multiple case study design

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Research Guides

Multiple Case Studies

Nadia Alqahtani and Pengtong Qu

Description

The case study approach is popular across disciplines in education, anthropology, sociology, psychology, medicine, law, and political science (Creswell, 2013). It is both a research method and a strategy (Creswell, 2013; Yin, 2017). In this type of research design, a case can be an individual, an event, or an entity, as determined by the research questions. There are two variants of the case study: the single-case study and the multiple-case study. The former design can be used to study and understand an unusual case, a critical case, a longitudinal case, or a revelatory case. On the other hand, a multiple-case study includes two or more cases or replications across the cases to investigate the same phenomena (Lewis-Beck, Bryman & Liao, 2003; Yin, 2017). …a multiple-case study includes two or more cases or replications across the cases to investigate the same phenomena

The difference between the single- and multiple-case study is the research design; however, they are within the same methodological framework (Yin, 2017). Multiple cases are selected so that “individual case studies either (a) predict similar results (a literal replication) or (b) predict contrasting results but for anticipatable reasons (a theoretical replication)” (p. 55). When the purpose of the study is to compare and replicate the findings, the multiple-case study produces more compelling evidence so that the study is considered more robust than the single-case study (Yin, 2017).

To write a multiple-case study, a summary of individual cases should be reported, and researchers need to draw cross-case conclusions and form a cross-case report (Yin, 2017). With evidence from multiple cases, researchers may have generalizable findings and develop theories (Lewis-Beck, Bryman & Liao, 2003).

Creswell, J. W. (2013). Qualitative inquiry and research design: Choosing among five approaches (3rd ed.). Los Angeles, CA: Sage.

Lewis-Beck, M., Bryman, A. E., & Liao, T. F. (2003). The Sage encyclopedia of social science research methods . Los Angeles, CA: Sage.

Yin, R. K. (2017). Case study research and applications: Design and methods . Los Angeles, CA: Sage.

Key Research Books and Articles on Multiple Case Study Methodology

Yin discusses how to decide if a case study should be used in research. Novice researchers can learn about research design, data collection, and data analysis of different types of case studies, as well as writing a case study report.

Chapter 2 introduces four major types of research design in case studies: holistic single-case design, embedded single-case design, holistic multiple-case design, and embedded multiple-case design. Novice researchers will learn about the definitions and characteristics of different designs. This chapter also teaches researchers how to examine and discuss the reliability and validity of the designs.

Creswell, J. W., & Poth, C. N. (2017). Qualitative inquiry and research design: Choosing among five approaches . Los Angeles, CA: Sage.

This book compares five different qualitative research designs: narrative research, phenomenology, grounded theory, ethnography, and case study. It compares the characteristics, data collection, data analysis and representation, validity, and writing-up procedures among five inquiry approaches using texts with tables. For each approach, the author introduced the definition, features, types, and procedures and contextualized these components in a study, which was conducted through the same method. Each chapter ends with a list of relevant readings of each inquiry approach.

This book invites readers to compare these five qualitative methods and see the value of each approach. Readers can consider which approach would serve for their research contexts and questions, as well as how to design their research and conduct the data analysis based on their choice of research method.

Günes, E., & Bahçivan, E. (2016). A multiple case study of preservice science teachers’ TPACK: Embedded in a comprehensive belief system. International Journal of Environmental and Science Education, 11 (15), 8040-8054.

In this article, the researchers showed the importance of using technological opportunities in improving the education process and how they enhanced the students’ learning in science education. The study examined the connection between “Technological Pedagogical Content Knowledge” (TPACK) and belief system in a science teaching context. The researchers used the multiple-case study to explore the effect of TPACK on the preservice science teachers’ (PST) beliefs on their TPACK level. The participants were three teachers with the low, medium, and high level of TPACK confidence. Content analysis was utilized to analyze the data, which were collected by individual semi-structured interviews with the participants about their lesson plans. The study first discussed each case, then compared features and relations across cases. The researchers found that there was a positive relationship between PST’s TPACK confidence and TPACK level; when PST had higher TPACK confidence, the participant had a higher competent TPACK level and vice versa.

Recent Dissertations Using Multiple Case Study Methodology

Milholland, E. S. (2015). A multiple case study of instructors utilizing Classroom Response Systems (CRS) to achieve pedagogical goals . Retrieved from ProQuest Dissertations & Theses Global. (Order Number 3706380)

The researcher of this study critiques the use of Classroom Responses Systems by five instructors who employed this program five years ago in their classrooms. The researcher conducted the multiple-case study methodology and categorized themes. He interviewed each instructor with questions about their initial pedagogical goals, the changes in pedagogy during teaching, and the teaching techniques individuals used while practicing the CRS. The researcher used the multiple-case study with five instructors. He found that all instructors changed their goals during employing CRS; they decided to reduce the time of lecturing and to spend more time engaging students in interactive activities. This study also demonstrated that CRS was useful for the instructors to achieve multiple learning goals; all the instructors provided examples of the positive aspect of implementing CRS in their classrooms.

Li, C. L. (2010). The emergence of fairy tale literacy: A multiple case study on promoting critical literacy of children through a juxtaposed reading of classic fairy tales and their contemporary disruptive variants . Retrieved from ProQuest Dissertations & Theses Global. (Order Number 3572104)

To explore how children’s development of critical literacy can be impacted by their reactions to fairy tales, the author conducted a multiple-case study with 4 cases, in which each child was a unit of analysis. Two Chinese immigrant children (a boy and a girl) and two American children (a boy and a girl) at the second or third grade were recruited in the study. The data were collected through interviews, discussions on fairy tales, and drawing pictures. The analysis was conducted within both individual cases and cross cases. Across four cases, the researcher found that the young children’s’ knowledge of traditional fairy tales was built upon mass-media based adaptations. The children believed that the representations on mass-media were the original stories, even though fairy tales are included in the elementary school curriculum. The author also found that introducing classic versions of fairy tales increased children’s knowledge in the genre’s origin, which would benefit their understanding of the genre. She argued that introducing fairy tales can be the first step to promote children’s development of critical literacy.

Asher, K. C. (2014). Mediating occupational socialization and occupational individuation in teacher education: A multiple case study of five elementary pre-service student teachers . Retrieved from ProQuest Dissertations & Theses Global. (Order Number 3671989)

This study portrayed five pre-service teachers’ teaching experience in their student teaching phase and explored how pre-service teachers mediate their occupational socialization with occupational individuation. The study used the multiple-case study design and recruited five pre-service teachers from a Midwestern university as five cases. Qualitative data were collected through interviews, classroom observations, and field notes. The author implemented the case study analysis and found five strategies that the participants used to mediate occupational socialization with occupational individuation. These strategies were: 1) hindering from practicing their beliefs, 2) mimicking the styles of supervising teachers, 3) teaching in the ways in alignment with school’s existing practice, 4) enacting their own ideas, and 5) integrating and balancing occupational socialization and occupational individuation. The study also provided recommendations and implications to policymakers and educators in teacher education so that pre-service teachers can be better supported.

Multiple Case Studies Copyright © 2019 by Nadia Alqahtani and Pengtong Qu is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License , except where otherwise noted.

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Common terms and phrases, about the author  (2022).

Daphne Halkias is Professor and Distinguished Research Fellow at École des Ponts Business School in Paris, France.

Michael Neubert is Associate Professor in Business and Management Studies and a Member of the Academic Council at UIBS in Zurich, Switzerland.

Paul W. Thurman is Professor of Management and Analytics at Columbia University's Mailman School of Public Health, New York, USA.

Nicholas Harkiolakis is on the Faculty of the School of Technology at Northcentral University, San Diego, California, USA.

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Continuing to enhance the quality of case study methodology in health services research

Shannon l. sibbald.

1 Faculty of Health Sciences, Western University, London, Ontario, Canada.

2 Department of Family Medicine, Schulich School of Medicine and Dentistry, Western University, London, Ontario, Canada.

3 The Schulich Interfaculty Program in Public Health, Schulich School of Medicine and Dentistry, Western University, London, Ontario, Canada.

Stefan Paciocco

Meghan fournie, rachelle van asseldonk, tiffany scurr.

Case study methodology has grown in popularity within Health Services Research (HSR). However, its use and merit as a methodology are frequently criticized due to its flexible approach and inconsistent application. Nevertheless, case study methodology is well suited to HSR because it can track and examine complex relationships, contexts, and systems as they evolve. Applied appropriately, it can help generate information on how multiple forms of knowledge come together to inform decision-making within healthcare contexts. In this article, we aim to demystify case study methodology by outlining its philosophical underpinnings and three foundational approaches. We provide literature-based guidance to decision-makers, policy-makers, and health leaders on how to engage in and critically appraise case study design. We advocate that researchers work in collaboration with health leaders to detail their research process with an aim of strengthening the validity and integrity of case study for its continued and advanced use in HSR.

Introduction

The popularity of case study research methodology in Health Services Research (HSR) has grown over the past 40 years. 1 This may be attributed to a shift towards the use of implementation research and a newfound appreciation of contextual factors affecting the uptake of evidence-based interventions within diverse settings. 2 Incorporating context-specific information on the delivery and implementation of programs can increase the likelihood of success. 3 , 4 Case study methodology is particularly well suited for implementation research in health services because it can provide insight into the nuances of diverse contexts. 5 , 6 In 1999, Yin 7 published a paper on how to enhance the quality of case study in HSR, which was foundational for the emergence of case study in this field. Yin 7 maintains case study is an appropriate methodology in HSR because health systems are constantly evolving, and the multiple affiliations and diverse motivations are difficult to track and understand with traditional linear methodologies.

Despite its increased popularity, there is debate whether a case study is a methodology (ie, a principle or process that guides research) or a method (ie, a tool to answer research questions). Some criticize case study for its high level of flexibility, perceiving it as less rigorous, and maintain that it generates inadequate results. 8 Others have noted issues with quality and consistency in how case studies are conducted and reported. 9 Reporting is often varied and inconsistent, using a mix of approaches such as case reports, case findings, and/or case study. Authors sometimes use incongruent methods of data collection and analysis or use the case study as a default when other methodologies do not fit. 9 , 10 Despite these criticisms, case study methodology is becoming more common as a viable approach for HSR. 11 An abundance of articles and textbooks are available to guide researchers through case study research, including field-specific resources for business, 12 , 13 nursing, 14 and family medicine. 15 However, there remains confusion and a lack of clarity on the key tenets of case study methodology.

Several common philosophical underpinnings have contributed to the development of case study research 1 which has led to different approaches to planning, data collection, and analysis. This presents challenges in assessing quality and rigour for researchers conducting case studies and stakeholders reading results.

This article discusses the various approaches and philosophical underpinnings to case study methodology. Our goal is to explain it in a way that provides guidance for decision-makers, policy-makers, and health leaders on how to understand, critically appraise, and engage in case study research and design, as such guidance is largely absent in the literature. This article is by no means exhaustive or authoritative. Instead, we aim to provide guidance and encourage dialogue around case study methodology, facilitating critical thinking around the variety of approaches and ways quality and rigour can be bolstered for its use within HSR.

Purpose of case study methodology

Case study methodology is often used to develop an in-depth, holistic understanding of a specific phenomenon within a specified context. 11 It focuses on studying one or multiple cases over time and uses an in-depth analysis of multiple information sources. 16 , 17 It is ideal for situations including, but not limited to, exploring under-researched and real-life phenomena, 18 especially when the contexts are complex and the researcher has little control over the phenomena. 19 , 20 Case studies can be useful when researchers want to understand how interventions are implemented in different contexts, and how context shapes the phenomenon of interest.

In addition to demonstrating coherency with the type of questions case study is suited to answer, there are four key tenets to case study methodologies: (1) be transparent in the paradigmatic and theoretical perspectives influencing study design; (2) clearly define the case and phenomenon of interest; (3) clearly define and justify the type of case study design; and (4) use multiple data collection sources and analysis methods to present the findings in ways that are consistent with the methodology and the study’s paradigmatic base. 9 , 16 The goal is to appropriately match the methods to empirical questions and issues and not to universally advocate any single approach for all problems. 21

Approaches to case study methodology

Three authors propose distinct foundational approaches to case study methodology positioned within different paradigms: Yin, 19 , 22 Stake, 5 , 23 and Merriam 24 , 25 ( Table 1 ). Yin is strongly post-positivist whereas Stake and Merriam are grounded in a constructivist paradigm. Researchers should locate their research within a paradigm that explains the philosophies guiding their research 26 and adhere to the underlying paradigmatic assumptions and key tenets of the appropriate author’s methodology. This will enhance the consistency and coherency of the methods and findings. However, researchers often do not report their paradigmatic position, nor do they adhere to one approach. 9 Although deliberately blending methodologies may be defensible and methodologically appropriate, more often it is done in an ad hoc and haphazard way, without consideration for limitations.

Cross-analysis of three case study approaches, adapted from Yazan 2015

The post-positive paradigm postulates there is one reality that can be objectively described and understood by “bracketing” oneself from the research to remove prejudice or bias. 27 Yin focuses on general explanation and prediction, emphasizing the formulation of propositions, akin to hypothesis testing. This approach is best suited for structured and objective data collection 9 , 11 and is often used for mixed-method studies.

Constructivism assumes that the phenomenon of interest is constructed and influenced by local contexts, including the interaction between researchers, individuals, and their environment. 27 It acknowledges multiple interpretations of reality 24 constructed within the context by the researcher and participants which are unlikely to be replicated, should either change. 5 , 20 Stake and Merriam’s constructivist approaches emphasize a story-like rendering of a problem and an iterative process of constructing the case study. 7 This stance values researcher reflexivity and transparency, 28 acknowledging how researchers’ experiences and disciplinary lenses influence their assumptions and beliefs about the nature of the phenomenon and development of the findings.

Defining a case

A key tenet of case study methodology often underemphasized in literature is the importance of defining the case and phenomenon. Researches should clearly describe the case with sufficient detail to allow readers to fully understand the setting and context and determine applicability. Trying to answer a question that is too broad often leads to an unclear definition of the case and phenomenon. 20 Cases should therefore be bound by time and place to ensure rigor and feasibility. 6

Yin 22 defines a case as “a contemporary phenomenon within its real-life context,” (p13) which may contain a single unit of analysis, including individuals, programs, corporations, or clinics 29 (holistic), or be broken into sub-units of analysis, such as projects, meetings, roles, or locations within the case (embedded). 30 Merriam 24 and Stake 5 similarly define a case as a single unit studied within a bounded system. Stake 5 , 23 suggests bounding cases by contexts and experiences where the phenomenon of interest can be a program, process, or experience. However, the line between the case and phenomenon can become muddy. For guidance, Stake 5 , 23 describes the case as the noun or entity and the phenomenon of interest as the verb, functioning, or activity of the case.

Designing the case study approach

Yin’s approach to a case study is rooted in a formal proposition or theory which guides the case and is used to test the outcome. 1 Stake 5 advocates for a flexible design and explicitly states that data collection and analysis may commence at any point. Merriam’s 24 approach blends both Yin and Stake’s, allowing the necessary flexibility in data collection and analysis to meet the needs.

Yin 30 proposed three types of case study approaches—descriptive, explanatory, and exploratory. Each can be designed around single or multiple cases, creating six basic case study methodologies. Descriptive studies provide a rich description of the phenomenon within its context, which can be helpful in developing theories. To test a theory or determine cause and effect relationships, researchers can use an explanatory design. An exploratory model is typically used in the pilot-test phase to develop propositions (eg, Sibbald et al. 31 used this approach to explore interprofessional network complexity). Despite having distinct characteristics, the boundaries between case study types are flexible with significant overlap. 30 Each has five key components: (1) research question; (2) proposition; (3) unit of analysis; (4) logical linking that connects the theory with proposition; and (5) criteria for analyzing findings.

Contrary to Yin, Stake 5 believes the research process cannot be planned in its entirety because research evolves as it is performed. Consequently, researchers can adjust the design of their methods even after data collection has begun. Stake 5 classifies case studies into three categories: intrinsic, instrumental, and collective/multiple. Intrinsic case studies focus on gaining a better understanding of the case. These are often undertaken when the researcher has an interest in a specific case. Instrumental case study is used when the case itself is not of the utmost importance, and the issue or phenomenon (ie, the research question) being explored becomes the focus instead (eg, Paciocco 32 used an instrumental case study to evaluate the implementation of a chronic disease management program). 5 Collective designs are rooted in an instrumental case study and include multiple cases to gain an in-depth understanding of the complexity and particularity of a phenomenon across diverse contexts. 5 , 23 In collective designs, studying similarities and differences between the cases allows the phenomenon to be understood more intimately (for examples of this in the field, see van Zelm et al. 33 and Burrows et al. 34 In addition, Sibbald et al. 35 present an example where a cross-case analysis method is used to compare instrumental cases).

Merriam’s approach is flexible (similar to Stake) as well as stepwise and linear (similar to Yin). She advocates for conducting a literature review before designing the study to better understand the theoretical underpinnings. 24 , 25 Unlike Stake or Yin, Merriam proposes a step-by-step guide for researchers to design a case study. These steps include performing a literature review, creating a theoretical framework, identifying the problem, creating and refining the research question(s), and selecting a study sample that fits the question(s). 24 , 25 , 36

Data collection and analysis

Using multiple data collection methods is a key characteristic of all case study methodology; it enhances the credibility of the findings by allowing different facets and views of the phenomenon to be explored. 23 Common methods include interviews, focus groups, observation, and document analysis. 5 , 37 By seeking patterns within and across data sources, a thick description of the case can be generated to support a greater understanding and interpretation of the whole phenomenon. 5 , 17 , 20 , 23 This technique is called triangulation and is used to explore cases with greater accuracy. 5 Although Stake 5 maintains case study is most often used in qualitative research, Yin 17 supports a mix of both quantitative and qualitative methods to triangulate data. This deliberate convergence of data sources (or mixed methods) allows researchers to find greater depth in their analysis and develop converging lines of inquiry. For example, case studies evaluating interventions commonly use qualitative interviews to describe the implementation process, barriers, and facilitators paired with a quantitative survey of comparative outcomes and effectiveness. 33 , 38 , 39

Yin 30 describes analysis as dependent on the chosen approach, whether it be (1) deductive and rely on theoretical propositions; (2) inductive and analyze data from the “ground up”; (3) organized to create a case description; or (4) used to examine plausible rival explanations. According to Yin’s 40 approach to descriptive case studies, carefully considering theory development is an important part of study design. “Theory” refers to field-relevant propositions, commonly agreed upon assumptions, or fully developed theories. 40 Stake 5 advocates for using the researcher’s intuition and impression to guide analysis through a categorical aggregation and direct interpretation. Merriam 24 uses six different methods to guide the “process of making meaning” (p178) : (1) ethnographic analysis; (2) narrative analysis; (3) phenomenological analysis; (4) constant comparative method; (5) content analysis; and (6) analytic induction.

Drawing upon a theoretical or conceptual framework to inform analysis improves the quality of case study and avoids the risk of description without meaning. 18 Using Stake’s 5 approach, researchers rely on protocols and previous knowledge to help make sense of new ideas; theory can guide the research and assist researchers in understanding how new information fits into existing knowledge.

Practical applications of case study research

Columbia University has recently demonstrated how case studies can help train future health leaders. 41 Case studies encompass components of systems thinking—considering connections and interactions between components of a system, alongside the implications and consequences of those relationships—to equip health leaders with tools to tackle global health issues. 41 Greenwood 42 evaluated Indigenous peoples’ relationship with the healthcare system in British Columbia and used a case study to challenge and educate health leaders across the country to enhance culturally sensitive health service environments.

An important but often omitted step in case study research is an assessment of quality and rigour. We recommend using a framework or set of criteria to assess the rigour of the qualitative research. Suitable resources include Caelli et al., 43 Houghten et al., 44 Ravenek and Rudman, 45 and Tracy. 46

New directions in case study

Although “pragmatic” case studies (ie, utilizing practical and applicable methods) have existed within psychotherapy for some time, 47 , 48 only recently has the applicability of pragmatism as an underlying paradigmatic perspective been considered in HSR. 49 This is marked by uptake of pragmatism in Randomized Control Trials, recognizing that “gold standard” testing conditions do not reflect the reality of clinical settings 50 , 51 nor do a handful of epistemologically guided methodologies suit every research inquiry.

Pragmatism positions the research question as the basis for methodological choices, rather than a theory or epistemology, allowing researchers to pursue the most practical approach to understanding a problem or discovering an actionable solution. 52 Mixed methods are commonly used to create a deeper understanding of the case through converging qualitative and quantitative data. 52 Pragmatic case study is suited to HSR because its flexibility throughout the research process accommodates complexity, ever-changing systems, and disruptions to research plans. 49 , 50 Much like case study, pragmatism has been criticized for its flexibility and use when other approaches are seemingly ill-fit. 53 , 54 Similarly, authors argue that this results from a lack of investigation and proper application rather than a reflection of validity, legitimizing the need for more exploration and conversation among researchers and practitioners. 55

Although occasionally misunderstood as a less rigourous research methodology, 8 case study research is highly flexible and allows for contextual nuances. 5 , 6 Its use is valuable when the researcher desires a thorough understanding of a phenomenon or case bound by context. 11 If needed, multiple similar cases can be studied simultaneously, or one case within another. 16 , 17 There are currently three main approaches to case study, 5 , 17 , 24 each with their own definitions of a case, ontological and epistemological paradigms, methodologies, and data collection and analysis procedures. 37

Individuals’ experiences within health systems are influenced heavily by contextual factors, participant experience, and intricate relationships between different organizations and actors. 55 Case study research is well suited for HSR because it can track and examine these complex relationships and systems as they evolve over time. 6 , 7 It is important that researchers and health leaders using this methodology understand its key tenets and how to conduct a proper case study. Although there are many examples of case study in action, they are often under-reported and, when reported, not rigorously conducted. 9 Thus, decision-makers and health leaders should use these examples with caution. The proper reporting of case studies is necessary to bolster their credibility in HSR literature and provide readers sufficient information to critically assess the methodology. We also call on health leaders who frequently use case studies 56 – 58 to report them in the primary research literature.

The purpose of this article is to advocate for the continued and advanced use of case study in HSR and to provide literature-based guidance for decision-makers, policy-makers, and health leaders on how to engage in, read, and interpret findings from case study research. As health systems progress and evolve, the application of case study research will continue to increase as researchers and health leaders aim to capture the inherent complexities, nuances, and contextual factors. 7

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• Developing the Quantitative Research Design
• Qualitative Descriptive Design
• Design and Development Research (DDR) For Instructional Design
• Qualitative Narrative Inquiry Research
• Action Research Resource
• Case Study Design in an Applied Doctorate

Qualitative Research Designs

Case study design, using case study design in the applied doctoral experience (ade), applicability of case study design to applied problem of practice, case study design references.

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The field of qualitative research there are a number of research designs (also referred to as “traditions” or “genres”), including case study, phenomenology, narrative inquiry, action research, ethnography, grounded theory, as well as a number of critical genres including Feminist theory, indigenous research, critical race theory and cultural studies. The choice of research design is directly tied to and must be aligned with your research problem and purpose. As Bloomberg & Volpe (2019) explain:

Choice of research design is directly tied to research problem and purpose. As the researcher, you actively create the link among problem, purpose, and design through a process of reflecting on problem and purpose, focusing on researchable questions, and considering how to best address these questions. Thinking along these lines affords a research study methodological congruence (p. 38).

Case study is an in-depth exploration from multiple perspectives of a bounded social phenomenon, be this a social system such as a program, event, institution, organization, or community (Stake, 1995, 2005; Yin, 2018). Case study is employed across disciplines, including education, health care, social work, sociology, and organizational studies. The purpose is to generate understanding and deep insights to inform professional practice, policy development, and community or social action (Bloomberg 2018).

Yin (2018) and Stake (1995, 2005), two of the key proponents of case study methodology, use different terms to describe case studies. Yin categorizes case studies as exploratory or descriptive . The former is used to explore those situations in which the intervention being evaluated has no clear single set of outcomes. The latter is used to describe an intervention or phenomenon and the real-life context in which it occurred. Stake identifies case studies as intrinsic or instrumental , and he proposes that a primary distinction in designing case studies is between single and multiple (or collective) case study designs. A single case study may be an instrumental case study (research focuses on an issue or concern in one bounded case) or an intrinsic case study (the focus is on the case itself because the case presents a unique situation). A longitudinal case study design is chosen when the researcher seeks to examine the same single case at two or more different points in time or to capture trends over time. A multiple case study design is used when a researcher seeks to determine the prevalence or frequency of a particular phenomenon. This approach is useful when cases are used for purposes of a cross-case analysis in order to compare, contrast, and synthesize perspectives regarding the same issue. The focus is on the analysis of diverse cases to determine how these confirm the findings within or between cases, or call the findings into question.

Case study affords significant interaction with research participants, providing an in-depth picture of the phenomenon (Bloomberg & Volpe, 2019). Research is extensive, drawing on multiple methods of data collection, and involves multiple data sources. Triangulation is critical in attempting to obtain an in-depth understanding of the phenomenon under study and adds rigor, breadth, and depth to the study and provides corroborative evidence of the data obtained. Analysis of data can be holistic or embedded—that is, dealing with the whole or parts of the case (Yin, 2018). With multiple cases the typical analytic strategy is to provide detailed description of themes within each case (within-case analysis), followed by thematic analysis across cases (cross-case analysis), providing insights regarding how individual cases are comparable along important dimensions. Research culminates in the production of a detailed description of a setting and its participants, accompanied by an analysis of the data for themes or patterns (Stake, 1995, 2005; Yin, 2018). In addition to thick, rich description, the researcher’s interpretations, conclusions, and recommendations contribute to the reader’s overall understanding of the case study.

Analysis of findings should show that the researcher has attended to all the data, should address the most significant aspects of the case, and should demonstrate familiarity with the prevailing thinking and discourse about the topic. The goal of case study design (as with all qualitative designs) is not generalizability but rather transferability —that is, how (if at all) and in what ways understanding and knowledge can be applied in similar contexts and settings. The qualitative researcher attempts to address the issue of transferability by way of thick, rich description that will provide the basis for a case or cases to have relevance and potential application across a broader context.

Qualitative research methods ask the questions of "what" and "how" a phenomenon is understood in a real-life context (Bloomberg & Volpe, 2019). In the education field, qualitative research methods uncover educational experiences and practices because qualitative research allows the researcher to reveal new knowledge and understanding. Moreover, qualitative descriptive case studies describe, analyze and interpret events that explain the reasoning behind specific phenomena (Bloomberg, 2018). As such, case study design can be the foundation for a rigorous study within the Applied Doctoral Experience (ADE).

Case study design is an appropriate research design to consider when conceptualizing and conducting a dissertation research study that is based on an applied problem of practice with inherent real-life educational implications. Case study researchers study current, real-life cases that are in progress so that they can gather accurate information that is current. This fits well with the ADE program, as students are typically exploring a problem of practice. Because of the flexibility of the methods used, a descriptive design provides the researcher with the opportunity to choose data collection methods that are best suited to a practice-based research purpose, and can include individual interviews, focus groups, observation, surveys, and critical incident questionnaires. Methods are triangulated to contribute to the study’s trustworthiness. In selecting the set of data collection methods, it is important that the researcher carefully consider the alignment between research questions and the type of data that is needed to address these. Each data source is one piece of the “puzzle,” that contributes to the researcher’s holistic understanding of a phenomenon. The various strands of data are woven together holistically to promote a deeper understanding of the case and its application to an educationally-based problem of practice.

Research studies within the Applied Doctoral Experience (ADE) will be practical in nature and focus on problems and issues that inform educational practice.  Many of the types of studies that fall within the ADE framework are exploratory, and align with case study design. Case study design fits very well with applied problems related to educational practice, as the following set of examples illustrate:

Elementary Bilingual Education Teachers’ Self-Efficacy in Teaching English Language Learners: A Qualitative Case Study

The problem to be addressed in the proposed study is that some elementary bilingual education teachers’ beliefs about their lack of preparedness to teach the English language may negatively impact the language proficiency skills of Hispanic ELLs (Ernst-Slavit & Wenger, 2016; Fuchs et al., 2018; Hoque, 2016). The purpose of the proposed qualitative descriptive case study was to explore the perspectives and experiences of elementary bilingual education teachers regarding their perceived lack of preparedness to teach the English language and how this may impact the language proficiency of Hispanic ELLs.

Exploring Minority Teachers Experiences Pertaining to their Value in Education: A Single Case Study of Teachers in New York City

The problem is that minority K-12 teachers are underrepresented in the United States, with research indicating that school leaders and teachers in schools that are populated mainly by black students, staffed mostly by white teachers who may be unprepared to deal with biases and stereotypes that are ingrained in schools (Egalite, Kisida, & Winters, 2015; Milligan & Howley, 2015). The purpose of this qualitative exploratory single case study was to develop a clearer understanding of minority teachers’ experiences concerning the under-representation of minority K-12 teachers in urban school districts in the United States since there are so few of them.

Exploring the Impact of an Urban Teacher Residency Program on Teachers’ Cultural Intelligence: A Qualitative Case Study

The problem to be addressed by this case study is that teacher candidates often report being unprepared and ill-equipped to effectively educate culturally diverse students (Skepple, 2015; Beutel, 2018). The purpose of this study was to explore and gain an in-depth understanding of the perceived impact of an urban teacher residency program in urban Iowa on teachers’ cultural competence using the cultural intelligence (CQ) framework (Earley & Ang, 2003).

Qualitative Case Study that Explores Self-Efficacy and Mentorship on Women in Academic Administrative Leadership Roles

The problem was that female school-level administrators might be less likely to experience mentorship, thereby potentially decreasing their self-efficacy (Bing & Smith, 2019; Brown, 2020; Grant, 2021). The purpose of this case study was to determine to what extent female school-level administrators in the United States who had a mentor have a sense of self-efficacy and to examine the relationship between mentorship and self-efficacy.

Suburban Teacher and Administrator Perceptions of Culturally Responsive Teaching to Promote Connectedness in Students of Color: A Qualitative Case Study

The problem to be addressed in this study is the racial discrimination experienced by students of color in suburban schools and the resulting negative school experience (Jara & Bloomsbury, 2020; Jones, 2019; Kohli et al., 2017; Wandix-White, 2020). The purpose of this case study is to explore how culturally responsive practices can counteract systemic racism and discrimination in suburban schools thereby meeting the needs of students of color by creating positive learning experiences. 

As you can see, all of these studies were well suited to qualitative case study design. In each of these studies, the applied research problem and research purpose were clearly grounded in educational practice as well as directly aligned with qualitative case study methodology. In the Applied Doctoral Experience (ADE), you will be focused on addressing or resolving an educationally relevant research problem of practice. As such, your case study, with clear boundaries, will be one that centers on a real-life authentic problem in your field of practice that you believe is in need of resolution or improvement, and that the outcome thereof will be educationally valuable.

Bloomberg, L. D. (2018). Case study method. In B. B. Frey (Ed.), The SAGE Encyclopedia of educational research, measurement, and evaluation (pp. 237–239). SAGE. https://go.openathens.net/redirector/nu.edu?url=https%3A%2F%2Fmethods.sagepub.com%2FReference%2Fthe-sage-encyclopedia-of-educational-research-measurement-and-evaluation%2Fi4294.xml

Bloomberg, L. D. & Volpe, M. (2019). Completing your qualitative dissertation: A road map from beginning to end . (4th Ed.). SAGE.

Stake, R. E. (1995). The art of case study research. SAGE.

Stake, R. E. (2005). Qualitative case studies. In N. K. Denzin and Y. S. Lincoln (Eds.), The SAGE handbook of qualitative research (3rd ed., pp. 443–466). SAGE.

Yin, R. (2018). Case study research and applications: Designs and methods. SAGE.

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Home > JCPS > Vol. 15 (2022) > No. 2

Journal of Counselor Preparation and Supervision

A systematic approach to multiple case study design in professional counseling and counselor education.

Charmayne R. Adams , University of Nebraska at Omaha Follow Casey A. Barrio Minton , University of Tennessee Follow Jennifer Hightower , Idaho State University Follow Ashley J. Blount , University of Nebraska at Omaha Follow

Document Type

Case study, multiple case study, qualitative research, research design, counseling

Subject Area

Counseling, Counselor Education, Higher Education Counseling, Mental Health Counseling, Rehabilitation Counseling, School Counseling

Case study research is a qualitative methodology that allows researchers to explore complex phenomena in a structured way, that is rigorous and provides an enormous amount of depth. Three scholars are credited with major contributions to the case study literature: Merriam (1998), Stake (1995/2006), and Yin (1994). The purpose of this paper is to explore case study design for use in the counseling profession. The authors provide instruction on the case study scholars, data collection, analysis, and reporting for both single and multiple case study research designs. Finally, implications for student counselors, counselor educators, and counseling professionals are provided.

Recommended Citation

Adams, C. R., Barrio Minton, C. A., Hightower, J., & Blount, A. J. (2022). A Systematic Approach to Multiple Case Study Design in Professional Counseling and Counselor Education. Journal of Counselor Preparation and Supervision, 15 (2). Retrieved from https://digitalcommons.sacredheart.edu/jcps/vol15/iss2/24

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• Volume 14, Issue 5
• Exploring the influence of health system factors on adaptive capacity in diverse hospital teams in Norway: a multiple case study approach
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• http://orcid.org/0000-0002-4689-8376 Birte Fagerdal 1 ,
• http://orcid.org/0000-0001-7107-4224 Hilda Bø Lyng 1 ,
• http://orcid.org/0000-0002-9124-1664 Veslemøy Guise 1 ,
• Janet E Anderson 2 ,
• http://orcid.org/0000-0003-0296-4957 Jeffrey Braithwaite 3 ,
• http://orcid.org/0000-0003-0186-038X Siri Wiig 1
• 1 SHARE, Faculty of Health Sciences , University of Stavanger , Stavanger , Norway
• 2 Anaesthesiology and Perioperative Medicine , Monash University Faculty of Medicine Nursing and Health Sciences , Melbourne , Victoria , Australia
• 3 Australian Institute of Health Innovation , Macquarie University , North Ryde , New South Wales , Australia
• Correspondence to Mrs Birte Fagerdal; birte.fagerdal{at}uis.no

Objectives Understanding flexibility and adaptive capacities in complex healthcare systems is a cornerstone of resilient healthcare. Health systems provide structures in the form of standards, rules and regulation to healthcare providers in defined settings such as hospitals. There is little knowledge of how hospital teams are affected by the rules and regulations imposed by multiple governmental bodies, and how health system factors influence adaptive capacity in hospital teams. The aim of this study is to explore the extent to which health system factors enable or constrain adaptive capacity in hospital teams.

Design A qualitative multiple case study using observation and semistructured interviews was conducted between November 2020 and June 2021. Data were analysed through qualitative content analysis with a combined inductive and deductive approach.

Setting Two hospitals situated in the same health region in Norway.

Participants Members from 8 different hospital teams were observed during their workday (115 hours) and were subsequently interviewed about their work (n=30). The teams were categorised as structural, hybrid, coordinating and responsive teams.

Results Two main health system factors were found to enable adaptive capacity in the teams: (1) organisation according to regulatory requirements to ensure adaptive capacity, and (2) negotiation of various resources provided by the governing authorities to ensure adaptive capacity. Our results show that aligning to local context of these health system factors affected the team’s adaptive capacity.

Conclusions Health system factors should create conditions for careful and safe care to emerge and provide conditions that allow for teams to develop both their professional expertise and systems and guidelines that are robust yet sufficiently flexible to fit their everyday work context.

• Health & safety
• Organisation of health services
• Quality in health care
• Protocols & guidelines
• QUALITATIVE RESEARCH

Data availability statement

Data are available upon reasonable request.

This is an open access article distributed in accordance with the Creative Commons Attribution Non Commercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited, appropriate credit is given, any changes made indicated, and the use is non-commercial. See:  http://creativecommons.org/licenses/by-nc/4.0/ .

https://doi.org/10.1136/bmjopen-2023-076945

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STRENGTHS AND LIMITATIONS OF THIS STUDY

Data for this study were collected during the COVID-19 pandemic, which enabled the research team to observe how novel national policy measures affected the frontline.

The study contributes to resilient healthcare as there have been few multilevel studies looking at how macrolevel factors affect microlevel adaptive capacity.

The combination of observations and interviews provided a substantial amount of data which were then triangulated.

Data collected at the national level are limited as our study focused on the hospital team level.

Introduction

Healthcare systems provide the formal healthcare delivery structures for a defined population, whose funding, management, scope and content are defined by laws, policies and regulations. They provide services to people, aiming to contribute to their health and well-being. Services are usually delivered in defined settings, such as homes, nursing homes and hospitals. Healthcare systems are complex and adaptive and continuously responsive to multiple factors including patients’ needs, innovations, pressures, pandemics and funding structures. 1 Understanding flexibility and adaptive capacities in these complex healthcare systems is a key focus of investigators of resilient healthcare. 2 3 Resilience in healthcare can briefly be defined as ‘the capacity to adapt to challenges and changes at different system levels, to maintain high quality care’ p6. 4

To date, research on resilient healthcare has paid most attention to work as done at the sharp end of the system. Less is therefore known about how actions, strategies and practices enacted by regulatory bodies and policy-makers affect every day work at the microlevel, such as hospital teams. 5 While regulations in the form of standards, rules and protocols are known to be key drivers in the structuring of healthcare activities and in the design of healthcare organisations, the interfaces between policy-making, regulation and resilience are subtle and nuanced, and regulatory strategies to improve quality and safety are therefore complex and multifarious. 6 7 However, the relationship between governmental bodies and adaptive capacity at the sharp end of the system has received insufficient attention and is thus in need of closer examination. 2 8 9

In this study, we define macrolevel healthcare system actors as governmental bodies, regulators and national and regional bodies, who act or intend to shape, monitor, control and modify practices within organisations in order to achieve an identifiable, desirable state of affairs. 10 They aim to constrain action, optimise performance and attempt to prevent error.

In complex systems like hospitals, much work is performed in teams. 11–13 Understanding the nature of teams and team performance is important to promote team effectiveness. The few studies that have been undertaken are limited in scope as they have not considered how teams are defined and structured, what their functions are or differences across healthcare teams. 11 14 Most research on teams in healthcare has focused on the dynamic domains in healthcare, such as emergency medicine or operating rooms, and teams that are similar to the teams in other industries, for instance in aviation. 15 16 However, not all teams in hospitals operate in an emergency setting. Teams in hospitals differ depending on their goals, tasks, structure, membership and situation, affecting how they adapt to a multitude of contingencies that are encountered in everyday work. 17 Hence, their requirements for support could differ depending on these attributes but this question has not been addressed sufficiently in previous research. Knowledge of these differences may enable optimisation of support and better function for the different teams. This study will address these knowledge gaps.

Aim and research question

This study aims to explore whether and how health system factors enable adaptive capacity in different types of hospital teams in Norway. We asked: What kind of health system factors enable adaptive capacity in hospital teams, and how do these factors affect adaptive capacity?

Design and setting

A qualitative exploratory methodology was chosen, using a multiple-embedded case study design. 11 18 A case was defined as one hospital containing four different types of teams. Two case hospitals were recruited to the study, featuring a total of eight teams. The study’s design was in line with that of an international comparative study, involving six countries (The Netherlands, Japan, Australia, England, Switzerland and Norway), where this article reports partial findings from the Norwegian case (see protocol of Anderson et al ). 11 The two Norwegian hospitals and the four team types were recruited in line with the study protocol. Findings from each of the countries will be written up as country case reports following an agreed on template. Furthermore, an international cross-case comparative analysis will be performed using the Qualitative Comparative Analysis method 19 with the aim of exploring how multilevel system factors interact to support or hinder adaptive capacity in different types of hospital teams in different countries, and how this leads to performance variability. This international comparative analysis is currently in progress. This article stands alone and uses Norwegian data only.

Recruitment and study context

The Norwegian health system is a semidecentralised system with the Norwegian Parliament as its highest decision-making body. The municipalities are responsible for providing primary care for their citizens, mainly through nursing homes, homecare, general practitioners and rehabilitation services. The hospitals are mainly state owned and administered by four Regional Health Authorities. The Norwegian Board of Health Supervision is a national regulatory body, organised under the Ministry of Health and Care Services. County Governors at the regional level oversee services within primary and specialised healthcare. Norway has a comprehensive set of legislation governing the health services, including requirements for the quality of services, regulations for authorised healthcare personnel and service users’ rights. These legislated requirements are subject to supervision and investigation by the Norwegian Board of Health Supervision and the County Governors. 20 21

The two hospitals in this study were selected and recruited based on their size and role in teaching provision. 11 Both hospitals are situated in the same health region in Norway. Hospital 1 is a large teaching hospital and hospital 2 is a middle-sized local hospital which is also responsible for educating healthcare professionals. The four different team types were structural, hybrid, responsive and coordinating, and are displayed in table 1 . See Fagerdal et al 22 for further descriptions of the teams.

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Descriptions of the four different teams studied in each hospital

Data were collected through observation, interviews and document analysis, all undertaken between December 2020 and June 2021. Researcher BF and HBL conducted the observations, which entailed following one or more team members for two workdays using an observation guide. Both researchers wrote their own individual fields notes which were both included in the data material. Using the observation guide enabled a structuring of the text in line with the central concepts used in resilience literature. 23 During observations, we looked for various types of demands from the different levels of the organisations, the teams’ capacities to meet the demands and types of adaptations that were performed by the teams and team members. The observed teams differed in how they work together and consequently our undertaking of the observations had to align with those differences. The structural and hybrid teams were observed during two shifts, including evening and dayshifts. With the responsive teams, we followed one team member during their workday and their response to acute alarms. The coordinating teams meet for 10 min every weekday, and the researchers attended all their meetings during a 14-day period. Due to the COVID-19 pandemic, one of the coordinating teams held their meetings digitally, which we also attended. The observations totalled 115 hours (see table 2 ).

Overview of data collection methods and data material according to team types and case sites

All interviews were undertaken post observation by researcher BF using a semistructured interview guide based on content from the Concepts for Applying Resilience Engineering (CARE) model, that is, demand, capacity, misalignments and adaptations, 24 and the four potentials of resilience; monitoring, anticipating, responding and learning. 23 Team members and one leader from each team were interviewed, resulting in 30 interviews (see table 3 ). Participants comprised 27 females and 3 males and their ages ranged between 24 and 56. The interview length varied from 40 to 90 min with a median of 55 min. All participants signed a written consent form and were given the opportunity to withdraw without any negative implications; all invited participants accepted the invitation to interview.

Overview of the interviewed participants in the study

Patient and public involvement statement

A coresearcher employed in the overall Resilience in Healthcare project, of which this study is a part, 11 collaborated in the planning and design of the study, and access to teams at hospital 1. In hospital 2, we used a local coordinator to help identify and facilitate access to the different teams.

All interviews were audio recorded and transcribed verbatim by researcher BF. Observation notes were included in the analysis, and all notes and interview transcripts were grouped according to hospital and team types to streamline the analysis work. We conducted a within-case analysis of each hospital and a cross-case analysis to identify patterns and themes in our overall material. 18 The data material was first read through in full by all the researchers to get a sense of the whole. The analysis was then done using a combined deductive and inductive approach. 25 The CARE model 24 was used as a framework to assist the deductive part of the analysis as visualised in figure 1 .

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Concepts for Applying Resilience Engineering model after Anderson et al 24 visualising the study’s focus on team adaptation.

Data were organised using three of the four key concepts in the CARE model matrix: capacities, misalignments and adaptations. The capacities were defined as health system factors in this analysis and represent the factors that influence teams’ ability to adapt. All data were in addition coded for team type and hospital which allowed for a cross hospital and cross-team analysis. After the data material had been divided into three parts of text, to enable further analysis, we proceeded with an inductive content analysis approach. 25 The categories were inductively reviewed and recoded and further developed into latent themes across the four teams. This process resulted in overarching themes representing health system factors, that influence teams’ adaptive capacity (see table 4 ).

Inductive coding structure

The national and regional health authorities set the scene for how the hospitals prioritises and arrange their work. System-level decisions filter down through the organisation and influence the team’s everyday work. Our analysis shows that the effect of system factors on teams’ everyday work and adaptive capacity can be divided into two main themes, each with associated subthemes: (1) organisation according to regulatory requirements to ensure adaptive capacity and (2) negotiation of various resources provided by the governing authorities to ensure adaptive capacity. In table 4 , we present the themes along with their subthemes, codes and examples of quotes from the participants or description from the observation.

Organising according to regulatory requirements to ensure adaptive capacity

National and regional guidelines, financial governance and regulatory inspections by the health supervision authorities all shaped the organisation of the hospitals.

Context and organisational structure

The organisational context was important. It affected how the teams enacted and performed patient care. For instance, the smaller hospital 2 had restrictions and limitations regarding both the types of diagnoses and the number of patients they were able to treat due to regional regulations. These regulations had a large impact on the smaller hospital and their teams in how they organised their work, their competence requirements and what kind of learning opportunities were available to the team members. For instance, since hospital provided an acute function for surgical patients, it could continue to be an educational institution for healthcare personnel, which also meant that healthcare professionals in the structural and hybrid teams could maintain and develop their skills in acute care. In addition, it also impacted the hybrid and structural teams in how they arranged their work by always being prepared for the admission of acute surgical patients during their workday. Furthermore, the regional health authority maintained overall flexibility in acute care provision by having this function in both hospitals.

Both the coordinating teams in our study had been established by the hospitals in response to a government policy of preventing corridor beds in hospitals as a means of improving patients’ safety. The teams were set up to include all ward managers cooperating to manage patient flow, and with a goal of evening out the overall strain across the hospital. These teams’ main assignment was to allocate patients to free beds within the hospital. In addition, a positive consequence of having these teams was that the team members got a better mutual understanding of the overall situation within the hospitals and an improved understanding of each other’s challenges across the hospital. This provided them with a greater range of solutions to use when making adaptations to avoid patients in the corridors. The coordinating team in hospital 2 also functioned as an arena for the team members to exchange advice and suggest solutions to other challenges in their work. This was to a certain extent also valid for the team in hospital 1, but due to the comparatively larger size of the team there, it was more difficult for the team members to get well acquainted. In addition to better patient flow and avoiding corridor patients, the hospitals aimed for the teams to focus on building a culture of helping each other across their respective hospitals and to foster a feeling of joint responsibility for the betterment of the hospital overall (see table 4 ). Similar to the responsive teams, the coordinating teams had been enabled to make quick decisions spanning hospital units, allowing for a wider range of alternative solutions to the problems encountered than if they were to make decisions on their own. Also, team members felt more of a responsibility to help each other and found that it was more difficult to say no to requests for free beds when meeting face to face with colleagues. Both the individual team members and the hospital organisation as a whole were thus found to have widened their adaptive capacities after establishing these teams.

Aligning with national and regional guidelines

The use of clinical guidelines provided teams with direction in the different treatment courses offered to patients. National guidelines were translated and aligned to work practices within the organisation to fit the current work in the teams. This gave the team a standard to maintain, a structure for their work and also brought them a sense of safety in knowing their boundaries and priorities for adaptation. For instance, the national guideline for sepsis treatment recommends starting antibiotics treatment within 1 hour of the start of symptoms and also lists early important diagnostic signs to look for in patients who are deteriorating. Early intervention and treatment improve the overall survival of these patients and both hospitals needed to ensure proper alignment to these standards (see table 4 ). The hybrid and structural teams were well aware of this, due to guidelines and information campaigns. The teams thus adapted their work to meet the national demands imposed here, prioritising this work over what were considered other less important tasks, such as helping patients with personal hygiene.

Another example of how guidelines shaped the organisation of hospital teams and how teams acted was seen in the work of both the responsive teams in the study. The two hospitals had to comply with the national requirements of diagnostic and treatment guidelines for cerebral infarction, and both hospitals had created responsive stroke teams to allow for quick diagnostics and treatment. Tailoring the responsive teams to fit the requirements of the national guidelines, reduced the ‘door to needle time’ in both hospitals significantly. This was accomplished by providing and designing equipment, procedures, role descriptions and facilities along with the right competent personnel. The responsive teams frequently made adaptations to the clinical procedure to fit with the patient’s condition, the proximity of the competent team members and the tailored equipment and location enabled for quick decision-making within the team, instead of encountering communication via phones or waiting for each other to finish other tasks.

Negotiating various resources provided by the governing authorities to ensure adaptive capacity

Financial incentives.

Incentives like the national funding model which generates income for the hospitals impacted both what kind of and how the hospitals prioritised treatment. Governing authorities use financial incentives to orient the hospitals towards planned direction. Budget cuts and other financial restraints imposed on hospitals demanded that both hospitals adapt their priorities, which consequently affected the teams’ delivery of treatment and care in the sharp end of the system. The government requirements for increased efficiency in the healthcare system, such as financial incentives for reducing beds, increased the pace of work and often required development of new work practices to cope with these demands. For instance, in both hospitals, there had been a decrease in hospital beds, and a shift towards outpatient treatment due to governing authorities funding schemes. To cope with this, both the hybrid and structural teams in both hospitals treated patients for a shorter amount of time. For example, the structural teams no longer admitted patients overnight preoperatively and discharged patients earlier postoperatively to primary healthcare service or the home. The teams coped with this by planning the discharge of the patient already at admittance to facilitate a safe and good-quality discharge. However, they often adapted their plans by not discharging patients due to either lack of capacity in primary care services, or disagreement and concern with the level of care offered in the municipalities. This example shows that the teams in practice negotiated the consequences of government funding restrictions to suit the patients’ needs.

In addition, they could to some extent handle some demands by determining how they could change procedures to fit certain requirements. For instance, one of the changes the structural team in hospital 2 made to manage earlier discharge was to have the nightshift staff remove the postoperative urine catheter from patients. The clinical procedure stated that for the patient to be discharged, they had to be able to urinate spontaneously after catheter removal. Catheter removal later in the day regularly meant that the patient had to stay an extra night, so by changing the timing of its removal staff still managed to provide care within the frame of guidelines given.

Physical surroundings

Both the hybrid and responsive teams in both hospitals had been placed in new premises designed specifically to accommodate their way of working, with well-designed spaces to facilitate their workday with proximity to necessary equipment, and a nearness to each other that enabled team members to easily assist if needed. Similarly, the structural team in hospital 2 had new premises, with a uniform design across the new hospital building making it easy for personnel to change teams and wards since their premises were already familiar to them. This uniformity in building design improved the teams’ overall adaptive capacity in peak situations, or when there was an absence of key personnel across wards and teams. Staff could easily assist personnel from other wards as they knew where equipment was stored and how the different facilities in the ward functioned (patient rooms, nurses’ stations, etc). The structural team in hospital 1, however, worked in old premises with narrow hallways and few physical meeting arenas for the team members, which hampered their workflow in that they had to spend time looking for each other, and otherwise had few opportunities to engage in direct communication with each other during their workday. The physical surroundings of the two coordinating teams differed. Due to the size of the team in hospital 1, the team there used digital software to manage the overall patient flow in the hospital. The smaller team in hospital 2 managed the same using a paper form that each member completed. However, both of the teams used the meeting to elaborate on their numbers with additional information as the numbers alone did not provide a sufficient representation of the overall situation on the wards.

Training and development resources

Training and development resources were crucial for a team’s adaptive capacity. The national attention on patient safety in recent decades has led to improved treatment courses and changed the focus on how healthcare personnel can learn from adverse events to avoid similar incidents in the future. Consequently, this has led to innovative solutions in how hospital managers organise learning activities for their employees. In accordance with a growing focus on simulation-based training and learning from regulatory bodies and policy-makers, all the teams in the study apart from the coordinating teams increasingly used simulation training (see table 4 ). Often, the teams would make simulation scenario cases based on adverse events or incidents that had happened on their ward and used them in their training. For the responsive teams, this type of training was mandatory and part of regulatory requirements for the teams. Also, for these teams that only worked together for limited episodes and had changing membership and different professional cultures, these simulation trainings were their only chance to practice and improve their team communication. During the period of our observation, they developed new cases with COVID-19 themes and used them to train and learn before they received actual COVID-19 patients. This improved their performance, as they had found several shortcomings in their COVID-19 procedure and thus changed it accordingly. For instance, they made efforts to prevent unnecessary contamination of team members and had detected a lack in the procedure of personal protective equipment. This shows that these types of prescribed training exercises enable teams to adapt procedures to fit their everyday work conditions.

Quality improvement resources

Quality improvement resources outside the hospital organisation supported team’s adaptive capacity. The national and regional healthcare authorities arrange various conferences and campaigns for hospitals and other healthcare institutions. Here, policy-makers, leaders and healthcare professionals meet and create reflexive spaces. As part of such efforts, the best practices are displayed and workshops are provided to encourage and translate quality and safety improvement into practice in different ways, alongside guidelines, learning tools and other materials for the different organisations to use and implement in their quality improvement work. Having this competence base within the health regions and at the national level to support teams added knowledge and increased adaptive capacity as it required knowledge transfer and new ideas anchored in research and practice. Moreover, the patient safety focus within the wards and teams like the safe care screening programme and safety huddles, launched by the Norwegian Directorate of Health and implemented through the regional health authorities, increased the team’s awareness of patient safety culture. The increased amount of quality measures the clinicians had to undertake and report on in their daily work were generally seen as good quality measures from both the organisations and the team’s point of view. However, it sometimes felt counterproductive constantly having to cope with balancing patients’ needs with the requirements of screening procedures, especially if staff felt they had little room for autonomous clinical assessment. For instance, the safe care screening programme where every patient over the age of 18 had to be screened for their risk of falling, bedsores and possible malnutrition within 24 hours was questioned. Screening young patients for this felt unnecessary and if there were other more pressing tasks that were seen as more important, they adapted the way they prioritised.

This study investigated the relationship between health system factors and adaptive capacity in hospital teams. Our results have shown that health system-level factors influence adaptive capacity in the teams through the provision of guidelines and resources, and how the teams align these to their current demands and capacity situation. Their effects on different teams are not uniform; some are advantageous to one team but disadvantageous to another. 5 6 We argue that it is the team’s opportunity to align these factors to context that are key for enabling adaptive capacity, as illustrated in figure 2 .

Illustrating the teams aligning system-levels factors to context for adaptive capacity.

All levels of a health system can influence each other, especially in an integrated and tightly coupled system. Higher system levels can affect lower levels through, for example, explicit instructions, by the provision or limitation of resources, or by establishing incentive systems. 26–28 On the other hand, lower system levels may use discretion when they interpret and implement directives from higher levels, and they may control the information flow to higher levels. 26 Our results show that decisions made at one level of the system can support or hinder adaptive capacity at other lower hierarchical levels of the system. 29–31 Accordingly, the system-level governing factors affect adaptive capacity at the sharp end by setting the framework and boundaries within which activity can take place. Regulatory bodies have system-wide responsibilities and must respond to system-wide disturbances, without detailed knowledge of how work is done in practice at the sharp end. Consequently, the sharp end must adapt to respond appropriately to disturbances within its own field of responsibility. 32

This study has operationalised adaptation using the CARE model 24 to see how different teams at the sharp end work in practice to negotiate system-level factors, such as regulations and guidelines. The findings show that factors at the macrolevel required different forms of adaptations within different team types to managing everyday work. Enabling adaptation at the team level by taking action at the macrolevel to attempt to reconcile work as imagined with work as done ( figure 1 ). The system-level factors also represent long-term planning and transformation of practices rather than short-term adaptations or adjustments in the system. 33 They envisage setting up the processes that design, produce and circulate resources that underpin safety, and prevent errors through standardisation, regulation and training. 32 How the teams negotiate these long-term transformations to their everyday work determines their adaptive capacity as our results have shown. Adaptation and adjustments to local context are inevitable in healthcare. 9 11 34 35 However, the vast number of protocols, policies, checklists, standards, guidelines, pathways and other regulatory requirements may lead those working at the sharp end to feel overwhelmed. 6 If not aligned with goals, tasks and current challenges, these governing factors may end up being counterproductive. 5 The teams studied talked about their everyday work and their primary focus on patient care along with their willingness to act in the best interest of the patients. 36 They talked about feeling a compound pressure in order to align system-level demands with their context and patients’ wishes and needs. 37 38 Taking the perspective of the patient into account was important to the teams. 39 40 Consequently, different teams had to align system-level demands differentially to ensure quality care for patients.

Our study showed that teams must balance continuous efficiency with thoroughness assessments 32 41–43 in everyday work (eg, making the nightshift prepare discharge adding more work to reduce corridor patients). Ways that the teams in our study continuously adapted regulatory requirements to their work context illuminated how resilient systems must have robust yet flexible structures to assist the system to deal with both everyday work and unexpected events. 8 30 44 45 System-level factors must therefore provide flexibility to fit different situations and types of teams, as teams differ in how they cooperate and function in everyday work. To ensure alignment of perspectives between macrolevel and microlevel actors, common arenas and structures for mutual feedback and reflections between stakeholders are crucial. 7 Furthermore, system factors need to entail robustness in the directions they provide to practice and the implementation of improvement efforts. 33

The findings show that for the responsive and coordinating teams the size of the hospital played a significant role in their ability to adapt. These two team types operated in part at the mesolevel of the hospital organisation, spanning hospital departments. Their work was characteristically ad hoc, dynamically changing team memberships and members who work primarily in other teams. The large size of hospital 1 hampered development of relationships between the team members in both the responsive and the coordinating team, whereas in the smaller hospital 2 it was easier to develop close relationships between colleagues. This implies that ad hoc teams, and especially large ones, need to have structure and guidelines in place that direct their work, and support to adapt their work based on the team members understanding of the tasks and their roles. The structural and hybrid teams were colocated and this seemed to allow for the development of long-term collegial relationships, better cooperation between team members, more flexible adaptation of their work and also seemed to allow for working with greater levels of independence and a larger room for self-organisation. Their work is influenced by system-level demands, but the size of the organisation does not affect their day-to-day work to the same degree as for the coordination and responsive teams.

Strengths and limitations

A strength of the study is that by combining observation and interviews we have gathered in-depth data of the team’s everyday work.

Data collection during COVID-19 pandemic could hamper everyday work practice; however, we collaborated closely with the sites to avoid any problems for the involved teams and units. Only two hospitals contributed to the data collection and including additional hospitals could add more than we have from two hospitals. However, the inclusion of eight teams, the total amount of data gave rich information to analyse our research questions.

Interview data from the macrolevel could have added additional perspectives from the regulators and policy-makers. We suggest further studies to integrate this in their activities to uncover the role of system factors seen from the policy-makers’ and regulators’ perspectives.

Conclusions and implications

This study illuminated how teams negotiate the health system factors that shape their work to provide as much adaptive capacity as possible and attempt to align system-level regulation and guidelines with everyday work demands. The results show that the size of both the organisation and team had an effect on adaptive capacity. Our findings imply that healthcare systems need to facilitate conditions that allow for teams to develop their professional expertise and develop systems that are robust and flexible to fit the context. Teams should be enabled to adapt to the functions and structure of the health system to carry out their everyday work in a changing environment.

Ethics statements

Patient consent for publication.

Not applicable.

Ethics approval

This study involves human participants and was approved by Regional committe for Medical and Health Research Ethics, ref.nr. 166280. Participants gave informed consent to participate in the study before taking part.

Acknowledgments

The authors would like to thank all participating teams and their leaders at the two hospitals who shared their valuable knowledge and reflections.

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X @fagerbirte

Contributors The study design was developed in collaboration with the whole research team. BF and HBL conducted the data collection. BF conducted and transcribed all the interviews. The analysis and interpretation of data were conducted in close collaboration between BF, HBL, VG, JEA and SW. SW is the guarantor of this study. All authors contributed with writing, critical revision and approval of the final version.

Funding This project is part of the Resilience in Healthcare Research program which has received funding from the Research Council of Norway from the FRIPRO TOPPFORSK program, grant agreement no. 275367. The University of Stavanger, Norway, NTNU Gjøvik, Norway supports the study with kind funding.

Competing interests None declared.

Patient and public involvement Patients and/or the public were not involved in the design, or conduct, or reporting, or dissemination plans of this research.

Provenance and peer review Not commissioned; externally peer reviewed.

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New technique for case study development published

May 9, 2024

Kevin Parker, ISU professor emeritus, recently published two papers in Communications of the Association for Information Systems (CAIS). Each paper was published by CAIS in their IS Education section, which has a 7% acceptance rate.

Modular Design of Teaching Cases: Reducing Workload While Maximizing Reusability presents a modular case study development concept for better managing the development of case studies. The approach achieves project extensibility through reusable case study modules, while at the same time helping to reduce instructor workload and solution reuse by students. The approach is based on the concept of creating different variations of a case study each semester by adding or replacing existing descriptive modules with new modules.

Wind Riders of the Lost River Range: A Modular Project-Based Case for Software Development focuses on the information technology needs of a simulated specialty sports shop in central Idaho that concentrates on wind sports equipment, like hang gliders, paragliders, and snowkites. The case study consists of a core case that describes both the IT system currently in use and the new system that provides updated business support. Students are tasked with analyzing the system and designing a new system that delivers enhanced functionality. This evolutionary case study is based on the Modular Design of Teaching Cases and consists of the core case and 17 modules that can be swapped in or out of both the current or future system to produce a wide variety of combinations and variations of the case study.

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Multiple case study design: the example of place marketing research

• Original Article
• Published: 05 February 2020
• Volume 17 , pages 50–62, ( 2021 )

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• Marek Ćwiklicki   ORCID: orcid.org/0000-0002-5298-0210 1 &
• Kamila Pilch 1  

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The objective of this paper is to discuss the specificity of multiple case study (MCS) research design using analysis of research description realized according to this strategy in the field of place branding and place marketing published between 1976 and 2016 in scholarly journals. Selecting cases and cases’ context are most frequently explained in place marketing articles where findings are results of MCS research. The choice of a case study as a research strategy and limitations of the studies are less frequently justified in investigated papers. Our analysis shows that the authors should pay more attention to elements characteristic for methodological rigour in their descriptions of the research method. For this purpose, we prepared a checklist. We have discussed in detail key methodological issues for MCS. Moreover, we have formulated guidelines for improving research methodology’s descriptions in scholarly papers. It should lead to an increase in methodological rigour in future research reports. Therefore, researchers will find out suggestions for studying phenomena within place branding domain.

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Acknowledgements

We would like to thank dr Renaud Vuignier for sharing his journal database and the reviewers of this journal for their insightful and constructive comments towards improving our manuscript.

Funding was provided by Cracow University of Economics.

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Ćwiklicki, M., Pilch, K. Multiple case study design: the example of place marketing research. Place Brand Public Dipl 17 , 50–62 (2021). https://doi.org/10.1057/s41254-020-00159-2

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Artificial intelligence  is being used in healthcare for everything from answering patient questions to assisting with surgeries and developing new pharmaceuticals.

According to  Statista , the artificial intelligence (AI) healthcare market, which is valued at $11 billion in 2021, is projected to be worth $187 billion in 2030. That massive increase means we will likely continue to see considerable changes in how medical providers, hospitals, pharmaceutical and biotechnology companies, and others in the healthcare industry operate.

Better  machine learning (ML)  algorithms, more access to data, cheaper hardware, and the availability of 5G have contributed to the increasing application of AI in the healthcare industry, accelerating the pace of change. AI and ML technologies can sift through enormous volumes of health data—from health records and clinical studies to genetic information—and analyze it much faster than humans.

Healthcare organizations are using AI to improve the efficiency of all kinds of processes, from back-office tasks to patient care. The following are some examples of how AI might be used to benefit staff and patients:

• Administrative workflow:  Healthcare workers spend a lot of time doing paperwork and other administrative tasks. AI and automation can help perform many of those mundane tasks, freeing up employee time for other activities and giving them more face-to-face time with patients. For example, generative AI can help clinicians with note-taking and content summarization that can help keep medical records as thoroughly as possible. AI might also help with accurate coding and sharing of information between departments and billing.
• Virtual nursing assistants:  One study found that  64% of patients  are comfortable with the use of AI for around-the-clock access to answers that support nurses provide. AI virtual nurse assistants—which are AI-powered chatbots, apps, or other interfaces—can be used to help answer questions about medications, forward reports to doctors or surgeons and help patients schedule a visit with a physician. These sorts of routine tasks can help take work off the hands of clinical staff, who can then spend more time directly on patient care, where human judgment and interaction matter most.
• Dosage error reduction:  AI can be used to help identify errors in how a patient self-administers medication. One example comes from a study in  Nature Medicine , which found that up to 70% of patients don’t take insulin as prescribed. An AI-powered tool that sits in the patient’s background (much like a wifi router) might be used to flag errors in how the patient administers an insulin pen or inhaler.
• Less invasive surgeries:  AI-enabled robots might be used to work around sensitive organs and tissues to help reduce blood loss, infection risk and post-surgery pain.
• Fraud prevention:  Fraud in the healthcare industry is enormous, at $380 billion/year, and raises the cost of consumers’ medical premiums and out-of-pocket expenses. Implementing AI can help recognize unusual or suspicious patterns in insurance claims, such as billing for costly services or procedures that are not performed, unbundling (which is billing for the individual steps of a procedure as though they were separate procedures), and performing unnecessary tests to take advantage of insurance payments.

A recent study found that  83% of patients  report poor communication as the worst part of their experience, demonstrating a strong need for clearer communication between patients and providers. AI technologies like  natural language processing  (NLP), predictive analytics, and  speech recognition  might help healthcare providers have more effective communication with patients. AI might, for instance, deliver more specific information about a patient’s treatment options, allowing the healthcare provider to have more meaningful conversations with the patient for shared decision-making.

According to  Harvard’s School of Public Health , although it’s early days for this use, using AI to make diagnoses may reduce treatment costs by up to 50% and improve health outcomes by 40%.

One use case example is out of the  University of Hawaii , where a research team found that deploying  deep learning  AI technology can improve breast cancer risk prediction. More research is needed, but the lead researcher pointed out that an AI algorithm can be trained on a much larger set of images than a radiologist—as many as a million or more radiology images. Also, that algorithm can be replicated at no cost except for hardware.

An  MIT group  developed an ML algorithm to determine when a human expert is needed. In some instances, such as identifying cardiomegaly in chest X-rays, they found that a hybrid human-AI model produced the best results.

Another  published study  found that AI recognized skin cancer better than experienced doctors.  US, German and French researchers used deep learning on more than 100,000 images to identify skin cancer. Comparing the results of AI to those of 58 international dermatologists, they found AI did better.

As health and fitness monitors become more popular and more people use apps that track and analyze details about their health. They can share these real-time data sets with their doctors to monitor health issues and provide alerts in case of problems.

AI solutions—such as big data applications, machine learning algorithms and deep learning algorithms—might also be used to help humans analyze large data sets to help clinical and other decision-making. AI might also be used to help detect and track infectious diseases, such as COVID-19, tuberculosis, and malaria.

One benefit the use of AI brings to health systems is making gathering and sharing information easier. AI can help providers keep track of patient data more efficiently.

One example is diabetes. According to the  Centers for Disease Control and Prevention , 10% of the US population has diabetes. Patients can now use wearable and other monitoring devices that provide feedback about their glucose levels to themselves and their medical team. AI can help providers gather that information, store, and analyze it, and provide data-driven insights from vast numbers of people. Using this information can help healthcare professionals determine how to better treat and manage diseases.

Organizations are also starting to use AI to help improve drug safety. The company SELTA SQUARE, for example, is  innovating the pharmacovigilance (PV) process , a legally mandated discipline for detecting and reporting adverse effects from drugs, then assessing, understanding, and preventing those effects. PV demands significant effort and diligence from pharma producers because it’s performed from the clinical trials phase all the way through the drug’s lifetime availability. Selta Square uses a combination of AI and automation to make the PV process faster and more accurate, which helps make medicines safer for people worldwide.

Sometimes, AI might reduce the need to test potential drug compounds physically, which is an enormous cost-savings.  High-fidelity molecular simulations  can run on computers without incurring the high costs of traditional discovery methods.

AI also has the potential to help humans predict toxicity, bioactivity, and other characteristics of molecules or create previously unknown drug molecules from scratch.

As AI becomes more important in healthcare delivery and more AI medical applications are developed, ethical, and regulatory governance must be established. Issues that raise concern include the possibility of bias, lack of transparency, privacy concerns regarding data used for training AI models, and safety and liability issues.

“AI governance is necessary, especially for clinical applications of the technology,” said Laura Craft, VP Analyst at  Gartner . “However, because new AI techniques are largely new territory for most [health delivery organizations], there is a lack of common rules, processes, and guidelines for eager entrepreneurs to follow as they design their pilots.”

The World Health Organization (WHO) spent 18 months deliberating with leading experts in ethics, digital technology, law, and human rights and various Ministries of Health members to produce a report that is called  Ethics & Governance of Artificial Intelligence for Health . This report identifies ethical challenges to using AI in healthcare, identifies risks, and outlines six  consensus principles  to ensure AI works for the public’s benefit:

• Protecting autonomy
• Promoting human safety and well-being
• Ensuring transparency
• Fostering accountability
• Ensuring equity
• Promoting tools that are responsive and sustainable

The WHO report also provides recommendations that ensure governing AI for healthcare both maximizes the technology’s promise and holds healthcare workers accountable and responsive to the communities and people they work with.

AI provides opportunities to help reduce human error, assist medical professionals and staff, and provide patient services 24/7. As AI tools continue to develop, there is potential to use AI even more in reading medical images, X-rays and scans, diagnosing medical problems and creating treatment plans.

AI applications continue to help streamline various tasks, from answering phones to analyzing population health trends (and likely, applications yet to be considered). For instance, future AI tools may automate or augment more of the work of clinicians and staff members. That will free up humans to spend more time on more effective and compassionate face-to-face professional care.

When patients need help, they don’t want to (or can’t) wait on hold. Healthcare facilities’ resources are finite, so help isn’t always available instantaneously or 24/7—and even slight delays can create frustration and feelings of isolation or cause certain conditions to worsen.

IBM® watsonx Assistant™ AI healthcare chatbots  can help providers do two things: keep their time focused where it needs to be and empower patients who call in to get quick answers to simple questions.

IBM watsonx Assistant  is built on deep learning, machine learning and natural language processing (NLP) models to understand questions, search for the best answers and complete transactions by using conversational AI.

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