the case study approach to research

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

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

Research question

• Conceptual framework
• Conceptual vs. theoretical framework

Data collection

• Qualitative research methods
• Focus groups
• Observational research

What is a case study?

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

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

Case studies

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

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

Definition of a case study

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

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

Characteristics of case studies

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

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

The role of case studies in research

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

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

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

What is the purpose of a case study?

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

Why use case studies in qualitative research?

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

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

The explanatory, exploratory, and descriptive roles of case studies

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

The impact of case studies on knowledge development

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

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

Types of case studies

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

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

Exploratory case studies

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

Descriptive case studies

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

Explanatory case studies

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

Intrinsic, instrumental, and collective case studies

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

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

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

Critical information systems research

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

Health research

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

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

Asthma research studies

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

Other fields

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

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

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

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

Propositions

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

Units of analysis

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

Argumentation

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

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

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

Defining the research question

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

Selecting and defining the case

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

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

Developing a detailed case study protocol

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

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

Collecting data

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

Analyzing and interpreting data

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

Writing the case study report

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

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

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

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

Observations

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

Documents and artifacts

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

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

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

Ensuring the quality of data collection

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

Data analysis

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

Organizing the data

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

Categorizing and coding the data

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

Identifying patterns and themes

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

Interpreting the data

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

Verification of the data

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

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

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

Benefits include the following:

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

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

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

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

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Case Study – Methods, Examples and Guide

Table of Contents

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

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

Types of Case Study

Types and Methods of Case Study are as follows:

Single-Case Study

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

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

Multiple-Case Study

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

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

Exploratory Case Study

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

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

Descriptive Case Study

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

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

Instrumental Case Study

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

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

Case Study Data Collection Methods

Here are some common data collection methods for case studies:

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

Observations

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

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

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

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

How to conduct Case Study Research

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

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

Examples of Case Study

Here are some examples of case study research:

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

Application of Case Study

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

Business and Management

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

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

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

Social Sciences

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

Law and Ethics

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

Purpose of Case Study

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

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

Case studies can also serve other purposes, including:

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

Advantages of Case Study Research

There are several advantages of case study research, including:

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

Limitations of Case Study Research

There are several limitations of case study research, including:

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

About the author

Muhammad Hassan

Researcher, Academic Writer, Web developer

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• 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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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.

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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. 

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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.

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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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The case study approach

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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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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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22 Case Study Research: In-Depth Understanding in Context

Helen Simons, School of Education, University of Southampton

• Published: 01 July 2014
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This chapter explores case study as a major approach to research and evaluation. After first noting various contexts in which case studies are commonly used, the chapter focuses on case study research directly Strengths and potential problematic issues are outlined and then key phases of the process. The chapter emphasizes how important it is to design the case, to collect and interpret data in ways that highlight the qualitative, to have an ethical practice that values multiple perspectives and political interests, and to report creatively to facilitate use in policy making and practice. Finally, it explores how to generalize from the single case. Concluding questions center on the need to think more imaginatively about design and the range of methods and forms of reporting requiredto persuade audiences to value qualitative ways of knowing in case study research.

Introduction

This chapter explores case study as a major approach to research and evaluation using primarily qualitative methods, as well as documentary sources, contemporaneous or historical. However, this is not the only way in which case study can be conceived. No one has a monopoly on the term. While sharing a focus on the singular in a particular context, case study has a wide variety of uses, not all associated with research. A case study, in common parlance, documents a particular situation or event in detail in a specific sociopolitical context. The particular can be a person, a classroom, an institution, a program, or a policy. Below I identify different ways in which case study is used before focusing on qualitative case study research in particular. However, first I wish to indicate how I came to advocate and practice this form of research. Origins, context, and opportunity often shape the research processes we endorse. It is helpful for the reader, I think, to know how I came to the perspective I hold.

The Beginnings

I first came to appreciate and enjoy the virtues of case study research when I entered the field of curriculum evaluation and research in the 1970s. The dominant research paradigm for educational research at that time was experimental or quasi- experimental, cost-benefit, or systems analysis, and the dominant curriculum model was aims and objectives ( House, 1993 ). The field was dominated, in effect, by a psychometric view of research in which quantitative methods were preeminent. But the innovative projects we were asked to evaluate (predominantly, but not exclusively, in the humanities) were not amenable to such methodologies. The projects were challenging to the status quo of institutions, involved people interpreting the policy and programs, were implemented differently in different contexts and regions, and had many unexpected effects.

We had no choice but to seek other ways to evaluate these complex programs, and case study was the methodology we found ourselves exploring, in order to understand how the projects were being implemented, why they had positive effects in some regions of the country and not others, and what the outcomes meant in different sociopolitical and cultural contexts. What better way to do this than to talk with people to see how they interpreted the “new” curriculum; to watch how teachers and students put it into practice; to document transactions, outcomes, and unexpected consequences; and to interpret all in the specific context of the case ( Simons, 1971 , 1987 , pp. 55–89). From this point on and in further studies, case study in educational research and evaluation came to be a major methodology for understanding complex educational and social programs. It also extended to other practice professions, such as nursing, health, and social care ( Zucker, 2001 ; Greenhalgh & Worrall, 1997 ; Shaw & Gould, 2001 ). For further details of the evolution of the case study approach and qualitative methodologies in evaluation, see House, 1993 , pp. 2–3; Greene, 2000 ; Simons, 2009 , pp. 14–18; Simons & McCormack, 2007 , pp. 292–311).

This was not exactly the beginning of case study, of course. It has a long history in many disciplines ( Simons, 1980; Ragin, 1992; Gomm, Hammersley, & Foster, 2004 ; Platt, 2007 ), many aspects of which form part of case study practice to this day. But its evolution in the context just described was a major move in the contemporary evolution of the logic of evaluative inquiry ( House, 1980 ). It also coincided with movement toward the qualitative in other disciplines, such as sociology and psychology. This was all part of what Denzin & Lincoln (1994) termed “a quiet methodological revolution” (p. ix) in qualitative inquiry that had been evolving over the course of forty years.

There is a further reason why I continue to advocate and practice case study research and evaluation to this day and that is my personal predilection for trying to understand and represent complexity, for puzzling through the ambiguities that exist in many contexts and programs and for presenting and negotiating different values and interests in fair and just ways.

Put more simply, I like interacting with people, listening to their stories, trials and tribulations—giving them a voice in understanding the contexts and projects with which they are involved, and finding ways to share these with a range of audiences. In other words, the move toward case study methodology described here suited my preference for how I learn.

Concepts and Purposes of Case Study

Before exploring case study as it has come to be established in educational research and evaluation over the past forty years, I wish to acknowledge other uses of case study. More often than not, these relate to purpose, and appropriately so in their different contexts, but many do not have a research intention. For a study to count as research, it would need to be a systematic investigation generating evidence that leads to “new” knowledge that is made public and open to scrutiny. There are many ways to conduct research stemming from different traditions and disciplines, but they all, in different ways, involve these characteristics.

Everyday Usage: Stories We Tell

The most common of these uses of case study is the everyday reference to a person, an anecdote or story illustrative of a particular incident, event, or experience of that person. It is often a short, reported account commonly seen in journalism but also in books exploring a phenomenon, such as recovery from serious accidents or tragedies, where the author chooses to illustrate the story or argument with a “lived” example. This is sometimes written by the author and sometimes by the person whose tale it is. “Let me share with you a story,” is a phrase frequently heard

The spirit behind this common usage and its power to connect can be seen in a report by Tim Adams of the London Olympics opening ceremony’s dramatization by Danny Boyle.

It was the point when we suddenly collectively wised up to the idea that what we are about to receive over the next two weeks was not only about “legacy collateral” and “targeted deliverables,” not about G4S failings and traffic lanes and branding opportunities, but about the second-by-second possibilities of human endeavour and spirit and communality, enacted in multiple places and all at the same time. Stories in other words. ( Adams, 2012 )

This was a collective story, of course, not an individual one, but it does convey some of the major characteristics of case study—that richness of detail, time, place, multiple happenings and experiences—that are also manifest in case study research, although carefully evidenced in the latter instance. We can see from this common usage how people have come to associate case study with story. I return to this thread in the reporting section.

Professions Individual Cases

In professional settings, in health and social care, case studies, often called case histories , are used to accurately record a person’s health or social care history and his or her current symptoms, experience, and treatment. These case histories include facts but also judgments and observations about the person’s reaction to situations or medication. Usually these are confidential. Not dissimilar is the detailed documentation of a case in law, often termed a case precedent when referred to in a court case to support an argument being made. However in law there is a difference in that such case precedents are publicly documented.

Case Studies in Teaching

Exemplars of practice.

In education, but also in health and social care training contexts, case studies have long been used as exemplars of practice. These are brief descriptions with some detail of a person or project’s experience in an area of practice. Though frequently reported accounts, they are based on a person’s experience and sometimes on previous research.

Case scenarios

Management studies are a further context in which case studies are often used. Here, the case is more like a scenario outlining a particular problem situation for the management student to resolve. These scenarios may be based on research but frequently are hypothetical situations used to raise issues for discussion and resolution. What distinguishes these case scenarios and the case exemplars in education from case study research is the intention to use them for teaching purposes.

Country Case Studies

Then there are case studies of programs, projects, and even countries, as in international development, where a whole-country study might be termed a case study or, in the context of the Organization for Economic Co-operation and Development (OECD), where an exploration is conducted of the state of the art of a subject, such as education or environmental science in one or several countries. This may be a contemporaneous study and/or what transpired in a program over a period of time. Such studies often do have a research base but frequently are reported accounts that do not detail the design, methodology, and analysis of the case, as a research case study would do, or report in ways that give readers a vicarious experience of what it was like to be there. Such case studies tend to be more knowledge and information-focused than experiential.

Case Study as History

Closer to a research context is case study as history—what transpired at a certain time in a certain place. This is likely to be supported by documentary evidence but not primary data gathering unless it is an oral history. In education, in the late 1970s, Stenhouse (1978) experimented with a case study archive. Using contemporaneous data gathering, primarily through interviewing, he envisaged this database, which he termed a “case record,” forming an archive from which different individuals,, at some later date, could write a “case study.” This approach uses case study as a documentary source to begin to generate a history of education, as the subtitle of Stenhouse’s 1978 paper indicates “Towards a contemporary history of education.”

Case Study Research

From here on, my focus is on case study research per se, adopting for this purpose the following definition:

Case study is an in-depth exploration from multiple perspectives of the complexity and uniqueness of a particular project, policy, institution or system in a “real-life” context. It is research based, inclusive of different methods and is evidence-led. ( Simons, 2009 , p. 21).

For further related definitions of case study, see Stake (1995) , Merriam (1998), and Chadderton & Torrance (2011) . And for definitions from a slightly different perspective, see Yin (2004) and Thomas (2011a) .

Not Defined by Method or Perspective

The inclusion of different methods in the definition quoted above definition signals that case study research is not defined by methodology or method. What defines case study is its singularity and the concept and boundary of the case. It is theoretically possible to conduct a case study using primarily quantitative data if this is the best way of providing evidence to inform the issues the case is exploring. It is equally possible to conduct case study that is mainly qualitative, to engage people with the experience of the case or to provide a rich portrayal of an event, project, or program.

Or one can design the case using mixed methods. This increases the options for learning from different ways of knowing and is sometimes preferred by stakeholders who believe it provides a firmer basis for informing policy. This is not necessarily the case but is beyond the scope of this chapter to explore. For further discussion of the complexities of mixing methods and the virtue of using qualitative methods and case study in a mixed method design, see Greene (2007) .

Case study research may also be conducted from different standpoints—realist, interpretivist, or constructivist, for example. My perspective falls within a constructivist, interpretivist framework. What interests me is how I and those in the case perceive and interpret what we find and how we construct or co-construct understandings of the case. This not only suits my predilection for how I see the world, but also my preferred phenomenological approach to interviewing and curiosity about people and how they act in social and professional life.

Qualitative Case Study Research

Qualitative case study research shares many characteristics with other forms of qualitative research, such as narrative, oral history, life history, ethnography, in-depth interview, and observational studies that utilize qualitative methods. However, its focus, purpose, and origins, in educational research at least, are a little different.

The focus is clearly the study of the singular. The purpose is to portray an in-depth view of the quality and complexity of social/educational programs or policies as they are implemented in specific sociopolitical contexts. What makes it qualitative is its emphasis on subjective ways of knowing, particularly the experiential, practical, and presentational rather than the propositional ( Heron, 1992 , 1999 ) to comprehend and communicate what transpired in the case.

Characteristic Features and Advantages

Case study research is not method dependent, as noted earlier, nor is it constrained by resources or time. Although it can be conducted over several years, which provides an opportunity to explore the process of change and explain how and why things happened, it can equally be carried out contemporaneously in a few days, weeks, or months. This flexibility is extremely useful in many contexts, particularly when a change in policy or unforeseen issues in the field require modifying the design.

Flexibility extends to reporting. The case can be written up in different lengths and forms to meet different audience needs and to maximize use (see the section on Reporting). Using the natural language of participants and familiar methods (like interview, observation, oral history) also enables participants to engage in the research process, thereby contributing significantly to the generation of knowledge of the case. As I have indicated elsewhere ( Simons, 2009 ), “This is both a political and epistemological point. It signals a potential shift in the power base of who controls knowledge and recognizes the importance of co-constructing perceived reality through the relationships and joint understandings we create in the field” (p. 23).

Possible Disadvantages

If one is an advocate, identifying advantages of a research approach is easier than pointing out its disadvantages, something detractors are quite keen to do anyway! But no approach is perfect, and here are some of the issues that often trouble people about case study research. The “sample of one” is an obvious issue that worries those convinced that only large samples can constitute valid research and especially if this is to inform policy. Understanding complexity in depth may not be a sufficient counterargument, and I suspect there is little point in trying to persuade otherwise For frequently, this perception is one of epistemological and methodological, if not ideological, preference.

However, there are some genuine concerns that many case researchers face: the difficulty of processing a mass of data; of “telling the truth” in contexts where people may be identifiable; personal involvement, when the researcher is the main instrument of data gathering; and writing reports that are data-based, yet readable in style and length. But one issue that concerns advocates and nonadvocates alike is how inferences are drawn from the single case.

Answers to some of these issues are covered in the sections that follow. Whether they convince may again be a question of preference. However, it is worth noting here that I do not think we should seek to justify these concerns in terms identified by other methodologies. Many of them are intrinsic to the nature and strength of qualitative case study research.

Subjectivity, for instance, both of participants and researcher is inevitable, as it is in many other qualitative methodologies. This is often the basis on which we act. Rather than see this as bias or something to counter, it is an intelligence that is essential to understanding and interpreting the experience of participants and stakeholders. Such subjectivity needs to be disciplined, of course, through procedures that examine both the validity of individuals’ representations of “their truth”, and demonstrate how the researcher took a reflexive approach to monitoring how his or her own values and predilections may have unduly influenced the data.

Types of Case Study

There are numerous types of case study, too many to categorize, I think, as there are overlaps between them. However, attempts have been made to do this and, for those who value typologies, I refer them to Bassey (1999) and, for a more extended typology, to Thomas (2011b) . A slightly different approach is taken by Gomm, Hammersley, and Foster (2004) in annotating the different emphases in major texts on case study. What I prefer to do here is to highlight a few familiar types to focus the discussion that follows on the practice of case study research.

Stake (1995) offers a threefold distinction that is helpful when it comes to practice, he says, because it influences the methods we choose to gather data (p. 4). He distinguishes between an intrinsic case study , one that is studied to learn about the particular case itself and an instrumental case study , in which we choose a case to gain insight into a particular issue (i.e., the case is instrumental to understanding something else; p. 3). The collective case study is what its name suggests: an extension of the instrumental to several cases.

Theory-led or theory-generated case study is similarly self-explanatory, the first starting from a specific theory that is tested through the case; the second constructing a theory through interpretation of data generated in the case. In other words, one ends rather than begins with a theory. In qualitative case study research, this is the more familiar route. The theory of the case becomes the argument or story you will tell.

Evaluation case study requires a slightly longer description as this is my context of practice, one which has influenced the way I conduct case study and what I choose to emphasize in this chapter. An evaluation case study has three essential features: to determine the value of the case, to include and balance different interests and values, and to report findings to a range of stakeholders in ways that they can use. The reasons for this may be found in the interlude that follows, which offers a brief characterization of the social and ethical practice of evaluation and why qualitative methods are so important in this practice.

Interlude: Social and Ethical Practice of Evaluation

Evaluation is a social practice that documents, portrays, and seeks to understand the value of a particular project, program, or policy. This can be determined by different evaluation methodologies, of course. But the value of qualitative case study is that it is possible to discern this value without decontextualizing the data. While the focus of the case is usually a project, program, policy, or some unit within, studies of key individuals, what I term case profiles , may be embedded within the overall case. In some instances, these profiles, or even shorter cameos of individuals, may be quite prominent. For it is through the perceptions, interpretations, and interactions of people that we learn how policies and programs are enacted ( Kushner, 2000 , p. 12). The program is still the main focus of analysis, but, in exploring how individuals play out their different roles in the program, we get closer to the actual experience and meaning of the program in practice.

Case study evaluation is often commissioned from an external source (government department or other agency) keen to know the worth of publicly funded programs and policies to inform future decision making. It needs to be responsive to issues or questions identified by stakeholders, who often have different values and interests in the expected outcomes and appreciate different perspectives of the program in action. The context also is often highly politicized, and interests can conflict. The task of the evaluator in such situations becomes one of including and balancing all interests and values in the program fairly and justly.

This is an inherently political process and requires an ethical practice that offers participants some protection over the personal data they give as part of the research and agreed audiences access to the findings, presented in ways they can understand. Negotiating what information becomes public can be quite difficult in singular settings where people are identifiable and intricate or problematic transactions have been documented. The consequences that ensue from making knowledge public that hitherto was private may be considerable for those in the case. It may also be difficult to portray some of the contextual detail that would enhance understanding for readers.

The ethical stance that underpins the case study research and evaluation I conduct stems from a theory of ethics that emphasizes the centrality of relationships in the specific context and the consequences for individuals, while remaining aware of the research imperative to publicly report. It is essentially an independent democratic process based on the concepts of fairness and justice, in which confidentiality, negotiation, and accessibility are key principles ( MacDonald, 1976 ; Simons, 2009 , pp. 96–111; and Simons 2010 ). The principles are translated into specific procedures to guide the collection, validation, and dissemination of data in the field. These include:

engaging participants and stakeholders in identifying issues to explore and sometimes also in interpreting the data;

documenting how different people interpret and value the program;

negotiating what data becomes public respecting both the individual’s “right to privacy” and the public’s “right to know”;

offering participants opportunities to check how their data are used in the context of reporting;

reporting in language and forms accessible to a wide range of audiences;

disseminating to audiences within and beyond the case.

For further discussion of the ethics of democratic case study evaluation and examples of their use in practice, see Simons (2000 , 2006 , 2009 , chapter 6, 2010 ).

Designing Case Study Research

Design issues in case study sometimes take second place to those of data gathering, the more exciting task perhaps in starting research. However, it is critical to consider the design at the outset, even if changes are required in practice due to the reality of what is encountered in the field. In this sense, the design of case study is emergent, rather than preordinate, shaped and reshaped as understanding of the significance of foreshadowed issues emerges and more are discovered.

Before entering the field, there are a myriad of planning issues to think about related to stakeholders, participants, and audiences. These include whose values matter, whether to engage them in data gathering and interpretation, the style of reporting appropriate for each, and the ethical guidelines that will underpin data collection and reporting. However, here I emphasize only three: the broad focus of the study, what the case is a case of, and framing questions/issues. These are steps often ignored in an enthusiasm to gather data, resulting in a case study that claims to be research but lacks the basic principles required for generation of valid, public knowledge.

Conceptualize the Topic

First, it is important that the topic of the research is conceptualized in a way that it can be researched (i.e., it is not too wide). This seems an obvious point to make, but failure to think through precisely what it is about your research topic you wish to investigate will have a knock-on effect on the framing of the case, data gathering, and interpretation and may lead, in some instances, to not gathering or analyzing data that actually informs the topic. Further conceptualization or reconceptualization may be necessary as the study proceeds, but it is critical to have a clear focus at the outset.

What Constitutes the Case

Second, I think it is important to decide what would constitute the case (i.e., what it is a case of) and where the boundaries of this lie. This often proves more difficult than first appears. And sometimes, partly because of the semifluid nature of the way the case evolves, it is only possible to finally establish what the case is a case of at the end. Nevertheless, it is useful to identify what the case and its boundaries are at the outset to help focus data collection while maintaining an awareness that these may shift. This is emergent design in action.

In deciding the boundary of the case, there are several factors to bear in mind. Is it bounded by an institution or a unit within an institution, by people within an institution, by region, or by project, program or policy,? If we take a school as an example, the case could be comprised of the principal, teachers, and students, or the boundary could be extended to the cleaners, the caretaker, the receptionist, people who often know a great deal about the subnorms and culture of the institution.

If the case is a policy or particular parameter of a policy, the considerations may be slightly different. People will still be paramount—those who generated the policy and those who implemented it—but there is likely also to be a political culture surrounding the policy that had an influence on the way the policy evolved. Would this be part of the case?

Whatever boundary is chosen, this may change in the course of conducting the study when issues arise that can only be understood by going to another level. What transpires in a classroom, for example, if this is the case, is often partly dependent on the support of the school leadership and culture of the institution and this, in turn, to some extent is dependent on what resources are allocated from the local education administration. Much like a series of Russian dolls, one context inside the other.

Unit of analysis

Thinking about what would constitute the unit of analysis— a classroom, an institution, a program, a region—may help in setting the boundaries of the case, and it will certainly help when it comes to analysis. But this is a slightly different issue from deciding what the case is a case of. Taking a health example, the case may be palliative care support, but the unit of analysis the palliative care ward or wards. If you took the palliative care ward as the unit of analysis this would be as much about how palliative care was exercised in this or that ward than issues about palliative care support in general. In other words, you would need to have specific information and context about how this ward was structured and managed to understand how palliative care was conducted in this particular ward. Here, as in the school example above, you would need to consider which of the many people who populate the ward form part of the case—nurses, interns, or doctors only, or does it extend to patients, cleaners, nurse aides, and medical students?

Framing Questions and Issues

The third most important consideration is how to frame the study, and you are likely to do this once you have selected the site or sites for study. There are at least four approaches. You could start with precise questions, foreshadowed issues ( Smith & Pohland, 1974 ), theories, or a program logic. To some extent, your choice will be dictated by the type of case you have chosen, but also by your personal preference for how to conduct it—in either a structured or open way.

Initial questions give structure; foreshadowed issues more freedom to explore. In qualitative case study, foreshadowed issues are more common, allowing scope for issues to change as the study evolves, guided by participants’ perspectives and events in the field. With this perspective, it is more likely that you will generate a theory of the case toward the end, through your interpretation and analysis.

If you are conducting an instrumental case study, staying close to the questions or foreshadowed issues is necessary to be sure you gain data that will illuminate the central focus of the study. This is critical if you are exploring issues across several cases, although it is possible to do a cross-case analysis from cases that have each followed a different route to discovering significant issues.

Opting to start with a theoretical framework provides a basis for formulating questions and issues, but it can also constrain the study to only those questions/issues that fit the framework. The same is true with using program logic to frame the case. This is an approach frequently adopted in evaluation case study where the evaluator, individually or with stakeholders, examines how the aims and objectives of the program relate to the activities designed to promote it and the outcomes and impacts expected. It provides direction, although it can lead to simply confirming what was anticipated, rather than documenting what transpired in the case.

Whichever approach you choose to frame the case, it is useful to think about the rationale or theory for each question and what methods would best enable you to gain an understanding of them. This will not only start a reflexive process of examining your choices—an important aspect of the process of data gathering and interpretation—it will also aid analysis and interpretation further down the track.

Methodology and Methods

Qualitative case study research, as already noted, appeals to subjective ways of knowing and to a primarily qualitative methodology, that captures experiential understanding ( Stake, 2010 , pp. 56–70). It follows that the main methods of data gathering to access this way of knowing will be qualitative. Interviewing, observation, and document analysis are the primary three, often supported by critical incidents, focus groups, cameos, vignettes, diaries/journals, and photographs. Before gathering any primary data, however, it is useful to search relevant existing sources (written or visual) to learn about the antecedents and context of a project, program, or policy as a backdrop to the case. This can sharpen framing questions, avoid unnecessary data gathering, and shorten the time needed in the field.

Given that there are excellent texts on qualitative methods (see, for example, Denzin & Lincoln, 1994 ; Seale, 1999 ; Silverman, 2000 , 2004 ), I will not discuss all potential relevant methods here, but simply focus on the qualities of the primary methods that are particularly appropriate for case study research.

Primary Qualitative Data Gathering Methods

Interviewing.

The most effective style of interviewing in qualitative case study research to gain in-depth data, document multiple perspectives and experiences and explore contested issues is the unstructured interview, active listening and open questioning are paramount, whatever prequestions or foreshadowed issues have been identified. This can include photographs—a useful starting point with certain cultural groups and the less articulate, to encourage them to tell their story through connecting or identifying with something in the image.

The flexibility of unstructured interviewing has three further advantages for understanding participants’ experiences. First, through questioning, probing, listening, and, above all, paying attention to the silences and what they mean, you can get closer to the meaning of participants’ experiences. It is not always what they say.

Second, unstructured interviewing is useful for engaging participants in the process of research. Instead of starting with questions and issues, invite participants to tell their stories or reflect on specific issues, to conduct their own self-evaluative interview, in fact. Not only will they contribute their particular perspective to the case, they will also learn about themselves, thereby making the process of research educative for them as well as for the audiences of the research.

Third, the open-endedness of this style of interviewing has the potential for creating a dialogue between participants and the researcher and between the researcher and the public, if enough of the dialogue is retained in the publication ( Bellah, Madsen, Sullivan, Swidler, & Tipton, 1985 ).

Observations

Observations in case study research are likely to be close-up descriptions of events, activities, and incidents that detail what happens in a particular context. They will record time, place, specific incidents, transactions, and dialogue, and note characteristics of the setting and of people in it without preconceived categories or judgment. No description is devoid of some judgment in selection, of course, but, on the whole, the intent is to describe the scene or event “as it is,” providing a rich, textured description to give readers a sense of what it was like to be there or provide a basis for later interpretation.

Take the following excerpt from a study of the West Bromwich Operatic Society. It is the first night of a new production, The Producers , by this amateur operatic society. This brief excerpt is from a much longer observation of the overture to the first evening’s performance, detailing exactly what the production is, where it is, and why there is such a tremendous sense of atmosphere and expectation surrounding the event. Space prevents including the whole observation, but I hope you can get a glimmer of the passion and excitement that precedes the performance:

Birmingham, late November, 2011, early evening.... Bars and restaurants spruce up for the evening’s trade. There is a chill in the air but the party season is just starting....

A few hundred yards away, past streaming traffic on Suffolk Street, Queensway, an audience is gathering at the New Alexandra Theatre. The foyer windows shine in the orange sodium night. Above each one is the rubric: WORLD CLASS THEATRE.

Inside the preparatory rituals are being observed; sweets chosen, interval drinks ordered and programmes bought. People swap news and titbits about the production.... The bubble of anticipation grows as the 5-minute warning sounds. People make their way to the auditorium. There have been so many nights like this in the past 110 years since a man named William Coutts invested £10,000 to build this palace of dreams.... So many fantasies have been played under this arch: melodramas and pantomimes, musicals and variety.... So many audiences, settling down in their tip-up seats, wanting to be transported away from work, from ordinariness and private troubles.... The dimming lights act like a mother’s hush. You could touch the silence. Boinnng! A spongy thump on a bass drum, and the horns pipe up that catchy, irrepressible, tasteless tune and already you’re singing under your breath, ‘Springtime for Hitler and Germany....’ The orchestra is out of sight in the pit. There’s just the velvet curtain to watch as your fingers tap along. What’s waiting behind? Then it starts it to move. Opening night.... It’s opening night! ( Matarasso, 2012 , pp. 1–2)

For another and different example—a narrative observation of an everyday but unique incident that details date, time, place, and experience—see Simons (2009 , p. 60).

Such naturalistic observations are also useful in contexts where we cannot understand what is going on through interviewing alone—in cultures with which we are less familiar or where key actors may not share our language or have difficulty expressing it. Careful description in these situations can help identify key issues, discover the norms and values that exist in the culture, and, if sufficiently detailed, allow others to cross corroborate what significance we draw from these observations. This last point is very important to avoid the danger in observation of ascribing motivations to people and meanings to transactions.

Finally, naturalistic observations are very important in highly politicized environments, often the case in commissioned evaluation case study, where individuals in interview may try to elude the “truth” or press on you that their view is the “right” view of the situation. In these contexts, naturalistic observations not only enable you to document interactions as you perceive them, but they also provide a cross-check on the veracity of information obtained in interviews.

Document analysis

Analysis of documents, as already intimated, is useful for establishing what historical antecedents might exist to provide a springboard for contemporaneous data gathering. In most cases, existing documents are also extremely pertinent for understanding the policy context.

In a national policy case study I conducted on a major curriculum change, the importance of preexisting documentation was brought home to me sharply when certain documentation initially proved elusive to obtain. It was difficult to believe that it did not exist, as the evolution of the innovation involved several parties who had not worked together before. There was bound, I thought, to be minuted meetings sharing progress and documentation of the “new” curriculum. In the absence of some crucial documents, I began to piece together the story through interviewing. Only there were gaps, and certain issues did not make sense.

It was only when I presented two versions of what I discerned had transpired in the development of this initiative in an interim report eighteen months into the study that things started to change. Subsequent to the meeting at which the report was presented, the “missing” documents started to appear. Suddenly found. What lay behind the “missing documents,” something I suspected from what certain individuals did and did not say in interview, was a major difference of view about how the innovation evolved, who was key in the process, and whose voice was more important in the context. Political differences, in other words, that some stakeholders were trying to keep from me. The emergence of the documents enabled me to finally produce an accurate and fair account.

This is an example of the importance of having access to all relevant documents relating to a program or policy in order to study it fairly. The other major way in which document analysis is useful in case study is for understanding the values, explicit and hidden, in policy and program documents and in the organization where the program or policy is implemented. Not to be ignored as documents are photographs, and these, too, can form the basis of a cultural and value analysis of an organization ( Prosser, 2000 ).

Creative artistic approaches

Increasingly, some case study researchers are employing creative approaches associated with the arts as a means of data gathering and analysis. Artistic approaches have often been used in representing findings, but less frequently in data gathering and interpretation ( Simons & McCormack, 2007 ). A major exception is the work of Richardson (1994) , who sees the very process of writing as an interpretative act, and of Cancienne and Snowber (2003) , who argue for movement as method.

The most familiar of these creative and artistic forms are written—narratives and short stories ( Clandinin & Connelly, 2000 ; Richardson, 1994 ; Sparkes, 2002 ), poems or poetic form ( Butler-Kisber, 2010 ; Duke, 2007 ; Richardson, 1997 ; Sparkes & Douglas, 2007 ), cameos of people, or vignettes of situations. These can be written by participants or by the researcher or developed in partnership. They can also be shared with participants to further interpret the data. But photographs also have a long history in qualitative research for presenting and constructing understanding ( Butler-Kisber, 2010 ; Collier, 1967 ; Prosser, 2000 ; Rugang, 2006 ; Walker, 1993 ).

Less common are other visual forms of gathering data, such as “draw and write” ( Sewell, 2011 ), artefacts, drawings, sketches, paintings, and collages, although all forms are now on the increase. For examples of the use of collage in data gathering, see Duke (2007) and Butler-Kisber (2010) , and for charcoal drawing, Elliott (2008) .

In qualitative inquiry broadly, these creative approaches are now quite common. And in the context of arts and health in particular (see, for example, Frank, 1997 ; Liamputtong & Rumbold, 2008 ; Spouse, 2000 ), we can see how artistic approaches illuminate in-depth understanding. However, in case study research to date, I think narrative forms have tended to be most prominent.

Finally, for capturing the quality and essence of peoples’ experience, nothing could be more revealing than a recording of their voices. Video diaries—self-evaluative portrayals by individuals of their perspectives, feelings, or experience of an event or situation—are a most potent way both of gaining understanding and communicating that to others. It is rather more difficult to gain access for observational videos, but they are useful for documentation and have the potential to engage participants and stakeholders in the interpretation.

Getting It All Together

Case study is so often associated with story or with a report of some event or program that it is easy to forget that much analysis and interpretation has gone on before we reach this point. In many case study reports, this process is hidden, leaving the reader with little evidence on which to assess the validity of the findings and having to trust the one who wrote the tale.

This section briefly outlines possibilities, first, for analyzing and interpreting data, and second, for how to communicate the findings to others. However it is useful to think of these together and indeed, at the start, because decisions about how you report may influence how you choose to make sense of the data. Your choice may also vary according to the context of the study—what is expected or acceptable—and your personal predilections, whether you prefer a more rational than intuitive mode of analysis, for example, or a formal or informal style of writing up that includes images, metaphor, narratives, or poetic forms.

Analyzing and Interpreting Data

When it comes to making sense of data, I make a distinction between analysis—a formal inductive process that seeks to explain—and interpretation, a more intuitive process that gains understanding and insight from a holistic grasp of data, although these may interact and overlap at different stages.

The process, whichever emphasis you choose, is one of reducing or transforming a large amount of data to themes that can encapsulate the overarching meaning in the data. This involves sorting, refining, and refocusing data until they make sense. It starts at the beginning with preliminary hunches, sometimes called “interpretative asides” or “working hypotheses,” later moving to themes, analytic propositions, or a theory of the case.

There are many ways to conduct this process. Two strategies often employed are concept mapping —a means of representing data visually to explore links between related concepts—and progressive focusing ( Parlett & Hamilton, 1976 ), the gradual reframing of initially identified issues into themes that are then further interpreted to generate findings. Each of these strategies tends to have three stages: initial sense making, identification of themes, and examination of patterns and relationships between them.

If taking a formal analytic approach to the task, the data would likely be broken down into segments or datasets (coded and categorized) and then reordered and explored for themes, patterns, and possible propositions. If adopting a more intuitive process, you might focus on identifying insights through metaphors and images, lateral thinking, or puzzling over paradoxes and ambiguities in the data, after first immersing yourself in the total dataset, reading and re-reading interview scripts, observations and field notes to get a sense of the whole. Trying out different forms of making sense through poetry, vignettes, cameos, narratives, collages, and drawing are further creative ways to interpret data, as are photographs taken in the case arranged to explain or tell the story of the case.

Reporting Case Study Research

Narrative structure and story.

As indicated in the introduction, telling a story is often associated with case study and some think this is what a case study is. In one sense, it is and, given that story is the natural way in which we learn ( Okri, 1997 ), it is a useful framework both for gathering data and for communicating case study findings. Not any story will do however. To count as research, it must be authentic, grounded in data, interpreted and analyzed to convey the meaning of the case.

There are several senses in which story is appropriate in qualitative case study: in capturing stories participants tell, in generating a narrative structure that makes sense of the case (i.e., the story you will tell), and in deciding how you communicate this narrative (i.e., in story form). If you choose a written story form (and advice here can be sought from Harrington (2003) and Caulley (2008) ), it needs to be clearly structured, well written, and contain only the detail that is necessary to give readers the vicarious experience of what it was like in the case. If the story is to be communicated in other ways, through, for example, audio or videotape, or computer or personal interaction, the same applies, substituting visual and interpersonal skill for written.

Matching forms of reporting to audience

The art of reporting is strongly connected to usability, so forms of reporting need to connect to the audiences we hope to inform: how they learn, what kind of evidence they value, and what kind of reporting maximizes the chances they will use the findings to promote policies and programs in the interests of beneficiaries. As Okri (1997) further reminds us, the writer only does half the work; the reader does the other (p. 41).

There may be other considerations as well: how open are commissioners to receiving stories of difficulties, as well as success stories? What might they need to hear beyond what is sought in the technical brief? And through what style of reporting would you try and persuade them? If conducting noncommissioned case study research, the scope for different forms of reporting is wider. In academia, for instance, many institutions these days accept creative and artistic forms of reporting when supported by supervisors and appreciated by examiners.

Styles of Reporting

The most obvious form of reporting is linear, often starting with a short executive summary and a brief description of focus and context, followed by methodology, the case study or thematic analysis, findings, and conclusions or implications. Conclusion-led reporting is similar in terms of its formality, but simply starts the other way around. From the conclusions drawn from the analyzed data, it works backward to tell the story through narrative, verbatim, and observational data of how these conclusions were reached. Both have a strong story line. The intent is analytic and explanatory.

Quite a different approach is to engage the reader in the experience and veracity of the case. Rather like constructing a portrait or editing a documentary film, this involves the sifting, constructing, re-ordering of frames, events and episodes to tell a coherent story primarily through interview excerpts, observations, vignettes, and critical incidents that depict what transpired in the case. Interpretation is indirect through the weaving of the data. The story can start at any point provided the underlying narrative structure is maintained to establish coherence ( House, 1980 , p. 116).

Different again, and from the other end of a continuum, is a highly interpretative account that may use similar ways of presenting data but weaves a story from the outset that is highly interpretative. Engaging metaphor, images, short stories, contradictions, paradoxes, and puzzles, it is invariably interesting to read and can be most persuasive. However, the evidence is less visible and therefore less open to alternative interpretations.

Even more persuasive is a case study that uses artistic forms to communicate the story of the case. Paintings, poetic form, drawings, photography, collage, and movement can all be adopted to report findings, whether the data was acquired using these forms or by other means. The arts-based inquiry movement ( Mullen & Finley, 2003 ) has contributed hugely to the validation and legitimation of artistic and creative ways of representing qualitative research findings. The journal Qualitative Inquiry contains many good examples, but see also Liamputtong & Rumbold (2008) . Such artistic forms of representation may not be for everyone or appropriate in some contexts, but they do have the power to engage an audience and the potential to facilitate use.

Generalization in Case Study Research

One of the potential limitations of case study often proposed is that it is impossible to generalize. This is not so. However, the way in which one generalizes from a case is different from that adopted in traditional forms of social science research that utilize large samples (randomly selected) and statistical procedures and which assume regularities in the social world that allow cause and effect to be determined. In this form of research inferences from data are stated as formal propositions that apply to all in the target population. See Donmoyer (1990) for an argument on the restricted nature of this form of generalization when considering single-case studies.

Making inferences from cases with a qualitative data set arises more from a process of interpretation in context, appealing to tacit and situated understanding for acceptance of their validity. Such inferences are possible where the context and experience of the case is richly described so the reader can recognize and connect with the events and experiences portrayed. There are two ways to examine how to reach these generalized understandings. One is to generalize from the case to other cases of a similar or dissimilar nature. The other is to see what we learn in-depth from the uniqueness of the single case itself.

Generalizing from the Single Case

A common approach to generalization and one most akin to a propositional form is cross-case generalization. In a collective or multi-site case study, each case is explored to see if issues that arise in one case also exist in other cases and what interconnecting themes there are between them. This kind of generalization has a degree of abstraction and potential for theorizing and is often welcomed by commissioners of research concerned that findings from the single case do not provide an adequate or “safe” basis for policy determination.

However, there are four additional ways to generalize from the single case, all of which draw more on tacit knowledge and recognition of context, although in different ways. In naturalistic generalization , first proposed by Stake (1978) , generalization is reached on the basis of recognition of similarities and differences to cases with which we are familiar. To enable such recognition, the case needs to feature rich description; people’s voices; and enough detail of time, place, and context to provide a vicarious experience to help readers discern what is similar and dissimilar to their own context ( Stake, 1978 ).

Situated generalization ( Simons, Kushner, Jones, & James, 2003 ) is close to the concept of naturalistic generalization in relying for its generality on retaining a connectedness with the context in which it first evolved. However, it has an extra dimension in a practice context. This notion of generalization was identified in an evaluation of a research project that engaged teachers in and with research. Here, in addition to the usual validity criteria to establish the warrant for the findings, the generalization was seen as dependable if trust existed between those who conducted the research (teachers, in this example) and those thinking about using it (other teachers). In other words, beyond the technical validity of the research, teachers considered using the findings in their own practice because they had confidence in those who generated them. This is a useful way to think about generalization if we wish research findings to improve professional practice.

The next two concepts of generalization— concept and process generalization —relate more to what you discover in making sense of the case. As you interpret and analyze, you begin to generate a theory of the case that makes sense of the whole. Concepts may be identified that make sense in the one case but have equal significance in other cases of a similar kind, even if the contexts are different.

It is the concept that generalizes, not the specific content or context. This may be similar to the process Donmoyer (2008) identifies of “intellectual generalization” (quoted by Butler-Kisber, 2010 , p. 15) to indicate the cognitive understanding one can gain from qualitative accounts even if settings are quite different.

The same is true for generalization of a process. It is possible to identify a significant process in one case (or several cases) that is transferable to other contexts, irrespective of the precise content and contexts of those other cases. An example here is the collaborative model for sustainable school self-evaluation I identified in researching school self-evaluation in a number of schools and countries ( Simons, 2002 ). Schools that successfully sustained school self-evaluation had an infrastructure that was collaborative at all stages of the evaluation process from design to conduct of the study, to analyzing the results and to reporting the findings. This ensured that the whole school was involved and that results were discussed and built into the ongoing development of school policies and practice. In other cases, different processes may be discovered that have applicability in a range of contexts. As with concept generalization, it is the process that generalizes not the substantive content or specific context.

Particularization

The forms of generalization discussed above are useful when we have to justify case study in a research or policy context. But the overarching justification for how we learn from case study is particularization —a rich portrayal of insights and understandings interpreted in the particular context. Several authors have made this point ( Stake, 1995 ; Flyvberg, 2006 ; Simons 2009 ). Stake puts it most sharply when he observes that “The real business of case study is particularization, not generalization” (p. 8), referring here to the main reason for studying the singular, which is to understand the uniqueness of the case itself.

My perspective (explored further in Simons, 1996 ; Simons, 2009 , p. 239; Simons & McCormack, 2007 ) is similar in that I believe the “real” strength of case study lies in the insights we gain from in-depth study of the particular. But I also argue for the universality of such insights—if we get it “right.” By which I mean that if we are able to capture and report the uniqueness, the essence, of the case in all its particularity and present this in a way we can all recognize, we will discover something of universal significance. This is something of a paradox. The more you learn in depth about the particularity of one person, situation, or context, the more likely you are to discover something universal. This process of reaching understanding has support both from the way in which many discoveries are made in science and in how we learn from artists, poets, and novelists, who reach us by communicating a recognizable truth about individuals, human relationships, and/or social contexts.

This concept of particularization is far from new, as the quotation from a preface to a book written in 1908 attests. Stephen Reynolds, the author of A Poor Man’s House , notes that the substance of the book was first recorded in a journal, kept for purposes of fiction, and in letters to one of his friends, but fiction proved an inappropriate medium. He felt that the life and the people were so much better than anything he could invent. The book therefore consists of the journal and letters drawn together to present a picture of a typical poor man’s house and life, much as we might draw together a range of data to present a case study. It is not the substance of the book that concerns us here but the methodological relevance to case study research. Reynolds notes that the conclusions expressed are tentative and possibly go beyond this man’s life, so he thought some explanation of the way he arrived at them was needed:

Educated people usually deal with the poor man’s life deductively; they reason from the general to the particular; and, starting with a theory, religious, philanthropic, political, or what not, they seek, and too easily find, among the millions of poor, specimens—very frequently abnormal—to illustrate their theories. With anything but human beings, that is an excellent method. Human beings, unfortunately, have individualities. They do what, theoretically, they ought not to do, and leave undone those things they ought to do. They are even said to possess souls—untrustworthy things beyond the reach of sociologists. The inductive method—reasoning from the particular to the general... should at least help to counterbalance the psychological superficiality of the deductive method. ( Reynolds, 1908 : preface) 1

Slightly overstated perhaps, but the point is well made. In our search for general laws, we not only lose sight of the uniqueness and humanity of individuals, but reduce them in the process, failing to present their experience in any “real” sense. What is astonishing about the quotation is that it was written over a century ago and yet many still argue today that you cannot generalize from the particular.

Going even further back, in 1798, Blake proclaimed that “To Generalize is to be an Idiot. To Particularize is the Alone Distinction of Merit.” In research, we may not wish to make such a strong distinction: these processes both have their uses in different kinds of research. But there is a major point here for the study of the particular that Wilson (2008) notes in commenting on Blake’s perception when he says: “Favouring the abstract over the concrete, one ‘sees all things only thro’ the narrow chinks of his cavern”’ (referring here to Blake’s The Marriage of Heaven and Hell [1793]; in Wilson, 2008 , p. 62). The danger Wilson is pointing to here is that abstraction relies heavily on what we know from our past understanding of things, and this may prevent us experiencing a concrete event directly or “apprehend[ing] a particular moment” ( Wilson, 2008 , p. 63).

Blake had a different mission, of course, than case researchers, and he was not himself free from abstractions, as Wilson points out, although he fought hard “to break through mental barriers to something unique and living” ( Wilson, 2008 , p. 65). It is this search for the “unique and living” and experiencing the “isness” of the particular that we should take from the Blake example to remind ourselves of the possibility of discovering something “new,” beyond our current understanding of the way things are.

Focusing on particularization does not diminish the usefulness of case study research for policy makers or practitioners. Grounded in recognizable experience, the potential is there to reach a range of audiences and to facilitate use of the findings. It may be more difficult for those who seek formal generalizations that seem to offer a safe basis for policy making to accept case study reports. However, particular stories often hold the key to why policies have or have not worked well in the past. It is not necessary to present long cases—a criticism frequently levelled—to demonstrate the story of the case. Such case stories can be most insightful for policy makers who, like many of us in everyday life, often draw inferences from a single instance or case, whatever the formal evidence presented. “I am reminded of the story of....”

The case for studying the particular to inform practice in professional contexts needs less persuasion because practitioners can recognize the content and context quite readily and make the inference to their own particular context ( Simons et al., 2003 ). In both sets of circumstances—policy and practice—it is more a question of whether the readers of our case research accept the validity of findings determined in this way, how they choose to learn, and our skill in telling the case study story.

Conclusion and Future Directions

In this chapter, I have presented an argument for case study research, making the case, in particular, for using qualitative methods to highlight what it is that qualitative case study research can bring to the study of social and educational programs. I outlined the various ways in which case study is commonly used before focusing directly on case study as a major mode of research inquiry, noting characteristics it shares with other qualitative methodologies, as well as itsdifference and the difficulties it is sometimes perceived to have. The chapter emphasizes the importance of thinking through what the case is, to be sure that the issues explored and the data generated do illuminate this case and not any other.

But there is still more to be done. In particular, I think we need to be more adventurous in how we craft and report the case. I suspect we may have been too cautious in the past in how we justified case study research, borrowing concepts from other disciplines and forms of educational research. More than 40 years on, it is time to take a greater risk—in demonstrating the intrinsic nature of case study and what it can offer to our understanding of human and social situations.

I have already drawn attention to the need to design the case, although this could be developed further to accentuate the uniqueness of the particular case. One way to do this is to feature individuals more in the design itself, not only to explore programs and policies through perspectives of key actors or groups and transactions between them, which to some extent happens already, but also to get them to characterize what makes the context unique. This is the reversal of many a design framework that starts with the logic of a program and takes forward the argument for personal evaluation ( Kushner, 2000 ), noted in the interlude on evaluation. Apart from this attention to design, there are three other issues I think we need to explore further: the warrant for creative methods in case study, more imaginative reporting; and how we learn from a study of the singular.

Warrant for More Creative Methods in Case Study Research

The promise that creative methods have for eliciting in-depth understanding and capturing the unusual, the idiosyncratic, the uniqueness of the case, was mentioned in the methods section. Yet, in case study research, particularly in program and policy contexts, we have few good examples of the use of artistic approaches for eliciting and interpreting data, although more, as acknowledged later, for presenting it. This may be because case study research is often conducted in academic or policy environments, where propositional ways of knowing are more valued.

Using creative and artistic forms in generating and interpreting case study data offers a form of evidence that acknowledges experiential understanding in illuminating the uniqueness of the case. The question is how to establish the warrant for this way of knowing and persuade others of its virtue. The answer is simple. By demonstrating the use of these methods in action, by arguing for a different form of validity that matches the intrinsic nature of the method, and, above all, by good examples.

Representing Findings to Engage Audiences in Learning

In evaluative and research policy contexts, where case study is often the main mode of inquiry or part of a broader study, case study reports often take a formal structure or sometimes, where the context is receptive, a portrayal or interpretative form. But, too often, the qualitative is an add-on to a story told by other means or reduced to issues in which the people who gave rise to the data are no longer seen. However, there are many ways to put them center stage.

Tell good stories and tell them well. Or, let key actors tell their own stories. Explore the different ways technology can help. Make video clips that demonstrate events in context, illustrate interactions between people, give voice to participants—show the reality of the program, in other words. Use graphics to summarize key issues and interactive, cartoon technology, as seen on some TED presentations, to summarize and visually show the complexity of the case. Video diaries were mentioned in the methods section: seeing individuals tell their tales directly is a powerful way of communicating, unhindered by “our” sense making. Tell photo stories. Let the photos convey the narrative, but make sure the structure of the narrative is evident to ensure coherence. These are just the beginnings. Those skilled in information technology could no doubt stretch our imagination further.

One problem and a further question concerns our audiences. Will they accept these modes of communication? Maybe not, in some contexts. However, there are three points I wish to leave you with. First, do not presume that they won’t. If people are fully present in the story and the complexity is not diminished, those reading, watching, or hearing about the case will get the message. If you are worried about how commissioners might respond, remember that they are no different from any other stakeholder or participant when it comes to how they learn from human experience. Witness the reference to Okri (1997) earlier about how we learn.

Second, when you detect that the context requires a more formal presentation of findings, respond according to expectation but also include elements of other forms of presentation. Nudge a little in the direction of creativity. Third, simply take a chance, that risk I spoke about earlier. Challenge the status quo. Find situations and contexts where you can fully represent the qualitative nature of the experience in the cases you study with creative forms of interpretation and representation. And let the audience decide.

Learning from a Study of the Singular

Finally, to return to the issue of “generalization” in case study that worries some audiences. I pointed out in the generalization section several ways in which it is possible to generalize from case study research, not in a formal propositional sense or from a case to a population, but by retaining a connection with the context in which the generalization first arose—that is, to realize in-depth understanding in context in different circumstances and situations. However, I also emphasized that, in many instances, it is particularization from which we learn. That is the point of the singular case study, and it is an art to perceive and craft the case in ways that we can.

Acknowledgments

Parts of this chapter build on ideas first explored in Simons, 2009 .

I am grateful to Bob Williams for pointing out the relevance of this quotation from Reynolds to remind us that “there is nothing new under the sun” and that we sometimes continue to engage endlessly in debates that have been well rehearsed before.

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

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

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

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

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

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

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

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

 Famous Case Studies

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

Clinical Case Studies

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

Child Psychology Case Studies

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

Types of Case Studies

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

Where Do You Find Data for a Case Study?

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

1. Primary sources

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

2. Secondary sources

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

3. Archival records

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

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

4. Organizational records

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

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

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

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

How do I Write a Case Study in Psychology?

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

1. Introduction

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

2. Case Presentation

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

3. Management and Outcome

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

4. Discussion

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

5. Additional Items

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

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

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

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

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

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

Limitations

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Further Information

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

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What the Case Study Method Really Teaches

• Nitin Nohria

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

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

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

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

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• Open access
• Published: 01 May 2024

A critical assessment of using ChatGPT for extracting structured data from clinical notes

• Jingwei Huang   ORCID: orcid.org/0000-0003-2155-6107 1 ,
• Donghan M. Yang 1 ,
• Ruichen Rong 1 ,
• Kuroush Nezafati   ORCID: orcid.org/0000-0002-6785-7362 1 ,
• Colin Treager 1 ,
• Zhikai Chi   ORCID: orcid.org/0000-0002-3601-3351 2 ,
• Shidan Wang   ORCID: orcid.org/0000-0002-0001-3261 1 ,
• Xian Cheng 1 ,
• Yujia Guo 1 ,
• Laura J. Klesse 3 ,
• Guanghua Xiao 1 ,
• Eric D. Peterson 4 ,
• Xiaowei Zhan 1 &
• Yang Xie   ORCID: orcid.org/0000-0001-9456-1762 1  

npj Digital Medicine volume  7 , Article number:  106 ( 2024 ) Cite this article

Metrics details

• Non-small-cell lung cancer

Existing natural language processing (NLP) methods to convert free-text clinical notes into structured data often require problem-specific annotations and model training. This study aims to evaluate ChatGPT’s capacity to extract information from free-text medical notes efficiently and comprehensively. We developed a large language model (LLM)-based workflow, utilizing systems engineering methodology and spiral “prompt engineering” process, leveraging OpenAI’s API for batch querying ChatGPT. We evaluated the effectiveness of this method using a dataset of more than 1000 lung cancer pathology reports and a dataset of 191 pediatric osteosarcoma pathology reports, comparing the ChatGPT-3.5 (gpt-3.5-turbo-16k) outputs with expert-curated structured data. ChatGPT-3.5 demonstrated the ability to extract pathological classifications with an overall accuracy of 89%, in lung cancer dataset, outperforming the performance of two traditional NLP methods. The performance is influenced by the design of the instructive prompt. Our case analysis shows that most misclassifications were due to the lack of highly specialized pathology terminology, and erroneous interpretation of TNM staging rules. Reproducibility shows the relatively stable performance of ChatGPT-3.5 over time. In pediatric osteosarcoma dataset, ChatGPT-3.5 accurately classified both grades and margin status with accuracy of 98.6% and 100% respectively. Our study shows the feasibility of using ChatGPT to process large volumes of clinical notes for structured information extraction without requiring extensive task-specific human annotation and model training. The results underscore the potential role of LLMs in transforming unstructured healthcare data into structured formats, thereby supporting research and aiding clinical decision-making.

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Large language models streamline automated machine learning for clinical studies

Introduction.

Large Language Models (LLMs) 1 , 2 , 3 , 4 , 5 , 6 , such as Generative Pre-trained Transformer (GPT) models represented by ChatGPT, are being utilized for diverse applications across various sectors. In the healthcare industry, early applications of LLMs are being used to facilitate patient-clinician communication 7 , 8 . To date, few studies have examined the potential of LLMs in reading and interpreting clinical notes, turning unstructured texts into structured, analyzable data.

Traditionally, the automated extraction of structured data elements from medical notes has relied on medical natural language processing (NLP) using rule-based or machine-learning approaches or a combination of both 9 , 10 . Machine learning methods 11 , 12 , 13 , 14 , particularly deep learning, typically employ neural networks and the first generation of transformer-based large language models (e.g., BERT). Medical domain knowledge needs to be integrated into model designs to enhance performance. However, a significant obstacle to developing these traditional medical NLP algorithms is the limited existence of human-annotated datasets and the costs associated with new human annotation 15 . Despite meticulous ground-truth labeling, the relatively small corpus sizes often result in models with poor generalizability or make evaluations of generalizability impossible. For decades, conventional artificial intelligence (AI) systems (symbolic and neural networks) have suffered from a lack of general knowledge and commonsense reasoning. LLMs, like GPT, offer a promising alternative, potentially using commonsense reasoning and broad general knowledge to facilitate language processing.

ChatGPT is the application interface of the GPT model family. This study explores an approach to using ChatGPT to extract structured data elements from unstructured clinical notes. In this study, we selected lung cancer pathology reports as the corpus for extracting detailed diagnosis information for lung cancer. To accomplish this, we developed and improved a prompt engineering process. We then evaluated the effectiveness of this method by comparing the ChatGPT output with expert-curated structured data and used case studies to provide insights into how ChatGPT read and interpreted notes and why it made mistakes in some cases.

Data and endpoints

The primary objective of this study was to develop an algorithm and assess the capabilities of ChatGPT in processing and interpreting a large volume of free-text clinical notes. To evaluate this, we utilized unstructured lung cancer pathology notes, which provide diagnostic information essential for developing treatment plans and play vital roles in clinical and translational research. We accessed a total of 1026 lung cancer pathology reports from two web portals: the Cancer Digital Slide Archive (CDSA data) ( https://cancer.digitalslidearchive.org/ ) and The Cancer Genome Atlas (TCGA data) ( https://cBioPortal.org ). These platforms serve as public data repositories for de-identified patient information, facilitating cancer research. The CDSA dataset was utilized as the “training” data for prompt development, while the TCGA dataset, after removing the overlapping cases with CDSA, served as the test data for evaluating the ChatGPT model performance.

From all the downloaded 99 pathology reports from CDSA for the training data, we excluded 21 invalid reports due to near-empty content, poor scanning quality, or missing report forms. Seventy-eight valid pathology reports were included as the training data to optimize the prompt. To evaluate the model performance, 1024 pathology reports were downloaded from cBioPortal. Among them, 97 overlapped with the training data and were excluded from the evaluation. We further excluded 153 invalid reports due to near-empty content, poor scanning quality, or missing report forms. The invalid reports were preserved to evaluate ChatGPT’s handling of irregular inputs separately, and were not included in the testing data for accuracy performance assessment. As a result, 774 valid pathology reports were included as the testing data for performance evaluation. These valid reports still contain typos, missing words, random characters, incomplete contents, and other quality issues challenging human reading. The corresponding numbers of reports used at each step of the process are detailed in Fig. 1 .

Exclusions are accounted for due to reasons such as empty reports, poor scanning quality, and other factors, including reports of stage IV or unknown conditions.

The specific task of this study was to identify tumor staging and histology types which are important for clinical care and research from pathology reports. The TNM staging system 16 , outlining the primary tumor features (T), regional lymph node involvement (N), and distant metastases (M), is commonly used to define the disease extent, assign prognosis, and guide lung cancer treatment. The American Joint Committee on Cancer (AJCC) has periodically released various editions 16 of TNM classification/staging for lung cancers based on recommendations from extensive database analyses. Following the AJCC guideline, individual pathologic T, N, and M stage components can be summarized into an overall pathologic staging score of Stage I, II, III, or IV. For this project, we instructed ChatGPT to use the AJCC 7 th edition Cancer Staging Manual 17 as the reference for staging lung cancer cases. As the lung cancer cases in our dataset are predominantly non-metastatic, the pathologic metastasis (pM) stage was not extracted. The data elements we chose to extract and evaluate for this study are pathologic primary tumor (pT) and pathologic lymph node (pN) stage components, overall pathologic tumor stage, and histology type.

Overall Performance

Using the training data in the CDSA dataset ( n  = 78), we experimented and improved prompts iteratively, and the final prompt is presented in Fig. 2 . The overall performance of the ChatGPT (gpt-3.5-turbo-16k model) is evaluated in the TCGA dataset ( n  = 774), and the results are summarized in Table 1 . The accuracy of primary tumor features (pT), regional lymph node involvement (pN), overall tumor stage, and histological diagnosis are 0.87, 0.91, 0.76, and 0.99, respectively. The average accuracy of all attributes is 0.89. The coverage rates for pT, pN, overall stage and histological diagnosis are 0.97, 0.94, 0.94 and 0.96, respectively. Further details of the accuracy evaluation, F1, Kappa, recall, and precision for each attribute are summarized as confusion matrices in Fig. 3 .

Final prompt for information extraction and estimation from pathology reports.

For meaningful evaluation, the cases with uncertain values, such as “Not Available”, “Not Specified”, “Cannot be determined”, “Unknown”, et al. in reference and prediction have been removed. a Primary tumor features (pT), b regional lymph node involvement (pN), c overall tumor stage, and d histological diagnosis.

Inference and Interpretation

To understand how ChatGPT reads and makes inferences from pathology reports, we demonstrated a case study using a typical pathology report in this cohort (TCGA-98-A53A) in Fig. 4a . The left panel shows part of the original pathology report, and the right panel shows the ChatGPT output with estimated pT, pN, overall stage, and histology diagnosis. For each estimate, ChatGPT gives the confidence level and the corresponding evidence it used for the estimation. In this case, ChatGPT correctly extracted information related to tumor size, tumor features, lymph node involvement, and histology information and used the AJCC staging guidelines to estimate tumor stage correctly. In addition, the confidence level, evidence interpretation, and case summary align well with the report and pathologists’ evaluations. For example, the evidence for the pT category was described as “The pathology report states that the tumor is > 3 cm and < 5 cm in greatest dimension, surrounded by lung or visceral pleura.” The evidence for tumor stage was described as “Based on the estimated pT category (T2a) and pN category (N0), the tumor stage is determined to be Stage IB according to AJCC7 criteria.” It shows that ChatGPT extracted relevant information from the note and correctly inferred the pT category based on the AJCC guideline (Supplementary Fig. 1 ) and the extracted information.

a TCGA-98-A53A. An example of a scanned pathological report (left panel) and ChatGPT output and interpretation (right panel). All estimations and support evidence are consistent with the pathologist’s evaluations. b The GPT model correctly inferred pT as T2a based on the tumor’s size and involvement according to AJCC guidelines.

In another more complex case, TCGA-50-6590 (Fig. 4b ), ChatGPT correctly inferred pT as T2a based on both the tumor’s size and location according to AJCC guidelines. Case TCGA-44-2656 demonstrates a more challenging scenario (Supplementary Fig. 2 ), where the report only contains some factual data without specifying pT, pN, and tumor stage. However, ChatGPT was able to infer the correct classifications based on the reported facts and provide proper supporting evidence.

Error analysis

To understand the types and potential reasons for misclassifications, we performed a detailed error analysis by looking into individual attributes and cases where ChatGPT made mistakes, the results of which are summarized below.

Primary tumor feature (pT) classification

In total, 768 cases with valid reports and reference values in the testing data were used to evaluate the classification performance of pT. Among them, 15 cases were reported with unknown or empty output by ChatGPT, making the coverage rate 0.97. For the remaining 753 cases, 12.6% of pT was misclassified. Among these misclassification cases, the majority were T1 misclassified as T2 (67 out of 753 or 8.9%) or T3 misclassified as T2 (12 out of 753, or 1.6%).

In most cases, ChatGPT extracted the correct tumor size information but used an incorrect rule to distinguish pT categories. For example, in the case TCGA-22-4609 (Fig. 5a ), ChatGPT stated, “Based on the tumor size of 2.0 cm, it falls within the range of T2 category according to AJCC 7th edition for lung carcinoma staging manual.” However, according to the AJCC 7 th edition staging guidelines for lung cancer, if the tumor is more than 2 cm but less than 3 cm in greatest dimension and does not invade nearby structures, pT should be classified as T1b. Therefore, ChatGPT correctly extracted the maximum tumor dimension of 2 cm but incorrectly interpreted this as meeting the criteria for classification as T2. Similarly, for case TCGA-85-A4JB, ChatGPT incorrectly claimed, “Based on the tumor size of 10 cm, the estimated pT category is T2 according to AJCC 7th edition for lung carcinoma staging manual.” According to the AJCC 7 th edition staging guidelines, a tumor more than 7 cm in greatest dimension should be classified as T3.

a TCGA-22-4609 illustrates a typical case where the GPT model uses a false rule, which is incorrect by AJCC guideline. b Case TCGA-39-5028 shows a complex case where there exist two tumors and the GPT model only capture one of them. c Case TCGA-39-5016 reveals a case where the GPT model made a mistake for getting confused with domain terminology.

Another challenging situation arose when multiple tumor nodules were identified within the lung. In the case of TCGA-39-5028 (Fig. 5b ), two separate tumor nodules were identified: one in the right upper lobe measuring 2.1 cm in greatest dimension and one in the right lower lobe measuring 6.6 cm in greatest dimension. According to the AJCC 7 th edition guidelines, the presence of separate tumor nodules in a different ipsilateral lobe results in a classification of T4. However, ChatGPT classified this case as T2a, stating, “The pathology report states the tumor’s greatest diameter as 2.1 cm”. This classification would be appropriated if the right upper lobe nodule were a single isolated tumor. However, ChatGPT failed to consider the presence of the second, larger nodule in the right lower lobe when determining the pT classification.

Regional lymph node involvement (pN)

The classification performance of pN was evaluated using 753 cases with valid reports and reference values in the testing data. Among them, 27 cases were reported with unknown or empty output by ChatGPT, making the coverage rate 0.94. For the remaining 726 cases, 8.5% of pN was misclassified. Most of these misclassification cases were N1 misclassified as N2 (32 cases). The AJCC 7th edition staging guidelines use the anatomic locations of positive lymph nodes to determine N1 vs. N2. However, most of the misclassification cases were caused by ChatGPT interpreting the number of positive nodes rather than the locations of the positive nodes. One such example is the case TCGA-85-6798. The report states, “Lymph nodes: 2/16 positive for metastasis (Hilar 2/16)”. Positive hilar lymph nodes correspond to N1 classification according to AJCC 7th edition guidelines. However, ChatGPT misclassifies this case as N2, stating, “The pathology report states that 2 out of 16 lymph nodes are positive for metastasis. Based on this information, the pN category can be estimated as N2 according to AJCC 7th edition for lung carcinoma staging manual.” This interpretation is incorrect, as the number of positive lymph nodes is not part of the criteria used to determine pN status according to AJCC 7th edition guidelines. The model misinterpreted pN2 predictions in 22 cases due to similar false assertions.

In some cases, the ChatGPT model made classification mistakes by misunderstanding the locations’ terminology. Figure 5c shows a case (TCGA-39-5016) where the ChatGPT model recognized that “6/9 peribronchial lymph nodes involved, “ corresponding with classification as N1, but ChatGPT misclassified this case as N2. By AJCC 7th edition guidelines, N2 is defined as “Metastasis in ipsilateral mediastinal and/or subcarinal lymph node(s)”. The ChatGPT model did not fully understand that terminology and made misclassifications.

Pathology tumor stage

The overall tumor stage classification performance was evaluated using 744 cases with valid reports and reference values as stage I, II and III in the testing data. Among them, 18 cases were reported as unknown or empty output by ChatGPT making the coverage rate as 0.94. For the remaining 726 cases, 23.6% of the overall stage was misclassified. Since the overall stage depends on individual pT and pN stages, the mistakes could come from misclassification of pT or pN (error propagation) or applying incorrect inference rules to determine the overall stage from pT and pN (incorrect rules). Looking into the 56 cases where ChatGPT misclassified stage II as stage III, 22 cases were due to error propagation, and 34 were due to incorrect rules. Figure 6a shows an example of error propagation (TCGA-MP-A4TK). ChatGPT misclassified the pT stage from T2a to T3, and then this mistake led to the incorrect classification of stage IIA to stage IIIA. Figure 6b illustrates a case (TCGA-49-4505) where ChatGPT made correct estimation of pT and pN but made false prediction about tumor stage by using a false rule. Among the 34 cases affected by incorrect rules, ChatGPT mistakenly inferred tumor stage as stage III for 26 cases where pT is T3 and pN is N0, respectively. For example, for case TCGA-55-7994, ChatGPT provided the evidence as “Based on the estimated pT category (T3) and pN category (N0), the tumor stage is determined to be Stage IIIA according to AJCC7 criteria”. According to AJCC7, tumors with T3 and N0 should be classified as stage IIB. Similarly, error analysis for other tumor stages shows that misclassifications come from both error propagation and applying false rules.

a Case TCGA-MP-A4TK: An example of typical errors GPT made in the experiments, i.e. GPT took false rule and further led to faulty propagation. b Case TCGA-49-4505: The GPT model made false estimation of Stage IIIA with a false rule, although it made correct inference with T2b and N1.

Histological diagnosis

The classification performance of histology diagnosis was evaluated using 762 cases with valid reports and reference values in the testing data. Among them, 17 cases were reported as either unknown or empty output by ChatGPT, making the coverage rate 0.96. For the remaining 745 cases, 6 ( < 1%) of histology types were misclassified. Among the mistakes that ChatGPT made for histology diagnosis, ChatGPT misclassified 3 of them as “other” type and 3 cases of actual “other” type (neither adenocarcinomas nor squamous cell carcinomas) as 2 adenocarcinomas and 1 squamous cell carcinoma. In TCGA-22-5485, two tumors exist: one squamous cell carcinoma and another adenocarcinoma, which should be classified as the ‘other’ type. However, ChatGPT only identified and extracted information for one tumor. In the case TCGA-33-AASB, which is the “other” type of histology, ChatGPT captured the key information and gave it as evidence: “The pathology report states the histologic diagnosis as infiltrating poorly differentiated non-small cell carcinoma with both squamous and glandular features”. However, it mistakenly estimated this case as “adenocarcinoma”. In another case (TCGA-86-8668) of adenocarcinoma, ChatGPT again captured key information and stated as evidence, “The pathology report states the histologic diagnosis as Bronchiolo-alveolar carcinoma, mucinous” but could not tell it is a subtype of adenocarcinoma. Both cases reveal that ChatGPT still has limitations in the specific domain knowledge in lung cancer pathology and the capability of correcting understanding its terminology.

Analyzing irregularities

The initial model evaluation and prompt-response review uncovered irregular scenarios: the original pathology reports may be blank, poorly scanned, or simply missing report forms. We reviewed how ChatGPT responded to these anomalies. First, when a report was blank, the prompt contained only the instruction part. ChatGPT failed to recognize this situation in most cases and inappropriately generated a fabricated case. Our experiments showed that, with the temperature set at 0 for blank reports, ChatGPT converged to a consistent, hallucinated response. Second, for nearly blank reports with a few random characters and poorly scanned reports, ChatGPT consistently converged to the same response with increased variance as noise increased. In some cases, ChatGPT responded appropriately to all required attributes but with unknown values for missing information. Last, among the 15 missing report forms in a small dataset, ChatGPT responded “unknown” as expected in only 5 cases, with the remaining 10 still converging to the hallucinated response.

Reproducibility evaluation

Since ChatGPT models (even with the same version) evolve over time, it is important to evaluate the stability and reproducibility of ChatGPT. For this purpose, we conducted experiments with the same model (“gpt-3.5-turbo-0301”), the same data, prompt, and settings (e.g., temperature = 0) twice in early April and the middle of May of 2023. The rate of equivalence between ChatGPT estimations in April and May on key attributes of interest (pT, pN, tumor stage, and histological diagnosis) is 0.913. The mean absolute error between certainty degrees in the two experiments is 0.051. Considering the evolutionary nature of ChatGPT models, we regard an output difference to a certain extent as reasonable and the overall ChatGPT 3.5 model as stable.

Comparison with other NLP methods

In order to have a clear perspective on how ChatGPT’s performance stands relative to established methods, we conducted a comparative analysis of the results generated by ChatGPT with two established methods: a keyword search algorithm and a deep learning-based Named Entity Recognition (NER) method.

Data selection and annotation

Since the keyword search and NER methods do not support zero-shot learning and require human annotations on the entity level, we carefully annotated our dataset for these traditional NLP methods. We used the same training and testing datasets as in the prompt engineering for ChatGPT. The training dataset underwent meticulous annotation by experienced medical professionals, adhering to the AJCC7 standards. This annotation process involved identifying and highlighting all relevant entities and text spans related to stage, histology, pN, and pT attributes. The detailed annotation process for the 78 cases required a few weeks of full-time work from medical professionals.

Keyword search algorithm using wordpiece tokenizer

For the keyword search algorithm, we employed the WordPiece tokenizer to segment words into subwords. We compiled an annotated entity dictionary from the training dataset. To assess the performance of this method, we calculated span similarities between the extracted spans in the validation and testing datasets and the entries in the dictionary.

Named Entity Recognition (NER) classification algorithm

For the NER classification algorithm, we designed a multi-label span classification model. This model utilized the pre-trained Bio_ClinicalBERT as its backbone. To adapt it for multi-label classification, we introduced an additional linear layer. The model underwent fine-tuning for 1000 epochs using the stochastic gradient descent (SGD) optimizer. The model exhibiting the highest overall F1 score on the validation dataset was selected as the final model for further evaluation in the testing dataset.

Performance evaluation

We evaluated the performance of both the keyword search and NER methods on the testing dataset. We summarized the predicted entities/spans and their corresponding labels. In cases where multiple related entities were identified for a specific category, we selected the most severe entities as the final prediction. Moreover, we inferred the stage information for corpora lacking explicit staging information by aggregating details from pN, pT, and diagnosis, aligning with the AJCC7 protocol. The overall predictions for stage, diagnosis, pN, and pT were compared against the ground truth table to gauge the accuracy and effectiveness of our methods. The results (Supplementary Table S1 ) show that the ChatGPT outperforms WordPiece tokenizer and NER Classifier. The average accuracy for ChatGPT, WordPiece tokenizer, and NER Classifier are 0.89, 0.51, and 0.76, respectively.

Prompt engineering process and results

Prompt design is a heuristic search process with many elements to consider, thus having a significantly large design space. We conducted many experiments to explore better prompts. Here, we share a few typical prompts and the performance of these prompts in the training data set to demonstrate our prompt engineering process.

Output format

The most straightforward prompt without special design would be: “read the pathology report and answer what are pT, pN, tumor stage, and histological diagnosis”. However, this simple prompt would make ChatGPT produce unstructured answers varying in format, terminology, and granularity across the large number of pathology reports. For example, ChatGPT may output pT as “T2” or “pT2NOMx”, and it outputs histological diagnosis as “Multifocal invasive moderately differentiated non-keratinizing squamous cell carcinoma”. The free-text answers will require a significant human workload to clean and process the output from ChatGPT. To solve this problem, we used a multiple choice answer format to force ChatGPT to pick standardized values for some attributes. For example, for pT, ChatGPT could only provide the following outputs: “T0, Tis, T1, T1a, T1b, T2, T2a, T2b, T3, T4, TX, Unknown”. For the histologic diagnosis, ChatGPT could provide output in one of these categories: Lung Adenocarcinoma, Lung Squamous Cell Carcinoma, Other, Unknown. In addition, we added the instruction, “Please make sure to output the whole set of answers together as a single JSON file, and don’t output anything beyond the required JSON file,” to emphasize the requirement for the output format. These requests in the prompt make the downstream analysis of ChatGPT output much more efficient. In order to know the certainty degree of ChatGPT’s estimate and the evidence, we asked ChatGPT to provide the following 4 outputs for each attribute/variable: extracted value as stated in the pathology report, estimated value based on AJCC 7th edition for lung carcinoma staging manual, the certainty degree of the estimation, and the supporting evidence for the estimation. The classification accuracy of this prompt with multiple choice output format (prompt v1) in our training data could achieve 0.854.

Evidence-based inference

One of the major concerns for LLM is that the results from the model are not supported by any evidence, especially when there is not enough information for specific questions. In order to reduce this problem, we emphasize the use of evidence for inference in the prompt by adding this instruction to ChatGPT: “Please ensure to make valid inferences for attribute estimation based on evidence. If there is no available evidence provided to make an estimation, please answer the value as “Unknown.” In addition, we asked ChatGPT to “Include “comment” as the last key of the JSON file.” After adding these two instructions (prompt v2), the performance of the classification in the training data increased to 0.865.

Chain of thought prompting by asking intermediate questions

Although tumor size is not a primary interest for diagnosis and clinical research, it plays a critical role in classifying the pT stage. We hypothesize that if ChatGPT pays closer attention to tumor size, it will have better classification performance. Therefore, we added an instruction in the prompt (prompt v3) to ask ChatGPT to estimate: “tumor size max_dimension: [<the greatest dimension of tumor in Centimeters (cm)>, ‘Unknown’]” as one of the attributes. After this modification, the performance of the classification in the training data increased to 0.90.

Providing examples

Providing examples is an effective way for humans to learn, and it should have similar effects for ChatGPT. We provided a specific example to infer the overall stage based on pT and pN by adding this instruction: “Please estimate the tumor stage category based on your estimated pT category and pN category and use AJCC7 criteria. For example, if pT is estimated as T2a and pN as N0, without information showing distant metastasis, then by AJCC7 criteria, the tumor stage is “Stage IB”.” After this modification (prompt v4), the performance of the classification in the training data increased to 0.936.

Although we can further refine and improve prompts, we decided to use prompt v4 as the final model and apply it to the testing data and get the final classification accuracy of 0.89 in the testing data.

ChatGPT-4 performance

LLM evolves rapidly and OpenAI just released the newest GPT-4 Turbo model (GPT-4-1106-preview) in November 2023. To compare this new model with GPT-3.5-Turbo, we applied this newest GPT model GPT-4-1106 to analyze all the lung cancer pathology notes in the testing data. The classification result and the comparison with the GPT-3.5-Turbo-16k are summarized in Supplementary Table 1 . The results show that GPT-4-turbo performs better in almost every aspect; overall, the GPT-4-turbo model increases performance by over 5%. However, GPT-4-Turbo is much more expensive than GPT-3.5-Turbo. The performance of GPT-3.5-Turbo-16k is still comparable and acceptable. As such, this study mainly focuses on assessing GPT-3.5-Turbo-16k, but highlights the fast development and promise of using LLM to extract structured data from clinical notes.

Analyzing osteosarcoma data

To demonstrate the broader application of this method beyond lung cancer, we collected and analyzed clinical notes from pediatric osteosarcoma patients. Osteosarcoma, the most common type of bone cancer in children and adolescents, has seen no substantial improvement in patient outcomes for the past few decades 18 . Histology grades and margin status are among the most important prognostic factors for osteosarcoma. We collected pathology reports from 191 osteosarcoma cases (approved by UTSW IRB #STU 012018-061). Out of these, 148 cases had histology grade information, and 81 had margin status information; these cases were used to evaluate the performance of the GPT-3.5-Turbo-16K model and our prompt engineering strategy. Final diagnoses on grade and margin were manually reviewed and curated by human experts, and these diagnoses were used to assess ChatGPT’s performance. All notes were de-identified prior to analysis. We applied the same prompt engineering strategy to extract grade and margin information from these osteosarcoma pathology reports. This analysis was conducted on our institution’s private Azure OpenAI platform, using the GPT-3.5-Turbo-16K model (version 0613), the same model used for lung cancer cases. ChatGPT accurately classified both grades (with a 98.6% accuracy rate) and margin status (100% accuracy), as shown in Supplementary Fig. 3 . In addition, Supplementary Fig. 4 details a specific case, illustrating how ChatGPT identifies grades and margin status from osteosarcoma pathology reports.

Since ChatGPT’s release in November 2022, it has spurred many potential innovative applications in healthcare 19 , 20 , 21 , 22 , 23 . To our knowledge, this is among the first reports of an end-to-end data science workflow for prompt engineering, using, and rigorously evaluating ChatGPT in its capacity of batch-processing information extraction tasks on large-scale clinical report data.

The main obstacle to developing traditional medical NLP algorithms is the limited availability of annotated data and the costs for new human annotations. To overcome these hurdles, particularly in integrating problem-specific information and domain knowledge with LLMs’ task-agnostic general knowledge, Augmented Language Models (ALMs) 24 , which incorporate reasoning and external tools for interaction with the environment, are emerging. Research shows that in-context learning (most influentially, few-shot prompting) can complement LLMs with task-specific knowledge to perform downstream tasks effectively 24 , 25 . In-context learning is an approach of training through instruction or light tutorial with a few examples (so called few-shot prompting; well instruction without any example is called 0-shot prompting) rather than fine-tuning or computing-intensive training, which adjusts model weights. This approach has become a dominant method for using LLMs in real-world problem-solving 24 , 25 , 26 . The advent of ALMs promises to revolutionize almost every aspect of human society, including the medical and healthcare domains, altering how we live, work, and communicate. Our study shows the feasibility of using ChatGPT to extract data from free text without extensive task-specific human annotation and model training.

In medical data extraction, our study has demonstrated the advantages of adopting ChatGPT over traditional methods in terms of cost-effectiveness and efficiency. Traditional approaches often require labor-intensive annotation processes that may take weeks and months from medical professionals, while ChatGPT models can be fine-tuned for data extraction within days, significantly reducing the time investment required for implementation. Moreover, our economic analysis revealed the cost savings associated with using ChatGPT, with processing over 900 pathology reports incurring a minimal monetary cost (less than $10 using GPT 3.5 Turbo and less than $30 using GPT-4 Turbo). This finding underscores the potential benefits of incorporating ChatGPT into medical data extraction workflows, not only for its time efficiency but also for its cost-effectiveness, making it a compelling option for medical institutions and researchers seeking to streamline their data extraction processes without compromising accuracy or quality.

A critical requirement for effectively utilizing an LLM is crafting a high-quality “prompt” to instruct the LLM, which has led to the emergence of an important methodology referred to as “prompt engineering.” Two fundamental principles guide this process: firstly, the provision of appropriate context, and secondly, delivering clear instructions about subtasks and the requirements for the desired response and how it should be presented. For a single query for one-time use, the user can experiment with and revise the prompt within the conversation session until a satisfactory answer is obtained. However, prompt design can become more complex when handling repetitive tasks over many input data files using the OpenAI API. In these instances, a prompt must be designed according to a given data feed while maintaining the generality and coverage for various input data features. In this study, we found that providing clear guidance on the output format, emphasizing evidence-based inference, providing chain of thought prompting by asking for tumor size information, and providing specific examples are critical in improving the efficiency and accuracy of extracting structured data from the free-text pathology reports. The approach employed in this study effectively leverages the OpenAI API for batch queries of ChatGPT services across a large set of tasks with similar input data structures, including but not limited to pathology reports and EHR.

Our evaluation results show that the ChatGPT (gpt-3.5-turbo-16k) achieved an overall average accuracy of 89% in extracting and estimating lung cancer staging information and histology subtypes compared to pathologist-curated data. This performance is very promising because some scanned pathology reports included in this study contained random characters, missing parts, typos, varied formats, and divergent information sections. ChatGPT also outperformed traditional NLP methods. Our case analysis shows that most misclassifications were due to a lack of knowledge of detailed pathology terminology or very specialized information in the current versions of ChatGPT models, which could be avoided with future model training or fine-tuning with more domain-specific knowledge.

While our experiments reveal ChatGPT’s strengths, they also underscore its limitations and potential risks, the most significant being the occasional “hallucination” phenomenon 27 , 28 , where the generated content is not faithful to the provided source content. For example, the responses to blank or near-blank reports reflect this issue, though these instances can be detected and corrected due to convergence towards an “attractor”.

The phenomenon of ‘hallucination’ in LLMs presents a significant challenge in the field. It is important to consider several key factors to effectively address the challenges and risks associated with ChatGPT’s application in medicine. Since the output of an LLM depends on both the model and the prompt, mitigating hallucination can be achieved through improvements in GPT models and prompting strategies. From a model perspective, model architecture, robust training, and fine-tuning on a diverse and comprehensive medical dataset, emphasizing accurate labeling and classification, can reduce misclassifications. Additionally, enhancing LLMs’ comprehension of medical terminology and guidelines by incorporating feedback from healthcare professionals during training and through Reinforcement Learning from Human Feedback (RLHF) can further diminish hallucinations. Regarding prompt engineering strategies, a crucial method is to prompt the GPT model with a ‘chain of thought’ and request an explanation with the evidence used in the reasoning. Further improvements could include explicitly requesting evidence from input data (e.g., the pathology report) and inference rules (e.g., AJCC rules). Prompting GPT models to respond with ‘Unknown’ when information is insufficient for making assertions, providing relevant context in the prompt, or using ‘embedding’ of relevant text to narrow down the semantic subspace can also be effective. Harnessing hallucination is an ongoing challenge in AI research, with various methods being explored 5 , 27 . For example, a recent study proposed “SelfCheckGPT” approach to fact-check black-box models 29 . Developing real-time error detection mechanisms is crucial for enhancing the reliability and trustworthiness of AI models. More research is needed to evaluate the extent, impacts, and potential solutions of using LLMs in clinical research and care.

When considering using ChatGPT and similar LLMs in healthcare, it’s important to thoughtfully consider the privacy implications. The sensitivity of medical data, governed by rigorous regulations like HIPAA, naturally raises concerns when integrating technologies like LLMs. Although it is a less concern to analyze public available de-identified data, like the lung cancer pathology notes used in this study, careful considerations are needed for secured healthcare data. More secured OpenAI services are offered by OpenAI security portal, claimed to be compliant to multiple regulation standards, and Microsoft Azure OpenAI, claimed could be used in a HIPAA-compliant manner. For example, de-identified Osteosarcoma pathology notes were analyzed by Microsoft Azure OpenAI covered by the Business Associate Agreement in this study. In addition, exploring options such as private versions of these APIs, or even developing LLMs within a secure healthcare IT environment, might offer good alternatives. Moreover, implementing strong data anonymization protocols and conducting regular security checks could further protect patient information. As we navigate these advancements, it’s crucial to continuously reassess and adapt appropriate privacy strategies, ensuring that the integration of AI into healthcare is both beneficial and responsible.

Despite these challenges, this study demonstrates our effective methodology in “prompt engineering”. It presents a general framework for using ChatGPT’s API in batch queries to process large volumes of pathology reports for structured information extraction and estimation. The application of ChatGPT in interpreting clinical notes holds substantial promise in transforming how healthcare professionals and patients utilize these crucial documents. By generating concise, accurate, and comprehensible summaries, ChatGPT could significantly enhance the effectiveness and efficiency of extracting structured information from unstructured clinical texts, ultimately leading to more efficient clinical research and improved patient care.

In conclusion, ChatGPT and other LLMs are powerful tools, not just for pathology report processing but also for the broader digital transformation of healthcare documents. These models can catalyze the utilization of the rich historical archives of medical practice, thereby creating robust resources for future research.

Data processing, workflow, and prompt engineering

The lung cancer data we used for this study are publicly accessible via CDSA ( https://cancer.digitalslidearchive.org/ ) and TCGA ( https://cBioPortal.org ), and they are de-identified data. The institutional review board at the University of Texas Southwestern Medical Center has approved this study where patient consent was waived for using retrospective, de-identified electronic health record data.

We aimed to leverage ChatGPT to extract and estimate structured data from these notes. Figure 7a displays our process. First, scanned pathology reports in PDF format were downloaded from TCGA and CDSA databases. Second, R package pdftools, an optical character recognition tool, was employed to convert scanned PDF files into text format. After this conversion, we identified reports with near-empty content, poor scanning quality, or missing report forms, and those cases were excluded from the study. Third, the OpenAI API was used to analyze the text data and extract structured data elements based on specific prompts. In addition, we extracted case identifiers and metadata items from the TCGA metadata file, which was used to evaluate the model performance.

a Illustration of the use of OpenAI API for batch queries of ChatGPT service, applied to a substantial volume of clinical notes — pathology reports in our study. b A general framework for integrating ChatGPT into real-world applications.

In this study, we implemented a problem-solving framework rooted in data science workflow and systems engineering principles, as depicted in Fig. 7b . An important step is the spiral approach 30 to ‘prompt engineering’, which involves experimenting with subtasks, different phrasings, contexts, format specifications, and example outputs to improve the quality and relevance of the model’s responses. It was an iterative process to achieve the desired results. For the prompt engineering, we first define the objective: to extract information on TNM staging and histology type as structured attributes from the unstructured pathology reports. Second, we assigned specific tasks to ChatGPT, including estimating the targeted attributes, evaluating certainty levels, identifying key evidence of each attribute estimation, and generating a summary as output. The output was compiled into a JSON file. In this process, clinicians were actively formulating questions and evaluating the results.

Our study used the “gpt-3.5-turbo” model, accessible via the OpenAI API. The model incorporates 175 billion parameters and was trained on various public and authorized documents, demonstrating specific Artificial General Intelligence (AGI) capabilities 5 . Each of our queries sent to ChatGPT service is a “text completion” 31 , which can be implemented as a single round chat completion. All LLMs have limited context windows, constraining the input length of a query. Therefore, lengthy pathology reports combined with the prompt and ChatGPT’s response might exceed this limit. We used OpenAI’s “tiktoken” Python library to estimate the token count to ensure compliance. This constraint has been largely relaxed by the newly released GPT models with much larger context windows. We illustrate the pseudocode for batch ChatGPT queries on a large pathology report set in Supplementary Fig. 5 .

Model evaluation

We evaluated the performance of ChatGPT by comparing its output with expert-curated data elements provided in the TCGA structured data using the testing data set. Some staging records in the TCGA structured data needed to be updated; our physicians curated and updated those records. To mimic a real-world setting, we processed all reports regardless of data quality to collect model responses. For performance evaluation, we only used valid reports providing meaningful text and excluded the reports with near-empty content, poor scanning quality, and missing report forms, which were reported as irregular cases. We assessed the classification accuracy, F1, Kappa, recall, and precision for each attribute of interest, including pT, pN, overall stage, and histology types, and presented results as accuracy and confusion matrices. Missing data were excluded from the accuracy evaluation, and the coverage rate was reported for predicted values as ‘unknown’ or empty output.

Reporting summary

Further information on research design is available in the Nature Research Reporting Summary linked to this article.

Data availability

The lung cancer dataset we used for this study is “Pan-Lung Cancer (TCGA, Nat Genet2016)”, ( https://www.cbioportal.org/study/summary?id=nsclc_tcga_broad_2016 ) and the “luad” and “lusc” subsets from CDSA ( https://cancer.digitalslidearchive.org/ ). We have provided a reference regarding how to access the data 32 . We utilized the provided APIs to retrieve clinical information and pathology reports for the LUAD (lung adenocarcinoma) and LUSC (lung squamous cell carcinoma) cohorts. The pediatric data are the EHR data from UTSW clinic services. The data is available from the corresponding author upon reasonable request and IRB approval.

Code availability

All codes used in this paper were developed using APIs from OpenAI. The prompt for the API is available in Fig. 2 . Method-specific code is available from the corresponding author upon request.

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Acknowledgements

This work was partially supported by the National Institutes of Health [P50CA70907, R35GM136375, R01GM140012, R01GM141519, R01DE030656, U01CA249245, and U01AI169298], and the Cancer Prevention and Research Institute of Texas [RP230330 and RP180805].

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Jingwei Huang, Donghan M. Yang, Ruichen Rong, Kuroush Nezafati, Colin Treager, Shidan Wang, Xian Cheng, Yujia Guo, Guanghua Xiao, Xiaowei Zhan & Yang Xie

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Contributions

J.H., Y.X., X.Z. and G.X. designed the study. X.Z., K.N., C.T. and J.H. prepared, labeled, and curated lung cancer datasets. D.M.Y., X.C., Y.G., L.J.K. prepared, labeled, and curated osteosarcoma datasets. Z.C. provided critical inputs as pathologists. Y.X., G.X., E.P. provided critical inputs for the study. J.H. implemented experiments with ChatGPT. R.R. and K.N. implemented experiments with N.L.P. J.H., Y.X., G.X. and S.W. conducted data analysis. Y.X., G.X., J.H., X.Z., D.M.Y. and R.R. wrote the manuscript. All co-authors read and commented on the manuscript.

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Correspondence to Xiaowei Zhan or Yang Xie .

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Huang, J., Yang, D.M., Rong, R. et al. A critical assessment of using ChatGPT for extracting structured data from clinical notes. npj Digit. Med. 7 , 106 (2024). https://doi.org/10.1038/s41746-024-01079-8

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Received : 24 July 2023

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Published : 01 May 2024

DOI : https://doi.org/10.1038/s41746-024-01079-8

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Please note you do not have access to teaching notes, harmonizing public health with individual liberties: exploring the interplay of right to health, privacy, and autonomy during recent and future pandemics.

International Journal of Human Rights in Healthcare

ISSN : 2056-4902

Article publication date: 2 May 2024

The purpose of publishing this viewpoint is to critically analyze the relationship between public health interventions and individual liberties, during recent and potential future pandemics. By exploring the interplay of the right to health, privacy, and autonomy, this viewpoint seeks to highlight the complexities and challenges faced by decision-makers in balancing collective well-being with the protection of individual rights. Through detailed discussions on lockdowns, contact tracing, and international border closures, this paper aims to foster a deeper understanding of how these measures impact fundamental human rights and proposes ways to achieve harmony in future pandemics and crises.

Design/methodology/approach

The viewpoint has a qualitative approach, using critical analysis and examination of legal frameworks, scholarly literature, and real-world examples to explore the interplay between public health interventions and individual liberties during pandemics. It draws upon diverse sources, including international declarations, legal instruments, and empirical studies to elucidate the complexities of balancing collective well-being with the protection of fundamental human rights. Through in-depth discussions on lockdowns, contact tracing, and international border closures, this paper aims to provide a comprehensive understanding of the challenges and opportunities in harmonizing public health with individual liberties.

The findings of the viewpoint highlight the relationship between public health interventions and individual liberties during pandemics. It underscores the importance of balancing collective well-being with the protection of fundamental human rights, particularly the right to health, privacy, and autonomy. The analysis reveals the shortcomings of certain measures, such as restrictive lockdowns, border closure, and unchecked contact tracing in respecting individual rights. However, it also identifies opportunities for adopting an altruistic approach that upholds both public health imperatives and individual freedoms. Ultimately, the findings emphasize the need for a nuanced understanding and inclusive approach to crisis management.

Research limitations/implications

This analysis predominantly addresses the COVID-19 pandemic, potentially limiting the generalizability of findings to other public health crises. It is limited by its focus primarily on legal frameworks and theoretical analysis, which may not fully capture the practical complexities and nuances of implementing public health measures. Furthermore, while proposing ways to achieve harmony between public health and individual liberties, practical challenges and potential trade-offs in implementation are not extensively explored. Future research could benefit from empirical studies and case analyses to better understand the real-world implications of balancing public health imperatives with individual rights.

Practical implications

The viewpoint underscores the importance of adopting an altruistic approach that respects both public health imperatives and individual rights during pandemics. Practical implications include the need for policymakers to prioritize transparency, accountability, and citizen engagement in implementing public health measures. By fostering trust, ensuring data privacy, and promoting active participation, authorities can mitigate concerns about infringements on individual liberties while effectively managing public health threats. Furthermore, the analysis highlights the importance of considering diverse perspectives and potential trade-offs in decision-making processes to achieve a balanced and inclusive approach to crisis management.

Social implications

The viewpoint’s social implications lie in building a society where public health measures are implemented with respect for individual rights, fostering trust, and promoting community engagement. By prioritizing transparency, accountability and inclusivity, authorities can mitigate social tensions and promote collective resilience during pandemics. Moreover, upholding principles of equity and fairness in crisis management can help address disparities and ensure that vulnerable populations are not disproportionately affected. Overall, the viewpoint advocates for a societal framework that values both public health and individual liberties, thereby contributing to the development of a more cohesive and resilient society in the face of future health crises.

Originality/value

The originality and value of this viewpoint lie in its comprehensive exploration of the interplay between public health interventions and individual liberties during pandemics. By synthesizing legal frameworks, scholarly literature, and real-world examples, it offers unique insights into the complexities and challenges faced by decision-makers in balancing collective well-being with the protection of fundamental human rights. Additionally, the viewpoint’s proposal of an altruistic approach that respects both public health imperatives and individual freedoms contributes to the discourse on ethical crisis management. Overall, it provides valuable perspectives and recommendations for achieving harmony between public health and individual liberties in future pandemics.

• Public health
• Human rights
• Health care
• Civil society
• Contact tracing
• Individual rights
• Individual liberties
• Right to health

Pokhrel, A. (2024), "Harmonizing public health with individual liberties: exploring the interplay of right to health, privacy, and autonomy during recent and future pandemics", International Journal of Human Rights in Healthcare , Vol. ahead-of-print No. ahead-of-print. https://doi.org/10.1108/IJHRH-02-2024-0011

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