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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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• 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.

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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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Writing a Case Study

What is a case study?

A Case study is: 

• An in-depth research design that primarily uses a qualitative methodology but sometimes​​ includes quantitative methodology.
• Used to examine an identifiable problem confirmed through research.
• Used to investigate an individual, group of people, organization, or event.
• Used to mostly answer "how" and "why" questions.

What are the different types of case studies?

Note: These are the primary case studies. As you continue to research and learn

about case studies you will begin to find a robust list of different types. 

Who are your case study participants?

What is triangulation ? 

Validity and credibility are an essential part of the case study. Therefore, the researcher should include triangulation to ensure trustworthiness while accurately reflecting what the researcher seeks to investigate.

How to write a Case Study?

When developing a case study, there are different ways you could present the information, but remember to include the five parts for your case study.

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• Last Updated: Apr 18, 2024 3:46 PM
• URL: https://resources.nu.edu/researchtools

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• Published: 27 June 2011

The case study approach

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

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

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

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Introduction

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

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

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

What is a case study?

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

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

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

What are case studies used for?

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

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

How are case studies conducted?

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

Defining the case

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

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

Selecting the case(s)

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

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

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

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

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

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

Collecting the data

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

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

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

Analysing, interpreting and reporting case studies

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

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

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

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

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

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

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

Conclusions

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

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Acknowledgements

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

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

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Crowe, S., Cresswell, K., Robertson, A. et al. The case study approach. BMC Med Res Methodol 11 , 100 (2011). https://doi.org/10.1186/1471-2288-11-100

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Case Study Research Design

The case study research design have evolved over the past few years as a useful tool for investigating trends and specific situations in many scientific disciplines.

This article is a part of the guide:

• Research Designs
• Quantitative and Qualitative Research
• Literature Review
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• Descriptive Research

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• 1 Research Designs
• 2.1 Pilot Study
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• 2.3 Qualitative Research Design
• 2.4 Quantitative and Qualitative Research
• 3.1 Case Study
• 3.2 Naturalistic Observation
• 3.3 Survey Research Design
• 3.4 Observational Study
• 4.1 Case-Control Study
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• 4.3 Longitudinal Study
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• 6.3 Double Blind Experiment
• 6.4 Factorial Design
• 7.1 Literature Review
• 7.2 Systematic Reviews
• 7.3 Meta Analysis

The case study has been especially used in social science, psychology, anthropology and ecology.

This method of study is especially useful for trying to test theoretical models by using them in real world situations. For example, if an anthropologist were to live amongst a remote tribe, whilst their observations might produce no quantitative data, they are still useful to science.

What is a Case Study?

Basically, a case study is an in depth study of a particular situation rather than a sweeping statistical survey . It is a method used to narrow down a very broad field of research into one easily researchable topic.

Whilst it will not answer a question completely, it will give some indications and allow further elaboration and hypothesis creation on a subject.

The case study research design is also useful for testing whether scientific theories and models actually work in the real world. You may come out with a great computer model for describing how the ecosystem of a rock pool works but it is only by trying it out on a real life pool that you can see if it is a realistic simulation.

For psychologists, anthropologists and social scientists they have been regarded as a valid method of research for many years. Scientists are sometimes guilty of becoming bogged down in the general picture and it is sometimes important to understand specific cases and ensure a more holistic approach to research .

H.M.: An example of a study using the case study research design.

The Argument for and Against the Case Study Research Design

Some argue that because a case study is such a narrow field that its results cannot be extrapolated to fit an entire question and that they show only one narrow example. On the other hand, it is argued that a case study provides more realistic responses than a purely statistical survey.

The truth probably lies between the two and it is probably best to try and synergize the two approaches. It is valid to conduct case studies but they should be tied in with more general statistical processes.

For example, a statistical survey might show how much time people spend talking on mobile phones, but it is case studies of a narrow group that will determine why this is so.

The other main thing to remember during case studies is their flexibility. Whilst a pure scientist is trying to prove or disprove a hypothesis , a case study might introduce new and unexpected results during its course, and lead to research taking new directions.

The argument between case study and statistical method also appears to be one of scale. Whilst many 'physical' scientists avoid case studies, for psychology, anthropology and ecology they are an essential tool. It is important to ensure that you realize that a case study cannot be generalized to fit a whole population or ecosystem.

Finally, one peripheral point is that, when informing others of your results, case studies make more interesting topics than purely statistical surveys, something that has been realized by teachers and magazine editors for many years. The general public has little interest in pages of statistical calculations but some well placed case studies can have a strong impact.

How to Design and Conduct a Case Study

The advantage of the case study research design is that you can focus on specific and interesting cases. This may be an attempt to test a theory with a typical case or it can be a specific topic that is of interest. Research should be thorough and note taking should be meticulous and systematic.

The first foundation of the case study is the subject and relevance. In a case study, you are deliberately trying to isolate a small study group, one individual case or one particular population.

For example, statistical analysis may have shown that birthrates in African countries are increasing. A case study on one or two specific countries becomes a powerful and focused tool for determining the social and economic pressures driving this.

In the design of a case study, it is important to plan and design how you are going to address the study and make sure that all collected data is relevant. Unlike a scientific report, there is no strict set of rules so the most important part is making sure that the study is focused and concise; otherwise you will end up having to wade through a lot of irrelevant information.

It is best if you make yourself a short list of 4 or 5 bullet points that you are going to try and address during the study. If you make sure that all research refers back to these then you will not be far wrong.

With a case study, even more than a questionnaire or survey , it is important to be passive in your research. You are much more of an observer than an experimenter and you must remember that, even in a multi-subject case, each case must be treated individually and then cross case conclusions can be drawn .

How to Analyze the Results

Analyzing results for a case study tends to be more opinion based than statistical methods. The usual idea is to try and collate your data into a manageable form and construct a narrative around it.

Use examples in your narrative whilst keeping things concise and interesting. It is useful to show some numerical data but remember that you are only trying to judge trends and not analyze every last piece of data. Constantly refer back to your bullet points so that you do not lose focus.

It is always a good idea to assume that a person reading your research may not possess a lot of knowledge of the subject so try to write accordingly.

In addition, unlike a scientific study which deals with facts, a case study is based on opinion and is very much designed to provoke reasoned debate. There really is no right or wrong answer in a case study.

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From David E. Gray \(2014\). Doing Research in the Real World \(3rd ed.\) London, UK: Sage.

Research Methods and Design

• Action Research
• Case Study Design
• Literature Review
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a research strategy whose characteristics include

• a focus on the interrelationships that constitute the context of a specific entity (such as an organization, event, phenomenon, or person),
• analysis of the relationship between the contextual factors and the entity being studied, and
• the explicit purpose of using those insights (of the interactions between contextual relationships and the entity in question) to generate theory and/or contribute to extant theory. 

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What is the value of working with case studies.

Professor Todd Landman explains how case studies can be used in research. He discusses the importance of choosing a case study correctly and warns about limitations of case study research.

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This study uses systems thinking as a duplicated research methodology to define and validate a case study early. This case study is a part of a complex sociotechnical research project. We use a systemigram to visualize the case study, including its different aspects, also called embedded units of analysis. This visualization aids in sharing, understanding, and stimulating discussion, explanation, and communication among heterogeneous stakeholders from industry and academia. We support the systemigram as a conceptual model with other systems thinking tools, including a context diagram, and customers, actors, transformation, worldview, owner, and environment (CATWOE) analysis. In addition, we applied other tools, such as workflow analysis and stakeholder analysis. We found that using systems thinking and its tools, mainly systemigram, aids researchers in well-defining, understanding, validating, and communicating the case study, its context, its aspects, its goals, and its relations among the heterogeneous stakeholders.

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Acknowledgments

This research is part of a larger research project, the second iteration of the Human Systems Engineering Innovation Framework (HSEIF-2), funded by The Research Council of Norway (Project number 317862). We also take the opportunity to thank the Norwegian Industrial Systems Engineering Research Group (NISE) for their valuable discussions and feedback. We also acknowledge the company for their practical cooperation, especially Bjørn Stokkeland. We also thank Aline Pereira Da Silva for contributing as a co-researcher in the workshops.

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Appendix A: Stakeholders, an Elaboration of These Stakeholders and Their Interests

This Appendix lists Table 2 . Table 2 illustrates the stakeholders (who), an elaboration of these stakeholders (what), and their interests (who).

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Ali, H.B., Muller, G. (2024). Systems Thinking Design in Action: A Duplicated Novel Approach to Define Case Studies. In: Verma, D., Madni, A.M., Hoffenson, S., Xiao, L. (eds) The Proceedings of the 2023 Conference on Systems Engineering Research. CSER 2023. Conference on Systems Engineering Research Series. Springer, Cham. https://doi.org/10.1007/978-3-031-49179-5_37

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ORIGINAL RESEARCH article

Identification of factors associated with hospitalization in an outpatient population with mental health conditions: a case–control study.

• 1 Pôle Centre Rive Gauche, CH Le Vinatier, Bron, France
• 2 Université Claude Bernard Lyon 1, Villeurbanne, France
• 3 Pôle Santé Publique, Hospices Civils de Lyon, Lyon, France
• 4 UMR 5992 CNRS, U1028 INSERM, Centre de Recherche en Neurosciences de Lyon, Bron, France
• 5 Hospices Civils de Lyon, Lyon, France
• 6 UMR 5229 CNRS, Centre Ressource de Réhabilitation psychosociale, Le Vinatier, Bron, France

Introduction: Addressing relevant determinants for preserved person-centered rehabilitation in mental health is still a major challenge. Little research focuses on factors associated with psychiatric hospitalization in exclusive outpatient settings. Some variables have been identified, but evidence across studies is inconsistent. This study aimed to identify and confirm factors associated with hospitalization in a specific outpatient population.

Methods: A retrospective monocentric case-control study with 617 adult outpatients (216 cases and 401 controls) from a French community-based care facility was conducted. Participants had an index outpatient consultation between June 2021 and February 2023. All cases, who were patients with a psychiatric hospitalization from the day after the index outpatient consultation and up to 1 year later, have been included. Controls have been randomly selected from the same facility and did not experience a psychiatric hospitalization in the 12 months following the index outpatient consultation. Data collection was performed from electronic medical records. Sociodemographic, psychiatric diagnosis, historical issues, lifestyle, and follow-up-related variables were collected retrospectively. Uni- and bivariate analyses were performed, followed by a multivariable logistic regression.

Results: Visit to a psychiatric emergency within a year (adjusted odds ratio (aOR): 13.02, 95% confidence interval (CI): 7.32–23.97), drug treatment discontinuation within a year (aOR: 6.43, 95% CI: 3.52–12.03), history of mental healthcare without consent (aOR: 5.48, 95% CI: 3.10–10.06), medical follow-up discontinuation within a year (aOR: 3.17, 95% CI: 1.70–5.95), history of attempted suicide (aOR: 2.50, 95% CI: 1.48–4.30) and unskilled job (aOR: 0.26, 95% CI: 0.10–0.65) are the independent variables found associated with hospitalization for followed up outpatients.

Conclusions: Public health policies and tools at the local and national levels should be adapted to target the identified individual determinants in order to prevent outpatients from being hospitalized.

1 Introduction

The deinstitutionalization process in psychiatry began in the late twentieth century. This shift, especially seen in high-income countries, consists of a decrease in specialized psychiatric hospital beds for an increase of patients with a mental health condition, followed up in general medical hospitals, community-based care, and various outpatient settings ( 1 ). Between the mid-twentieth century and the 1990s, the number of psychiatric beds dropped to more than 80% in most western regions around the world ( 1 ).

However, the transition from an inpatient setting paradigm to an outpatient one needs to be carefully organized, with the necessary and appropriate structures and funding. Indeed, patients who suffer from a mental health disease need a deep consideration of the multifaceted world in which they live, to integrate and adapt their rehabilitation process for the outside world. The strengthening of community services has been heterogenous around the world ( 1 ). This deinstitutionalization failed, for example, in many places in the USA, leading to an increase in homelessness and crime among people with psychiatric diseases in the 1990s ( 2 ). More recently, there are still concerns about the good transitioning process that have been raised in central and eastern Europe, with a large body of evidence showing failures in deinstitutionalization and reinstitutionalization outcomes. Some of the causes found are lack of personal assistance, development and adaptation of social housing, and cuts to social support ( 3 ). The limited scaling up of community-based and primary care mental health services has also been identified as a failure factor of deinstitutionalization, along with fundamental concerns with the model. A deeper work on addressing social determinants is indeed also evoked, which are known to be fundamental structural drivers of mental illness ( 1 ). A relatively recent dramatic event that has to be remembered regarding the deinstitutionalization failure has been the “Life Esidimeni scandal” in 2016 in South Africa. Qualified as a humanitarian crisis, this event caused the deaths of a thousand psychiatric patients (94 according to an official report issued in 2017 ( 4 )) following their transfer from an inpatient setting to multiple outpatient settings without the appropriate care and follow-up required. Indeed, the cut in this 2,000-bed facility budget led to patients’ discharge regardless of individual autonomy and psychosocial disability into inadequately resourced nongovernmental facilities ( 5 ).

Deinstitutionalization requires strong, continuous efforts and should always stay person-centered. In this approach, the multidisciplinary team caring for the patient must bear in mind the individual factors that can predict the maintained recovery of the patient in the outpatient setting ( 6 ). Few settings succeed yet to address all structural determinants, even in high-income countries ( 1 ). Indeed, the Lancet Commission on Global Mental Health and Sustainable Development reminded us that regarding mental health, all countries are “developing” due to the relative underfunding of mental health services in relation to the burden of the condition ( 7 ). Ways to achieve success with deinstitutionalization may involve legislation with a mandate to establish community-based services (like in Italy ( 8 )) and to adapt them to a local context. Improvements will probably require a multitude of paradigm shifts within these structures, considering factors enabling their enhancement. If no adequate care is provided during deinstitutionalization or after it, patients may relapse after being discharged from the hospital and consequently readmitted. Many studies therefore considered readmission rate to be an indicator for intervention studies ( 1 ) and to identify protective and risk factors of relapses ( 9 ) ( 10 , 11 ).

A rich scientific literature is available on the study of risk factors of hospitalization in patients suffering from mental health pathologies. Nonexhaustively, for depression ( 12 ), the type of illness diagnosis, psychiatric comorbidity, treatment-related factors, and sociodemographic factors were associated with hospitalization. For bipolar disorders ( 13 ), characteristics of the index hospitalization (transfer, discharge disposition, length of stay), all-cause acute health service utilization in the year prior to it, and comorbidity were identified. For schizophrenia ( 14 , 15 ), recent medical follow-up discontinuation, medication nonadherence, life events, comorbidity, sex, age, and medication type were variables associated with hospitalization. Finally, for other psychiatric conditions ( 16 ) ( 9 , 10 ) ( 17 ) ( 11 ) ( 18 ), factors associated with hospitalization were shown to be recent medical follow-up discontinuation, multiple psychiatric hospitalization history, history of mental healthcare without consent, social isolation, socioeconomic status, violence history, psychiatric diagnosis, and patient’s satisfaction with treatment. A suicide attempt was found to be a risk factor for hospitalization in some studies and a protective factor at 1 year in others.

Nonetheless, the studies cited above only evaluate risk factors for readmission, i.e., for patients that are originally coming from an inpatient hospital setting. Literature focusing on an exclusive outpatient setting is scarce ( 19 , 20 ). It confirmed some previously identified risk factors in studies with an inpatient setting, such as alcohol/substance use, family history of mental health disease, and marital status, but have also diverging results for negative attitude/poor compliance with medication, identified by Antonio Ciudad et al. ( 20 ) as lowering the hazard of relapse during outpatient follow-up.

A systematic review of the literature carried out by Donisi et al. ( 11 ) additionally underlined some inhomogeneous results for identified risk factors associated with readmissions regarding sociodemographic variables, and a literature weakness for social support, considered only in a few papers. Furthermore, the authors emphasized that some factors were only identified in uni- or bivariate analyses and not in multiple regression.

More people are followed up in outpatient settings, and the minimal use of hospitalization remains a challenge in mental health. This study is of interest to mental health professionals and policymakers because more data on factors associated with hospitalization in followed up outpatients could help tailor appropriate follow-up care and adapt existing tools to reduce the need for hospitalization. Our study, therefore, aimed to identify and confirm risk factors of hospitalization in a specific outpatient population.

2.1 Study design

We conducted an observational, retrospective, monocentric case-control study based on hospitalization in one of the largest university-affiliated public psychiatric hospitals in France, with around 500 beds and 26,500 patients followed up on an outpatient basis, the Centre Hospitalier le Vinatier (CHV) in Bron. The CHV has several community-based care facilities called “Centre Médico-Psychologique” or “CMP”, providing medical–psychological and social consultations to anyone experiencing psychological difficulties. The present study was made in one of them. We reported this case-control study according to Strengthening the Reporting of Observational Studies in Epidemiology (STROBE). For details, see Supplementary File 1 .

This retrospective study investigated the data from patients followed up in an outpatient setting from June 2021 to February 2023. This study period has been chosen in order not to have repercussions of the health restrictions due to the COVID-19 pandemic on our variables. The studied sample comes from the Centre Médico-Psychologique Centre Rive Gauche facility, administratively attached to the CHV but which has an independent operation for outpatients requiring mental healthcare in a defined geographic area (third, sixth, and eighth districts of Lyon).

In this facility, participants were eligible if they were aged 18 or older and had at least one outpatient psychiatric medical consultation between June 2021 and February 2023 (defined as the index consultation).

The sample size for this study was determined considering an odds ratio of 1.5 to 3 clinically meaningful based on previous literature. With a significance level of 0.05, a type I error of 0.025, and a power of 0.9, the required sample size was calculated using R and its Epicalc package 2.9.0.1. An estimate was then made with the lowest and highest expected frequencies for the studied variables. An ideal sample size was calculated and ranged between 807 and 423, with an approximate 1:2 case/control ratio.

2.3 Outcome

The studied outcome was full psychiatric hospitalization from the day after the index outpatient consultation and up to 1 year later. Full psychiatric hospitalization was defined in this study as more than 24 h of hospitalization in a psychiatric hospital. Thus, participants who had this outcome of interest were referred to as cases, whereas others who did not have the outcome of interest were referred to as controls.

2.4 Selection of cases and controls

Cases were patients who had a full psychiatric hospitalization from the day after the index outpatient consultation and up to 1 year later.

Controls were patients who did not experience full psychiatric hospitalization in the 12 months following the index outpatient consultation (therefore, controls have an index outpatient consultation before February 2022 to have at least a 1-year psychiatric hospitalization-free period).

All cases in the sample responding to the case definition were included ( n = 216).

Controls ( n = 401) were then randomly selected from the sample list of patients who met the definition of controls in order to approximately respect a 1:2 case/control ratio and the sample size determination. The random selection was performed with simple random sampling using computer-generated random numbers to ensure an unbiased selection process.

All the detailed characteristics of cases and controls can be found in Table 1 .

Table 1 Descriptive analysis.

2.5 Variables

The following exposure or potential confounder variables were collected retrospectively from patients’ medical records (collected in a binary yes/no format for qualitative variables):

I. Sociodemographic variables: age (in years, quantitative variable), gender, birth in France, unemployed (including patients on sick leave but not retired patients), unskilled worker (i.e., job accessible without special qualifications, only job category collected), homeless, partner of life (in a relationship).

II. Main psychiatric diagnosis (1 only), according to the ICD-11: depressive disorders, schizophrenia or other primary psychotic disorders, bipolar or related disorders, anxiety or fear-related disorders, neurodevelopmental disorders, another psychiatric diagnosis (other diagnosis belonging to the ICD-11 category 6: mental, behavioral, or neurodevelopmental disorders).

III. Psychiatric comorbidity: the presence of a psychiatric comorbidity (in addition to the main diagnosis, the presence of another psychiatric disorder falling under category 6 of the ICD-11).

IV. Historical issues and lifestyle: traumatic history (exhaustively: rape and/or sexual assault and/or loss of first-degree relative before the patient’s age of 18 and/or torture and/or major physical assault and/or loss of a child by suicide and/or violent death of a first-degree relative in front of the patient and/or patient placed in foster care during childhood, and/or direct witness to a homicide), history of mental healthcare without consent (medical treatment undertaken without the consent of the patient being treated, as permitted by law), multiple psychiatric hospitalization history (> 5 full psychiatric hospitalization), alcohol abuse within the year (diagnosed by the psychiatrist as pathologic, and corresponding to the ICD-11 codes 6C40.0, 6C40.1, 6C40.20, 6C40.21, and 6C40.3), illicit drug abuse within the year (regular consumption of an illicit substance greater than 1/week), family history of mental health disease (known psychiatric disorder within the patient’s biological family), history of attempted suicide, and drug side effect reported within the year (presence of a side effect documented on the patient’s medical record).

V. Follow-up-related variables: visit to psychiatric emergency within the year (excluding the one that led to full psychiatric hospitalization of the case definition), drug treatment discontinuation within the year (discontinuation by the patient, without medical agreement, of a psychiatric background treatment regimen over a period of more than 1 week), medical follow-up discontinuation within the year, additional support within the year (follow-up by a psychiatrist at least twice a year and/or regular follow-up by a medical mobile team (> 1/trimester) and/or included in a psychoeducation care program with a total hourly volume > 15 h/year), and time since first admission to the psychiatric hospital in outpatient or inpatient setting (in years, quantitative variable).

The term “within the year” refers to the variable being present 12 months prior to hospitalization for cases or 12 months following the index consultation for controls.

These variables were chosen because they have already been identified in the literature as factors associated with psychiatric hospitalization or suggested to be potential risk factors or confounders.

We hypothesized that all variables might be potential confounders and were indiscriminately tested to include them in the regression model (see Section 2.6) and to control for potential confounders.

2.6 Analysis

Statistical analysis was conducted using R software version 4.2.1 (23 June 2022) (R Core Team, 2022). Collected variables in case and control groups have been compared using a bivariate analysis ( Table 1 ). For quantitative variables, the Student’s t -test was used. For qualitative variables (dichotomous variables collected in a yes/no format), a Chi-square ( χ 2 ) test was performed.

Multivariable logistic regression was used to study the relationship between the outcome and the assessed covariables (listed in Section 2.2) with adjusted odds ratios (aORs) and 95% confidence intervals (CIs). In the analysis and to interpret its results, control group variables were considered baseline/reference category and case variables were compared to them. Based on the significant factors identified in the univariate analysis, variables were added to the model when p < 0.10. The model was built using a forward, stepwise selection procedure. It involves iteratively adding variables to the model one at a time, based on their individual contribution to improving the model’s fit. The fitness of the models was compared with a likelihood-ratio test. The choice was made to work on a subset of patients without missing data (complete case analysis). Interactions between variables included in the model were tested. They were considered when they appeared significant ( p -value < 0.01 to avoid multiple testing problems) and had an interpretable clinical meaning. The multiple logistic regression model was adjusted for all the risk factor variables included in the full model ( Table 2 ). The data normality of residuals for this multiple logistic regression was assessed by the Shapiro–Wilk normality test.

Table 2 Univariate analysis and results of a multiple logistic regression model predicting psychiatric hospital admission of outpatients (on a no missing values dataset, n = 521).

2.7 Data collection and ethical approval

Data were retrieved from the CHV’s electronic medical record system by reading through each medical record one by one. It was collected anonymously and entered directly into a secure document to ensure the confidentiality and privacy of participants. Personal identifying information such as names, addresses, and contact details were not recorded. Instead, each participant was assigned a unique identification code, which was used to perform the analyses with the studied variables. All data were stored securely and accessible only to authorized research personnel. Only the first author acquired data to guarantee reproducibility. Only the selected variables cited above were collected in the binary format “yes” or “no”, except for the two quantitative variables “age” and “time since first admission to the psychiatric hospital in outpatient or inpatient setting” collected in years (whole number).

To ensure data reliability, data were directly collected during the reading of each medical record.

Ethical approval was obtained by the Ethics Committee of the CHV with the registration number CEREVI/2023/003 on 27 February 2023. The study was conducted in accordance with the Declaration of Helsinki.

3.1 Participants and missing data investigation

All eligible cases have been included in the study (216 cases). Based on the number of cases and the predetermined targeted sample size, 401 controls were included out of a total eligible population of 1,044. The included controls were randomly selected from the sample list of eligible controls.

No missing data were observed for n = 521 patients out of the 617 included in the study.

When considering the mechanism underlying these missing data, it is important to note that they predominantly pertain to variables that necessitate investigating past events. Specifically, these pertain to the presence or absence of a family history of mental health diseases ( n = 55 missing data points out of 617, i.e., 8.9%), the presence or not of personal traumatic history ( n = 38 missing data points out of 617, i.e., 6.2%), and whether or not there was a history of suicide attempt ( n = 17 missing data out of 617, i.e., 2.8%). The other variables have less than 10 missing data points each. The details regarding missing data points for each of the variables within cases and controls are available in Table 1 .

3.2 Sociodemographic characteristics and descriptive analysis

Data from N = 617 patients followed up in an outpatient setting from June 2021 to February 2023 have been investigated for descriptive analysis (216 cases and 401 controls). Men were a higher proportion of cases (65.3%) than controls (56.1%). Cases were slightly younger than controls, with a mean age of 42.7 years old versus 45.1 years, respectively. Unemployment was higher among cases than controls (75.9% of unemployment for cases versus 62.8% for controls), and in parallel, more people had unskilled work in the control group (18.0% versus 5.1% in the case group ( p < 0.001)). Homelessness was much more prevalent among cases than controls, with 13.4% of homeless individuals among cases versus 2.5% for controls ( p < 0.001).

There was a difference in proportion for the main psychiatric diagnosis between groups for depression, schizophrenia or other primary psychotic disorders, anxiety or fear-related disorders, and neurodevelopmental disorders. Schizophrenia, or other primary psychotic disorders, was the main diagnosed psychiatric disease in our population (66.7% and 51.4% for cases and controls, respectively, p < 0.001).

For historical issues and lifestyle variables: case and control groups significantly differed in proportion for history of mental healthcare without consent, multiple psychiatric hospitalization history (> 5), alcohol or illicit drug abuse within the year, family history of mental health disease, and history of attempted suicide ( p < 0.001 except for family history of mental health disease with p = 0.007).

Finally, considering follow-up-related variables, strong significant proportion differences between groups for the following variables were observed ( p < 0.001): visit to a psychiatric emergency, drug treatment discontinuation, medical follow-up discontinuation, and additional support (all within the year). For the variables: visit to psychiatric emergency, drug treatment discontinuation, and medical follow-up discontinuation, the rates were all higher among cases than controls with respectively 60.2%, 58.3%, and 49.1% (cases) versus 7.7%, 12.5%, and 14.0% (controls). Conversely, additional support had a higher proportion in controls (93.8%) than in cases (84.3%) ( p < 0.001).

Table 1 describes the detailed sociodemographic, clinical, personal history, and follow-up characteristics of cases and controls ( N = 617).

3.3 Analytic statistics: multivariable modeling using multiple logistic regression

For the analytic statistics, modeling was conducted using a subset of patients without missing data (complete case analysis) with n = 521. According to our model, we found that six independent variables are significantly associated with full psychiatric hospitalization for patients being followed up in an outpatient setting. Indeed, in multivariable analysis, psychiatric hospitalization of outpatients remained strongly associated with a visit to a psychiatric emergency within a year (aOR: 13.02 [95% CI: 7.32–23.97]), a drug treatment or medical follow-up discontinuation within a year (aOR: 6.43 [95% CI: 3.52–12.03] and aOR: 3.17 [95% CI: 1.70–5.95], respectively), a history of mental healthcare without consent (aOR: 5.48 [95% CI: 3.10–10.06]), and a history of attempted suicide (aOR: 2.50 [95% CI: 1.48–4.30]). Finally, having a work (unskilled work) was conversely associated with a smaller risk of psychiatric hospitalization (aOR: 0.26 [95% CI: 0.10–0.65]). Estimates of adjusted odds ratio were calculated using logistic regression adjusted for the variables included in the model: “visit to a psychiatric emergency within a year”, “drug treatment discontinuation within a year”, “history of mental healthcare without consent”, “medical follow-up discontinuation within a year”, “history of attempted suicide”, and “unskilled job”.

Table 2 presents these identified variables with their respective odds ratios and confidence intervals.

4 Discussion

This study aimed to identify and confirm variables associated with hospitalization, including both protective and risk factors. This information aims to guide and establish appropriate vigilance and follow-up care for mental health in an outpatient setting.

According to our multivariable logistic regression model, six variables have been independently found to be significantly associated with full hospitalization in psychiatry for patients followed up in an outpatient setting: visit to a psychiatric emergency within a year, drug treatment discontinuation within a year, history of mental healthcare without consent, medical follow-up discontinuation within a year, history of attempted suicide, and unskilled job. These findings highlight the importance of considering follow-up-related, historical issues and sociodemographic determinants for successful outpatient rehabilitation and, by extension, deinstitutionalization.

Visit to a psychiatric emergency within the year was the most strongly associated variable with hospitalization and had an aOR of 13.02 (95% CI: 7.32–23.97) in our model. This result is in line with literature that identified emergency visits associated with hospitalization, but to a lesser extent and not in an exclusive outpatient setting like in our study ( 21 ) ( 10 ). Drug treatment discontinuation within the year was associated with an aOR of 6.43 (95% CI: 3.52–12.03). A systematic literature review by Donisi et al. ( 11 ) identified medication compliance as a factor associated with readmissions of psychiatric patients, but Antonio Ciudad et al. ( 20 ) found conflicting results for schizophrenic outpatients. A recent study on early psychiatric rehospitalization also found mental health prescription adherence as a predictor of rehospitalization with a random forest analysis ( 10 ). Medication compliance is known to be an important and challenging factor in the care of psychiatric patients ( 22 ). Our study identified and confirmed the importance of medication compliance in an outpatient setting. History of mental healthcare without consent was also associated with hospitalization (aOR: 5.48, 95% CI: 3.10–10.06). We can assume that patients with a history of care without consent are the ones with bad insight into their illness and are therefore more complex patients, requiring more frequent hospitalization. This risk factor has already been identified, particularly in schizophrenic patients ( 23 ). In another study, conducted without distinction of psychiatric pathology and still in an inpatient setting, no statistical association was found ( 18 ). Medical follow-up discontinuation in psychiatry has also already been studied in the literature. Anne Nelson et al. examined whether patients discharged from inpatient psychiatric care (and not originated from outpatient care like in our study) would have lower rehospitalization rates if they kept an outpatient follow-up appointment after discharge ( 17 ). The authors showed a greater rate of rehospitalization for patients who did not keep an appointment after discharge. The same conclusions have been drawn on a general psychiatric inpatient population ( 10 ) and on a study focused on schizophrenia ( 14 ). In our study, where patients come from an outpatient setting, we also found that medical follow-up discontinuation is a risk factor for hospitalization (aOR: 3.17, 95% CI: 1.70–5.95). A history of attempted suicide also appeared to be a risk factor for psychiatric hospitalization for patients followed up in an outpatient mental health setting, with a 2.50 aOR (95% CI: 1.48–4.30). However, the literature shows conflicting results. Some studies also confirm this risk factor, which has previously been identified in studies conducted in inpatient settings ( 18 , 24 ); in other studies, this risk factor was unclear, with nonsignificant results ( 11 , 21 , 25 ). The ability to have a job, which has been collected in our study with the variable “unskilled worker”, has been identified as a protective factor in the multivariable logistic regression model ( p -value: 0.006) adjusted for potential confounders, as illustrated in Table 2 : aOR of 0.26 (95% CI: 0.10–0.65). We explain this protective effect by assuming that controls, supposed to be clinically less severe than cases, with fewer symptoms, are more likely to get and keep a work. Having a job is indeed linked with cognitive remediation and the recovery process ( 26 ). “Unskilled worker” has been the only job category collected because other job categories were almost nonexistent in our population.

The community-based outpatient setting of the present study is particularly interesting regarding its population characteristics. Indeed, it offers multi-professional monitoring, which is valuable for patients with severe illnesses. With 75.9% of cases and 62.8% of controls unemployed in our study, this strongly suggests that mental disability significantly impacts psychosocial determinants, highlighting its importance. As with other chronic illnesses, psychological disability is a barrier to employment, and the severity of the condition is related to the ability to work ( 26 ). This might also explain the protective effect found in the association of the variable “unskilled worker”. Patients followed up regularly in this setting are also considered “severe” for other reasons. They often cannot follow a liberal mental health specialist due to poor socioeconomic conditions and may have a too severe psychiatric disorder requiring hospital practitioners (due to complex pharmacotherapeutics or illness) to reach a stable medical state. From a clinical point of view, most patients having a main diagnosis of schizophrenia or other primary psychotic disorders (66.7% among cases and 51.4% among controls) is another argument for the population severity, with patients who cannot be adequately followed up by general practitioners and/or private psychiatrists. Interestingly, this does not represent the psychiatric diseases distribution of general population and is even the opposite. Indeed, in France, anxiety disorders have the highest prevalence, followed by depression, bipolar disorders, and finally, psychotic disorders ( 27 ). Regarding historical issues and lifestyle, the prevalence of traumatic history was notably high in both groups, with around 60% prevalence. Mental health conditions are well-known to have multifactorial origins ( 28 ). Nevertheless, it is noteworthy to observe the prevalence of traumatic exposure within our study population. The high proportions of patients with mental healthcare without consent history and multiple psychiatric hospitalization histories (> 5) also underline the specificities of our outpatient population, which have a certain severity. Multidisciplinary community-based care has the potential to address the specific needs of the population within the framework of deinstitutionalization when considering the identified determinants.

The case–control design and the multivariable logistic regression utilized have, however, their limitations. Firstly, the population selection has been made through “hospital recruitment” (outpatient service attached to the CHV public psychiatric hospital). It can therefore introduce a selection bias regarding the admission probability of participants to that public outpatient service (e.g., patients with poorest socioeconomic conditions). Nonetheless, as the probability of admission to that service relies on the geographical sectorization (population originating from a defined geographic urban area: third, sixth, and eighth districts of Lyon) and has few equivalents in the private sector, we consider this bias to be existent but limited. To limit classification bias, classification was made on electronical medical records identically for cases and controls. Sectorization also prevents the risk of missing a hospitalization in another facility by ensuring the patient is ultimately hospitalized in his or her local hospital. Confusion bias has been considered via modeling with multivariable logistic regression. We assessed interactions in our model with one being significant (variable history of mental healthcare without consent with variable history of attempted suicide, adjusted p -value of 0.004). We, however, decided not to include this interaction in the model because (i) the clinical relevance of this interaction was not key in our exploratory investigation, and we do not seek a predictive model; (ii) considering that this interaction barely improves our overall model significance (residual deviance of 361 when considered versus 370, p -value: 0.003). Lastly, a limitation of our model is the absence of residuals normality for this multiple logistic regression. Indeed, residuals do not seem independent of the predicted values. Some explanatory variables would thus be lacking and not exhaustively listed in this study, such as variables on education level or on patient’s attitude and perception.

The highlights of this study are, however, its overall consistency with literature data on previously identified risk factors associated with hospitalization and the confirmation of these factors in an exclusive outpatient setting. The recruitment method used in this study with the sectorization principle of the service is also a robust point because it allowed to limit selection bias and consider all the patients followed up in this special outpatient setting.

5 Conclusion

Our study identified several independent risk and protective factors for hospitalization among patients with a mental health condition who are being treated in an outpatient setting. These factors include variables related to follow-up, such as a recent visit to a psychiatric emergency and recent discontinuation of drug treatment or medical follow-up (within the year), as well as historical issues or lifestyle-related factors.

To our knowledge, this is the first time that these factors are assessed statistically together in a specific outpatient setting, with patients not originating exclusively from a hospital. That is of great interest in the deinstitutionalization era. Public health policies at local and to a bigger extent, at the national scale, should consider these new data to target and tailor appropriate follow-up of care in outpatient settings. Tools to distinguish patients with the identified risk factors and prevent them from being hospitalized should also be created and adapted.

Data availability statement

The data analyzed in this study is subject to the following licenses/restrictions: Medical information that cannot be shared according to the ethical approval obtained by the Ethics Committee of the CHV with the registration number CEREVI/2023/003 on 02/27/2023. Requests to access these datasets should be directed to [email protected].

Ethics statement

The studies involving humans were approved by Ethics Committee of the CHV with the registration number CEREVI/2023/003 on 02/27/2023. The studies were conducted in accordance with the local legislation and institutional requirements. The ethics committee/institutional review board waived the requirement of written informed consent for participation from the participants or the participants’ legal guardians/next of kin because the data were obtained in routine care practice with patient information and possible retraction. The study was carried out in accordance with current legislations.

Author contributions

ML: Writing – review & editing, Writing – original draft, Visualization, Validation, Software, Resources, Project administration, Methodology, Investigation, Formal Analysis, Data curation, Conceptualization. RM: Writing – review & editing, Supervision, Resources, Project administration, Funding acquisition, Conceptualization. CD: Writing – review & editing, Visualization, Validation, Supervision, Methodology. LZ: Writing – review & editing, Validation, Supervision, Project administration. JP: Writing – review & editing, Software, Data curation. NF: Writing – review & editing, Validation, Supervision, Resources, Project administration, Funding acquisition.

The author(s) declare that no financial support was received for the research, authorship, and/or publication of this article.

Conflict of interest

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

Publisher’s note

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

Supplementary material

The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fpsyt.2024.1341160/full#supplementary-material

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Keywords: mental health system, outpatient clinic, deinstitutionalization, epidemiology, public health

Citation: Lebrat M, Megard R, Dananché C, Zimmer L, Plasse J and Franck N (2024) Identification of factors associated with hospitalization in an outpatient population with mental health conditions: a case–control study. Front. Psychiatry 15:1341160. doi: 10.3389/fpsyt.2024.1341160

Received: 19 November 2023; Accepted: 28 March 2024; Published: 18 April 2024.

Reviewed by:

Copyright © 2024 Lebrat, Megard, Dananché, Zimmer, Plasse and Franck. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY) . The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

*Correspondence: Matthieu Lebrat, [email protected]

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

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The case study approach

Sarah crowe.

1 Division of Primary Care, The University of Nottingham, Nottingham, UK

Kathrin Cresswell

2 Centre for Population Health Sciences, The University of Edinburgh, Edinburgh, UK

Ann Robertson

3 School of Health in Social Science, The University of Edinburgh, Edinburgh, UK

Anthony Avery

Aziz sheikh.

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

Introduction

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

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

This paper is structured around the following main questions: What is a case study? What are case studies used for? How are case studies conducted? What are the potential pitfalls and how can these be avoided? We draw in particular on four of our own recently published examples of case studies (see Tables ​ Tables1, 1 , ​ ,2, 2 , ​ ,3 3 and ​ and4) 4 ) and those of others to illustrate our discussion[ 3 - 7 ].

Example of a case study investigating the reasons for differences in recruitment rates of minority ethnic people in asthma research[ 3 ]

Example of a case study investigating the process of planning and implementing a service in Primary Care Organisations[ 4 ]

Example of a case study investigating the introduction of the electronic health records[ 5 ]

Example of a case study investigating the formal and informal ways students learn about patient safety[ 6 ]

What is a case study?

A case study is a research approach that is used to generate an in-depth, multi-faceted understanding of a complex issue in its real-life context. It is an established research design that is used extensively in a wide variety of disciplines, particularly in the social sciences. A case study can be defined in a variety of ways (Table ​ (Table5), 5 ), the central tenet being the need to explore an event or phenomenon in depth and in its natural context. It is for this reason sometimes referred to as a "naturalistic" design; this is in contrast to an "experimental" design (such as a randomised controlled trial) in which the investigator seeks to exert control over and manipulate the variable(s) of interest.

Definitions of a case study

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

These are however not necessarily mutually exclusive categories. In the first of our examples (Table ​ (Table1), 1 ), we undertook an intrinsic case study to investigate the issue of recruitment of minority ethnic people into the specific context of asthma research studies, but it developed into a instrumental case study through seeking to understand the issue of recruitment of these marginalised populations more generally, generating a number of the findings that are potentially transferable to other disease contexts[ 3 ]. In contrast, the other three examples (see Tables ​ Tables2, 2 , ​ ,3 3 and ​ and4) 4 ) employed collective case study designs to study the introduction of workforce reconfiguration in primary care, the implementation of electronic health records into hospitals, and to understand the ways in which healthcare students learn about patient safety considerations[ 4 - 6 ]. Although our study focusing on the introduction of General Practitioners with Specialist Interests (Table ​ (Table2) 2 ) was explicitly collective in design (four contrasting primary care organisations were studied), is was also instrumental in that this particular professional group was studied as an exemplar of the more general phenomenon of workforce redesign[ 4 ].

What are case studies used for?

According to Yin, case studies can be used to explain, describe or explore events or phenomena in the everyday contexts in which they occur[ 1 ]. These can, for example, help to understand and explain causal links and pathways resulting from a new policy initiative or service development (see Tables ​ Tables2 2 and ​ and3, 3 , for example)[ 1 ]. In contrast to experimental designs, which seek to test a specific hypothesis through deliberately manipulating the environment (like, for example, in a randomised controlled trial giving a new drug to randomly selected individuals and then comparing outcomes with controls),[ 9 ] the case study approach lends itself well to capturing information on more explanatory ' how ', 'what' and ' why ' questions, such as ' how is the intervention being implemented and received on the ground?'. The case study approach can offer additional insights into what gaps exist in its delivery or why one implementation strategy might be chosen over another. This in turn can help develop or refine theory, as shown in our study of the teaching of patient safety in undergraduate curricula (Table ​ (Table4 4 )[ 6 , 10 ]. Key questions to consider when selecting the most appropriate study design are whether it is desirable or indeed possible to undertake a formal experimental investigation in which individuals and/or organisations are allocated to an intervention or control arm? Or whether the wish is to obtain a more naturalistic understanding of an issue? The former is ideally studied using a controlled experimental design, whereas the latter is more appropriately studied using a case study design.

Case studies may be approached in different ways depending on the epistemological standpoint of the researcher, that is, whether they take a critical (questioning one's own and others' assumptions), interpretivist (trying to understand individual and shared social meanings) or positivist approach (orientating towards the criteria of natural sciences, such as focusing on generalisability considerations) (Table ​ (Table6). 6 ). Whilst such a schema can be conceptually helpful, it may be appropriate to draw on more than one approach in any case study, particularly in the context of conducting health services research. Doolin has, for example, noted that in the context of undertaking interpretative case studies, researchers can usefully draw on a critical, reflective perspective which seeks to take into account the wider social and political environment that has shaped the case[ 11 ].

Example of epistemological approaches that may be used in case study research

How are case studies conducted?

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

Defining the case

Carefully formulated research question(s), informed by the existing literature and a prior appreciation of the theoretical issues and setting(s), are all important in appropriately and succinctly defining the case[ 8 , 12 ]. Crucially, each case should have a pre-defined boundary which clarifies the nature and time period covered by the case study (i.e. its scope, beginning and end), the relevant social group, organisation or geographical area of interest to the investigator, the types of evidence to be collected, and the priorities for data collection and analysis (see Table ​ Table7 7 )[ 1 ]. A theory driven approach to defining the case may help generate knowledge that is potentially transferable to a range of clinical contexts and behaviours; using theory is also likely to result in a more informed appreciation of, for example, how and why interventions have succeeded or failed[ 13 ].

Example of a checklist for rating a case study proposal[ 8 ]

For example, in our evaluation of the introduction of electronic health records in English hospitals (Table ​ (Table3), 3 ), we defined our cases as the NHS Trusts that were receiving the new technology[ 5 ]. Our focus was on how the technology was being implemented. However, if the primary research interest had been on the social and organisational dimensions of implementation, we might have defined our case differently as a grouping of healthcare professionals (e.g. doctors and/or nurses). The precise beginning and end of the case may however prove difficult to define. Pursuing this same example, when does the process of implementation and adoption of an electronic health record system really begin or end? Such judgements will inevitably be influenced by a range of factors, including the research question, theory of interest, the scope and richness of the gathered data and the resources available to the research team.

Selecting the case(s)

The decision on how to select the case(s) to study is a very important one that merits some reflection. In an intrinsic case study, the case is selected on its own merits[ 8 ]. The case is selected not because it is representative of other cases, but because of its uniqueness, which is of genuine interest to the researchers. This was, for example, the case in our study of the recruitment of minority ethnic participants into asthma research (Table ​ (Table1) 1 ) as our earlier work had demonstrated the marginalisation of minority ethnic people with asthma, despite evidence of disproportionate asthma morbidity[ 14 , 15 ]. In another example of an intrinsic case study, Hellstrom et al.[ 16 ] studied an elderly married couple living with dementia to explore how dementia had impacted on their understanding of home, their everyday life and their relationships.

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

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

The selected case study site(s) should allow the research team access to the group of individuals, the organisation, the processes or whatever else constitutes the chosen unit of analysis for the study. Access is therefore a central consideration; the researcher needs to come to know the case study site(s) well and to work cooperatively with them. Selected cases need to be not only interesting but also hospitable to the inquiry [ 8 ] if they are to be informative and answer the research question(s). Case study sites may also be pre-selected for the researcher, with decisions being influenced by key stakeholders. For example, our selection of case study sites in the evaluation of the implementation and adoption of electronic health record systems (see Table ​ Table3) 3 ) was heavily influenced by NHS Connecting for Health, the government agency that was responsible for overseeing the National Programme for Information Technology (NPfIT)[ 5 ]. This prominent stakeholder had already selected the NHS sites (through a competitive bidding process) to be early adopters of the electronic health record systems and had negotiated contracts that detailed the deployment timelines.

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

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

Collecting the data

In order to develop a thorough understanding of the case, the case study approach usually involves the collection of multiple sources of evidence, using a range of quantitative (e.g. questionnaires, audits and analysis of routinely collected healthcare data) and more commonly qualitative techniques (e.g. interviews, focus groups and observations). The use of multiple sources of data (data triangulation) has been advocated as a way of increasing the internal validity of a study (i.e. the extent to which the method is appropriate to answer the research question)[ 8 , 18 - 21 ]. An underlying assumption is that data collected in different ways should lead to similar conclusions, and approaching the same issue from different angles can help develop a holistic picture of the phenomenon (Table ​ (Table2 2 )[ 4 ].

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

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

Analysing, interpreting and reporting case studies

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

The Framework approach is a practical approach, comprising of five stages (familiarisation; identifying a thematic framework; indexing; charting; mapping and interpretation) , to managing and analysing large datasets particularly if time is limited, as was the case in our study of recruitment of South Asians into asthma research (Table ​ (Table1 1 )[ 3 , 24 ]. Theoretical frameworks may also play an important role in integrating different sources of data and examining emerging themes. For example, we drew on a socio-technical framework to help explain the connections between different elements - technology; people; and the organisational settings within which they worked - in our study of the introduction of electronic health record systems (Table ​ (Table3 3 )[ 5 ]. Our study of patient safety in undergraduate curricula drew on an evaluation-based approach to design and analysis, which emphasised the importance of the academic, organisational and practice contexts through which students learn (Table ​ (Table4 4 )[ 6 ].

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

When reporting findings, it is important to provide the reader with enough contextual information to understand the processes that were followed and how the conclusions were reached. In a collective case study, researchers may choose to present the findings from individual cases separately before amalgamating across cases. Care must be taken to ensure the anonymity of both case sites and individual participants (if agreed in advance) by allocating appropriate codes or withholding descriptors. In the example given in Table ​ Table3, 3 , we decided against providing detailed information on the NHS sites and individual participants in order to avoid the risk of inadvertent disclosure of identities[ 5 , 25 ].

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

The case study approach is, as with all research, not without its limitations. When investigating the formal and informal ways undergraduate students learn about patient safety (Table ​ (Table4), 4 ), for example, we rapidly accumulated a large quantity of data. The volume of data, together with the time restrictions in place, impacted on the depth of analysis that was possible within the available resources. This highlights a more general point of the importance of avoiding the temptation to collect as much data as possible; adequate time also needs to be set aside for data analysis and interpretation of what are often highly complex datasets.

Case study research has sometimes been criticised for lacking scientific rigour and providing little basis for generalisation (i.e. producing findings that may be transferable to other settings)[ 1 ]. There are several ways to address these concerns, including: the use of theoretical sampling (i.e. drawing on a particular conceptual framework); respondent validation (i.e. participants checking emerging findings and the researcher's interpretation, and providing an opinion as to whether they feel these are accurate); and transparency throughout the research process (see Table ​ Table8 8 )[ 8 , 18 - 21 , 23 , 26 ]. Transparency can be achieved by describing in detail the steps involved in case selection, data collection, the reasons for the particular methods chosen, and the researcher's background and level of involvement (i.e. being explicit about how the researcher has influenced data collection and interpretation). Seeking potential, alternative explanations, and being explicit about how interpretations and conclusions were reached, help readers to judge the trustworthiness of the case study report. Stake provides a critique checklist for a case study report (Table ​ (Table9 9 )[ 8 ].

Potential pitfalls and mitigating actions when undertaking case study research

Stake's checklist for assessing the quality of a case study report[ 8 ]

Conclusions

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

Competing interests

The authors declare that they have no competing interests.

Authors' contributions

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

Pre-publication history

The pre-publication history for this paper can be accessed here:

http://www.biomedcentral.com/1471-2288/11/100/prepub

Acknowledgements

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

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