’t go the way that it went with the SPs. Because it sometimes doesn’t matter how good your rapport is, they just aren’t gonna do what’s suggested... I think I run into much harder stops with some of my real non-SP patients.

Another clinician echoed this description, noting that:

My experience is that patients don’t normally accept what you say so easily. […] The interactions that I have with my patients are not anything like these, ‘cause these were very calm, very reasonable, willing to listen to you; they seemed to have a health literacy level that is well beyond a lot of the patients I deal with.

While these concerns were not voiced by every clinician, they were voiced by clinicians who experienced different scenarios with the SPs, indicating that patients may not always be agreeable to the actions suggested in the algorithm—and that that lack of agreement would be something that would need to be managed in an ongoing patient relationship, rather than disappearing at the end of the role play with the SP.

In this study, we used the SP methodology in combination with one-on-one interviews guided by CFIR to develop an implementation bundle for 6 algorithms designed to address common and challenging behaviors associated with LTOT. We found the use of the SP methodology to be a valuable tool for highlighting important components of an implementation bundle. Specifically, we found that an implementation bundle addressing (1) the importance of having the algorithm use endorsed by practice leadership, and of having a local “champion” who promoted their use; (2) integration of the algorithm workflow into practice EHRs; and (3) practice and location-specific inputs into the algorithms would be most effective in promoting the successful adoption and implementation of the EPBs for the LTOT algorithms. We also found that the SPs were realistic in their portray of patients with LTOT; however, it was noted that patients of the clinicians that participated in the simulations were likely to be more resistant to the adoption of the recommendations outlined by the algorithms than the SP portrayal. SPs are trained to recognize and reward participant skill, which may account for this observation.

Of methodological note in the realm of qualitative research: completing the interviews just after the SP interactions set an excellent stage for collecting qualitative data, likely because clinicians had just had a novel experience that was fresh in their minds. They could also talk about the details of the SP cases without concern for inappropriately describing actual patient cases in too much detail and contrast the SPs with their patients in general. This made for highly engaging interviews in which rapport building between interviewer and interviewee was more easily built. Additionally, interviews were conducted by qualitative research specialists who were not personally invested in the development of the algorithms or orientation to the algorithms, setting the stage for open and honest feedback.

In this study, we used the SP methodology in combination with one-on-one interviews guided by CFIR to develop an implementation bundle for 6 algorithms designed to address common and challenging behaviors associated with LTOT. Our findings underscore the value of the SP methodology in elucidating essential components of the implementation bundle. Specifically, we found that an implementation bundle addressing (1) the importance of having the algorithm use endorsed by practice leadership, and of having a local “champion” who promoted their use; (2) integration of the algorithm workflow into practice EHRs; and (3) practice and location-specific inputs into the algorithms would be most effective in promoting the successful adoption and implementation of the EPBs for the LTOT algorithms. We also found that the SPs were realistic in their portrayal of patients with LTOT; however, it was noted that patients of the clinicians that participated in the simulations were likely to be more resistant to the adoption of the recommendations outlined by the algorithms than the SP portrayal.

Of methodological note in the realm of qualitative research: completing the interviews just after the SP interactions set an excellent stage for collecting qualitative data, likely because the experience was fresh in their minds. They could also talk about the details of the SP cases without concern for inappropriately describing actual patient cases in too much detail and contrast the SPs with their patients in general. This made for highly engaging interviews in which rapport building between interviewer and interviewee was more easily built. Additionally, interviews were conducted by qualitative research specialists who were not personally invested in the development of the algorithms or orientation to the algorithms, setting the stage for open and honest feedback.

Despite the merits of the SP approach in examining EBP implementation, several limitations warrant consideration. The applicability of SP methodology to diverse practices and various points in care management raises questions about its universal relevance. The effectiveness or practicality of SPs for EBP training and adoption may vary across different clinical settings, requiring careful consideration when extrapolating findings to practices with distinct characteristics or specific care management points.

The selection of long-term opioid therapy (LTOT)-related care as a case study introduces a contextual limitation. While SP methodology effectively addresses concerns within LTOT-related care, the transferability of findings to other healthcare scenarios might be constrained. The unique nature of LTOT-related care may not fully capture challenges present in different medical specialties or care contexts.

Additionally, while the goal of this manuscript is to illuminate the SP methodology, our study's findings may not be universally generalizable, considering factors such as regional variations in healthcare practices and differing levels of familiarity with EBP implementation. The dynamic nature of clinical practice introduces a limitation in capturing all potential scenarios through SP methodology. Clinician encounters with patients can vary widely, and SPs may not fully replicate the complexity of real-world situations.

Overall, this study demonstrates the potential of using the SP methodology guided by the CFIR framework to develop effective implementation strategies for improving care in real-world healthcare settings. The use of SPs allowed the research team to observe the EBP in practice with feedback from end-users with experience from different health care clinics. The CFIR framework provided a comprehensive approach to guiding the development of an implementation bundle that addressed the multiple factors that influence EBP implementation. The study’s success prompts further exploration of whether the developed implementation bundle correlates with increased EBP adoption levels to further validate the use of SPs for this purpose.

Availability of data and materials

The dataset supporting the conclusions of this article is available from the corresponding author on reasonable request.

Abbreviations

Consolidated framework for implementation research

Community Medical Inc.

Co-investigator

Evidence-based practice

Electronic health record

Long-term opioid therapy

Primary care physician

Patient-provider advisory board

Simulated patient

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Acknowledgements

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A completed SRQR checklist has been completed for this paper.

We gratefully acknowledge funding from NIDA Agency for our publication through an R34 grant mechanism entitled “Consensus-based algorithms to address opioid misuse behaviors among individuals prescribed long-term opioid therapy: developing implementation strategies and pilot testing.” Project Number: 5R34DA050004-03. Jessica Merlin is supported by the following grant from the NIH: K24DA05683701A1.

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Ellen Green

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Megan Hamm, Catherine Gowl, Reed Van Deusen, Jane M. Liebschutz & Jessica Merlin

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Contributions

EG contributed to the development of the standardized patient protocol and was a major contributor to writing the manuscript. MH conducted the interviews as well as analyzed and interpreted the data. CG and RVD developed the standardized patient protocol and conducted the simulations. JDW and JML contributed to the development of the standardized patient cases and interpretation of the data to ensure it aligned with primary care practice. JM provided oversight to the entire study.

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Correspondence to Ellen Green .

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This study was conducted at School of Medicine, University of Pittsburgh, Pittsburgh PA between June and July of 2021. The University of Pittsburgh IRB determined that the study was considered an exempt-level research project (STUDY20030189).

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Appendix: Instructions for participants

Dear Participant,

Thank you for participating in our study of opioid misuse in primary care.

In this exercise, you will encounter 2 different simulated patients played by standardized patients (SPs) of the SP Program of the University of Pittsburgh School of Medicine. The purpose of these visits is to help us study clinical algorithms for managing opioids.

Therefore, please be aware of the following expectations:

For this simulated scenario, each of these patients were started on opioid treatment by one of your partners who recently left your practice (Dr. Kia Williams). You may not have started opioids if it were up to you, but they have already been started and have an opioid agreement with this practice. Therefore, please focus your time on the algorithms and not on whether the patient should/should not have been started on opioids.

You will see each patient in 3 separate “telemedicine” visits via Zoom. Therefore, you do NOT need to perform a physical examination for these visits.

The “visits” will occur in break out rooms on the Zoom platform. The 1 st visit will be to establish care with you after Dr. Williams has left the practice. The next 2 visits will be follow-up visits.

For each scenario, there will be a “moderator” in the breakout room with you and the SP. The moderator’s camera will be off. This person will be helping with timing of the visits, and they will post updates about the patient’s case before each visit in the chat section.

Therefore, please enable the chat on your screen.

Also, please “hide nonvideo participants”, so the presence of the moderator is not a distraction for you as you conduct the visits. (If you need help in how to do this, please ask, so a team member can walk you through the steps)

In the interest of transparency, the moderators are also SPs. They are not clinicians.

After you are done with the visits, you will meet with researchers from the study to debrief your experience.

Timing of the whole activity:

Orientation: 30 min

Encounter with 1 st patient: 60 min

Break (including time to prepare for 2 nd patient): 15 min

Encounter with 2 nd patient: 60 min

Debrief with researchers: 75 min

Timing of your patient visits: You have 1 h for each session, which includes 3 distinct visits with the same patient. You will see timing banners at 15-min increments, and a 5-min warning. How you divide the time between the three visits is up to you.

If it would help you communicate with the patients in the simulation, here is some information about Dr. Kia Williams:

Dr. Williams recently left your practice to be closer to her family in South Carolina. Her father’s dementia has been worsening, and she wanted to be closer to her family in this time. As your partner, she was well liked by your colleagues, the staff, and her patients. She was an excellent doctor and a friend.

Prior to meeting each patient, we will share Dr. Williams’ last progress note with you. You will have time to review that information before starting the first visit. This note will have information about what work up has been done and what pain treatments have been tried.

The patients are aware of what has been tried, and they can answer these questions, but for the sake of this study, you do not need to explore this in great detail given the limited timeframe of each visit.

Dr. Williams had an opioid agreement with each of the patients you will meet today. This will also be shared with you in case you need to reference it during the sessions.

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Green, E., Hamm, M., Gowl, C. et al. Optimizing evidence-based practice implementation: a case study on simulated patient protocols in long-term opioid therapy. Implement Sci Commun 5 , 44 (2024). https://doi.org/10.1186/s43058-024-00575-y

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DOI : https://doi.org/10.1186/s43058-024-00575-y

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• Systematic Review
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• Published: 26 April 2024

Systematic review on the frequency and quality of reporting patient and public involvement in patient safety research

• Sahar Hammoud   ORCID: orcid.org/0000-0003-4682-9001 1 ,
• Laith Alsabek 1 , 2 ,
• Lisa Rogers 1 &
• Eilish McAuliffe 1  

BMC Health Services Research volume  24 , Article number:  532 ( 2024 ) Cite this article

Metrics details

In recent years, patient and public involvement (PPI) in research has significantly increased; however, the reporting of PPI remains poor. The Guidance for Reporting Involvement of Patients and the Public (GRIPP2) was developed to enhance the quality and consistency of PPI reporting. The objective of this systematic review is to identify the frequency and quality of PPI reporting in patient safety (PS) research using the GRIPP2 checklist.

Searches were performed in Ovid MEDLINE, EMBASE, PsycINFO, and CINAHL from 2018 to December, 2023. Studies on PPI in PS research were included. We included empirical qualitative, quantitative, mixed methods, and case studies. Only articles published in peer-reviewed journals in English were included. The quality of PPI reporting was assessed using the short form of the (GRIPP2-SF) checklist.

A total of 8561 studies were retrieved from database searches, updates, and reference checks, of which 82 met the eligibility criteria and were included in this review. Major PS topics were related to medication safety, general PS, and fall prevention. Patient representatives, advocates, patient advisory groups, patients, service users, and health consumers were the most involved. The main involvement across the studies was in commenting on or developing research materials. Only 6.1% ( n  = 5) of the studies reported PPI as per the GRIPP2 checklist. Regarding the quality of reporting following the GRIPP2-SF criteria, our findings show sub-optimal reporting mainly due to failures in: critically reflecting on PPI in the study; reporting the aim of PPI in the study; and reporting the extent to which PPI influenced the study overall.

Conclusions

Our review shows a low frequency of PPI reporting in PS research using the GRIPP2 checklist. Furthermore, it reveals a sub-optimal quality in PPI reporting following GRIPP2-SF items. Researchers, funders, publishers, and journals need to promote consistent and transparent PPI reporting following internationally developed reporting guidelines such as the GRIPP2. Evidence-based guidelines for reporting PPI should be encouraged and supported as it helps future researchers to plan and report PPI more effectively.

Trial registration

The review protocol is registered with PROSPERO (CRD42023450715).

Peer Review reports

Patient safety (PS) is defined as “the absence of preventable harm to a patient and reduction of risk of unnecessary harm associated with healthcare to an acceptable minimum” [ 1 ]. It is estimated that one in 10 patients are harmed in healthcare settings due to unsafe care, resulting in over three million deaths annually [ 2 ]. More than 50% of adverse events are preventable, and half of these events are related to medications [ 3 , 4 ]. There are various types of adverse events that patients can experience such as medication errors, patient falls, healthcare-associated infections, diagnostic errors, pressure ulcers, unsafe surgical procedures, patient misidentification, and others [ 1 ].

Over the last few decades, the approach of PS management has shifted toward actively involving patients and their families in managing PS. This innovative approach has surpassed the traditional model where healthcare providers were the sole managers of PS [ 5 ]. Recent research has shown that patients have a vital role in promoting their safety and decreasing the occurrence of adverse events [ 6 ]. Hence, there is a growing recognition of patient and family involvement as a promising method to enhance PS [ 7 ]. This approach includes involving patients in PS policy development, research, and shared decision making [ 1 ].

In the last decade, research involving patients and the public has significantly increased. In the United Kingdom (U.K), the National Institute for Health Research (NIHR) has played a critical role in providing strategic and infrastructure support to integrate Public and Patient Involvement (PPI) throughout publicly funded research [ 8 ]. This has established a context where PPI is recognised as an essential element in research [ 9 ]. In Ireland, the national government agency responsible for the management and delivery of all public health and social services; the National Health Service Executive (HSE) emphasise the importance of PPI in research and provide guidance for researchers on how to involve patients and public in all parts of the research cycle and knowledge translation process [ 10 ]. Similar initiatives are also developing among other European countries, North America, and Australia. However, despite this significant expansion of PPI research, the reporting of PPI in research articles continues to be sub-optimal, inconsistent, and lacks essential information on the context, process, and impact of PPI [ 9 ]. To address this problem, the Guidance for Reporting Involvement of Patients and the Public (GRIPP) was developed in 2011 following the EQUATOR methodology to enhance the quality, consistency, and transparency of PPI reporting. Additionally, to provide guidance for researchers, patients, and the public to advance the quality of the international PPI evidence-base [ 11 ]. The first GRIPP checklist was a significant start in producing higher-quality PPI reporting; however, it was developed following a systematic review, and did not include any input from the international PPI research community. Given the importance of reaching consensus in generating current reporting guidelines, a second version of the GRIPP checklist (GRIPP2) was developed to tackle this problem by involving the international PPI community in its development [ 9 ]. There are two versions of the GRIPP2 checklist, a long form (GRIPP2-LF) for studies with PPI as the primary focus, and a short form (GRIPP2-SF) for studies with PPI as secondary or tertiary focus.

Since the publication of the GRIPP2 checklist, several systematic reviews have been conducted to assess the quality of PPI reporting on various topics. For instance, Bergin et al. in their review to investigate the nature and impact of PPI in cancer research, reported a sub-optimal quality of PPI reporting using the GRIPP2-SF, mainly due to failure to address PPI challenges [ 12 ]. Similarly, Owyang et al. in their systematic review to assess the prevalence, extent, and quality of PPI in orthopaedic practice, described a poor PPI reporting following the GRIPP2-SF checklist criteria [ 13 ]. While a few systematic reviews have been conducted to assess theories, strategies, types of interventions, and barriers and enablers of PPI in PS [ 5 , 14 , 15 , 16 ], no previous review has assessed the quality of PPI reporting in PS research. Thus, our systematic review aims to address this knowledge gap. The objective of this review is to identify the frequency PPI reporting in PS research using the GRIPP2 checklist from 2018 (the year after GRIPP2 was published) and the quality of reporting following the GRIPP2-SF. The GRIPP2 checklist was chosen as the benchmark as it is the first international, evidence-based, community consensus informed guideline for the reporting of PPI in research and more specifically in health and social care research [ 9 ]. Additionally, it is the most recent report-focused framework and the most recommended by several leading journals [ 17 ].

We followed the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines to plan and report this review [ 18 ]. The review protocol was published on PROSPERO the International Database of Prospectively Registered Systematic Reviews in August 2023 (CRD42023450715).

Search strategy

For this review, we used the PICo framework to define the key elements in our research. These included articles on patients and public (P-Population) involvement (I- phenomenon of Interest) in PS (C-context). Details are presented in Table  1 . Four databases were searched including Ovid MEDLINE, EMBASE, PsycINFO, and CINAHL to identify papers on PPI in PS research. A systematic search strategy was initially developed using MEDLINE. MeSH terms and keywords relevant to specific categories (e.g., patient safety) were combined using the “OR” Boolean term (i.e. patient safety OR adverse event OR medical error OR surgical error) and categories were then combined using the “AND” Boolean term. (i.e. “patient and public involvement” AND “patient safety”). The search strategy was adapted for the other three databases. Full search strategies are provided in Supplementary file 1 . The search was conducted on July 27th, 2023, and was limited to papers published from 2018. As the GRIPP2 tool was published in 2017, this limit ensured the retrieval of relevant studies. An alert system was set on the four databases to receive all new published studies until December 2023, prior to the final analysis. The search was conducted without restrictions on study type, research design, and language. To reduce selection bias, hand searching was carried out on the reference lists of all the eligible articles in the later stages of the review. This was done by the first author. The search strategy was developed by the first author and confirmed by the research team and a Librarian. The database search was conducted by the first author.

Inclusion and exclusion criteria

Studies on PPI in PS research with a focus on health/healthcare were included in this review. We defined PPI as active involvement which is in line with the NIHR INVOLVE definition as “research being carried out ‘with’ or ‘by’ members of the public rather than ‘to’, ‘about’ or ‘for’ them” [ 19 ]. This includes any PPI including, being a co-applicant on a research project or grant application, identifying research priorities, being a member of an advisory or steering group, participating in developing research materials or giving feedback on them, conducting interviews with study participants, participating in recruitment, data collection, data analysis, drafting manuscripts and/or dissemination of results. Accordingly, we excluded studies where patients or the public were only involved as research participants.

We defined patients and public to include patients, relatives, carers, caregivers and community, which is also in line with the NIHR PPI involvement in National Health Service [ 19 ].

Patient safety included topics on medication safety, adverse events, communication, safety culture, diagnostic errors, and others. A full list of the used terms for PPI and PS is provided in Supplementary file 1 . Regarding the research type and design, we included empirical qualitative, quantitative, mixed methods, and case studies. Only articles published in peer-reviewed journals and in English were included.

Any article that did not meet the inclusion criteria was excluded. Studies not reporting outcomes were excluded. Furthermore, review papers, conference abstracts, letters to editor, commentary, viewpoints, and short communications were excluded. Finally, papers published prior to 2018 were excluded.

Study selection

The selection of eligible studies was done by the first and the second authors independently, starting with title and abstracts screening to eliminate papers that failed to meet our inclusion criteria. Then, full text screening was conducted to decide on the final included papers in this review. Covidence, an online data management system supported the review process, ensuring reviewers were blinded to each other’s decisions. Disagreements between reviewers were discussed first, in cases where the disagreement was not resolved, the fourth author was consulted.

Data extraction and analysis

A data extraction sheet was developed using excel then piloted, discussed with the research team and modified as appropriate. The following data were extracted: citation and year of publication, objective of the study, country, PS topic, design, setting, PPI participants, PPI stages (identifying research priorities, being a member of an advisory or steering group, etc.…), frequency of PPI reporting as per the GRIPP2 checklist, and the availability of a plain language summary. Additionally, data against the five items of GRIPP2-SF (aim of PPI in the study, methods used for PPI, outcomes of PPI including the results and the extent to which PPI influenced the study overall, and reflections on PPI) were extracted. To avoid multiple publication bias and missing outcomes, data extraction was done by the first and the second authors independently and then compared. Disagreements between reviewers were first discussed, and then resolved by the third and fourth authors if needed.

Quality assessment

The quality of PPI reporting was assessed using GRIPP2-SF developed by Staniszewska et al. [ 9 ] as it is developed to improve the quality, consistency, and reporting of PPI in social and healthcare research. Additionally the GRIPP2-SF is suitable for all studies regardless of whether PPI is the primary, secondary, or tertiary focus, whereas the GRIPP2-LF is not suitable for studies where PPI serves as a secondary or tertiary focus. The checklist includes five items (mentioned above) that authors should include in their studies. It is important to mention that Staniszewska et al. noted that “while GRIPP2-SF aims to guide consistent reporting, it is not possible to be prescriptive about the exact content of each item, as the current evidence-base is not advanced enough to make this possible” ([ 9 ] p5). For that reason, we had to develop criteria for scoring the five reporting items. We used three scoring as Yes, No, and partial for each of the five items of the GRIPP2-SF. Yes, was given when authors presented PPI information on the item clearly in the paper. No, when no information was provided, and partial when the information partially met the item requirement. For example, as per GRIPP2-SF authors should provide a clear description of the methods used for PPI in the study. In the example given by Staniszewska et al., information on patient/public partners and how many of them were provided, as well as the stages of the study they were involved in (i.e. refining the focus of the research questions, developing the search strategy, interpreting results). Thus, in our evaluation of the included studies, we gave a yes if information on PPI participants (i.e. patient partners, community partners, or family members etc..) and how many of them were involved was provided, and information on the stages or actions of their involvement in the study was provided. However, we gave a “partial” if information was not fully provided (i.e. information on patient/public partners and how many were involved in the study without describing in what stages or actions they were involved, and vice versa), and a “No” if no information was presented at all.

The quality of PPI reporting was done by the first and the second authors independently and then compared. Disagreements between reviewers were first discussed, and then resolved by the third and fourth author when needed.

Assessing the quality or risk of bias of the included studies was omitted, as the focus in this review was on appraising the quality of PPI reporting rather than assessing the quality of each research article.

Data synthesis

After data extraction, a table summarising the included studies was developed. Studies were compared according to the main outcomes of the review; frequency of PPI reporting following the GRIPP2 checklist and the quality of reporting as per GRIPP2-SF five items, and the availability of a plain language summary.

Search results and study selection

The database searches yielded a total of 8491 studies. First, 2496 were removed as duplicates. Then, after title and abstract screening, 5785 articles were excluded leaving 210 articles eligible for the full text review. After a careful examination, 68 of these studies were included in this review. A further 38 studies were identified from the alert system that was set on the four databases and 32 studies from the reference check of the included studies. Of these 70 articles, 56 were further excluded and 14 were added to the previous 68 included studies. Thus, 82 studies met the inclusion criteria and were included in this review. A summary of the database search results and the study selection process are presented in Fig.  1 .

PRISMA flow diagram of the study selection process. The PRISMA flow diagram details the review search results and selection process

Overview of included studies

Details of the study characteristics including first author and year of publication, objective, country, study design, setting, PS topic, PPI participants and involvement stages are presented in Supplementary file 2 . The majority of the studies were conducted in the U.K ( n  = 24) and the United States of America ( n  = 18), with the remaining 39 conducted in other high income countries, the exception being one study in Haiti. A range of study designs were identified, the most common being qualitative ( n  = 31), mixed methods ( n  = 13), interventional ( n  = 5), and quality improvement projects ( n  = 4). Most PS topics concerned medication safety ( n  = 17), PS in general (e.g., developing a PS survey or PS management application) ( n  = 14), fall prevention ( n  = 13), communication ( n  = 11), and adverse events ( n  = 10), with the remaining PS topics listed in Supplementary file 2 .

Patient representatives, advocates, and patient advisory groups ( n  = 33) and patients, service users, and health consumers ( n  = 32) were the main groups involved. The remaining, included community members/ organisations. Concerning PPI stages, the main involvement across the studies was in commenting on or developing research materials ( n  = 74) including, patient leaflets, interventional tools, mobile applications, and survey instruments. Following this stage, involvement in data analysis, drafting manuscripts, and disseminating results ( n  = 30), and being a member of a project advisory or steering group ( n  = 18) were the most common PPI evident in included studies. Whereas the least involvement was in identifying research priorities ( n  = 5), and being a co-applicant on a research project or grant application ( n  = 6).

Regarding plain language summary, only one out of the 82 studies (1.22%) provided a plain language summary in their paper [ 20 ].

Frequency and quality of PPI reporting

The frequency of PPI reporting following the GRIPP2 checklist was 6.1%, where only five of the 82 included studies reported PPI in their papers following the GRIPP2 checklist. The quality of PPI reporting in those studies is presented in Table  2 . Of these five studies, one study (20%) did not report the aim of PPI in the study and one (20%) did not comment on the extent to which PPI influenced the study overall.

The quality of PPI reporting of the remaining 77 studies is presented in Table  3 . The aim of PPI in the study was reported in 62.3% of articles ( n  = 48), while 3.9% ( n  = 3) partially reported this. A clear description of the methods used for PPI in the study was reported in 79.2% of papers ( n  = 61) and partially in 20.8% ( n  = 16). Concerning the outcomes, 81.8% of papers ( n  = 63) reported the results of PPI in the study, while 10.4% ( n  = 8) partially did. Of the 77 studies, 68.8% ( n  = 53) reported the extent to which PPI influenced the study overall and 3.9% ( n  = 3) partially reported this. Finally, 57.1% ( n  = 44) of papers critically reflected on the things that went well and those that did not and 2.6% ( n  = 2) partially reflected on this.

Summary of main findings

This systematic review assessed the frequency of reporting PPI in PS research using the GRIPP2 checklist and quality of reporting using the GRIPP2-SF. In total, 82 studies were included in this review. Major PS topics were related to medication safety, general PS, and fall prevention. Patient representatives, advocates, patient advisory groups, patients, service users, and health consumers were the most involved. The main involvement across the studies was in commenting on or developing research materials such as educational and interventional tools, survey instruments, and applications while the least was in identifying research priorities and being a co-applicant on a research project or grant application. Thus, significant effort is still needed to involve patients and the public in the earlier stages of the research process given the fundamental impact of PS on their lives.

Overall completeness and applicability of evidence

A low frequency of reporting PPI in PS research following the GRIPP2 guidelines was revealed in this review, where only five of the 82 studies included mentioned that PPI was reported as per the GRIPP2 checklist. This is despite it being the most recent report-focused framework and the most recommended by several leading journals [ 17 ]. This was not surprising as similar results were reported in recent reviews in other healthcare topics. For instance, Musbahi et al. in their systematic review on PPI reporting in bariatric research reported that none of the 90 papers identified in their review mentioned or utilised the GRIPP2 checklist [ 102 ]. Similarly, a study on PPI in orthodontic research found that none of the 363 included articles reported PPI against the GRIPP2 checklist [ 103 ].

In relation to the quality of reporting following the GRIPP2-SF criteria, our findings show sub-optimal reporting within the 77 studies that did not use GRIPP2 as a guide/checklist to report their PPI. Similarly, Bergin et al. in their systematic review to investigate the nature and impact of PPI in cancer research concluded that substandard reporting was evident [ 12 ]. In our review, this was mainly due to failure to meet three criteria. First, the lowest percentage of reporting (57.1%, n  = 44) was related to critical reflection on PPI in the study (i.e., what went well and what did not). In total, 31 studies (42.9%) did not provide any information on this, and two studies were scored as partial. The first study mentioned that only involving one patient was a limitation [ 27 ] and the other stated that including three patients in the design of the tool was a strength [ 83 ]. Both studies did not critically comment or reflect on these points so that future researchers are able to avoid such problems and enhance PPI opportunities. For instance, providing the reasons/challenges behind the exclusive inclusion of a single patient and explaining how this limits the study findings and conclusion would help future researchers to address these challenges. Likewise, commenting on why incorporating three patients in the design of the study tool could be seen as a strength would have been beneficial. This could be, fostering diverse perspectives and generating novel ideas for developing the tool. Similar to our findings, Bergin et al. in their systematic review reported that 40% of the studies failed to meet this criterion [ 12 ].

Second, only 48 out of 77 articles (62.3%) reported the aim of PPI in their study, which is unlike the results of Bergin et al. where most of the studies (93.1%) in their review met this criterion [ 12 ]. Of the 29 studies which did not meet this criterion in our review, few mentioned in their objective developing a consensus-based instrument [ 41 ], reaching a consensus on the patient-reported outcomes [ 32 ], obtaining international consensus on a set of core outcome measures [ 98 ], and facilitating a multi-stakeholder dialogue [ 71 ] yet, without indicating anything in relation to patients, patient representatives, community members, or any other PPI participants. Thus, the lack of reporting the aim of PPI was clearly evident in this review. Reporting the aim of PPI in the study is crucial for promoting transparency, methodological rigor, reproducibility, and impact assessment of the PPI.

Third, 68.8% ( n  = 53) of the studies reported the extent to which PPI influenced the study overall including positive and negative effects if any. This was again similar to the findings of Bergin et al., where 38% of the studies did not meet this criterion mainly due to a failure to address PPI challenges in their respective studies [ 12 ]. Additionally, Owyang et al. in their review on the extent, and quality of PPI in orthopaedic practice, also described a poor reporting of PPI impact on research [ 13 ]. As per the GRIPP2 guidelines, both positive and negative effects of PPI on the study should be reported when applicable. Providing such information is essential as it enhances future research on PPI in terms of both practice and reporting.

Reporting a clear description of the methods used for PPI in the study was acceptable, with 79.2% of the papers meeting this criterion. Most studies provided information in the methods section of their papers on the PPI participants, their number, stages of their involvement and how they were involved. Providing clear information on the methods used for PPI is vital to give the reader a clear understanding of the steps taken to involve patients, and for other researchers to replicate these methods in future research. Additionally, reporting the results of PPI in the study was also acceptable with 81.8% of the papers reporting the outcomes of PPI in the results section. Reporting the results of PPI is important for enhancing methodological transparency, providing a more accurate interpretation for the study findings, contributing to the overall accountability and credibility of the research, and informing decision making.

Out of the 82 studies included in this review, only one study provided a plain language summary. We understand that PS research or health and medical research in general is difficult for patients and the public to understand given their diverse health literacy and educational backgrounds. However, if we expect patients and the public to be involved in research then, it is crucial to translate this research that has a huge impact on their lives into an easily accessible format. Failing to translate the benefits that such research may have on patient and public lives may result in them underestimating the value of this research and losing interest in being involved in the planning or implementation of future research [ 103 ]. Thus, providing a plain language summary for research is one way to tackle this problem. To our knowledge, only a few health and social care journals (i.e. Cochrane and BMC Research Involvement and Engagement) necessitate a plain language summary as a submission requirement. Having this as a requirement for submission is crucial in bringing the importance of this issue to researchers’ attention.

Research from recent years suggests that poor PPI reporting in articles relates to a lack of submission requirements for PPI reporting in journals and difficulties with word limits for submitted manuscripts [ 13 ]. Price et al. assessed the frequency of PPI reporting in published papers before and after the introduction of PPI reporting obligations by the British Medical Journal (BMJ) [ 104 ]. The authors identified an increase in PPI reporting in papers published by BMJ from 0.5% to 11% between the periods of 2013–2014 and 2015–2016. The study findings demonstrate the impact of journal guidelines in shaping higher quality research outputs [ 13 ]. In our review, we found a low frequency of PPI reporting in PS research using the GRIPP2 checklist, alongside sub-optimal quality of reporting following GRIPP2-SF. This could potentially be attributed to the absence of submission requirements for PPI reporting in journals following the GRIPP2 checklist, as well as challenges posed by word limits.

Strengths and limitations

This systematic review presents an overview on the frequency of PPI reporting in PS research using the GRIPP2 checklist, as well as an evaluation of the quality of reporting following the GRIPP2-SF. As the first review to focus on PS research, it provides useful knowledge on the status of PPI reporting in this field, and the extent to which researchers are adopting and adhering to PPI reporting guidelines. Despite these strengths, our review has some limitations that should be mentioned. First, only English language papers were included in this review due to being the main language of the researchers. Thus, there is a possibility that relevant articles on PPI in PS research may have been omitted. Another limitation is related to our search which was limited to papers published starting 2018 as the GRIPP2 guidelines were published in 2017. Thus it is probable that the protocols of some of these studies were developed earlier than the publication of the GRIPP2 checklist, meaning that PPI reporting following GRIPP2 was not common practice and thus not adopted by these studies. This might limit the conclusions we can draw from this review. Finally, the use of GRIPP2 to assess the quality of PPI reporting might be a limitation as usability testing has not yet been conducted to understand how the checklist works in practice with various types of research designs. However, the GRIPP2 is the first international, evidence-based, community consensus informed guideline for the reporting of PPI in health and social care research. Reflections and comments from researchers using the GRIPP2 will help improve its use in future studies.

Implications for research and practice

Lack of PPI reporting not only affects the quality of research but also implies that others cannot learn from previous research experience. Additionally, without consistent and transparent reporting it is difficult to evaluate the impact of various PPI in research [ 9 ]: “if it is not reported it cannot be assessed” ([ 105 ] p19). Enhanced PPI reporting will result in a wider range and richer high-quality evidence-based PPI research, leading to a better understanding of PPI use and effectiveness [ 103 ]. GRIPP2 reporting guidelines were developed to provide guidance for researchers, patients, and the public to enhance the quality of PPI reporting and improve the quality of the international PPI evidence-base. The guidance can be used prospectively to plan PPI or retrospectively to guide the structure or PPI reporting in research [ 9 ]. To enhance PPI reporting, we recommend the following;

Publishers and journals

First, we encourage publishers and journals to require researchers to report PPI following the GRIPP2 checklist. Utilising the short or the long version should depend on the primary focus of the study (i.e., if PPI is within the primary focus of the research then the GRIPP2-LF is recommended). Second, we recommend that journals and editorial members advise reviewers to evaluate PPI reporting within research articles following the GRIPP2 tool and make suggestions accordingly. Finally, we encourage journals to add a plain language summary as a submission requirement to increase research dissemination and improve the accessibility of research for patients and the public.

Researchers

Though there is greater evidence of PPI in research, it is still primarily the researchers that are setting the research agenda and deciding on the research questions to be addressed. Thus, significant effort is still needed to involve patients and the public in the earlier stages of the research process given the fundamental impact of PS on their lives. To enhance future PPI reporting, perhaps adding a criterion following the GRIPP2 tool to existing EQUATOR checklists for reporting research papers such as STROBE, PRISMA, CONSORT, may support higher quality research. Additionally, currently, there is no detailed explanation paper for the GRIPP2 where each criterion is explained in detail with examples. Addressing this gap would be of great benefit to guide the structure of PPI reporting and to explore the applicability of each criterion in relation to different stages of PPI in research. For instance, having a detailed explanation for each criterion across different research studies having various PPI stages would be of high value to improve future PPI reporting given the growing interest in PPI research in recent years and the relatively small PPI evidence base in health and medical research.

Funding bodies can also enhance PPI reporting by adding a requirement for researchers to report PPI following the GRIPP2 checklist. In Ireland, the National HSE has already initiated this by requiring all PPI in HSE research in Ireland to be reported following the GRIPP2 guidelines [ 10 ].

This study represents the first systematic review on the frequency and quality of PPI reporting in PS research using the GRIPP2 checklist. Most PS topics were related to medication safety, general PS, and fall prevention. The main involvement across the studies was in commenting on or developing research materials. Thus, efforts are still needed to involve patients and the public across all aspects of the research process, especially earlier stages of the research cycle. The frequency of PPI reporting following the GRIPP2 guidelines was low, and the quality of reporting following the GRIPP2-SF criteria was sub-optimal. The lowest percentages of reporting were on critically reflecting on PPI in the study so future research can learn from this experience and work to improve it, reporting the aim of the PPI in the study, and reporting the extent to which PPI influenced the study overall including positive and negative effects. Researchers, funders, publishers, journals, editorial members and reviewers have a responsibility to promote consistent and transparent PPI reporting following internationally developed reporting guidelines such as the GRIPP2. Evidence-based guidelines for reporting PPI should be supported to help future researchers plan and report PPI more effectively, which may ultimately improve the quality and relevance of research.

Availability of data and materials

All data generated or analysed during this study are included in this published article and its Supplementary information files.

Abbreviations

• Patient safety

United Kingdom

National Institute for Health Research

Public and Patient Involvement

Health Service Executive

Guidance for Reporting Involvement of Patients and the Public

Second version of the GRIPP checklist

Long form of GRIPP2

Short form of GRIPP2

Preferred Reporting Items for Systematic Reviews and Meta-Analyses

The International Database of Prospectively Registered Systematic Reviews

British Medical Journal

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Acknowledgements

This research is funded as part of the Collective Leadership and Safety Cultures (Co-Lead) research programme which is funded by the Irish Health Research Board, grant reference number RL-2015–1588 and the Health Service Executive. The funders had no role in the study conceptualisation, design, data collection, analysis, decision to publish, or preparation of the manuscript.

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S.H and E.M.A designed the study. S.H developed the search strategies with feedback from L.A, L.R, and E.M.A. S.H conducted all searches. S.H and L.A screened the studies, extracted the data, and assessed the quality of PPI reporting. S.H analysed the data with feedback from E.M.A. S.H drafted the manuscript. All authors revised and approved the submitted manuscript. All authors agreed to be personally accountable for the author's own contributions and to ensure that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved.

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Hammoud, S., Alsabek, L., Rogers, L. et al. Systematic review on the frequency and quality of reporting patient and public involvement in patient safety research. BMC Health Serv Res 24 , 532 (2024). https://doi.org/10.1186/s12913-024-11021-z

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Received : 10 January 2024

Accepted : 21 April 2024

Published : 26 April 2024

DOI : https://doi.org/10.1186/s12913-024-11021-z

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