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• Published: 27 June 2011

The case study approach

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

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

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

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Introduction

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

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

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

What is a case study?

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

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

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

What are case studies used for?

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

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

How are case studies conducted?

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

Defining the case

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

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

Selecting the case(s)

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

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

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

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

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

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

Collecting the data

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

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

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

Analysing, interpreting and reporting case studies

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

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

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

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

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

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

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

Conclusions

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

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Acknowledgements

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

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Sarah Crowe & Anthony Avery

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

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• Exploratory Research | Definition, Guide, & Examples

Exploratory Research | Definition, Guide, & Examples

Published on December 6, 2021 by Tegan George . Revised on November 20, 2023.

Exploratory research is a methodology approach that investigates research questions that have not previously been studied in depth.

Exploratory research is often qualitative and primary in nature. However, a study with a large sample conducted in an exploratory manner can be quantitative as well. It is also often referred to as interpretive research or a grounded theory approach due to its flexible and open-ended nature.

Table of contents

When to use exploratory research, exploratory research questions, exploratory research data collection, step-by-step example of exploratory research, exploratory vs. explanatory research, advantages and disadvantages of exploratory research, other interesting articles, frequently asked questions about exploratory research.

Exploratory research is often used when the issue you’re studying is new or when the data collection process is challenging for some reason.

You can use this type of research if you have a general idea or a specific question that you want to study but there is no preexisting knowledge or paradigm with which to study it.

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Exploratory research questions are designed to help you understand more about a particular topic of interest. They can help you connect ideas to understand the groundwork of your analysis without adding any preconceived notions or assumptions yet.

Here are some examples:

• What effect does using a digital notebook have on the attention span of middle schoolers?
• What factors influence mental health in undergraduates?
• What outcomes are associated with an authoritative parenting style?
• In what ways does the presence of a non-native accent affect intelligibility?
• How can the use of a grocery delivery service reduce food waste in single-person households?

Collecting information on a previously unexplored topic can be challenging. Exploratory research can help you narrow down your topic and formulate a clear hypothesis and problem statement , as well as giving you the “lay of the land” on your topic.

Data collection using exploratory research is often divided into primary and secondary research methods, with data analysis following the same model.

Primary research

In primary research, your data is collected directly from primary sources : your participants. There is a variety of ways to collect primary data.

Some examples include:

• Survey methodology: Sending a survey out to the student body asking them if they would eat vegan meals
• Focus groups: Compiling groups of 8–10 students and discussing what they think of vegan options for dining hall food
• Interviews: Interviewing students entering and exiting the dining hall, asking if they would eat vegan meals

Secondary research

In secondary research, your data is collected from preexisting primary research, such as experiments or surveys.

Some other examples include:

• Case studies : Health of an all-vegan diet
• Literature reviews : Preexisting research about students’ eating habits and how they have changed over time
• Online polls, surveys, blog posts, or interviews; social media: Have other schools done something similar?

For some subjects, it’s possible to use large- n government data, such as the decennial census or yearly American Community Survey (ACS) open-source data.

How you proceed with your exploratory research design depends on the research method you choose to collect your data. In most cases, you will follow five steps.

We’ll walk you through the steps using the following example.

Therefore, you would like to focus on improving intelligibility instead of reducing the learner’s accent.

Step 1: Identify your problem

The first step in conducting exploratory research is identifying what the problem is and whether this type of research is the right avenue for you to pursue. Remember that exploratory research is most advantageous when you are investigating a previously unexplored problem.

Step 2: Hypothesize a solution

The next step is to come up with a solution to the problem you’re investigating. Formulate a hypothetical statement to guide your research.

Step 3. Design your methodology

Next, conceptualize your data collection and data analysis methods and write them up in a research design.

Step 4: Collect and analyze data

Next, you proceed with collecting and analyzing your data so you can determine whether your preliminary results are in line with your hypothesis.

In most types of research, you should formulate your hypotheses a priori and refrain from changing them due to the increased risk of Type I errors and data integrity issues. However, in exploratory research, you are allowed to change your hypothesis based on your findings, since you are exploring a previously unexplained phenomenon that could have many explanations.

Step 5: Avenues for future research

Decide if you would like to continue studying your topic. If so, it is likely that you will need to change to another type of research. As exploratory research is often qualitative in nature, you may need to conduct quantitative research with a larger sample size to achieve more generalizable results.

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It can be easy to confuse exploratory research with explanatory research. To understand the relationship, it can help to remember that exploratory research lays the groundwork for later explanatory research.

Exploratory research investigates research questions that have not been studied in depth. The preliminary results often lay the groundwork for future analysis.

Explanatory research questions tend to start with “why” or “how”, and the goal is to explain why or how a previously studied phenomenon takes place.

Like any other research design , exploratory studies have their trade-offs: they provide a unique set of benefits but also come with downsides.

• It can be very helpful in narrowing down a challenging or nebulous problem that has not been previously studied.
• It can serve as a great guide for future research, whether your own or another researcher’s. With new and challenging research problems, adding to the body of research in the early stages can be very fulfilling.
• It is very flexible, cost-effective, and open-ended. You are free to proceed however you think is best.

Disadvantages

• It usually lacks conclusive results, and results can be biased or subjective due to a lack of preexisting knowledge on your topic.
• It’s typically not externally valid and generalizable, and it suffers from many of the challenges of qualitative research .
• Since you are not operating within an existing research paradigm, this type of research can be very labor-intensive.

If you want to know more about statistics , methodology , or research bias , make sure to check out some of our other articles with explanations and examples.

• Normal distribution
• Degrees of freedom
• Null hypothesis
• Discourse analysis
• Control groups
• Mixed methods research
• Non-probability sampling
• Quantitative research
• Ecological validity

Research bias

• Rosenthal effect
• Implicit bias
• Cognitive bias
• Selection bias
• Negativity bias
• Status quo bias

Exploratory research is a methodology approach that explores research questions that have not previously been studied in depth. It is often used when the issue you’re studying is new, or the data collection process is challenging in some way.

Exploratory research aims to explore the main aspects of an under-researched problem, while explanatory research aims to explain the causes and consequences of a well-defined problem.

You can use exploratory research if you have a general idea or a specific question that you want to study but there is no preexisting knowledge or paradigm with which to study it.

Quantitative research deals with numbers and statistics, while qualitative research deals with words and meanings.

Quantitative methods allow you to systematically measure variables and test hypotheses . Qualitative methods allow you to explore concepts and experiences in more detail.

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Home » Exploratory Research – Types, Methods and Examples

Exploratory Research – Types, Methods and Examples

Table of Contents

Exploratory Research

Definition:

Exploratory research is a type of research design that is used to investigate a research question when the researcher has limited knowledge or understanding of the topic or phenomenon under study.

The primary objective of exploratory research is to gain insights and gather preliminary information that can help the researcher better define the research problem and develop hypotheses or research questions for further investigation.

Exploratory Research Methods

There are several types of exploratory research, including:

Literature Review

This involves conducting a comprehensive review of existing published research, scholarly articles, and other relevant literature on the research topic or problem. It helps to identify the gaps in the existing knowledge and to develop new research questions or hypotheses.

Pilot Study

A pilot study is a small-scale preliminary study that helps the researcher to test research procedures, instruments, and data collection methods. This type of research can be useful in identifying any potential problems or issues with the research design and refining the research procedures for a larger-scale study.

This involves an in-depth analysis of a particular case or situation to gain insights into the underlying causes, processes, and dynamics of the issue under investigation. It can be used to develop a more comprehensive understanding of a complex problem, and to identify potential research questions or hypotheses.

Focus Groups

Focus groups involve a group discussion that is conducted to gather opinions, attitudes, and perceptions from a small group of individuals about a particular topic. This type of research can be useful in exploring the range of opinions and attitudes towards a topic, identifying common themes or patterns, and generating ideas for further research.

Expert Opinion

This involves consulting with experts or professionals in the field to gain their insights, expertise, and opinions on the research topic. This type of research can be useful in identifying the key issues and concerns related to the topic, and in generating ideas for further research.

Observational Research

Observational research involves gathering data by observing people, events, or phenomena in their natural settings to gain insights into behavior and interactions. This type of research can be useful in identifying patterns of behavior and interactions, and in generating hypotheses or research questions for further investigation.

Open-ended Surveys

Open-ended surveys allow respondents to provide detailed and unrestricted responses to questions, providing valuable insights into their attitudes, opinions, and perceptions. This type of research can be useful in identifying common themes or patterns, and in generating ideas for further research.

Data Analysis Methods

Exploratory Research Data Analysis Methods are as follows:

Content Analysis

This method involves analyzing text or other forms of data to identify common themes, patterns, and trends. It can be useful in identifying patterns in the data and developing hypotheses or research questions. For example, if the researcher is analyzing social media posts related to a particular topic, content analysis can help identify the most frequently used words, hashtags, and topics.

Thematic Analysis

This method involves identifying and analyzing patterns or themes in qualitative data such as interviews or focus groups. The researcher identifies recurring themes or patterns in the data and then categorizes them into different themes. This can be helpful in identifying common patterns or themes in the data and developing hypotheses or research questions. For example, a thematic analysis of interviews with healthcare professionals about patient care may identify themes related to communication, patient satisfaction, and quality of care.

Cluster Analysis

This method involves grouping data points into clusters based on their similarities or differences. It can be useful in identifying patterns in large datasets and grouping similar data points together. For example, if the researcher is analyzing customer data to identify different customer segments, cluster analysis can be used to group similar customers together based on their demographic, purchasing behavior, or preferences.

Network Analysis

This method involves analyzing the relationships and connections between data points. It can be useful in identifying patterns in complex datasets with many interrelated variables. For example, if the researcher is analyzing social network data, network analysis can help identify the most influential users and their connections to other users.

Grounded Theory

This method involves developing a theory or explanation based on the data collected during the exploratory research process. The researcher develops a theory or explanation that is grounded in the data, rather than relying on pre-existing theories or assumptions. This can be helpful in developing new theories or explanations that are supported by the data.

Applications of Exploratory Research

Exploratory research has many practical applications across various fields. Here are a few examples:

• Marketing Research : In marketing research, exploratory research can be used to identify consumer needs, preferences, and behavior. It can also help businesses understand market trends and identify new market opportunities.
• Product Development: In product development, exploratory research can be used to identify customer needs and preferences, as well as potential design flaws or issues. This can help companies improve their product offerings and develop new products that better meet customer needs.
• Social Science Research: In social science research, exploratory research can be used to identify new areas of study, as well as develop new theories and hypotheses. It can also be used to identify potential research methods and approaches.
• Healthcare Research : In healthcare research, exploratory research can be used to identify new treatments, therapies, and interventions. It can also be used to identify potential risk factors or causes of health problems.
• Education Research: In education research, exploratory research can be used to identify new teaching methods and approaches, as well as identify potential areas of study for further research. It can also be used to identify potential barriers to learning or achievement.

Examples of Exploratory Research

Here are some more examples of exploratory research from different fields:

• Social Science : A researcher wants to study the experience of being a refugee, but there is limited existing research on this topic. The researcher conducts exploratory research by conducting in-depth interviews with refugees to better understand their experiences, challenges, and needs.
• Healthcare : A medical researcher wants to identify potential risk factors for a rare disease but there is limited information available. The researcher conducts exploratory research by reviewing medical records and interviewing patients and their families to identify potential risk factors.
• Education : A teacher wants to develop a new teaching method to improve student engagement, but there is limited information on effective teaching methods. The teacher conducts exploratory research by reviewing existing literature and interviewing other teachers to identify potential approaches.
• Technology : A software developer wants to develop a new app, but is unsure about the features that users would find most useful. The developer conducts exploratory research by conducting surveys and focus groups to identify user preferences and needs.
• Environmental Science : An environmental scientist wants to study the impact of a new industrial plant on the surrounding environment, but there is limited existing research. The scientist conducts exploratory research by collecting and analyzing soil and water samples, and conducting interviews with residents to better understand the impact of the plant on the environment and the community.

How to Conduct Exploratory Research

Here are the general steps to conduct exploratory research:

• Define the research problem: Identify the research problem or question that you want to explore. Be clear about the objective and scope of the research.
• Review existing literature: Conduct a review of existing literature and research on the topic to identify what is already known and where gaps in knowledge exist.
• Determine the research design : Decide on the appropriate research design, which will depend on the nature of the research problem and the available resources. Common exploratory research designs include case studies, focus groups, interviews, and surveys.
• Collect data: Collect data using the chosen research design. This may involve conducting interviews, surveys, or observations, or collecting data from existing sources such as archives or databases.
• Analyze data: Analyze the data collected using appropriate qualitative or quantitative techniques. This may include coding and categorizing qualitative data, or running descriptive statistics on quantitative data.
• I nterpret and report findings: Interpret the findings of the analysis and report them in a way that is clear and understandable. The report should summarize the findings, discuss their implications, and make recommendations for further research or action.
• Iterate : If necessary, refine the research question and repeat the process of data collection and analysis to further explore the topic.

When to use Exploratory Research

Exploratory research is appropriate in situations where there is limited existing knowledge or understanding of a topic, and where the goal is to generate insights and ideas that can guide further research. Here are some specific situations where exploratory research may be particularly useful:

• New product development: When developing a new product, exploratory research can be used to identify consumer needs and preferences, as well as potential design flaws or issues.
• Emerging technologies: When exploring emerging technologies, exploratory research can be used to identify potential uses and applications, as well as potential challenges or limitations.
• Developing research hypotheses: When developing research hypotheses, exploratory research can be used to identify potential relationships or patterns that can be further explored through more rigorous research methods.
• Understanding complex phenomena: When trying to understand complex phenomena, such as human behavior or societal trends, exploratory research can be used to identify underlying patterns or factors that may be influencing the phenomenon.
• Developing research methods : When developing new research methods, exploratory research can be used to identify potential issues or limitations with existing methods, and to develop new methods that better capture the phenomena of interest.

Purpose of Exploratory Research

The purpose of exploratory research is to gain insights and understanding of a research problem or question where there is limited existing knowledge or understanding. The objective is to explore and generate ideas that can guide further research, rather than to test specific hypotheses or make definitive conclusions.

Exploratory research can be used to:

• Identify new research questions: Exploratory research can help to identify new research questions and areas of inquiry, by providing initial insights and understanding of a topic.
• Develop hypotheses: Exploratory research can help to develop hypotheses and testable propositions that can be further explored through more rigorous research methods.
• Identify patterns and trends : Exploratory research can help to identify patterns and trends in data, which can be used to guide further research or decision-making.
• Understand complex phenomena: Exploratory research can help to provide a deeper understanding of complex phenomena, such as human behavior or societal trends, by identifying underlying patterns or factors that may be influencing the phenomena.
• Generate ideas: Exploratory research can help to generate new ideas and insights that can be used to guide further research, innovation, or decision-making.

Characteristics of Exploratory Research

The following are the main characteristics of exploratory research:

• Flexible and open-ended : Exploratory research is characterized by its flexible and open-ended nature, which allows researchers to explore a wide range of ideas and perspectives without being constrained by specific research questions or hypotheses.
• Qualitative in nature : Exploratory research typically relies on qualitative methods, such as in-depth interviews, focus groups, or observation, to gather rich and detailed data on the research problem.
• Limited scope: Exploratory research is generally limited in scope, focusing on a specific research problem or question, rather than attempting to provide a comprehensive analysis of a broader phenomenon.
• Preliminary in nature : Exploratory research is preliminary in nature, providing initial insights and understanding of a research problem, rather than testing specific hypotheses or making definitive conclusions.
• I terative process : Exploratory research is often an iterative process, where the research design and methods may be refined and adjusted as new insights and understanding are gained.
• I nductive approach : Exploratory research typically takes an inductive approach to data analysis, seeking to identify patterns and relationships in the data that can guide further research or hypothesis development.

Advantages of Exploratory Research

The following are some advantages of exploratory research:

• Provides initial insights: Exploratory research is useful for providing initial insights and understanding of a research problem or question where there is limited existing knowledge or understanding. It can help to identify patterns, relationships, and potential hypotheses that can guide further research.
• Flexible and adaptable : Exploratory research is flexible and adaptable, allowing researchers to adjust their methods and approach as they gain new insights and understanding of the research problem.
• Qualitative methods : Exploratory research typically relies on qualitative methods, such as in-depth interviews, focus groups, and observation, which can provide rich and detailed data that is useful for gaining insights into complex phenomena.
• Cost-effective : Exploratory research is often less costly than other research methods, such as large-scale surveys or experiments. It is typically conducted on a smaller scale, using fewer resources and participants.
• Useful for hypothesis generation : Exploratory research can be useful for generating hypotheses and testable propositions that can be further explored through more rigorous research methods.
• Provides a foundation for further research: Exploratory research can provide a foundation for further research by identifying potential research questions and areas of inquiry, as well as providing initial insights and understanding of the research problem.

Limitations of Exploratory Research

The following are some limitations of exploratory research:

• Limited generalizability: Exploratory research is typically conducted on a small scale and uses non-random sampling techniques, which limits the generalizability of the findings to a broader population.
• Subjective nature: Exploratory research relies on qualitative methods and is therefore subject to researcher bias and interpretation. The findings may be influenced by the researcher’s own perceptions, beliefs, and assumptions.
• Lack of rigor: Exploratory research is often less rigorous than other research methods, such as experimental research, which can limit the validity and reliability of the findings.
• Limited ability to test hypotheses: Exploratory research is not designed to test specific hypotheses, but rather to generate initial insights and understanding of a research problem. It may not be suitable for testing well-defined research questions or hypotheses.
• Time-consuming : Exploratory research can be time-consuming and resource-intensive, particularly if the researcher needs to gather data from multiple sources or conduct multiple rounds of data collection.
• Difficulty in interpretation: The open-ended nature of exploratory research can make it difficult to interpret the findings, particularly if the researcher is unable to identify clear patterns or relationships in the data.

About the author

Muhammad Hassan

Researcher, Academic Writer, Web developer

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Clinical Case Study

Appendiceal mucinous neoplasms: a case report and review of literature.

Appendiceal mucinous neoplasms (AMNs) are uncommon tumors accounting for less than 1% of all cancers, which include a heterogeneous group of diseases with varying malignant potential. Pseudomyxoma peritonei (PMP) is a clinical syndrome characterized by progressive accumulation of mucinous tumor throughout the peritoneal cavity ( Carr et al., 2017 ). Because of difficulty in recognition during diagnosis, it is important to identify special clinical manifestations in AMNs patients. We here report a case of a patient diagnosed with AMN.

Case Presentation

A 60-year-old man was referred to our hospital because of ascites of unknown origin for 4 years. During these 4 years, he had abdominal distension and been diagnosed with liver cirrhosis. He had undergone relevant treatment, such as ascites drainage and diuretics, but without satisfactory effect. The patient had an otherwise insignificant previous medical history. On admission to our hospital, physical examination showed a fluid-filled abdomen; however, shifting dullness was not obvious. The abdomen was soft, with no direct tenderness or rebound tenderness.

The patient had stable vital signs. Blood and urine analyses were normal. Electrocardiography, chest radiography, and arterial blood gas analysis were also normal. Abdominal computed tomography (CT) suggested that there was a huge cystic lesion in the patient's abdomen and it seemed to be a lump containing solid and cystic components arising from the appendix (Figure ​ (Figure1). 1 ). The patient also underwent colonoscopy examination, which showed that the colon and rectum as well as orifice of vermiform appendix were normal (Figure ​ (Figure2 2 ).

Abdominal CT scan showing appendiceal lump (red arrow) and cystic lesion (yellow arrow) with intestinal tube extruded.

Colonoscopy showing normal orifice of vermiform appendix.

To identify the cause of the illness, subsequent exploratory laparotomy was performed. During the operation, a large amount of yellow and jelly-like turbid liquid (about 4,000 ml) was observed in the abdominal cavity (Figure ​ (Figure3). 3 ). After eliminating these abnormal substances, an ileocecal appendix lump (Figure ​ (Figure4) 4 ) appeared. The appendix lump was removed subsequently.

A large amount of yellow and jelly-like turbid liquid from the abdominal cavity.

The appendix neoplasm (5 cm × 4 cm × 3 cm) during surgery.

Microscopically, the pathology of the appendix lump tissue (Figure ​ (Figure5) 5 ) suggested hyperplasia of the mucous columnar epithelium and the mucus lake, which was formed by a large amount of abnormal mucus. The pathology of peritoneal nodules showed that there was a large amount of mucous tissue in the hyperplastic fibrous tissue. The final diagnosis was low-grade AMN (LAMN) accompanied with PMP.

(H&E, ×40 magnification) Histopathology of the appendix neoplasm showing hyperplasia of the mucous columnar epithelium (black arrow) and the mucus lake (white arrow).

After confirming the diagnosis, the patient underwent a cytoreductive surgery (CRS), and 1 month after operation, repeat CT scan showed no abnormalities (Figure ​ (Figure6). 6 ). Three months after CRS, the patient underwent hyperthermic intraperitoneal chemotherapy (HIPEC) with paclitaxel and cisplatin. Furthermore, we conducted telephone follow-up every 6 months until April of this year, and the patient was alive and in good condition.

The normal abdominal CT scan results after surgery.

AMNs are uncommon neoplasms that are detected in only 1% of appendectomy specimens ( Fournier et al., 2017 ). Because of the low-incidence rate and atypical clinical presentation, these patients may be misdiagnosed in clinical practice. Most cases are diagnosed incidentally at the time of appendicitis surgery or a screening CT. In this report, the patient was diagnosed as having ascites caused by cirrhosis for 4 years. However, the diagnosis of AMN was eventually confirmed by CT and surgery combined with pathology. Therefore, it is crucial to distinguish between ascites and pseudoascites (PA).

PA is an uncommon cause of abdominal distention mimicking ascites. When PA is caused by large abdominal cysts, it is difficult to distinguish from simple ascites because of the presence of an ultrathin wall and large liquid collection ( Rossi et al., 2019 ). Shifting dullness and a ruler-pressing test are typical clinical signs of ascites upon physical examination. The typical radiological signs of PA—the fact that the loops appear conglomerated in the center of the abdomen, or the fact that there may be aspects of septations in the fluid collection—are not a consistent finding at imaging and also they are not specific ( Camilon & Chilstrom, 2014 ; Nett, Vo, & Chapman, 2015 ; Shafi, Malla, & Reshi, 2009 ). Ascites from liver cirrhosis appears on the basis of the symptoms and signs of liver function damage. Although surgery combined with pathology examination is the criterion standard for diagnosis, it is beneficial for patients to undergo a differential procedure before an invasive surgery.

According to the new 2016 consensus statement from the Peritoneal Surface Oncology Group International (PSOGI), mucinous neoplasms are classified as follows ( Carr et al., 2016 ): LAMN, high-grade AMN (HAMN), mucinous adenocarcinoma, and poorly differentiated mucinous adenocarcinoma with signet ring features. LAMN extends beyond the mucosa into the appendiceal wall but without infiltrative invasion and with low-grade cytologic atypia ( Carr et al., 2016 ). Regardless of the degree of cellular atypia, both LAMN and HAMN can develop transmural extension or can rupture. When this occurs, the patient is at risk for developing PMP ( Bartlett et al., 2019 ).

This patient was finally diagnosed with LAMN accompanied with PMP by pathology examination. In recent years, a consensus both domestically and internationally recommends the integrated therapy of CRS and HIPEC as the standard treatment for PMP ( Li, Yu, & Liu, 2014 ; Yang et al., 2019 ). Therefore, this patient received HIPEC treatment 3 months after surgery, rather than immediately following CRS. In subsequent telephone follow-up every 6 months until April of this year, the patient did not experience any discomfort or recurrence. These results indicate that similar to the effect of immediate HIPEC after surgery, treatment with HIPEC 3 months after surgery may still achieve satisfactory results. Further large-scale, multicenter clinical studies are required to explore this point.

It is of great importance to identify ascites related to PA for patients diagnosed with AMN accompanied by PMP. Firmly mastering basic knowledge and skill of physical examination and radiographic imaging is a necessary quality for modern clinicians. In addition, early and accurate diagnosis will provide a clear direction for early treatment, thereby laying a solid foundation for improving the patient's prognosis.

THE OFFICIAL JOURNAL OF THE SOCIETY OF GASTROENTEROLOGY NURSES AND ASSOCIATES, INC.

DEDICATED TO THE SAFE AND EFFECTIVE PRACTICE OF GASTROENTEROLOGY AND ENDOSCOPY NURSING

The authors declare no conflicts of interest.

• Bartlett D. J., Thacker P. G., Grotz T. E., Graham R. P., Fletcher J. G., VanBuren W. M., Sheedy S. P. (2019). Mucinous appendiceal neoplasms: Classification, imaging, and HIPEC . Abdominal Radiology , 44 ( 5 ), 1686–1702. doi:10.1007/s00261-018-01888-y [ PubMed ] [ Google Scholar ]
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• Fournier K., Rafeeq S., Taggart M., Kanaby P., Ning J., Chen H. C., Royal R. (2017). Low grade appendiceal mucinous neoplasm of uncertain malignant potential (LAMN-UMP): Prognostic factors and implications for treatment and follow-up . Annals of Surgical Oncology , 24 ( 1 ), 187–193. doi:10.1245/s10434-016-5588-2 [ PubMed ] [ Google Scholar ]
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• Yang L., Hongbin X., Zheng P., Shuzhong C., Wei W., Han L., Shaozhong W., et al. (2019). Expert consensus on cytoreductive surgery combined with hyperthermic intraperitoneal chemotherapy . National Medical Journal of China , 99 ( 20 ), 1527–1535. doi:10.3760/cma.j.issn.0376-2491.2019.20.003 [ Google Scholar ]

Understanding how front-line staff use patient experience data for service improvement: an exploratory case study evaluation

Affiliations.

• 1 Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, UK
• 2 Health Services Research Unit, University of Aberdeen, Aberdeen, UK
• 3 Picker Institute Europe, Oxford, UK
• 4 Nuffield Department of Population Health, University of Oxford, Oxford, UK
• 5 Lay representative, Oxford, UK
• 6 Critical Care, Royal Berkshire NHS Foundation Trust, Reading, UK
• 7 Division of Experience, Participation and Equalities, NHS England, London, UK
• 8 Saïd Business School, Oxford, UK
• PMID: 32182003
• Bookshelf ID: NBK554766
• DOI: 10.3310/hsdr08130

Background and aim: The NHS collects a large number of data on patient experience, but there are concerns that it does not use this information to improve care. This study explored whether or not and how front-line staff use patient experience data for service improvement.

Methods: Phase 1 – secondary analysis of existing national survey data, and a new survey of NHS trust patient experience leads. Phase 2 – case studies in six medical wards using ethnographic observations and interviews. A baseline and a follow-up patient experience survey were conducted on each ward, supplemented by in-depth interviews. Following an initial learning community to discuss approaches to learning from and improving patient experience, teams developed and implemented their own interventions. Emerging findings from the ethnographic research were shared formatively. Phase 3 – dissemination, including an online guide for NHS staff.

Key findings: Phase 1 – an analysis of staff and inpatient survey results for all 153 acute trusts in England was undertaken, and 57 completed surveys were obtained from patient experience leads. The most commonly cited barrier to using patient experience data was a lack of staff time to examine the data (75%), followed by cost (35%), lack of staff interest/support (21%) and too many data (21%). Trusts were grouped in a matrix of high, medium and low performance across several indices to inform case study selection. Phase 2 – in every site, staff undertook quality improvement projects using a range of data sources. The number and scale of these varied, as did the extent to which they drew directly on patient experience data, and the extent of involvement of patients. Before-and-after surveys of patient experience showed little statistically significant change. Making sense of patient experience ‘data’ Staff were engaged in a process of sense-making from a range of formal and informal sources of intelligence. Survey data remain the most commonly recognised and used form of data. ‘Soft’ intelligence, such as patient stories, informal comments and daily ward experiences of staff, patients and family, also fed into staff’s improvement plans, but they and the wider organisation may not recognise these as ‘data’. Staff may lack confidence in using them for improvement. Staff could not always point to a specific source of patient experience ‘data’ that led to a particular project, and sometimes reported acting on what they felt they already knew needed changing. Staff experience as a route to improving patient experience Some sites focused on staff motivation and experience on the assumption that this would improve patient experience through indirect cultural and attitudinal change, and by making staff feel empowered and supported. Staff participants identified several potential interlinked mechanisms: (1) motivated staff provide better care, (2) staff who feel taken seriously are more likely to be motivated, (3) involvement in quality improvement is itself motivating and (4) improving patient experience can directly improve staff experience. ‘Team-based capital’ in NHS settings We propose ‘team-based capital’ in NHS settings as a key mechanism between the contexts in our case studies and observed outcomes. ‘Capital’ is the extent to which staff command varied practical, organisational and social resources that enable them to set agendas, drive process and implement change. These include not just material or economic resources, but also status, time, space, relational networks and influence. Teams involving a range of clinical and non-clinical staff from multiple disciplines and levels of seniority could assemble a greater range of capital; progress was generally greater when the team included individuals from the patient experience office. Phase 3 – an online guide for NHS staff was produced in collaboration with The Point of Care Foundation.

Limitations: This was an ethnographic study of how and why NHS front-line staff do or do not use patient experience data for quality improvement. It was not designed to demonstrate whether particular types of patient experience data or quality improvement approaches are more effective than others.

Future research: Developing and testing interventions focused specifically on staff but with patient experience as the outcome, with a health economics component. Studies focusing on the effect of team composition and diversity on the impact and scope of patient-centred quality improvement. Research into using unstructured feedback and soft intelligence.

Funding: The National Institute for Health Research Health Services and Delivery Research programme.

Copyright © Queen’s Printer and Controller of HMSO 2020. This work was produced by Locock et al. under the terms of a commissioning contract issued by the Secretary of State for Health and Social Care. This issue may be freely reproduced for the purposes of private research and study and extracts (or indeed, the full report) may be included in professional journals provided that suitable acknowledgement is made and the reproduction is not associated with any form of advertising. Applications for commercial reproduction should be addressed to: NIHR Journals Library, National Institute for Health Research, Evaluation, Trials and Studies Coordinating Centre, Alpha House, University of Southampton Science Park, Southampton SO16 7NS, UK.

• Plain English summary
• Scientific summary
• Chapter 1. Background and rationale
• Chapter 2. Study methods
• Chapter 3. Patient and public panel methods and reflections
• Chapter 4. Phase 1 results
• Chapter 5. Overview of phase 2 findings
• Chapter 6. Making sense of patient experience ‘data’: where do ideas for change come from?
• Chapter 7. Improving staff experience
• Chapter 8. The effect of team-based capital on quality improvement projects in NHS settings
• Chapter 9. Dissemination and impact: developing guidance and training for the NHS
• Chapter 10. Conclusions and implications
• Acknowledgements
• Appendix 1. Case descriptions
• Appendix 2. Designing and analysing the patient experience survey
• Appendix 3. Baseline and post-intervention surveys: questionnaire
• Appendix 4. Patient experience survey: methodological considerations
• Appendix 5. Topic guide for the patient interviews
• Appendix 6. Pro forma
• Appendix 7. Information sheet and consent form
• Appendix 8. Qualitative coding and visual mapping
• Appendix 9. Visual mapping example
• Appendix 10. Themes on inpatient ward experience
• Appendix 11. ‘Quick Wins: Tips for Ward Improvement’ (making your life and patients’ lives better): developed by the lay panel to support participating teams
• Appendix 12. ’Tips for Involving Patients in Ward Improvement’ (working together to improve wards for you and your patients): developed by the lay panel to support participating teams
• List of abbreviations
• List of supplementary material

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