multi case study method

Multiple Case Research Design

First Online: 10 November 2021

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Stefan Hunziker 3 &
Michael Blankenagel 3

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This chapter addresses the peculiarities, characteristics, and major fallacies of multiple case research designs. The major advantage of multiple case research lies in cross-case analysis. A multiple case research design shifts the focus from understanding a single case to the differences and similarities between cases. Thus, it is not just conducting more (second, third, etc.) case studies. Rather, it is the next step in developing a theory about factors driving differences and similarities. Also, researchers find relevant information on how to write a multiple case research design paper and learn about typical methodologies used for this research design. The chapter closes with referring to overlapping and adjacent research designs.

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Hunziker, S., Blankenagel, M. (2021). Multiple Case Research Design. In: Research Design in Business and Management. Springer Gabler, Wiesbaden. https://doi.org/10.1007/978-3-658-34357-6_9

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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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Research Design Review

A discussion of qualitative & quantitative research design, multi-method & case-centered research: when the whole is greater than the sum of its parts.

A multi-method approach such as case study and narrative research are differentiated from other qualitative methods in many ways, a few include:

The focus of the research design is on the case itself – the subject of inquiry, such as the state’s drug-prevention program or the teaching of 8 th grade science – not the particular methods that are used to conduct the research.
Each case in a case study or narrative research project is treated as a unit throughout all phases of the research. It is the case as an entity that is important to the researcher, not the categorical reduction of its elements.
The subject matter and research objectives are typically complex. A case study of a non-profit organization, for instance, would have limited value if the qualitative researcher only explored one or two of the organization’s programs in one geographic location.
Likewise, case-centered research embraces the diversity of events, people, and circumstances that define a particular case.
The elements that make up the entity of a case-centered study are interrelated. Case research investigating employment practices at a large manufacturing company, for instance, would use various methods to look at the connections between many factors, including staff training and attitudes, outreach efforts, employment policies and benefits, union versus non-union opportunities, plant versus office working conditions, and the job pool.

Not unlike a fine wine, the case in case-centered research is made up of a complex web of interrelated facets, where the whole is greater than the sum of its parts. Multi-method research examines these parts while not disturbing the whole.

Case Study – Methods, Examples and Guide

Table of Contents

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

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

Types of Case Study

Types and Methods of Case Study are as follows:

Single-Case Study

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

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

Multiple-Case Study

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

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

Exploratory Case Study

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

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

Descriptive Case Study

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

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

Instrumental Case Study

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

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

Case Study Data Collection Methods

Here are some common data collection methods for case studies:

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

Observations

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

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

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

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

How to conduct Case Study Research

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

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

Examples of Case Study

Here are some examples of case study research:

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

Application of Case Study

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

Business and Management

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

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

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

Social Sciences

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

Law and Ethics

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

Purpose of Case Study

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

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

Case studies can also serve other purposes, including:

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

Advantages of Case Study Research

There are several advantages of case study research, including:

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

Limitations of Case Study Research

There are several limitations of case study research, including:

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

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How to Write a Multiple Case Study Effectively

Table of Contents

Have you ever been assigned to write a multiple case study but don’t know where to begin? Are you intimidated by the complexity and challenge it brings? Don’t worry! This article will help you learn how to write a multiple case study effectively that will make an impactful impression. So, let’s begin by defining a multiple case study.

What Is a Multiple Case Study?

A multiple case study is a research method examining several different entities. It helps researchers gain an understanding of the entities’ individual characteristics and disclose any shared patterns or insights. This type of investigation often uses both qualitative and quantitative data. These are usually collected from interviews, surveys, field observations, archival records, and other sources. This is done to analyze the relationships between each entity and its environment. The results can provide valuable insights for policymakers and decision-makers.

Why Is a Multiple Case Study Important?

A multiple case study is invaluable in providing a comprehensive view of a particular issue or phenomenon. Analyzing a range of cases allows for comparisons and contrasts to be drawn. And this can help identify broader trends, implications, and causes that might otherwise remain undetected. This method is particularly useful in developing theories and testing hypotheses. This is because the range of data collected provides more robust evidence than what could be achieved from one single case alone.

A person writing on a notebook with a laptop next to them

How to Write a Multiple Case Study

Below are the key steps on how to write a multiple case study :

1. Brainstorm Potential Case Studies

Before beginning your multiple case study, you should brainstorm potential cases suitable for the research project. Consider both theoretical and practical implications when deciding which cases are most appropriate. Think about how these cases can best illustrate the issue or question at hand. Make sure to consider all relevant information before making any decisions.

2. Conduct Background Research on Each Case

After selecting the individual cases for your multiple case study, the next step is to do background research for each case. Conducting extensive background research on each case will help you better understand the context of the study. This research will allow you to form an educated opinion and provide insight into the problems and challenges that each case may present.

3. Establish a Research Methodology

A successful multiple-case study requires a sound research methodology. This includes deciding on the methods of data collection and analysis and setting objectives. It also involves developing criteria for evaluating the results and determining what kind of data needs to be collected from each case. All of this must be done carefully, considering the purpose of the study and its outcomes.

4. Collect Data

Once a research method has been established, it is time to collect data from each case included in the study. Depending on the nature of the research project, this could involve interviewing participants, gathering statistics, or observing behaviors in certain settings. It is crucial to ensure that all data collected is accurate and reliable.

5. Analyze & Interpret Data

After the data has been collected, it must be analyzed to draw meaningful conclusions from it. This process involves examining patterns and trends within the data, identifying relationships between variables, and looking for commonalities among different cases. These findings must then be interpreted in light of the initial questions posed by the study.

6. Write the Report

After completing the analysis and interpretation of the data, it is finally time to write up the results of the multiple case study. This should include a summary of the key findings and an explanation of why these findings are significant. In addition, the limitations of the study should be acknowledged, along with recommendations for future research in this area.

Writing a multiple case study requires careful planning and execution. But the process becomes easier when you know the proper steps to conduct and create a multiple case study. It requires you to focus on the design of the study, including the sample chosen and the research methodology established. Conducting background research on each case and collecting data are also crucial steps in the process. To guide you through the process, this article outlines the key steps to help you easily write a well-structured multiple-case study .

How to Write a Multiple Case Study Effectively

Abir Ghenaiet

Abir is a data analyst and researcher. Among her interests are artificial intelligence, machine learning, and natural language processing. As a humanitarian and educator, she actively supports women in tech and promotes diversity.

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Open access
Published: 22 May 2024

Infection prevention practice in home healthcare: a mixed-method study in two Swiss home healthcare organisations

Lisa Brockhaus 1 ,
Claudia Lötscher 2 &
Niklaus Daniel Labhardt 1 , 3

BMC Health Services Research volume 24 , Article number: 657 ( 2024 ) Cite this article

Metrics details

Infection prevention and control (IPC) research has long neglected the home healthcare sector with its unique challenges. This study aimed to gain an understanding of the barriers to the implementation of infection prevention practices relevant to this setting, the related attitudes, perceived relevance and priorities from the home healthcare worker perspective in Switzerland.

The mixed-method study involved semi-structured interviews ( n = 18) and an anonymous web-based survey ( n = 144) among nursing assistants and nurses from two home healthcare organizations in northwest Switzerland. Questions in both sub-studies focused on perceived challenges to infection prevention practices, perceived relevance, and related attitudes and mitigation strategies. Using an exploratory-sequential design, survey questions were designed to quantify and complement the findings from the interview study.

Healthcare workers in these two organisations felt adequately protected, trained and supported by their organisations regarding IPC (survey agreement rates > 90%). General challenges to IPC in the home environment most agreed on were lack of cleanliness, lack of space, and the priorities of the patient to be respected (survey agreement rates 85.4%, 77.1%, and 70.8%, respectively). Practices and perceived challenges in the case of colonisation with multi-drug resistant organisms (MDRO) and potentially infectious diarrheal or respiratory illnesses varied highly regarding information transfer, use of protective equipment, and use and disinfection practices of multi-use equipment. Challenges to hand hygiene, sharps safety, waste management and decontamination of equipment did not feature as a prominent concern.

Conclusions

This study is the first to characterise the implementation of infection prevention practices and the related challenges in home healthcare in Switzerland. Home healthcare workers describe various challenges related to infection prevention practices as largely manageable in their work routine, and generally show satisfaction with the support provided by their organisations regarding IPC precautions. Key findings regarding challenges amenable to interventions include uncertainty and inconsistency regarding the management of MDRO colonisation and acute illnesses, and gaps in information transfer. Those challenges may benefit from both organisational interventions and further research into the level of precautions that are appropriate to the home healthcare setting.

Peer Review reports

The home healthcare (HHC) sector is rapidly expanding in many high-income countries, reflecting the demographic changes with an ageing population, as well as shifts from institutional to home-based care [ 1 , 2 , 3 ]. HHC is one of the fastest-growing segments of the Swiss healthcare sector. Over the last decade, the number of individuals receiving HHC has increased by more than 50% [ 4 , 5 ]. Furthermore, due to structural and financial pressures on acute care, patients are being discharged from hospitals earlier, resulting in an increase in the complexity and severity of home care patients [ 6 ].

Infection prevention and control (IPC) guidance specific to the HHC setting is largely lacking [ 7 , 8 , 9 ]. Understanding the factors that limit the implementation of IPC practices in the HHC context is essential to adapt established infection prevention strategies to this unique setting, and to develop tailored interventions to address adherence where necessary. However, IPC implementation research has long neglected the heterogeneous HHC sector [ 8 ]. The limited research has largely focused on injection safety, while other precautions remain poorly studied [ 10 ]. The lack of IPC strategies tailored to the HHC setting is demonstrated by low adherence to established infection prevention guidelines developed in hospital contexts, such as to prevention bundles for central line-associated bloodstream infections, or hand hygiene standards [ 11 , 12 , 13 ]. Also, the lack of context-specific guidance was perceived as a major challenge by HHC organisations during the COVID-19 pandemic [ 14 , 15 ].

In Switzerland, the HHC sector lacks structured IPC research and implementation efforts, with local organisations being responsible for the development of their individual IPC guidelines.

This study aimed to gain an understanding of the challenges to the implementation of IPC practices relevant to this setting, and the related attitudes, from the home healthcare worker (HHCW) perspective in Switzerland.

The objectives of the study were to (i) explore the HHCWs’ perspectives on barriers to the implementation of various IPC practices, and their attitudes towards implementation including mitigation strategies, perceived relevance of obstacles, and priorities; and (ii) to quantify relevant key findings.

We conducted a mixed-method study using an exploratory-sequential design [ 16 ]. Approval was obtained from the University Basel Ethics Committee (2023 − 129).

Participants were recruited from two regional HHC organisations in the northwest of Switzerland, one providing services in a suburban area (organisation A) and one in an urban area (organisation B), with 177 and 611 employees, respectively. Both organisations largely provide general HHC services for adults, and serve areas with diverse socio-economic contexts. Both are privately run not-for-profit organisations with a governmental mandate.

Participation was limited to employees providing healthcare services, including nurses and nursing assistants with medical treatment competencies, such as wound care and blood glucose management, and nursing assistants without medical treatment competencies providing basic healthcare such as assisting with personal hygiene. Employees exclusively providing household help were excluded.

Interview study

Interview participants were purposively sampled across the range of job profiles and training levels as outlined above. We aimed for a sample of 15–20 participants to reach code saturation [ 17 ], and finally stopped inclusion at 18 participants. No direct incentives were provided to the participants.

The interview guide was informed by our previous literature review [ 10 ], a field visit of the first author (LB), input from one HHC organisation (CL), and IPC expertise of the authors (LB, NDL). It was piloted on two HHCWs with subsequent refinements, and additional input from a researcher with vast experience in qualitative research was sought (JB).

The semi-structured interview started with an open question aiming to explore the participants’ personal definition of IPC precautions, and any aspects they spontaneously considered problematic. Then, 5–7 (depending on the participants’ job profile) questions about individual IPC practices and overarching issues were discussed focussing on aspects unique to the HHC setting. Two of them were phrased in the form of case vignettes ensuring practical understanding. Probing questions were asked to get more thorough descriptions of participants’ practices, and elicit a deeper understanding of their reasoning. Organisational and policy aspects were touched upon if perceived relevant by the participants, but were not the focus of the study. The interview concluded with open questions about any further aspects they considered important, and areas of improvement. The full interview guide is available in supplemental file 1 .

The interviews were conducted by the first author in July 2023, in Swiss German, audio-recorded, and manually transcribed into Standard German. The analysis was performed using MS Excel.

Within the dataset, the analysis focussed on describing factors impacting IPC practices from the HHCW perspective, while also contextualising those with the participants’ reasoning and general attitudes, such as views on relevance and priorities.

Identification and analysis of themes were performed as outlined by Braun et al. using a 6-step thematic analysis [ 18 ]. Both an inductive (not driven by an interest in a specific IP practice) and a deductive (by IP practice) approach were used in the coding process and identification of themes. Themes were identified at the semantic level. Coding was performed and themes were discussed and agreed on by two study team members (LB, CL). To maximise validity, we used negative case analysis, synthesised member checking [ 18 ] with two participants, and triangulation with the survey data.

17 survey items were designed to quantify the main themes identified from the interview study. Answering options were a 4-step Likert scale rating from ‘agree’ to ‘disagree’, or frequency of occurrence of specific situations, as appropriate. Questions were kept in jargon-free language and were piloted on 4 participants to ensure clarity.

The survey was delivered between 24 August and 21 October 2023 as a self-administered, anonymous, web-based online survey. All employees of the two organisations with the abovementioned job profiles were invited to participate, excluding nurses working exclusively in management roles. Eligible employees were invited to participate via e-mail by the HHC organisation management (directly in organisation A, and via team leaders in organisation B) including participant information. No incentives were paid to the participants, but they were provided time to complete the survey during working hours.

All survey results are reported descriptively using proportions. In the text, we report cumulative proportions of “agreeing” or “rather agreeing” to the respective item.

Furthermore, we performed a stratified analysis of all results by training level and by organisation. Therefore, responses were dichotomised to “agree/rather agree” versus “disagree/rather disagree”, and training levels were dichotomised to the two groups “participants without medical treatment competencies“ versus “participants with medical treatment competencies”. Results were analysed for significant differences using X2-test for trend, or Fisher’s exact test, as appropriate, and are only reported where significant differences (p < 0.05) were found.

Additional details on the survey methodology are provided in supplemental file 2 according to the CHERRIES-checklist [ 19 ].

Participant characteristics of the interview study ( n = 18) and the survey ( n = 144) are given in Table 1 .

Among the employees contacted for the interview study, one declined to participate. Among the 10 licensed nurses included in the interview study, 5 had additional management roles. The mean duration of the interviews was 16 min (SD 5.4, range 6–30).

The survey was completed by 144 HHCWs, equivalent to an overall response rate of 24.4% of the eligible employees (53.8% in organisation A, and 18.6% in organisation B). The complete survey results are provided in the supplemental file 3 .

General attitudes and perceptions

A key perception of participants was that implementation of infection prevention (IP) in terms of cleanliness or asepsis was not possible to the same extent as in the hospital setting. Participants then often went on to state their commitment to implementing precautions in the individual home, requiring their flexibility or even creativity:

«I also work a lot with common sense, simply setting up the environment in a way that I can achieve the best possible results, knowing that this is not like in the textbook.» (ID2)

However, some participants expressed a more dominant pragmatism resulting from the perceived limitations:

«You just do it [hand hygiene] when you know it’s really necessary. Also because you know that you can’t do it quite as properly as in hospital.» (ID13)

Participants also commonly expressed overall satisfaction with the precautions available for their protection, and IP implementation by the organisation. However, only few accounts did not include any aspect perceived as challenging IP practices in their setting.

95.8% of the survey participants agreed or rather agreed to feeling protected with the precautions in place in their organisation. 93.1% agreed or rather agreed to feeling safe to not transmit pathogens between patients with the precautions in place.

General challenges to IP practices eliciting the highest agreement in the survey were lack of cleanliness (85.4%), lack of space (77.1%), and the priorities of the patient having to be respected (70.8%). Participants with medical treatment competencies stated significantly more often being limited in their IP precautions by the space available in the home, compared to participants without medical treatment competencies (83.5% and 54.2%, respectively, p = 0.005).

Relationship with the patient

Patient and HCW roles being different from the hospital setting was an overarching theme across conversations about specific IP precautions. Participants highlighted the greater self-determination of the patient in many care decisions, and the motivational work necessary if they felt changes were needed for IP reasons:

«And also the understanding of the patient, you have to fight more for it. In the hospital you are the boss, at home the patient is the boss…» ( ID18)

However, there was broad consensus that in most cases, patients were very willing to comply if adequate explanations were given.

« If you talk to them, there is always a solution. So I really try to adapt, so far it always worked out somehow. » (ID6)

In the survey, 78.5% stated patients would usually be collaborative on IP precautions. 70.8% perceived the priorities of the patient having to be respected as a barrier to their IP management.

Risk assessment and information transfer

Most participants stated they would treat all their patients the same with regard to IP practices. A frequent reasoning for this attitude was the perception that some diagnoses relevant to IPC might not be reported to them as HHCW in charge. An alternative view was vaguely being “more cautious” with severely immunocompromised patients.

Participants expressed diverse views on the reliability of information flow relevant to IPC for specific patients. In Switzerland, discharge letters and diagnoses lists are not routinely sent to HHC organisations by hospitals or general practitioners. Some felt confident that the relevant information would be available from these external documents, and transferred to their information system, while others saw a lack of relevant information:

«So normally, when someone comes home from the hospital, the general practitioner, or relatives, actually contact the home care organisation…. in most situations this works well.» (ID2) «Honestly, I haven’t heard of anyone leaving the hospital with MRSA [methicillin-resistant Staphylococcus aureus], and I can’t imagine that nobody has it. And we don’t realise it.» (ID9)

Views on whether hospital discharge letters were easily available, or hard to obtain, were also divided, with occasional mention of the problem source being a hospital, GPs, or specific employees. It was also repeatedly pointed out that the legal basis for this information transfer seemed unclear:

«That’s difficult - we don’t always get a diagnosis list from the hospital, it depends on who you communicate with, ‘oops, that’s data protection…’ » (ID12)

Lack of medical information was perceived as a barrier to IP precautions by 55.6% of survey participants. When being asked about potential improvements, 78.5% and 85.4% saw a need for better information flow within the organisation, and between the organisation and other healthcare providers, respectively.

Hand hygiene

Hand hygiene featured prominently as the most important IP precaution to participants. Implementation of hand hygiene was rarely an issue for participants. Key explanations provided for this perspective were good availability of hand sanitiser, either stored at the home or carried along, and a feeling of being appropriately trained on these precautions.

Participants often expressed confidence in their individual decisions on what level of hand disinfection was appropriate:

«In any case, it can be implemented as I find it appropriate in home care… that the way I can disinfect my hands is sufficient.» (ID2) «(…) because there are always situations where you have to do one more move, where it might make sense to disinfect your hands immediately afterwards, but then I’m just disinfecting my hands…sometimes that’s not feasible.» (ID9)

While the risk of stains on furniture and floors with alcoholic hand sanitisers featured in some accounts, no other potential hassles were mentioned repeatedly. Time constraints were rarely mentioned.

Participants were divided on the indications and frequency of their hand washing in the patient’s home. Those who stated washing their hands occasionally provided individual strategies for drying their hands with something clean, including carrying along baby wipes, using toilet paper, or a cotton apron. Other participants described that they preferred disinfecting their hands or using gloves when they found the home too dirty to wash their hands, however acknowledging this was rarely the case.

«We should [wash hands] before we prepare food…but with certain patients it’s pointless because it’s so dirty. So then I prefer to disinfect my hands. Or I put on gloves.» (ID10)

While most participants described wearing gloves for limited indications such as intimate care or potential contact with body fluids, some also stated wearing gloves generously or even continuously for all care activities.

33.3% and 14.6% of survey participants stated using hand sanitisers more frequently, and less frequently, than recommended in the guidance, respectively. 57.6% used gloves more deliberately than as proposed in the guidance, and 21.3% stated using gloves for all care activities.

Multi-drug resistant organisms

A case vignette of a patient with known Methicillin-resistant Staphylococcus aureus (MRSA) colonisation elicited diverse themes:

(1) The awareness that colonisation with multi-drug resistant organisms (MDROs) in their patients may be unreported, or undiagnosed.

(2) Focusing on good hand hygiene without applying any additional precautions, an approach for which some of the nurses with management roles also offered explanations for:

«The patients take off their pants and reach into them… and then immediately touch other things again. In other words, we don’t actually know where anything has been distributed… And if we also don’t know that someone has MRSA or ESBL [Extended-spectrum beta-lactamase-producing Enterobacterales], and we only actually put the gloves on for personal hygiene, and otherwise, we touch things without gloves, then we can only hope that when we leave again and do our hand hygiene carefully, we don’t take anything with us. » (ID9)

(3) A belief that guidelines for the management of specified MDRO were available at the organisation, defining in what situations additional precautions would be necessary.

(4) Uncertainty about the necessary precautions, and doubts about the relevance of the MDRO diagnosis depending on the care activity, were expressed by nurses with management roles:

«There is a lack of clarity here. I do get this information when it is known at the patient’s discharge. They point out that the hygiene regulations are being strictly adhered to. But nothing special in any way… » (ID17) «But we still have to look into it, and first have to read and think about where the bug is sitting and whether it is relevant for us… » ( ID8)

(5) A frequent view among health assistants was that they had not come across a MDRO diagnosis up to now.

In the survey, an overall 87.5% of participants believed that MDRO guidelines existed in their organisation. 66.7% of the participants stated having seen patients with known MDRO colonisation. This proportion was significantly higher in participants with medical treatment competencies (79.5%) than in those without (27.3%) ( p < 0.001). Participants from organisation B also stated significantly more often having seen patients with known MDRO colonisation ( p = 0.02).

Potentially infectious acute illnesses

A case vignette of a patient with the notion of acute diarrhoea revealed two themes about IPC management of acute, potentially infectious illnesses:

(1) A key perception was that they had to make a new situation analysis when they visited the patients’ home:

«…you can’t judge the situation just at once. You come in and pay attention, prepare yourself, so I read through everything, meaning then I might need extra gloves, and also make sure the sanitiser bottle is full, like that. You don’t do anything special.» ( ID16)

(2) In addition, most participants described carrying an “emergency kit” with personal protective equipment, provided by the organisation, that allowed them to react to unexpected situations.

Paying extra attention to hand disinfection was largely agreed on in the survey for diarrheal and respiratory acute illnesses (91.0% and 77.1%, respectively). Paying extra attention to disinfecting equipment was stated by 77.1 and 61.8%, respectively.

Sharp safety

Participants stated unanimously that sharps disposal was generally well organised, with agency-provided sharp containers. Some spontaneously acknowledged that, similarly to the hospital setting, a residual risk of sharp injuries would always be present.

The rare cases in which issues arose showed three facets: (1), patients unable to comply with agreements, (2), caring for patients with intravenous drug use, and (3) plastic bottles being used if in the rare case, a container was not in place. However, participants readily presented their strategies to deal with those situations:

«So we had one [person with substance use] who had all the mess in his room, and then we said he had to come into the kitchen for wound care, and then you’re safe.» (ID10) «If we have patients where we know it won’t work, then we lock it away so that they can’t get to it.» (ID13)

It was further emphasised that patients who self-managed blood glucose measurements and insulin injections were also responsible for proper disposal of their equipment, although HHC staff could advise them and organise proper disposal boxes. The financing of safety devices and equipment was occasionally brought up but was generally perceived as a manageable challenge. 92.4% of survey participants stated that sharps were usually safely disposed of in the patients’ homes.

Waste management

Usually, waste disposal did not pose challenges to participants. They explained how materials contaminated with bodily fluids would be closed in a separate waste bag, and be disposed of in the household waste. Issues linked to this practice remained anecdotical across the accounts:

«We also have patients who empty the syringe container into the bin and then put it back… » (ID10)

In the survey, 83.2% stated that contaminated waste was usually being correctly disposed in the patients’ homes.

Decontamination/reprocessing

When asked about the handling of multi-use equipment, the dominant theme was a preventive effort of not laying down equipment in the patient’s home, in general, or at least not in a place they considered dirty. This often required creativity, as the following quote illustrates:

«There’s a loop at the back of the nursing bag where I hang the helmet and then I can put the tablet in there, so I don’t have to put it on the table.» (ID6 )

Multi-use equipment that was listed by participants included blood pressure meter, blood glucose meter, nurse bag and tablet. Participants described they would usually clean these items at the end of their working day, while cleaning strategies during the day were described inconsistently. However, this was rarely considered problematic:

«I take it inside [into the patients’ home] and clean it in the evening…There might be something on it. I put it down and then pick it up again, well my hands are then disinfected, and then I put it in the car and take it to the next patient and put it down again… » ( ID11) «And you go in with everything, including your bag, which you put or hang somewhere, and then you take it back into the car, so certain things you just drag around.» (ID13)

However, when survey participants were asked if they paid extra attention to disinfecting multi-use equipment after use in a patient colonised with MDRO, a patient with acute diarrhoea, or with acute respiratory symptoms, these statements were agreed to by 83.2%, 77.1%, and 61.8% of participants, respectively.

Wound care and asepsis

Establishing a clean surface for equipment and materials was the dominant theme in wound care conversations. Challenges most frequently described were a general lack of space, often associated with a “messy household”, or a generally dirty household, while descriptions of pets, pet hair, open windows, or lack of light were occasional accounts. However, participants acknowledged that those situations were not the common case and solutions would normally be found:

«There are the tidier and the less tidy households. But as a rule, I haven’t heard that this is a problem. I’ve also experienced a messy household, but that’s special, and even then I was able to make room for the dressings and drawing blood and so on.» ( ID17)

Participants emphasised adaptability and creativity were necessary and presented various strategies to create a satisfactory environment, including using newspaper piles, clean waste bags or packaging materials.

A second theme was the efforts to ensure the use of clean and appropriate wound care materials. Participants emphasised that some materials as well as equipment, such as single-use tweezers, were not paid for by health insurance. Re-sealing of opened materials to reduce costs was commonly reported in this context. Participants also mentioned organising a clean box from their HHC organisation to store all wound care materials and equipment in the home.

In the survey, the three most frequent barriers to clean wound care (rated “frequent” or “sometimes”) were lack of space to make a clean surface (68.7%), positioning of the patient difficult (63.5%), and lack of cleanliness in the home (59.2%).

Contextual themes: knowledge level, adherence, and COVID-19-related challenges

IP-related challenges during the COVID-19 pandemic were not explored explicitly and did not feature prominently in the accounts. When it was occasionally brought up spontaneously, the most common theme was not knowing whether a patient could be sick due to COVID-19 and infectious, and not being able to do anything about this:

«And he coughs right in my face (…)- then I’ve already told customers to test, and they didn’t know how to test, let alone had relatives to bring them to the test centre or anything else…and then we just let it be.» (ID18)

Participants unanimously rated their knowledge level about hygiene practices as sufficient or good, a perception that was shared by nurses with management roles who were asked about the knowledge level of their team. Justifications provided most frequently included training sessions, work supervision, the existence of guidance and knowing where to look up questions, and a positive feedback culture in the team.

Although not explicitly asked, an attitude of perceived importance of hygiene precautions and adherence was visible in many accounts.

«…you usually only find out afterwards whether there is a germ or something, … so I am very rigorous and strict, I don’t want to bring anything and take anything with me.» (ID6)

This view was echoed by nurses with management roles:

«Looking back on the corona period, it took a very long time for our people to become infected at all, and that was only when the measures were relaxed and people were able to meet again in their private lives… That’s why I believe that a minimum of precautions is being observed very carefully.» (ID9)

In the survey, 97.2% of participants perceived their own knowledge level to be sufficient for their work routine.

Summary of key findings

This study characterises IP implementation in the Swiss HHC setting with the following key findings: (i) HHCW in these two organisations felt sufficiently protected, trained and supported by their organisations regarding IPC, and felt committed to adapting their precautions to the diverse conditions they encounter in their work; (ii) Challenges of hand hygiene, sharps safety, waste management and decontamination of equipment did not feature prominently as a HHCW concern; (iii) Practices, perceived relevance and challenges in the case of colonisation with MDRO or potentially infectious diarrheal or respiratory illnesses were highly varied regarding information transfer, use of protective equipment, and use and disinfection practices of multi-use equipment; and (iv) main perceived challenges to IP practices in general in the home environment were lack of cleanliness, lack of space, and the priorities of the patient to be respected.

Strengths and limitations

Our study design allowed us to not only describe IP practices and self-perceived barriers in this setting, but also to provide a nuanced picture with regard to relevance and priorities as perceived by the HHCWs which we identify as a strength of our study.

The representativeness of this study may be limited due to the relatively small number of participants, the inclusion of only two organisations, and the rather low survey response rate in organisation B. However, we included two organisations characteristic for the Swiss setting: They are privately run not-for profit organisations with a governmental mandate, are members of the national umbrella association of HHC organisations, represent a typical range of organisation size in the Swiss setting, and serve socioeconomically diverse areas. The two organisations differ regarding area served (urban versus suburban), size (611 versus 177 employees), and, importantly, internal management mechanisms. The latter may explain the lower survey response rate in organisation B, with survey invitations being distributed indirectly via team leaders, in contrast to direct distribution by the management in organisation A. With the exception of the proportion of employees stating seeing patients with MDRO colonisation, we found no significant differences in the survey responses when stratifying for organisation. This finding makes us confident that the key findings may be transferable to other HHC organisations at least within the Swiss context.

Furthermore, it is possible that some potential gaps in a rather favourable picture were not disclosed. Particularly, we found a discrepancy in self-declaration of decontamination practices of equipment when seeing patients with MDRO or acute illnesses between the interview study and the survey results that may be explained by some desirability bias. However, in all other practices under question, survey results aligned with the qualitative findings.

Lastly, the study deliberately focused on the HHCWs perspectives, meaning that factors that currently may lack awareness in the setting are potentially not identified as barriers by participants.

Comparison with existing literature

Key challenges highlighted by participants in this study have been described previously in other high-income countries, most notably including space and cleanliness in the home [ 20 , 21 , 22 , 23 ], patient priorities [ 21 , 24 ], and inconsistency regarding management of MDRO colonisation [ 21 , 24 ]. When compared to this limited evidence from other high-income countries, it is noticeable that participants in our study largely describe the challenges encountered as manageable rather than overwhelming. For example, survey studies from the US reported multiple barriers to IPC practices upon each home visit [ 20 , 22 ]. Apart from inter-country differences, this potential discrepancy may be explained by our study design putting encountered barriers into perspective by asking interview participants to elaborate on the perceived relevance of factors they identified, and by differences in how the survey questions were phrased. Furthermore, our study does not identify sharps safety as a predominant IP concern for HHCW in this setting, a finding that contrasts with the focus of previous research [ 10 ]. We argue that with device improvements and improved knowledge about transmission of blood-borne pathogens over the last decades, other concerns have become more dominant.

Literature about IP management of patients with MDRO colonisation in HHC is particularly scarce. One study from the US revealed that practices of taking equipment into the home or using dedicated equipment varied widely [ 25 ], a finding that aligns with our study. Another study from the US showed 48% of HHC nursing bags were contaminated with bacterial pathogens on the inside, of which 6% were MDRO [ 26 ]. To our knowledge, effectiveness of the decontamination practices for equipment in HHC has not been studied. Inconsistency around the management of various MDROs reflects evidence gaps that are not limited to the HHC setting: the use of contact precautions is increasingly questioned as evidence for their effectiveness is missing even in the hospital setting [ 27 , 28 , 29 ].

Challenges related to IP practices during the Covid-19 pandemic, somehow surprisingly, did not spontaneously dominate participants’ accounts. However, our study focused on practices rather than the policy and organisational level, and thus does not conflict with findings of other publications describing the policy response in and for HHC during the Covid-19 pandemic as inappropriate [ 14 , 15 ].

Implications for future research, and policy and practice

Identified barriers around information transfer, and inconsistency regarding the management of MDRO or acute illnesses, may primarily benefit from operational interventions. This may include clarification of the legal situation for health records transfer to HHC organisations, at least in the Swiss context. From a technical perspective however, evidence about what level of precautions is effective in and appropriate to the HHC setting is further missing. This is particularly true for the management of various MDROs as an increasingly relevant challenge to all healthcare sectors [ 9 ]. Robust epidemiological and clinical data on colonisation and healthcare-associated infections in the setting are missing [ 7 , 30 , 31 ], and would provide a very first step towards clarification of precautions appropriate in this context.

This study is the first to characterise the implementation of IP practices and the related challenges in HHC in Switzerland. HHCW describe various specific challenges related to IP practices as largely manageable in their work routine, and generally show satisfaction with the support provided by their organisations regarding IPC precautions. Key findings regarding challenges include uncertainty and inconsistency around the IP management of MDRO colonisation and acute illnesses, and gaps in information transfer. Those challenges may benefit from both organisational interventions and further research into the level of precautions that are appropriate to the HHC setting.

Data availability

The original data of this study are available from the correspondig author on reasonable request.

Abbreviations

Home healthcare

Home healthcare worker

Infection prevention
Infection prevention and control

Methicillin-resistant Staphylococcus aureus

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Acknowledgements

We are grateful for the support of the home healthcare employees who provided their time and expertise for this study, and the management support of the two home healthcare organisations taking part in this study. We are further indebted to Dr Jennifer Belus (JB) for her advice on qualitative research methodology.

Open access funding provided by University of Basel. There was no specific funding for this research project. NDL reveived an Eccellenza Grant from the Swiss National Science Foundation (PCEFP3_181355).

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LB conceptualised the study, undertook the data collection, led the analysis, and wrote the first draft and final manuscript. CL contributed to the conceptualisation, data collection and analysis of the data and commented on the draft. NDL supervised the study conceptualisation and provided feedback on the draft. All authors reviewed and approved the final manuscript.

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Published: 23 May 2024

Partial discharge localization in power transformer tanks using machine learning methods

Farzin Khodaveisi 1 ,
Hamidreza Karami 2 ,
Matin Zarei Karimpour 3 ,
Marcos Rubinstein 2 &
Farhad Rachidi 4

Scientific Reports volume 14 , Article number: 11785 ( 2024 ) Cite this article

Metrics details

Electrical and electronic engineering
Power stations

This paper presents a comparison of machine learning (ML) methods used for three-dimensional localization of partial discharges (PD) in a power transformer tank. The study examines ML and deep learning (DL) methods, ranging from support vector machines (SVM) to more complex approaches like convolutional neural networks (CNN). Multiple case studies are considered, each with different attributes, including sensor position, frequency content of the PD signal, and size of the transformer tank. The paper focuses on predicting the PD location in three-dimensional space using single-sensor electric field measurements. Various aspects of each method are analyzed, such as the input signal, core methodology, correlation coefficient between the predicted location and the actual location, and root mean square error (RMSE). These features are discussed and compared across the different methods. The results indicate that the CNN model exhibits superior performance in terms of location accuracy among the methods considered.

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Introduction.

Source localization has many applications in fields such as medicine, acoustics, electromagnetics, and lightning 1 . In the realm of electromagnetics, Partial discharges (PDs) are electrical breakdowns that occur within electrical insulations, such as those in transformers. Over time, PDs can lead to the complete breakdown of the insulation system, causing extensive damage to the transformer. PDs are a major contributor to the failure of power transformers, transmission lines, and gas insulation, among other components. Any malfunction in a power transformer can result in power outages and reduced reliability of electrical power networks. Therefore, the early detection and localization of PDs is crucial in order to prevent potential hazards and minimize further damage 2 , 3 .

PD localization techniques 2 can be categorized into two groups: acoustic and electromagnetic. Acoustic detection and localization methods 2 , 4 , 5 , 6 , 7 rely on detecting the sound waves emitted by PD sources. Compared to electromagnetic methods, acoustic methods are less sensitive to weak PDs and those that occur within the winding 7 , 8 . Acoustic sensors can be mounted on the external walls of the power transformer, making acoustic detection a non-invasive technique. Nevertheless, the acoustic signal may be contaminated by external acoustic environmental noise.

Electromagnetic localization based methods 2 , 4 , 7 , 8 , 9 , 10 utilize electromagnetic waves emitted by PD sources. The detection methods that employ ultra high frequency (UHF) radiation are particularly sensitive to weak PDs occurring within the winding. Moreover, UHF measurements are commonly electromagnetically shielded by grounding the transformer tank to mitigate external disturbances such as corona and environmental noise 11 .

Classical acoustic and electromagnetic three-dimensional localization methods rely on the Time Difference of Arrival (TDoA) of signals. However, these methods are highly sensitive to noise due to the need for precise determination of the onset time of the arriving signals 11 . Moreover, they require a minimum of four time-synchronized sensors as well as direct propagation paths from the PD sources to the multiple sensors to operate effectively. In the case of acoustic methods, reasonable accuracy can be achieved by implementing appropriate signal processing techniques. However, TDoA-based electromagnetic methods encounter inaccuracies caused by inhomogeneities and scattering within transformers.

Recently, a novel approach based on time reversal has been proposed in the electromagnetic and acoustic regimes. This approach can localize the sources of partial discharges inside a transformer using only a single sensor. In comparison to the conventional TDoA method, the time reversal-based method demonstrates robustness to noise in the experimental signals. Moreover, it remains effective even in the presence of obstacles that obstruct the direct line of sight between the sensor and the PD source. The technique requires a model of the transformer tank to carry out the backward propagation stage 12 .

The creation of a PD localization system demands a significant level of accuracy, sensitivity, and robustness. These qualities have been essential for power grid operators and installers over the past decades. Traditionally, PD diagnostics primarily rely on features extracted through conventional techniques such as statistical analysis, and time–frequency analysis. Simple threshold values are then computed to make decisions 11 . Advanced signal processing techniques like the discrete wavelet transform (DWT) are employed to extract more sophisticated and powerful features, while conventional machine learning (ML) methods, including Back-Propagation Neural Networks (BPNN), support vector machines (SVM), and fuzzy inference systems (FIS), are gradually utilized for classification and regression tasks 11 . In recent times, with advancements in computing and information technologies, deep learning (DL) has gained significant attention as a subset of ML for intelligent PD diagnostics 13 .

Table S1 , based on the review of 13 and including articles published since 2021, presents an overview of papers that utilize ML methods for PD diagnosis. The studies predominantly focus on detection, pattern recognition, and classification, as shown in the table. However, only about 12.7% of these studies address the problem of localization. In terms of applications, only 16.50% of the studies are focused on transformers, while others examine PDs in gas-insulated transmission lines (GIL), gas-insulated switchgear (GIS), high-voltage cables, and electrical equipment. One of the primary reasons for the limited attention given to PD localization is its inherent difficulty compared to detection and classification. Notably, the CNN model has garnered researchers’ attention due to its exceptional performance in signal and image processing, as depicted in Table S1 . For PD localization, the bagging-kernel extreme learning machine (Bagging-KELM) 14 achieves the best result in GIL, with an average error of 0.93 cm. Neural networks, bagging techniques, and SVMs are the most frequently employed models in PD localization studies. It should be noted that all the methods presented in Table S1 have been individually investigated, considering various configurations and scenarios. This diversity in approaches has made the task of comparison quite challenging. The aim of this paper is to provide a comprehensive comparison of these models within specific and well-defined scenarios. In particular, several well-known ML-based methods are investigated for the three-dimensional localization of partial discharges inside a power transformer tank. ML and DL methods frequently used in recent articles on PDs are examined, considering their compatibility for regression problems. Multiple case studies involving various attributes are presented. These attributes encompass sensor positioning, number of sensors, frequency content of the PD signal, and the size of the transformer tank. The PD location in the three-dimensional space is determined using single-sensor electric field measurements for all case studies, except for one case study in which three sensors are considered. The features of each method, such as input signal, core methodology, correlation coefficient of predicted location with the real location, and root mean square error (RMSE) analysis, are discussed and compared.

The novelty of the paper lies in the three-dimensional localization of PD sources within a power transformer tank using only a single sensor, achieved through ML and DL techniques. These techniques include BPNN, CNN, SVR, and XGBoost methods. The paper also provides a comprehensive comparison of the performance of each method in localizing the PD sources.

The remainder of the paper is organized as follows: “ Case studies ” presents the data generation, “ Data preprocessing ” illustrates the data preprocessing procedure, and in “ Machine learning methods ”, explanations for each model are provided. “ Results and discussion ” focuses on the model comparison and presents the results. Finally, in “ Conclusion ”, concluding remarks are provided.

Case studies

This section presents the various case studies considered in the analysis. All the case studies have been simulated using microwave studio (CST) software. The geometry of the transformer tank is illustrated in Fig. 1 . The origin of the coordinate system is located at the center of the transformer tank. For simplicity, the study does not include the windings and ferromagnetic cores. The transformer tank is made of steel with a conductivity σ of 7.69e6 S/m. The volume of the transformer tank, as shown in Fig. 1 , is 1000 × 500 × 500 mm 3 . The thickness of the tank walls is 10 mm. In the study, the PD sources are modeled as small dipole antennas with a length of 10 mm, excited by a Gaussian pulse. The figure does not depict the dipole antenna used to model the PD source. Different orientations of the PDs are considered in the considered case studies. Please refer to Table 1 for further details.

The transformer tank including three identical monopole antennas representing sensors aligned in three different axes. The inset is a zoom of antenna 2.

The emitted fields from the PD sources are detected by three different sensors, represented by monopole antennas, as shown in Fig. 1 . The length and radius of the monopole antennas are 67.8 and 2.5 mm, respectively. The red cones in Fig. 1 depict the antenna inputs. Five different case studies are discussed in the paper, as shown in Table 1 . The description of each case study is presented in the subsequent subsections.

Case study #1

In the first case study, a single monopole antenna placed along the x-axis is employed to receive the PD signal (see Fig. 1 and Table 1 ). The coordinates of the monopole antenna are (x = −500 mm, y = −150 mm, z = −150 mm). The PD is modeled as a 10 mm y-polarized dipole antenna positioned randomly within the transformer tank of Fig. 1 . The PD signal in the simulation is a Gaussian pulse with a frequency bandwidth of 0.5–3 GHz, see Table 1 . A total of 600 Monte-Carlo simulations were conducted for this case study. The location of the PD source within the transformer tank was randomly selected using a uniform probability distribution function for each direction.

Case study #2

The second case study is similar to CS#1, except for the location and polarization of the monopole antenna used to receive the PD signals. In CS#2, a y-axis polarized monopole antenna is employed as the receiving sensor, situated at coordinates (x = −400 mm, y = −240 mm, z = −150 mm). This case study utilizes 1000 Monte-Carlo simulations, see Table 1 .

Case study #3

In the third case study, the location of the PD sources is randomly selected inside the transformer tank using a uniform probability function, similar to case study #1. However, unlike the previous case studies, the PD source polarization is arbitrary. To achieve this, three new variables are introduced to indicate the rotation angles along the x, y, and z axes. These three angles are selected using a uniform probability distribution function in the range of 0–360 degrees. The three dipole antennas are used to record the electric fields emitted by the PD source. However, even though the fields are captured by all three antennas, each at a different location and with a different polarization, the captured signals from the monopole antennas are considered separately. In other words, for the purpose of localizing the PD source, the electric field components are utilized individually. The locations of the receiving antenna can be found in Fig. 1 .

The PD signal used in this case study is a Gaussian pulse with a frequency bandwidth covering the range of 0.5 to fmax GHz. The value of fmax is randomly selected using a uniform probability distribution function between 1 and 3 GHz. A total of 1000 Monte Carlo simulations were conducted for this case study to ensure a consistent number of instances and maintain uniform fairness across all case studies. Additionally, another 1000 samples are planned for consideration in the triple sensor case study.

Case study #4

The fourth case study is similar to the previous case study (CS#3), but it uses a larger transformer tank size: 1000 × 1000 × 500 mm 3 , which is twice as long in the y-axis direction compared to the tank size used in the previous three cases. The locations of the monopole antennas used in this study are, respectively, at positions (x = −500 mm, y = −400 mm, z = −150 mm), (x = 400 mm, y = −500 mm, z = −150 mm), and (x = 400 mm, y = 400 mm, z = −250 mm).

Case study #5

The transformer tank size used in this last case study is 2000 × 1000 × 1000 mm 3 , which means that the length of the tank sides along the axes is twice that of the tank used in CS#1 to CS#3. The monopole antennas used in this study are, respectively, at positions (x = −500 mm, y = −400 mm, z = −150 mm), (x = 400 mm, y = −500 mm, z = −150 mm), and (x = 400 mm, y = 400 mm, z = −250 mm).

Data preprocessing

Data preprocessing is a crucial step in signal processing that plays a vital role in achieving accurate and efficient solutions with minimal complexity. Moreover, preliminary experiments have demonstrated the necessity of preprocessing of both the PD signals and actual labels (locations). In this study, data preprocessing consists of five key steps: cut-off, normalization, resampling, label shifting, and train-test dataset splitting.

The first stage of preprocessing involves trimming a specific duration of time from all signal instances. This step is crucial in simulations because the wave maintains a constant speed, and the onset time provides information about the location of the PD (partial discharge) in the radial direction. However, this approach can lead to unfair predictions when compared to practical tests. To achieve a more robust model, it is beneficial to implement this preprocessing step.

Specifically, the duration of the signal is cut down to 40 ns by trimming the beginning and the end as follows: A starting threshold is defined as the time at which the signal begins to fluctuate more than 0.001 V in amplitude. This threshold is denoted as t , representing the starting time. Any data prior to this threshold is discarded. The remaining time duration (duration of the signal– t ) is then trimmed from the end of the signal so that the total duration is 40 ns. However, since the initial sample rates of the instances differ, they will have varying numbers of samples.

Normalization

The next step in preprocessing is normalization, which aims to expedite the training of the model. To achieve this, the entire signal is divided by the absolute value of the maximum signal amplitude, which can vary significantly for PD signals in the database. Consequently, the output signal is constrained to fluctuate between −1 and 1.

The simulated signals generated using the CST-MWS software have varying sampling rates due to the implementation of the finite integration technique during the simulations. Consequently, these signals have different numbers of samples. To ensure consistency in the input shape of the model, resampling becomes a crucial step, aiming to achieve an equal number of samples for all signals. In this paper, the down-sampling procedure is based on polyphase filtering, which offers computationally efficient resampling and filtering capabilities with high accuracy when applied to signals with defined sample rates.

Based on the conducted experiments, the best performance was observed when using 400, 800, and 1200 samples as the number of input features for the model for values ranging between 50 and 4800 for SVR. The number of samples does not have a significant effect on XGBoost performance. Consequently, a value of 400 samples was selected. Increasing the number of samples beyond this value would not significantly enhance accuracy but would significantly prolong the model training process. Additionally, polyphase filtering has proven to be a suitable approach, preserving over 98% of the signal content, as indicated by the computed correlation coefficient between the original and resampled signals.

In Fig. 2 , the effect of sample rates ranging from 50 to 4800 is depicted across three separate databases (used for CS#1, CS#2, and CS#3) for the x, y, and z directions (horizontal axis). The coefficient of determination (R) for predicted locations by the Support Vector Regression (SVR) model is shown on the vertical axis. The best result is obtained with a number of samples ranging from 400 to 1200. Therefore, signals with a sample rate of 400 are used throughout the paper to reduce computational tasks.

The variation of the R metric versus the number of samples for three separate datasets (CS#1, CS#2, and CS#3) along the y-axis using the SVR method.

Shifting labels (location of PD sources)

Since labels are ranged from negative to positive numbers, which correspond to the location of the PD source inside the cavity along the x-, y-, and z-axis, the model might not be able to distinguish the sign of numbers during both the training and evaluation stages. The solution used here involves shifting all the labels to positive regions, thereby yielding a more resilient model. Nonetheless, the amount of shifting may vary depending on the specific tank shape in each case study.

Splitting training and test dataset

Before training, the datasets are divided into train and test data, with 80% of the dataset used for training and 20% for testing. To ensure a fair comparison of results between models, the training dataset is randomly shuffled using a seed number of 11. This procedure guarantees that each partition undergoes a complete pattern randomization.

Machine learning methods

A flowchart depicting the ML and DL-based approaches proposed in the paper is presented in Figure S1 in the Supplementary Information. The initial step involves data collection, which is simulated using CST-MWS software. Once the data is collected, it needs to undergo preprocessing before being fed into the models. During preprocessing, the data is initially trimmed and then normalized to fall within the range of 1 to −1. Following normalization, the data is resampled to consist of 400 samples. Given that the labels span from negative to positive numbers, representing the PD source’s location within the cavity along the x-, y-, and z-axis, the location labels' origin is shifted to ensure all labels are positive.

After the data is prepared for model input, a range of models is assessed to identify the one with the highest accuracy. Subsequently, these models are trained using 80% of the preprocessed data and evaluated using the remaining preprocessed data for testing purposes. Finally, the model that performs the best is selected as the optimal choice.

Four frequent models have been chosen from those in Table S1 : Support Vector Machine (SVM), neural networks (NN), convolution neural networks (CNN) and XGBoost which encompasses boosting methods.

Each model used in this paper has gone through a grid search for hyperparameters. The grid search condition is slightly different depending on each model architecture. All ML methods used in this paper have the same input: a 1D preprocessed PD signal in the time domain with 400 samples, except for three sensor case study (see “ Three sensors ”, for which 1200 samples were used. To assess the degree of association between two variables, correlation coefficients are used.

Support vector regression

A simple linear support vector machine (SVM) classifier operates by drawing a straight line between two classes. This means that all the data points on one side of the line will be classified as one category, while the data points on the other side will be assigned to a different category. As a result, there are numerous possible lines to select from.

Support vector regression (SVR) applies the same principle as SVM, but it is used for regression problems. SVR is a widely used algorithm with various applications 15 . To optimize SVR, a grid search is performed on the gamma, regularization parameter, and kernel. The best outcome was achieved by setting the gamma value to 0.01, the regularization parameter to 1000 (where the strength of regularization is inversely proportional) and employing the radial basis function (“RBF”) as the kernel. Since SVR does not inherently support multidimensional regression, the multi-target regression strategy is employed to expand its capabilities, fitting one regressor per target.

A gradient boosting decision tree (GBDT) is an ensemble learning algorithm, similar to random forest, used for both classification and regression tasks. Ensemble learning algorithms combine multiple machine learning algorithms to obtain improved models. XGBoost is an example of a parallel tree boosting algorithm 16 and it is implemented using the XGBoost library. In this case, default hyperparameters are used as the model performance does not improve after grid search. Additionally, XGBoost also supports multi-target regression strategy.

back-propagation neural network (BPNN)

DL has made significant progress in various applications. One of the first DL models that has been extensively examined is the Backpropagation Neural Network (BPNN). The BPNN consists of multiple layers, with each layer containing a number of neurons that adapt complex functions through a series of nonlinear transformations. The architecture of this model is illustrated in Fig. 3 . It comprises three main parts: the input layer, hidden layers, and output layer.

Architecture of BPNN model. The first layer is the input layer, each fully connected layer has 512 units, and the output layer estimates the x, y and z coordinates of the PD source.

The input layer serves as a simple fully connected layer that feeds into the hidden layers. The hidden layers consist of three dense layers, each containing 512 units with the rectified linear unit (ReLU) activation function. On the other hand, the output layer is another dense layer with three units representing the 3D source location. To optimize the model, the Nadam optimizer 17 is used, and the learning rate gradually decreases from 0.1 to 0.001.

Convolutional neural network (CNN)

A convolutional neural network (CNN) operates in a similar manner to conventional fully connected multilayer perceptron neural networks, but with additional convolutional layers positioned at the front of the network 18 . The model considered in this study is the 1D CNN model 19 . This particular model yielded the best results, as indicated in Table 2 . In comparison to the back-propagation neural network (BPNN), the CNN 1D model is more complex, which leads to higher computational cost but also improved accuracy. All layers in the model employ the rectified linear unit (ReLU) activation function, and the optimizer used is similar to that of the back propagation neural network (BPNN). For a comprehensive representation of the model’s architecture, please refer to Fig. 4 .

Architecture of the CNN model. The input layer consists of 400 nodes. Layer 1 is a 1D CNN layer with dimensions (394, 64) followed by an average pooling layer with dimensions (98, 64). Layer 2 is another 1D CNN layer with dimensions (89, 256) followed by an average pooling layer with dimensions (44, 256). The first fully connected (FC) layer has 512 units, the second FC layer has 256 units, and the third FC layer has 512 units. The output of the model represents the x, y, and z coordinates of PD.

CNN-based methods automatically identify and utilize hierarchical features in signals received by sensors. In CNN-based methods, multiple layers of convolutional filters are applied to the signal, progressively obtaining higher-level features. This is crucial for localizing partial discharges, where the spatial information of the source is encoded in the signal. Other methods like SVM and XGBoost rely on global features extracted from the signal in partial discharge localization applications. It should be noted that in other applications, feature engineering can improve the performance by selecting the best features to achieve better results. For example, SVM-based approaches excel in classification tasks where feature boundaries can be distinctly defined in a high-dimensional space but do not inherently extract features from complex patterns like images. Therefore, through the use of convolutional layers and pooling operations, CNNs can capture spatial hierarchies and dependencies between different parts of the data, such as the location and spread of discharge patterns within a transformer tank.

Results and discussion

All models are evaluated based on their performance measured by the root mean square error (RMSE) and correlation coefficient (R) criteria in each coordinate for all case studies. The Pearson correlation coefficient 20 is a numerical measure that determines the linear correlation between measured values and values simulated by the model, with an optimal value of 1.

In Eq. ( 1 ), the variable i represents the actual location, while j represents the predicted location in the same direction, such as the y direction. The parameter C ij denotes the covariance between i and j , and C ii represents the standard deviation of i .

One way to evaluate the goodness of fit of a regression model to a dataset is by calculating the Root Mean Square Error (RMSE). RMSE is a metric that measures the distance between the predicted values from the model and the actual values in the dataset. A lower RMSE indicates a better fit of the model to the dataset. The formal definition of RMSE is as follows:

where, \({\widehat{x}}_{l}\) , \({\widehat{y}}_{l}\) , and \({\widehat{z}}_{l}\) are predicted values, x i , y i , and zi are observed values respectively. The quantity n is the number of samples.

The constructed models were trained and tested using eightfold cross-validation. However, for all the results presented in this paper, the seed number 11 was used to split the training and test datasets. The implementation was done using the Python programming language, and the models were trained and evaluated on a computer with an NVIDIA GeForce GTX 1660 TI and 4 GB of graphics memory. To facilitate further research, all codes and datasets used in this study have been made available on GitHub. ( https://github.com/Farzinkh/Partial_Discharge . )

Single sensor

Table 2 presents the R metric (corresponding to the correlation coefficient of the PD source estimation) and the RMSE value (corresponding to the three-dimensional localization error) for four different models: SVR, XGBoost, BPNN, and CNN. The first main column provides experiment details, including the case study number (refer to Table 1 ), and displays the R metric or the RMSE. The second to fifth columns present the results for the SVR, XGBoost, BPNN, and CNN models, respectively. For instance, the shaded row in Table 2 represents the R metric for the z-coordinate of all the different models in the first case study (CS#1).

In CS#1, the CNN model performs the best, with accuracies of 0.99, 0.97, and 0.94 for the x, y, and z-coordinates, respectively. The RMSE is 39.89 mm, which is considered excellent for partial discharge applications. In this case study, the receiving antenna is oriented along the x-axis, and the PD source polarization is along the y-axis. The second-best model is BPNN, which achieves accuracies of 0.94, 0.96, and 0.80 for the x, y, and z-coordinates, respectively. SVR exhibits similar performance to BPNN, with a slight reduction (2 percent) in the estimation accuracy along the z-coordinate. Finally, XGBoost achieves accuracies of 0.8, 0.91, and 0.73 for the x, y, and z-coordinates, respectively. The localization error averages for SVR, XGBoost, BPNN, and CNN are 74.46, 95.57, 73.22, and 39.89 mm, respectively. Figure 5 (a) (b), and (c) show the evaluation curves (the estimated versus the actual location of the PD source) for the CNN method for the x, y, and z coordinates in CS#1.

The CNN model’s estimated location compared to the actual location of PD sources for CS#1: ( a ) x-coordinate, ( b ) y-coordinate, and ( c ) z-coordinate. The number of instances for all the curves is 120.

In CS#2, the performance of the CNN method is better than in other models, similar to the previous case study. It can estimate the PD source location with accuracies of 0.99, 0.98, and 0.97 for the x, y, and z-coordinates, respectively. In contrast to CS#1, the SVR performs slightly better than the BPNN method. The accuracies of SVR and BPNN are (0.94, 0.96, 0.94) and (0.94, 0.95, 0.89), respectively, with each parenthesis representing the x, y, and z coordinates. Finally, the XGBoost method presents the worst results in terms of accuracy in estimating the PD source. Its accuracy is lower than 0.89 for all coordinates. In CS#2, both the antenna direction and PD polarization are along the y-axis. The localization error averages for SVR, XGBoost, BPNN, and CNN are 58.76, 83.62, 60.98, and 27.04 mm, respectively. Figure 6 (a), (b), and (c) show the evaluation curves (the estimated versus the actual location of the PD source) for the CNN method for the x, y, and z coordinates in CS#2.

The CNN model’s estimated location compared to the actual location of PD sources for CS#2: ( a ) x-coordinate, ( b ) y-coordinate, and ( c ) z-coordinate. The number of instances for all the curves is 196.

The last three main rows in Table 2 are devoted to CS#3. Unlike the two previous case studies (i.e., CS#1 and CS#2), the performance of all models is reduced. This is because in CS#3, the polarization of the PD source is randomly changed in the simulation. Generally, the CNN method performs better than the other methods, similar to the previous case studies. In CS#3, when the receiving antenna along the x-axis is used, the performance of BPNN is better than SVR; otherwise, SVR outperforms the BPNN method. In this case study, like the previous ones, the performance of XGBoost is the worst. The evaluation curves for all four models are shown in Fig. 7 . It can be observed from the figure that the performance of the CNN method is superior to that of the other methods. The CNN exhibits higher accuracy for the x-coordinate compared to the y and z coordinates, as indicated in Table 2 . According to Table 2 and Fig. 7 , it is evident that, across all techniques and case studies (especially for CS#3), the accuracy of PD source estimation yields better results for the x-coordinate. To investigate the reason behind this observation, CS#4 and CS#5 were employed.

Evaluation curves for ( a – c ) SVR, ( d – f ) XGBoost, ( g – i ) BPNN, and ( j – l ) CNN (the y direction is considered for the receiving antenna) methods. The number of instances for all the curves is 200.

Observe the antenna oriented along the y axis in CS#3 (forth column of Table 2 ). In this case, the minimum and maximum localization errors are 12.1 and 347.54 mm, respectively, and the mean value is 98.09 mm. Figure 8 displays the density of the three-dimensional localization error obtained from the CNN model on the test dataset. For better insight, all predicted errors are classified in Fig. 8 into eight 42 mm bins, starting with zero and ending with the maximum error. The blue bars represent the local density in each stage, while the yellow bars represent the overall density. According to this figure, 88% of PD source localizations have errors less than 168 mm (lower than 17 cm), which validates the relatively accurate nature of this model in predicting locations of PD sources.

The density of three-dimensional localization error obtained from the CNN model. The blue bars represent the local density, while the yellow bars represent the overall density.

To investigate the effects of the PD’s location on the obtained results, Fig. 9 presents the RMSE of the CNN model results for CS#3 with the y-direction receiving antenna. The transformer tank is divided into three sections based on the distance between each section and the corner of the transformer tank. The vertical axis represents the RMSE for each section. It can be observed that the CNN method can accurately estimate the PD’s location anywhere inside the tank, as the localization error associated with the PD's location in the CNN method is negligible.

The RMSE versus the location of the PD source inside the transformer for CNN method in CS#3 in ranges less than 162, between 162 and 336 and more than 336.

Three sensors

A possible approach to increase the accuracy of the model is to increase the number of sensors, as increasing the number of samples for each signal does not provide any significant advantage (see Fig. 2 ). Using three separate sensors in different directions is beneficial when dealing with a variety of PD frequencies (ranging from 0.5 to 3 GHz). The procedure becomes slightly more complex in terms of the model architecture, as shown in Fig. 10 . Based on the conducted experiments, since simple preprocessing methods for merging PD signals like summation, subtraction, and averaging as feature extraction on three signals (each containing 400 samples) in the element-wise procedure do not improve the accuracy of the model, a more advanced method was required. One solution for achieving high model accuracy is by employing CNN models once again as feature extraction layers to achieve a 400-sample signal which is the desired input shape for the base model and utilizes the transfer learning technique. According to the figure, a solution for achieving high model accuracy in merging the PD preprocessed signals (each containing 400 samples) is to employ CNN models once again and to utilize transfer learning techniques. The architecture used to adapt a signal with 400 samples for input into the preceding model (the base model) to include a CNN 1D layer (1137,10), a Max pooling layer (162,10), and an FC layer with 400 units as embedding layer. These extra layers are added before the base model. It is important to note that the preceding model plays a vital role and was specifically trained for one sensor operating within the 0 to 3 GHz PD frequency range.

Architecture of the CNN model to utilize transfer learning techniques.

For this experiment, two scenarios are considered. The datasets of CS#3 are divided into two equal parts, each with a length of 1000 samples. The first scenario involves training the base model on CS#3 (Part I) and CS#3 (Part II) for 3-sensor transfer learning. The second scenario reverses the order of the datasets. The results of these two experiments are presented in Table 3 . In the first scenario, the RMSE error decreases from 67.14 to 46.13 mm for the single-sensor and the three-sensor CNN models, respectively, leading to a 31.2% improvement. In the second scenario, the RMSE error decreases from 84.2 to 61.85 mm for the single and the three-sensor CNN models, respectively, leading to a 22.35% improvement. According to these records, the use of three sensors lead to an improvement in the accuracy of about 26%.

Figures 11 and 12 display the overall density of the three-dimensional localization error obtained from the CNN models on CS#3 (Part I) and (Part II) (refer to the fourth column of Table 3 ). The dashed lines represent the overall density for the single-sensor pre-trained CNN model, while the solid lines represent the overall density for the three-sensor CNN model. According to these figures, using three sensors leads to a more robust model while evaluating unseen data for 80% density regarding transfer model performance on the base model’s dataset (DS) and base model performance on the transfer model’s DS.

The density of three-dimensional localization error obtained from the CNN models for the first scenario. The dashed lines represent the overall density for the single-sensor CNN model trained on CS#3 (Part I) with a y-direction receiving antenna, while the solid lines represent the overall density for the three sensor CNN model.

The density of three-dimensional localization error obtained from the CNN models for the second scenario. The dashed lines represent the overall density for the single-sensor CNN model trained on CS#3 (Part II) with a y-direction receiving antenna, while the solid lines represent the overall density for the three-sensor CNN model.

Effect of the cavity shape and size

In the previous case studies, the localization accuracy in the x direction was observed to be higher than in the other directions. The only difference between the directions in the procedure is in the lengths of the transformer tank sides. Specifically, the x direction is longer than the others. Two experiments were conducted in CS#4 and CS#5 changing the tank dimensions and using the same single-sensor CNN model used in the preceding case studies to examine the impact of the shape and size of the cavity (see Table 1 ).

In the first experiment, CS#4 was used to determine the relationship between the accuracy of the predicted PD localization in all coordinates and the shape of the cavity. Since the cavity in this dataset has dimensions of 1000 × 1000 × 500 mm 3 , (compare to the 500 × 1000 × 500 mm 3 dimensions of the previous case studies), it is expected that the accuracy in the x and y directions will be approximately the same. This is indeed the case, as indicated in Table 4 .

In the second experiment, CS#5, the cavity size was increased by a factor of 2 compared to case study CS#3, resulting in dimensions of 2000 × 1000 × 1000 mm 3 . Comparatively, the accuracy remains approximately constant compared to the CS#3 (refer to the fourth column of Table 2 and fourth column of Table 4 ).

Conclusions

In this study, a DL-based approach was presented for the 3D localization of PDs within the transformer tanks. Four models were examined, namely BPNN, CNN, SVR, and XGBoost, which were selected based on their frequency in recent related articles and their previous success in localization tasks. Five case studies were considered for this study, each encompassing various conditions such as the maximum and minimum frequency content of the PD signals, antenna and PD source polarization, and the size of the transformer tank. These case studies were generated through Monte Carlo simulations. The models were developed using the Python language on a GPU processor to enhance the computational process.

CNN showed significant accuracy compared to the other models, with an average correlation coefficient of 0.98 and 0.86 for all dimensions in the case studies CS#2 (maximum frequency of 3 GHz) and CS#3 (random maximum frequency in the y-direction), respectively. In the former case study, 99.2% of the localizations had an error of less than 13.3 cm, and in the latter, 88% had an error of less than 17 cm. However, CNN still exhibited limitations in practical robustness. To address this problem, a three-sensor CNN model was introduced, which demonstrated a 26% improvement in robustness compared to the single sensor model, as well as at least a 22% improvement in accuracy. The accuracy of the models is related to the size of the cavity; however, there is no simple relationship. Based on the experiments, the models performed much better in a cavity with two equal dimensions.

The most challenging aspect of implementing this research in practice is collecting enough signals from different types of real power transformers in various locations where PD sources occur. In future work, the proposed method will be applied to a practical power transformer using signals received by a single antenna inside the transformer tank under real-world conditions.

Data availability

The datasets generated and analyzed during the current study, as well as the source codes and all computed results, figures, and other related materials, are available in the “Partial_Discharge” repository at github.com/Farzinkh/Partial_Discharge.

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Department of Electrical Engineering, Bu-Ali Sina University, Hamedan, Iran

Farzin Khodaveisi

University of Applied Sciences of Western Switzerland (HES-SO), 1400, Yverdon-Les-Bains, Switzerland

Hamidreza Karami & Marcos Rubinstein

Electrical Engineering Department, Hamedan University of Technology, Hamedan, Iran

Matin Zarei Karimpour

Electromagnetic Compatibility Laboratory, Swiss Federal Institute of Technology (EPFL), 1015, Lausanne, Switzerland

Farhad Rachidi

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F.K. and M.K. developed the theory and performed the computations, designed the model and the computational framework and analyzed the data and carried out the implementation. H.K. developed the theoretical formalism, verified the analytical methods and performed the numerical simulations. H.K. M.R. and F.R. reviewed the manuscript.

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Correspondence to Hamidreza Karami .

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Khodaveisi, F., Karami, H., Karimpour, M.Z. et al. Partial discharge localization in power transformer tanks using machine learning methods. Sci Rep 14 , 11785 (2024). https://doi.org/10.1038/s41598-024-62527-9

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http://orcid.org/0000-0003-0414-1225 Richard Nicholas 1 ,
http://orcid.org/0000-0002-3760-6634 Emma Clare Tallantyre 2 ,
James Witts 3 ,
http://orcid.org/0000-0002-1855-5595 Ruth Ann Marrie 4 ,
http://orcid.org/0000-0002-3432-9942 Elaine M Craig 3 ,
Sarah Knowles 3 ,
http://orcid.org/0000-0002-2712-0200 Owen Rhys Pearson 5 ,
Katherine Harding 6 ,
Karim Kreft 2 ,
J Hawken 2 ,
Gillian Ingram 5 ,
Bethan Morgan 7 ,
http://orcid.org/0000-0002-2130-4420 Rodden M Middleton 3 ,
Neil Robertson 2 ,
UKMS Register Research Group 8
1 Division of Neuroscience, Department of Brain Sciences , Imperial College London , London , UK
2 Division of Psychological Medicine and Clinical Neurosciences, School of Medicine , Cardiff University , Cardiff , UK
3 Population Data Science, Singleton Park , Swansea University Medical School , Swansea , UK
4 Departments of Medicine and Community Health Sciences , University of Manitoba Max Rady College of Medicine , Winnipeg , Manitoba , Canada
5 Department of Neurology , Swansea Bay University Health Board , Swansea , UK
6 Royal Gwent Hospital , Aneurin Bevan University Health Board , Newport , UK
7 Uplands and Mumbles Surgery , Swansea Bay University Health Board , Swansea , UK
8 UK MS Register , Swansea , UK
Correspondence to Dr Rodden M Middleton, Population Data Science, Swansea University, Swansea, Swansea, UK; R.M.Middleton{at}swansea.ac.uk

Background Identification of multiple sclerosis (MS) cases in routine healthcare data repositories remains challenging. MS can have a protracted diagnostic process and is rarely identified as a primary reason for admission to the hospital. Difficulties in identification are compounded in systems that do not include insurance or payer information concerning drug treatments or non-notifiable disease.

Aim To develop an algorithm to reliably identify MS cases within a national health data bank.

Method Retrospective analysis of the Secure Anonymised Information Linkage (SAIL) databank was used to identify MS cases using a novel algorithm. Sensitivity and specificity were tested using two existing independent MS datasets, one clinically validated and population-based and a second from a self-registered MS national registry.

Results From 4 757 428 records, the algorithm identified 6194 living cases of MS within Wales on 31 December 2020 (prevalence 221.65 (95% CI 216.17 to 227.24) per 100 000). Case-finding sensitivity and specificity were 96.8% and 99.9% for the clinically validated population-based cohort and sensitivity was 96.7% for the self-declared registry population.

Discussion The algorithm successfully identified MS cases within the SAIL databank with high sensitivity and specificity, verified by two independent populations and has important utility in large-scale epidemiological studies of MS.

MULTIPLE SCLEROSIS
EPIDEMIOLOGY

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/jnnp-2024-333532

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Introduction

The challenge of monitoring changing patterns of disease at population levels 1 arises from variations in healthcare systems, how data are collected in community and hospital settings and the need to verify findings through capture-recapture methodology. 2 Anonymised repositories of highly codified ‘routine’ data provides opportunities for incidence and prevalence monitoring or tracking impacts of pandemics on a population level but adds complexity varying both by the system and by how they are maintained. Repositories exist because of the need for accurate audit, reporting, health surveillance and billing data. Where the reporting of the condition is not mandated, cases can be missed and in public health systems where insurance or reimbursement do not drive reporting, the priority of how treatment codes are applied and reporting can vary.

Ascertainment of cases of multiple sclerosis (MS) is challenging since diagnostic criteria have changed over time and the coding systems for the detail required for accurate disease subtypes are rudimentary. Since 1965, the MS diagnostic approach has evolved from purely clinical criteria where a diagnosis could take years, to criteria that allow integration of paraclinical data into the diagnostic process, including allowing diagnosis after a single clinical event. 3

Wales is a country, within the UK, with a population of 3.2 million. In common with the UK, it shares a National Health Service (NHS). Uniquely, in Wales, the Secure Anonymised Information Linkage (SAIL) 4 is a data repository from sources including the Welsh NHS (hospitals and specialist services) and from general practice. Data are collected from the Welsh NHS as International Classification of Diseases (ICD) V.10 5 codes, and from more than 85% of general practitioners (GPs) as ‘Read’ codes, 6 a UK-specific general practice coding system. There are no older coding systems (ICD 8,9) as supplying hospital systems carried out this transition prior to the creation of SAIL. A crucial element of SAIL is that diverse datasets can be robustly linked to existing individual patients. There are several approaches to finding people with MS (pwMS) within routine data repositories. Iterating on Al-Sakran and colleagues’ work in Manitoba 7 we developed an algorithm to identify MS cases within Wales. The performance of the algorithm was tested against two other independent MS datasets.

Study setting

We used a cross-sectional population-based cohort study to develop an algorithm to identify pwMS living in Wales, using SAIL. SAIL uses a trusted third party (NHS Wales) to implement a unique Anonymous Linking Field (ALF) as records are loaded into the repository. 4 Users of SAIL are not allowed to use any method to identify patients within it. We used the following datasets:

Welsh Demographics Service Dataset

Individuals registered with Welsh GPs ( https://data.sail.ukserp.ac.uk/Asset/View/20 ).

Patient Episode Data for Wales

Routine and emergency hospital admissions and ‘spells of care’ that include transfers between wards and discharge information ( https://data.sail.ukserp.ac.uk/Asset/View/15 ).

Welsh Longitudinal General Practice Dataset

Welsh Primary Care data covering ~85% of GP practices ( https://data.sail.ukserp.ac.uk/Asset/View/17 ).

Annual Digital Death Extract

UK Deaths ( https://data.sail.ukserp.ac.uk/Asset/View/03 ).

Outpatient Dataset for Wales

Comprises all Welsh Hospitals and includes ‘did not attend’ information ( https://data.sail.ukserp.ac.uk/Asset/View/14 ).

Independent patient with MS datasets

The South-East Wales MS cohort , comprises clinical data from the Cardiff area in South Wales, updated by clinical and research staff based on patient contact (Ethics: 19/WA/0289, 05/WSE03/111). 8 We included pwMS known to be based in Cardiff diagnosed up to 1 January 2020. Inclusion criteria were appropriate to the epoch of MS diagnosis 9 known to be alive and resident in the Cardiff and Vale area on 31 December 2020.

The UK MS Register patient portal is a nationwide registry capturing data from self-registered pwMS (Ethics: 21/SW/0085). Participants confirm a clinical diagnosis of MS. Data are updated every 6 months. Cases from the UK MS Register (UKMSR) were included if they had a complete date of birth, gender, postcode and Welsh Demographics Service Dataset (WDSD) entry.

SAIL MS Algorithm

SAIL data were included if individuals had a valid ALF code from between January 1970 and December 2020, and a week of birth ≥1910.

Next, we checked to see if patients were alive and resident in Wales on 31 December 2020. Datasets were searched in order: Annual Digital Death Extract, WDSD, Welsh Longitudinal General Practice Dataset (WLGP), Patient Episode Data for Wales (PEDW) and checked for dates of death.

Next, patients were required to have an ICD-10 code ‘G35’, ‘Multiple Sclerosis’ within PEDW/Outpatient Dataset for Wales (OPDW) or a Read code ‘F20’. ‘Multiple Sclerosis’ within WLGP.

Once the ICD-10 code G35 was established, the algorithm identified the earliest code from the following list to determine onset date as used previously, 10 optic neuritis (H46/F4H3.), acute transverse myelitis (G373/F037.), acute disseminated encephalomyelitis (G369/F21X.), demyelinating disease of central nervous system not otherwise specified (G378/F21yz), other acute disseminated demyelination unspecified (G368/F21y.), MS (G35/F20.) or neuromyelitis optica (G360/F210). 10

Cases were included if:

There were <3 F20./G35 codes and the established onset date was ≥6 months of the earliest WLGP entry for the patient

OR there were ≥3 F20./G35 codes.

Data analysis

Provisioned data are stored and accessed via the SAIL Secure eResearch Platform. 11 All analyses were conducted using R V.4.1.3. 95% CIs were calculated for prevalence and incidence using Poisson’s method. Using the following formulas sensitivity (true positive/(true positive+false negative)) and specificity (true negative/(false positive+true negative)) were calculated.

Incidence and prevalence in an algorithmically identified Welsh MS population

Of the SAIL population of 4 757 428 subjects, the algorithm identified 6194 prevalent cases of MS within Wales on 31 December 2020 ( figure 1 ). Using WDSD for those in Wales in 2020, 209 were incidents (diagnosed in 2020). Given the WLGP population size of 2 794 484 at the end of 2020, incidence is estimated at 7.48 (95% CI 6.5 to 8.56) per 100 000 and prevalence 221.65 (95% CI 216.17 to 227.24) per 100 000.

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Flowchart of case finding for each of the algorithms. ICD, International Classification of Diseases; MS, multiple sclerosis; SAIL, Secure Anonymised Information Linkage; WDSD, Welsh Demographics Service Dataset; WLGP, Welsh Longitudinal General Practice Dataset;CNS, Central Nervous System; SAMSA, SAil Multiple Sclerosis Algorithm.

Comparison of the algorithm versus two independent Welsh MS datasets

We used two validation cohorts to confirm the diagnostic accuracy of the SAIL algorithm ( table 1 ). Of 713 in the South-East Wales MS cohort, 690 (96.8% sensitivity) were identified by the SAIL algorithm. Using the Cardiff dataset as a gold standard 156 people were identified by the algorithm as having MS, but were not in the South East Wales MS dataset, giving a specificity of 99.9%. 69 of the 156 ‘false positives’ had Cardiff hospital data, but were unknown to the MS Service. The remainder were only known to primary care. Among the 836 Welsh pwMS from the UKMSR who had self-registered MS via an online portal, 808 (96.7% sensitivity) were identified by the SAIL algorithm. Specificity analysis was not applied to UKMSR since this is not a population-based cohort. For both populations age of diagnosis was similar between the algorithm and the cohort.

View inline

Sensitivity and specificity of the UK MS Register algorithm in the South Wales and UK MS Register cohorts. The population shown is Cardiff and Vale as of 31 December 2020. Sensitivity=true positive/true positive+false negative. Specificity=true negative/(true negative+false positives), gold standard=South East Wales MS Cohort

We describe an algorithm to identify pwMS within a national routine data repository. We used a clinician-confirmed population-based cohort, and a self-declared previously validated 12 cohort to validate the algorithm, confirming high sensitivity and specificity. We were able to demonstrate that the age at diagnosis calculated by the algorithm is similar to the age at diagnosis self-reported by patients and confirmed by clinicians within both validation cohorts.

Population-wide repositories of health data provide a valuable opportunity to study trends in disease patterns over time. However, identification of all subjects with a disease depends on the reach of the system and how regularly and rigorously it is maintained. A claims-based registry in Canada has developed a reliable methodology for capturing incidents and prevalent cases of MS. 10 However, its transferability is affected by different coding mechanisms and collection drivers. SAIL contains primary and secondary care health data 4 but not disease-modifying therapy prescribing/billing data unlike Canada’s insurance-based healthcare system, where it can be used as a confirmatory code for MS diagnosis. In contrast, in SAIL diagnostic confirmation is based, potentially more reliably, on healthcare professional input. Given these differences, we were able to develop an algorithm within this UK-based public health system, relying on a combination of hospital and GP reporting that was able to capture>96% of known MS cases.

Using the SAIL algorithm, we estimated the incidence of MS in our region to be 7.48 (95% CI 6.5 to 8.56) per 100 000. An earlier SAIL study using only hospital-identified MS cases and only one MS code found that Welsh incidence was 9.10 (95% CI 8.80 to 9.40) per 100 000. 13 Whereas a population-based study undertaken in South Wales in 2007 found the incidence was 9.65 (95% CI 7.71 to 13.1) per 100 000. 14 Our lower incidence reflects the more conservative approach taken to case ascertainment in this algorithm, but we have confirmed its sensitivity and specificity in two independent cohorts. Our prevalence finding of 221.65 per 100 000 people for Wales is higher than other reported figures for the UK as a whole at 199 per 100 000, 15 but consistent with the predictions made in the South-East Wales region, 14 suggesting a rise of MS prevalence to 260 per 100 000 population by 2028–2048.

We also identified a group of 2260 people in Wales who had a demyelination code but did not have MS. By confirming the high sensitivity and specificity of our algorithm versus a clinically diagnosed MS population, we conclude that we were correct to exclude this group at this point, but it will inevitably contain people who later go on to develop MS.

In this study, we present a robust, sensitive algorithm to ascertain cases of MS in large populations that, with pragmatic adaptations, could be adapted to effectively identify cases in other large geographical areas with similarly structured data systems.

Ethics statements

Patient consent for publication.

Not applicable.

Acknowledgments

This study makes use of anonymised data held in the Secure Anonymised Information Linkage (SAIL) Databank. We would like to acknowledge all the data providers who make anonymised data available for research. All interpretations of SAIL data are the authors’ own.

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Collaborators Professor Nikos Evangelou, Kellie Allen, Professor Klaus Schmierer, Dr Ian Galea, Dr Matt Craner, Professor Jeremy Chataway, Dr Gavin McDonnell, Ms Annemieke Fox, Dr Heather Wilson, Dr David Rog, Dr Chris Kipps, Dr Andrew Gale, Dr Monica Marta, Sarah Fuller, Dr Judy Archer, Dr Brendan McLean, Dr Agne Straukiene, Dr Joe Guadango, Jo Kitley, Dr Andrew Graham, Dr Carlo Canepa, Dr Helen Ford, Professor Alasdair Coles, Professor H Emsley, Professor Jeremy Hobart, Julie Foxton, Dr Dreedharan Harikrishnan, Dr Laura Petzold, Dr Tim Harrower, Professor Ruth Dobson London, Dr Zbignew Slowinski, Professor Basil Sharrack.

Contributors RN, RMM, RAM, ECT and NR: Conceptualisation, Resources, Writing, Review and editing. GI, ORP, KH, JH, KLK, BM: Review and editing. JW, SK, EMC: Data Analysis, Review and editing.

Funding This study was funded by Multiple Sclerosis Society (147).

Competing interests None declared.

Provenance and peer review Not commissioned; externally peer reviewed.

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Multiple Case Research Design
The major advantage of multiple case research lies in cross-case analysis. A multiple case research design shifts the focus from understanding a single case to the differences and similarities between cases. Thus, it is not just conducting more (second, third, etc.) case studies. Rather, it is the next step in developing a theory about factors ...
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In a case study research, multiple methods of data collection are used, as it involves an in-depth study of a phenomenon. It must be noted, as highlighted by Yin , a case study is not a method of data collection, rather is a research strategy or design to study a social unit.
Multiple Case Studies
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.
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By Janet Salmons, PhD Manager, Sage Research Methods Community. What is Case Study Methodology?. Case studies in research are both unique and uniquely confusing. The term case study is confusing because the same term is used multiple ways. The term can refer to the methodology, that is, a system of frameworks used to design a study, or the methods used to conduct it.
What Is a Case Study?
A case study is a detailed study of a specific subject, such as a person, group, place, event, organization, or phenomenon. Case studies are commonly used in social, educational, clinical, and business research. A case study research design usually involves qualitative methods, but quantitative methods are sometimes also used.
Case Study Method: A Step-by-Step Guide for Business Researchers
The authors have recently conducted an in-depth case study in the Information and Communication Technology (ICT) industry of New Zealand. A multiple case studies approach was adopted that spanned over 2 years, as it is difficult to investigate all the aspects of a phenomenon in a single case study (Cruzes, Dybå, Runeson, & Höst, 2015). The ...
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This essay sharpens and refreshes the multi-case theory-building approach, sometimes termed The "Eisenhardt Method.". The Method's singular aim is theory building, especially with multiple cases and theoretical logic. Its defining features (e.g. research questions without obvious answers, careful case selection, well-identified constructs ...
The Multiple Case Study Design
The multiple case study design is a valuable qualitative research tool in studying the links between the personal, social, behavioral, psychological, organizational, cultural, and environmental factors that guide organizational and leadership development. Case study research is essential for the in-depth study of participants' perspectives on ...
A Systematic Approach to Multiple Case Study Design in Professional
Multiple case study is the intentional analysis of two or more complete single case reports (Stake, 1995). When well-selected and crafted, researchers can use multiple case study to increase external validity and generalizability of their single case study findings (Merriam, 1998). Although multiple case study is well-suited for counseling and
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A multi-case study is often a relevant technique and a method to describe the best possible applicable method in our research. At times it is useful in selecting the most appropriate methodology ...
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A multi-method approach such as case study and narrative research are differentiated from other qualitative methods in many ways, a few include: The focus of the research design is on the case itself - the subject of inquiry, such as the state's drug-prevention program or the teaching of 8 th grade science - not the particular methods ...
8
This chapter turns the tables to consider integrative multi-method research designs in which qualitative research structures the overall study and produces the final causal inference, and in which quantitative research design components are used to test and refine critical assumptions. The relative length of the discussion of these two ...
The Multiple Case Study Design: Methodology and ...
The multiple case study design is an effective qualitative research method for investigating the relationships between personal, social, behavioral, psychological, organizational, cultural, and ...
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The case study method, and in particular the multiple-case studies design, offers LIS researchers a proven tool for achieving a deep understanding of a specific phenomenon—-for example, the ...
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The multiple case study design is a valuable qualitative research tool in studying the links between the personal, social, behavioral, psychological, organizational, cultural, and environmental factors that guide organizational and leadership development. Case study research is essential for the in-depth study of participants' perspectives on ...
Case Study
Defnition: A case study is a research method that involves an in-depth examination and analysis of a particular phenomenon or case, such as an individual, organization, community, event, or situation. It is a qualitative research approach that aims to provide a detailed and comprehensive understanding of the case being studied.
PDF Mixed Methods Case Study Research
The Mixed Methods Case Study Purpose: •a multiple case study design. Each case was selected as a tool to illuminating a particular issue"(p.101). The case study was instrumental. Bound •"Each case study was bounded by one individual and by the time he or she matriculated in the ELHE program" (p.101). Case Selection
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The multiple-case design was the best research design for this study, as it allowed the researcher to use best practices from the two international universities in order to develop a conceptual framework for the University of Johannesburg. The major benefit of using a multiple-case design was that multiple perspectives of the individuals
Single case studies vs. multiple case studies: A comparative study
This study attempts to answer when to write a single case study and when to write a multiple case study. It will further answer the benefits and disadvantages with the different types. The literature review, which is based on secondary sources, is about case studies. Then the literature review is discussed and analysed to reach a conclusion ...
Multiple-case Study Methods in Governance-related Research
Multiple-case studies; research methods; Notes. Author's estimate, based on the pattern of publication in the period 2004-9 (inclusive) in the journals discussed below. Ragin (Citation 1989) focuses on Boolean algebraic techniques for moving beyond holistic case studies in comparative work.
PDF Single case studies vs. multiple case studies: A comparative study
single case studies with multiple case studies. 2. Method This study is based on qualitative material and therefore secondary sources. This includes scientific articles, case studies and other literature. To find out what the differences are between single- and multiple case studies, these subjects are defined in the literature review.
How to Write a Multiple Case Study Effectively
A multiple case study is a research method examining several different entities. It helps researchers gain an understanding of the entities' individual characteristics and disclose any shared patterns or insights. This type of investigation often uses both qualitative and quantitative data. These are usually collected from interviews, surveys ...
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The mixed-method study involved semi-structured interviews (n = 18) and an anonymous web-based survey (n = 144) among nursing assistants and nurses from two home healthcare organizations in northwest Switzerland. ... and 70.8%, respectively). Practices and perceived challenges in the case of colonisation with multi-drug resistant organisms ...
Partial discharge localization in power transformer tanks ...
Multiple case studies are considered, each with different attributes, including sensor position, frequency content of the PD signal, and size of the transformer tank. ... the CNN method performs ...
Sustainability
This study tackles the challenge of optimizing scrap blends in steel production to achieve sustainability and environmental consciousness. Focusing on a steel-casting company as a case study, we develop a mathematical model that minimizes cost, emissions, and energy consumption while maximizing scrap utilization. This model considers the specific elemental composition of various scrap piles ...
Algorithmic approach to finding people with multiple sclerosis using
Background Identification of multiple sclerosis (MS) cases in routine healthcare data repositories remains challenging. MS can have a protracted diagnostic process and is rarely identified as a primary reason for admission to the hospital. Difficulties in identification are compounded in systems that do not include insurance or payer information concerning drug treatments or non-notifiable ...
The Association between Rainfall and Taxi Travel Activities: A Case
Rainfall has a significant impact on urban population mobility, posing great challenges to traffic management and urban planning. An understanding of this influence from multiple perspectives is urgently needed. In this study, we devised a multiscale comparative research framework to explore the spatiotemporal effects of rainfall on taxi travel patterns, aiming to provide a new perspective on ...

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Case Study – Methods, Examples and Guide

Types of Case Study

Single-Case Study

Multiple-Case Study

Descriptive Case Study

Instrumental Case Study

Case Study Data Collection Methods

Observations

How to conduct Case Study Research

Examples of Case Study

Application of Case Study

Business and Management

Social Sciences

Law and Ethics

Purpose of Case Study

Advantages of Case Study Research

Limitations of Case Study Research

About the author

Muhammad Hassan

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1. Brainstorm Potential Case Studies

2. Conduct Background Research on Each Case

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Infection prevention practice in home healthcare: a mixed-method study in two Swiss home healthcare organisations

Conclusions

Interview study

General attitudes and perceptions

Relationship with the patient

Risk assessment and information transfer

Potentially infectious acute illnesses

Sharp safety

Waste management

Decontamination/reprocessing

Wound care and asepsis

Contextual themes: knowledge level, adherence, and COVID-19-related challenges

Summary of key findings

Strengths and limitations

Comparison with existing literature

Implications for future research, and policy and practice

Data availability

Abbreviations

Acknowledgements

Author information