embedded case study research design

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Marshall M, Davies H, Ward V, et al. Optimising the impact of health services research on the organisation and delivery of health services: a mixed-methods study. Southampton (UK): NIHR Journals Library; 2022 Feb. (Health and Social Care Delivery Research, No. 10.3.)

Optimising the impact of health services research on the organisation and delivery of health services: a mixed-methods study.

Chapter 6 designing and supporting embedded research.

Text throughout this chapter has been reproduced with permission from Ward et al. 178 This is an Open Access article distributed in accordance with the terms of the Creative Commons Attribution (CC BY 4.0) license, which permits others to distribute, remix, adapt and build upon this work, for commercial use, provided the original work is properly cited. See: https://creativecommons.org/licenses/by/4.0/ . The text below includes minor additions and formatting changes to the original text.

• Introduction

Despite becoming an increasingly popular strategy, designing and managing the embedded co-production of knowledge is far from straightforward. As we saw in Chapters 3 and 4 , initiatives can come in a wide variety of shapes and sizes and are usually both complex and emergent in nature. 158 Moreover, the nature of knowledge co-production itself is complex and multifaceted, as explored in Chapter 2 . Dilemmas and challenges include the types of co-production sought, the extent to which researchers should be embedded in the organisational setting, how to manage boundaries and the (sometimes conflicting) interests of different parties, and how best to respond to the knowledge needs of organisations grappling with complex and changing hinterlands. 65 , 155 , 156

Consequentially, as we saw in the previous chapters, those involved in initiatives can struggle to consider and fully articulate the range of issues that are germane or the design options that are open to them. This, in turn, can lead to tensions within initiatives due to, for example, the differing (and often unexamined) expectations of those involved, the difficulty of evaluating and demonstrating the value of initiatives (especially to those investing time and/or money), the need to respond to changing internal staffing and external influences, and the difficulty of reconciling diverse and potentially discordant aspects of an initiative. 65 , 162

In this and the following chapter, we begin to integrate the findings from the research-based workstreams 1–3 into a design framework, supporting metaphors, and tools and resources to aid the design, analysis and management of embedded research initiatives. The framework is based on the extensive research reported in Chapters 2 – 5 , work that enabled us to tease apart and map the various features of embedded co-production initiatives. It also draws on the extensive engagement and influencing activities of workstream 4, to focus on the practical manifestations of the programme of research. We present the design framework in the form of a ‘landscape map’ with accompanying materials, including a series of reflective questions. Other outputs and outcomes from the range of engagement activities deployed in workstream 4 (and outlined in Chapter 1 ) are also covered here and in the following chapter, to give a fully rounded picture of the contributions we have made to supporting the embedded co-production of knowledge. We conclude this chapter by drawing on our experiences of being involved in embedded research initiatives to discuss the potential utility and value of the materials for initiating and sustaining embedded research initiatives.

• Approaches and methods

In this section, we describe the approaches and methods used to create the portfolio of tools, resources and supporting materials that emerged from this project. First, we explain how we developed the design framework for embedded research initiatives, drawing heavily on the research reported in Chapters 3 – 5 . Then we revisit the engaging and influencing activities (workstream 4, outlined in Chapter 1 ) to show how these activities led to a wide range of supporting materials that promote and support the development of embedded research initiatives.

Developing the design framework

As documented in Chapters 2 – 5 , the supporting research comprised a wide review of literatures on knowledge co-production and on embedded research (workstream 1), a scoping exercise of embedded research initiatives in health settings across the UK (workstream 2), and a co-production workshop with embedded researchers and their managers (part of workstream 4). This was followed by more detailed exploration of four ongoing initiatives, including about 50 in-depth interviews and an additional workshop with follow-up discussions to validate our thinking and create and refine the supporting materials (workstream 3).

The features of embedded co-production

As described in Chapter 3 , our research identified 10 themes representing the common concerns of an embedded research initiative, and these were grouped under three categories relating to the underlying intent, structure and processes involved in an initiative. From each of these main themes, we then identified a number of additional subthemes that teased out the various aspects of each feature (see Table 5 ); their underlying rationales and evidence can be traced in Chapter 3 .

Given the ongoing uncertainty about the link between what embedded research initiatives do and their outcomes, the features described should not be viewed prescriptively. Instead, they are descriptions of the conceptual and operational components that characterise embedded initiatives – areas that require focused attention and detailed articulation. Importantly, each feature illustrates the complexity and nuance of embedded research that sometimes fails to be acknowledged, disaggregated or given due regard. This led us to think of the features as a common language for describing, discussing, planning and managing initiatives, a set of tools and resources for more thorough and reflective analysis – and with greater specificity – than is usually the case.

Co-producing a basic design framework

Having identified the 10 features (and associated subthemes) from our literature review and empirical work, we held a day-long co-design workshop with embedded researchers and their managers ( n  = 18). The purpose of the workshop was twofold. First, we aimed to test, validate and (if necessary) amend the features. Second, we aimed to understand how best to communicate the features and work them into a practical framework for those designing or becoming involved in an embedded research initiative.

The workshop made use of a range of creative activities and was facilitated by an experienced team of researchers in design and health care (see Acknowledgements ). The workshop began with participants adding comments and questions to postcards that depicted each of the 10 features with a simple icon (e.g. a location icon for proximity, a house for belonging). These thoughts were then discussed in small groups before being summarised and fed back to the rest of the group. Small groups also discussed and fed back whether or not some features were more of a priority than others. Insights that emerged during this part of the workshop included the emergent and often underarticulated nature of intended outcomes; the central (yet usually hidden) nature of power dynamics; the sense of ‘homelessness’ often felt by embedded researchers; the need to consider when and where to involve others in an initiative; the idea of proximity as a journey as much as a set of locations; and the need to manage expectations about researcher skills, expertise, roles and activities.

The next section of the workshop focused on visualising the features. Participants worked individually and then in small groups to produce physical models as three-dimensional metaphors of the features, before turning these into two-dimensional pictures. The aim was to give participants the time and space to explore and make sense of the features in different ways, and to develop insights about how they could best be communicated and shared with others involved in embedded research initiatives. Recurring metaphors drew on the natural world (e.g. trees, ferns, ponds, waves, soil), with many participants focusing on ideas of growth, tending and nurturing.

Other activities during the workshop included a discussion about formulating a network to support those in embedded researcher roles (informed by insights about the potential loneliness and ‘homelessness’ of embedded researchers) and a discussion about how to share the insights from our research and workshop (participants’ strong preference was for materials to be shared via a website and in open-access publications).

Visualising the framework and devising supporting materials

Although the workshop validated the features our research had identified, provided some powerful metaphors to describe those features and coalesced a group of people involved in embedded research, we still needed to develop the features into a practical framework to support the design, analysis and management of embedded research initiatives.

This additional work comprised three main activities: first, working with a professional illustrator to develop a single visual representation of the features; second, developing a series of reflective questions to help unpack the features and subthemes; and, third, providing a rich and evidence-rooted explanation of the themes and subthemes that would allow interested parties to track back to the published and grey literature.

To develop the visual representation of the major themes, we shared photographs and materials from the co-design workshop with a professional illustrator (see Acknowledgements ) before holding a number of telephone and e-mail discussions. These focused on how adequately to represent the features in a coherent single image that captured both our research findings and the insights from participants at the co-design workshop. Between interactions with the illustrator, we discussed draft illustrations and metaphors with members of the wider team and sought further feedback from some of the workshop participants.

To support this visual re-working of the framework, we also developed a series of reflective questions for each theme, and we constructed more detailed and technical accounts of the themes and subthemes, drawing on our wide range of study materials. These additional materials drew on extensive discussions with members of our wider team and workshop participants, and, collectively, they provided material that was easy for potential stakeholders in embedded research initiatives to understand. All of these materials are freely available at the Embedded Research website. 67

Developing other supporting materials

Throughout the project, considerable attention was paid to engaging and influencing activities (workstream 4; see Chapter 1 for an outline of its approach, audience and actions). Activities ran in parallel with workstreams 1–3, drawing on and contributing to those research activities.

A project website was constructed at an early stage, 67 and a series of webinars was run to create interest and drive web traffic. As research-based materials (such as the design framework) became available, these were converted to web resources and released for wider community commentary. The collaborative workshops programmed into the lifetime of the project created opportunities for the co-production of resources, which, in turn, were used to enhance the resources section of the website. The planned workshop that could not run because of COVID-19 restrictions was replaced with podcast interviews with other members of the team to provide a readily accessible record of the thinking behind the design framework. Articles in widely read practitioner journals ( Health Service Journal , Local Government Chronicle ) created additional interest, spread the core messages from the research, drove further website traffic and created additional useful discussions that fed back into the creation of supporting materials. Blogs and an active Twitter account (@_embedded) offered further channels to stimulate interest, and allowed followers to engage with the supporting materials under development.

• Outputs: tools and resources for embedded research

The varied activities outlined in the preceding section produced a wide range of resources for those interested in developing embedded co-production to draw on. The translation of our research output (the core features of embedded research initiatives) into these resources was seen as essential if this work was to have wider influence and we were to work in ways that were sympathetic to an engaged, inclusive and co-productive orientation to knowledge. In this section, we explore the outputs created by these processes in collaboration with, and usually co-productively with, wider partners.

Because our workshop participants, and our own research and practical experience, had alerted us to the often emergent and dynamic nature of embedded research initiatives, the potentially complex relationships between the features, and the limited evidence to date on the outcomes of initiatives, we did not seek to create a prescriptive or instructional manual for embedded research initiatives. Instead, the materials were designed to provide a structured way of engaging with the complexities, nuance and choices involved in designing and developing an embedded research initiative.

So, although considerable work went into developing, sharing and validating these outputs, we do not regard them as definitive: their nature will remain provisional, their uses conditional and contextually contingent, and the contribution they make to the design and management of schemes necessarily partial. It remains our hope, however, that they will provide useful guidance.

Three groups of outputs are presented in this chapter and in Chapter 7 : first, the design framework, its visual representation and its supporting metaphors; second, the set of interrogatory questions intended to build dialogical engagement with the framework; and, third (in Chapter 7 ), a suite of materials for those considering employing an embedded researcher or blended research and service team. Thus, those interested in learning more about embedded research can have an easy introduction and then ‘drill down’ into the nuance, substance and evidence for each theme (see the Embedded Research website 67 ).

These three groups allow engagement in a multilayered way. The first uses the metaphor of the garden to introduce concepts of complexity, multifacetedness and interconnectivity. Then individual aspects of the garden (the themes and subthemes) can be explored both as metaphors and dialogically through questions such as those set out in Table 15 . Finally, these web resources contain click-throughs to other resources such as more detailed expositions of the themes linked to the literature, published papers, case studies, job-related resources, an animation and the opportunity to join a network of like-minded practitioners in embedded research.

Reflective questions to aid discussion and consideration of an embedded research initiative

The design framework: an illustrated metaphor

The visual landscape seen in Figure 12 represents the features of an embedded research initiative. Drawing on the insights of the participants at workshop 1, we selected a garden as an overarching metaphor to represent the growing, emergent nature of embedded research initiatives and the active work that individuals and organisations need to put into planning and maintaining such initiatives.

A visual landscape of the features of an embedded research initiative.

Each theme is represented as a separate area in the garden, with relevant visual metaphors as follows:

• Intended outcomes are represented by the range of desirable produce emerging from the garden as a whole.
• Power dynamics are seen as a river flowing through the whole space, with the scope both to power initiatives (the water wheel) and also (implicitly) to overwhelm (e.g. by flooding).
• Scale is hinted at by the idea of a wood containing trees of different size, species and maturity.
• Involvement uses ideas of the hive (honeybees) to suggest that collective engagement is needed to produce more than individuals can alone.
• Proximity hints at ideas of distinct choices (the signpost), purposeful navigation (the map) and boundaries to be negotiated (the fence).
• Belonging is represented by both a summer house (a structural space for belonging) and a picnic (reflecting informal social spaces for belonging).
• Functional activities suggests the range of activities needed for success, their interconnectedness, ideas of investment for the future, and the toil sometimes involved in the tasks.
• Researcher skill and expertise are represented by gardening equipment and tools.
• Relational roles playfully suggests that actors from very different backgrounds and abilities may need to find ways to get along.
• Learning mechanisms points towards growth (the baby birds), maturity and stillness (the wise heron) and calm reflection (seen here literally but intended metaphorically).

Questions for engaging with the framework

The integrated nature of the overarching metaphor in Figure 12 illustrates the complex interconnectivity of all its aspects, yet disaggregating the overall picture into its constituent parts also has value in teasing out distinct design components and ensuring focused discussion. Table 15 shows each of these aspects in turn and lists a series of questions designed to prompt contemplation and discussion of each feature. These questions can be used alongside the visual landscape to prompt further enquiry, articulation and discussion of key design and/or management issues at the outset and again as initiatives unfold.

Clearly, the questions laid out in Table 15 do not present the last word on how to explore the themes. As new or extant collaborations grapple with the concerns laid out, new ways of digging deeper will emerge appropriate to the specific initiative under scrutiny. To aid this, the framework on the Embedded Research website 67 contains click-throughs to other resources, to which we now turn.

Other supporting materials from workstream 4

The design framework described above lies at the centre of a suite of practice-focused materials that aim to assist in the exploration of embedded forms of knowledge co-production. Other resources developed across the team included:

• An animation to provide a visually attractive and engaging view of the possibilities.
• Real-world case studies to showcase possible manifestations of embedded strategies.
• A network of interested actors through which views and resources can be exchanged.
• A ‘recruitment resources pack’ containing sample job adverts, job description and person specification, which can be adapted and adopted for different circumstances. The pack also includes guidance and training resources, to help the collaborating institutions consider what will make for a successful and sustainable initiative.

Given the practical nature and the target audience for the recruitment resources pack (specifically, managers, clinicians and potential embedded researchers), the recruitment resources are presented separately from the other supporting materials in Chapter 7 .

Animated introduction to embeddedness

As the limitations of knowledge transfer approaches became evident, there was a need to be able to explain the nature and (potential) advantages of more co-productive approaches, such as those embraced by the term ‘embedded research’. The animation we created is designed to stimulate discussions on how embedded research is viewed alongside more traditional research methods. It asks viewers to consider how we better align academic and non-academic perspectives, to ensure that academic outputs are useful to the service and that the service outlines real-world problems for the academic community to help solve. It is intended as a resource to enable teams to consider whether or not there are models other than traditional research that may provide a more useful approach for them.

In addition, we hope that it will help to build the credibility of embedded researchers who are already in post, and support them to be accepted into the research world. Our intention is for the animation to be provocative in order to drive discussion and enable people to think more broadly about research approaches. We believe that it will interest all three of our target audience groups: NHS and local government leaders, the academic community, and front-line staff and service users.

Real-world exemplars

Real-world exemplars of embedded research initiatives are presented on the website in multimedia format, including webinars and documentation. These introduce some of the diverse manifestations of embedded co-production, allowing those interested in pursuing this approach to better understand the challenges and benefits. These examples also highlight the great variability of approaches, suggesting that there is no simple template, thus reinforcing the use of the design framework as a guide for discussions and design decisions, rather than a prescription.

Networked activities

The research proposal envisaged the creation of a network or community for embedded researchers, and the subsequent work confirmed its relevance: one of the themes identified through the literature review, and validated further through the case studies, spoke to how lonely the embedded researcher role could be. Moreover, the tension between belonging to an academic world while being embedded in a service organisation was highlighted.

This could imply an easy opportunity to create a peer network to offer support to people who may feel isolated in their roles. Our experience in this project, however, demonstrated something rather different.

When we discussed the desirability of greater peer support at the first workshop, the participants expressed considerable enthusiasm for establishing a self-managed and lightly facilitated network to allow researchers to support and learn from each other. A network was established, but, despite the initial enthusiasm, activity in the group was limited. The project team experimented with various communication tools (e-mail lists, a Google Group, WhatsApp groups), but, despite prompting within these resources, subsequent communications traffic remained generally light.

From these experiences, and analyses of the interactions through our website, actors in this domain appear to coalesce around specific activities, rather than as peers wanting to connect more broadly. This may be reflective of the diverse models of embedded research initiatives, and the very different specific subject matter people are working on; that is, the potential for common ground could be rather more limited than might first be thought. It also suggests that engagement and connecting activities across this domain may need to be more actively managed and resourced.

In contrast, we successfully connected with existing bodies, such as the AHSN network and the ARC network, to involve them in planning the resources we were developing, commenting on emerging artefacts and improving their knowledge of embedded research models. Colleagues from these networks participated in our various workshops and publicised our work widely in their own geographic and virtual networks. Project team members also attended AHSN and ARC implementation events to speak about the potential of embedded research to deliver improved outcomes and impact.

• Reflecting on the tools and resources developed

As researchers and leaders of embedded research initiatives, we had seen that interest in developing this approach was increasing in both the UK and overseas. In UK health and related services alone, we identified almost 50 such schemes (see Chapter 3 ), and enquiries of interest in the approach from people in health service settings showed no sign of slowing.

Many of those contacting us cited their frustration with the often limited utility and impact of academic research, their desire for better situated and ‘useful’ knowledge to help them address service delivery issues, and their desire for closer interactions with researchers. But, although they recognised the promise of embedded research in addressing some of these issues, they were often unclear about the possible components of an embedded initiative, or how to go about designing a programme to meet their particular needs. This was compounded by the largely ad hoc and somewhat opaque nature of many of the initiatives that had been developed by others. We were told that this made it difficult to see exactly how existing schemes had been designed and developed, for what purposes, and with what obstacles and success.

Recognising these challenges, we extended our research in workstreams 1–3 (on the common core components of embedded co-production and the microdynamics of implementation) to create a multilayered practical framework to guide those designing, managing or analysing embedded research initiatives. That work has been described in this chapter, augmented by an account of the range of practical tools and resources that we have co-developed. The framework, scheme-related resources and associated web-based tools are now available on open access. 67

Next, we will discuss the potential for such tools to address common concerns over embedded research initiatives.

Published experience, as well as our own extensive interactions with embedded research teams, highlighted the need for open dialogue and discussion, both at scheme inception and as part of ongoing scheme development and management. 162 , 166 This included the need for a common understanding, agreed goals and a bespoke design that fitted the local context and met the needs and ambitions of those involved. The framework we developed provides a structured way of encouraging such shared understandings, and a means of drawing on the experience of others (through the detailed linkages in deeper layers of the framework).

Used as part of an ongoing dialogue, the framework has the potential to deepen shared understandings, highlight divergent assumptions and reveal potential (and often hidden) tensions in the design options being taken.

None of the core components identified in the framework is simple or straightforward, and many will be context dependent, or even contested by the divergent actors involved in embedded research schemes. A willingness to invest the time necessary to properly explore and negotiate ideas and expectations across the components may be advantageous, as could be a willingness to return and review these issues as schemes bed in. There is also a need for those involved to know and understand what they are getting into, and to be alert to the potential risks (and not just benefits) at different levels. This might include, for example, articulating the career risks for researchers associated with not following a traditional academic route: embedded research is often not well aligned to the norms or incentives of academia, and is demanding of time and skills. It might also cover the extensive commitment needed from key players on the service side to help navigate local politics and competing priorities within operational settings. Use of the framework and exploratory questions might usefully extend and deepen discussions in these and other areas.

In similar fashion, the practice-related artefacts co-created as part of this project offer starting points for local discussion and adaptation. Although drawing on items that are (or have been) in use, we can expect considerable extension and diversification of these materials over time. It is expected that the Embedded Research website 67 will be hosted and maintained, at least in the medium term, by the Research Unit for Research Utilisation at the University of St Andrews (co-investigators VW and HD are co-directors of this unit). 179 The Embedded Research website 67 will act as a repository for the tools, resources and materials developed as part of this project, with the potential to expand the materials held as the field matures.

As the ideas explored in this project spread and evolve, there will be a need for further evaluation of such schemes: for the purposes of learning and adaptation, for development of specific schemes over time (initiatives are often emergent and not rigorously defined at the outset), for comparison between initiatives of differing designs, and as a means to build a business case for further initiatives and investment.

There is also a conspicuous need for more rigorous and evaluative research on the process and outcomes of such schemes, so that more prescriptive advice can be given to those wishing to invest scarce resources. The framework and theoretical underpinnings reported here and previously have the potential to provide a common language and structured means of engagement with all these concerns, and the practical tools and resources developed have the potential to expedite scheme development.

This chapter has provided an account of the processes and outcomes that integrated insights from the research presented in Chapters 2 – 5 with some of components of the engaging and influencing work of workstream 4. In Chapter 7 , we present the recruitment resource pack, which we hope will be practically useful to the embedded researchers, managers and clinicians who wish to design and deliver an embedded research scheme in their locality.

• Cite this Page Marshall M, Davies H, Ward V, et al. Optimising the impact of health services research on the organisation and delivery of health services: a mixed-methods study. Southampton (UK): NIHR Journals Library; 2022 Feb. (Health and Social Care Delivery Research, No. 10.3.) Chapter 6, Designing and supporting embedded research.
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• Published: 23 October 2020

Advancing integrated care evaluation in shifting contexts: blending implementation research with case study design in project SUSTAIN

• Jenny Billings   ORCID: orcid.org/0000-0002-5660-9478 1 ,
• Simone R. de Bruin 2 ,
• Caroline Baan 2 &
• Giel Nijpels 3  

BMC Health Services Research volume  20 , Article number:  971 ( 2020 ) Cite this article

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Despite many studies evaluating the effectiveness of integrated care, evidence remains inconsistent. There is increasing commentary pointing out the mismatch between the ability to capture the somewhat ‘illusive’ impact of integrated care initiatives and programmes, and the most appropriate way to do this. Focusing on methodology, this paper describes and critically reviews the experiences of SUSTAIN, a Horizon 2020 funded project (2015–2019) with the purpose of advancing knowledge and understanding of cross-European integrated care evaluation. SUSTAIN sought to improve integrated care initiatives for older people in seven countries, and to maximise the potential for knowledge transfer and application across Europe. The methods approach drew from implementation research, employing the participative Evidence Integration Triangle (EIT) and incorporating a mixed method, multiple embedded case study design. A core set of qualitative and quantitative indicators, alongside context and process data, were created and tested within four key project domains (person-centredness, prevention-orientation, safety and efficiency). The paper critically discusses the overall approach, highlighting the value of the EIT and case study design, and signalling the challenges of data collection with frail older people and stakeholder involvement at the sites, as well as difficulties developing the core set of indicators.

Conclusions

Lessons learned and recommendations for advancing integrated care evaluation are put forward that focus on the status of integrated care as a complex intervention and a process. The use of implementation research methods and case study design are recommended as an additional evaluation approach for researchers to consider, alongside suggested ways of improving methods of data collection with frail populations and cost analysis.

Peer Review reports

Despite many studies evaluating the effectiveness of integrated care, evidence remains inconsistent. Increasingly, commentary on the subject of integrated care evaluation is pointing out the mismatch between the ability to capture the impact of integrated care initiatives and programmes, and the selection of the most appropriate methodology to do this. Authors have highlighted a range of evaluation challenges that include the stability and sustainability of initiatives; data collection and the suitability of measures [ 1 , 2 ]; and a lack of appreciation of the complexity [ 3 ]. In addition, the status of integrated care as a ‘process’ must be recognised [ 4 ], meaning it is not a ‘fixed’ intervention, but susceptible to constant development and change. These factors all affect the sturdiness of evaluation designs and what constitutes an outcome. This in turn is prompting the need to fit the evaluation design more with how integrated care is implemented in practice and what integrated care is there to achieve and improve [ 5 , 6 ]. The value of mixed methods studies and suitable frameworks that examine both processes and outcomes has therefore been recognised in this field [ 2 , 7 ].

Regarding the examination of processes, the wide variation in how integrated care is operationalised calls for evaluations that include a range of qualitative methods, so that important contextual information can be examined to identify what seems to work and why. Regarding outcomes, there is a need to ensure that outcome measures have a good pragmatic fit with the shifting context of integrated care interventions and the population group under study. There is a tendency for example for measures for frail older people and people with multimorbidity to focus on general health outcomes (e.g. health status, physical functioning, quality of life), which may not be appropriate to their fluctuating physical and mental status. Outcomes such as experiences of care, independence and autonomy may be more suited to this vulnerable target group, and these inclusions may be more appropriate to ascertain the link between the integrated care processes and what improvements can be expected for the service user in receipt of care.

Given this impetus, researchers are adopting more ‘real world’ methodologies for the evaluation of complex interventions such as integrated care. While mixed methodologies have been advocated for some time to gain a better appreciation of the ‘grass root’ processes involved in integrated care implementation [ 8 ], the emergence of realist approaches drawn from Pawson and Tilley (1997) [ 9 ] has become evident [ 10 , 11 ]. Realist researchers seek to explain the underlying “cause” or mechanisms that generate observed phenomena through the construction of context, mechanism and outcome (CMO) configurations [ 12 ]. To support this, academics are developing frameworks such as the COMIC model for the evaluation of integrated care [ 13 ]. In addition to realist methods, and continuing with the focus on context, researchers are turning to implementation research. This is described as the scientific study of the processes used in the implementation of an initiative alongside the context within which it is taking place [ 14 ]. Its intention is to promote the systematic uptake of research findings and other evidence-based practices into routine practice, and, hence, to improve the quality and effectiveness of health services and care [ 15 ].

This broadening of methods appeal is becoming reflected in funding opportunities. The European Union (EU) funded research initiatives (Horizon 2020) are encouraging the adoption of innovative approaches to the evaluation of integrated care to better understand the impact on vulnerable populations with complex needs. One such project is SUSTAIN – Sustainable Tailored Integrated Care for older people in Europe (2015–2019). This paper focuses on the use of innovative approaches within SUSTAIN, and describes the method by which implementation research and case study design were blended for the evaluation of European integrated care initiatives. The aim is to share methodological experiences and lessons learned with the research community, in order to advance understanding of integrated care evaluation in context and add to the international ‘toolbox’ of methodological approaches. It will commence with a brief introduction to SUSTAIN and an overview of the design. This is followed by a critical discussion of the strengths and weaknesses of our approach and concludes with an assessment of the extent to which we advanced understanding of integrated care evaluation. Lessons learned and recommendations for future integrated care evaluations are put forward.

Overview of the SUSTAIN project

The SUSTAIN project was carried out by thirteen partners from eight European countries: Austria, Belgium, Estonia, Germany, Norway, Spain, the Netherlands, and the United Kingdom. With the exception of Belgium, in all other countries two integrated care initiatives (also referred to as ‘sites’) per country were invited to participate in the SUSTAIN-project, each developing and evaluating two integrated care initiatives ( n  = 14) focusing on older people with complex needs. Sites that were recruited needed to have integrated care initiatives in place, but were motivated to improve and adapt their programmes.

The overall aim of SUSTAIN was to improve integrated care for older people, and to maximise the potential for knowledge transfer and application across Europe [ 16 ]. SUSTAIN had four main themes, pre-set by the Horizon 2020 funding programme of person-centredness, prevention-orientation, safety and efficiency in integrated care, which guided the development, implementation and evaluation of the initiatives. The objectives of SUSTAIN were to:

Support and evaluate improvements to established integrated care initiatives for older people over 65 living at home with multiple long-term conditions and complex needs; and.

Contribute to the adoption and application of these improvements to other health and social care systems, and regions in Europe.

The development and evaluation of SUSTAIN initiatives took place over an 18 month time span, with a 6 month phase of identifying the sites and creating improvement plans, followed by a year long implementation and evaluation period. Ethical approval for the evaluations was obtained through the site-specific governance structures. This paper focuses on the evaluation aspect.

Integrated care comes in many forms, and given that our focus was on developing improvements according to local needs, SUSTAIN has not been exempt from this variability in the selection of initiatives. However, despite the considerable differences in structure and context, two common approaches could be identified within our 14 initiatives;

Sites that aimed to improve services to older people and/or expand collaboration, communication and coordination with other care and support organisations, while also enhancing knowledge and understanding of each other’s roles and responsibilities;

Sites that aimed to improve the actual care delivery process to older people, for example providing care in a more person-centred way, or improving case- and discharge management [ 17 ].

The challenge for the evaluation design thus became one of developing a robust and consistent approach applicable across all country sites, in the face of several important variabilities. These included differing integrated care configurations and contexts, the site-specific pace of implementation, the throughput of service users and their length of exposure to the intervention, and the enduring problem of data accessibility and comparability [ 18 ].

Methods: the evaluation design of SUSTAIN

Evidence integration triangle.

The overall approach to the project was guided by the Evidence Integration Triangle (EIT). This participatory approach is derived from implementation research and aims to tackle the process of translating research and best-practice evidence to implementation [ 19 ].

While evidence on the contribution the EIT is making is still emerging, it is claimed that by focusing on the perspective of stakeholders and the context for application of scientific findings, pragmatic approaches can hasten the integration of research, policy, and practice [ 20 ].

There are three main components to the EIT model, namely the evidence-based intervention programme or policy, participatory implementation processes with stakeholder involvement, and practical measures of progress and outcome (Fig.  1 ). These three components enable stakeholders to use scientific evidence to encourage development and sharing of new knowledge to inform decision-making. In SUSTAIN, stakeholders were involved by organising steering group meetings. Steering groups consisted of local stakeholders (e.g. managers, health and social care professionals, representatives of older people and carers, commissioners, local policy officers) considered relevant to the integrated care initiative. These steering groups were created to design and implement improvement plans, that is, sets of improvements that apply to local, site-specific priorities.

Evidence Integration Triangle [ 19 ]

The high participation levels within this model enabled the research to be relevant and applicable from the onset and ensured that indicators and measures generated to gather evidence remain sensitive of the research and practice environment. Qualitative and quantitative evidence is accumulated and used throughout so that the change process remains dynamic and responsive to improvement.

Context is also pivotal to the EIT. Glasgow [ 19 ] describes the multilevel context as the conditions surrounding health problems and intervention opportunities in a particular place with a particular population, and is a key starting point. Context also changes over time, giving a dynamic aspect to the EIT, with context continually informing the other key components. It is clear that this approach was well suited to the fluctuating integrated care environments within which SUSTAIN was taking place.

• Case study design

Within the practical measures aspect of the EIT (the focus of the development of our evaluation tools and approach), we adopted Yin’s [ 21 ] case study design. A strength of case study design is its ability to support the analysis of multiple qualitative and quantitative data sources – described as ‘embedded’ - to investigate complex phenomena in their everyday contexts and across different contexts [ 22 ]. It was therefore deemed appropriate for examining implementation processes as they unfolded within the EIT cycle in our differing interventions. In addition, it allowed for multiple cases, taking into account the different types of intervention, data source availability and sample size variations across our study sites. As such, with the case study design we aimed to tackle several of the challenges in integrated care evaluation.

Cases are defined by a unit of analysis, common across all sites. With SUSTAIN, our unit of analysis became ‘set of improvements for integrated care initiatives’, as this was a core objective. In addition, we adopted an explanatory approach to our case study design [ 21 ], as we were seeking to develop explanatory models and greater theoretical understanding around two main propositions linked to the four SUSTAIN themes:

Integrated care activities will maintain or enhance person-centredness, prevention orientation, safety and efficiency in care delivery;

Explanations for succeeding in improving existing integrated care initiatives will be identified.

These propositions were accompanied by a number of analytical questions to support analysis, described in the analysis section. Thus Fig. 2 illustrates our overall approach - multiple embedded case study design that is explanatory in nature . In SUSTAIN we differentiated between qualitative and quantitative indicators, a requirement of the Horizon 2020 call. Both produce quantitative data but the former measures attitudes, perceptions and beliefs, and the latter focuses on audit-style data such as hospital admissions [ 23 ]. In addition, the figure includes the data sources and the minimum anticipated samples that were seen as achievable, gauged through discussion at partner sites and within the consortium as a whole, taking into consideration the variability previously mentioned such as the differing speed of service user throughput and variable length of the intervention.

Multiple Embedded Case study design showing data sources and planned samples per site and overall in 14 sites

Practical measures

A key feature of the design was to develop and test a core set of indicators that could be used across our partner countries and potentially be transferable to other areas. While Fig.  2 maps out the discreet data sources aligned to case study design, Table  1 unpacks our data sources further, describing distinct data items and data collection tools that were core to the evaluation of our sites in more detail. For clarity and linkage to Fig. 2 , qualitative and quantitative indicators are highlighted in colour under the data items column. For data collection tools, instrument selection depended upon the goodness of fit with our objectives and four key themes; availability especially in different languages; validation within our population group; and length. Sites also included some site-specific measures in addition to our core set, that were particular to their interventions, such as audit forms to track new general practice referrals (UK), numbers of GPs, nurses and social workers (Spain), and reasons for not using the integrated care centre for people with dementia (Austria). The selection of our core instruments is elaborated upon and critically reviewed in the discussion.

Data collection

In keeping with the EIT cycles and approach to rapid knowledge transfer, data collection took place over 1 year in two waves following a 6 month development phase where baseline information was collected. Stakeholder reviews of preliminary findings (at the 12 month period) and final findings (at the 18 month period) (Fig.  3 ) were built in through steering groups to ascertain what seemed to be working well, and where solutions to problems needed to be identified. In order to enable comparison, we used uniform procedures for data collection for all initiatives.

Data collection and Feedback plan

Analysis strategies within multiple case study designs are focused on triangulation of data, purported by Yin [ 21 ] to strengthen the construct validity of the research. Each data source became one piece of a jigsaw with each piece contributing to understanding of the whole phenomenon [ 25 ]. In SUSTAIN, within each site, data sources were analysed according to their requirements before proceeding to a specific analytical process [ 26 , 27 ]. Uniform templates for analysis of each data source were generated through a discussion among research partners. All data was entered on a shared anonymised database. Of Yin’s [ 21 ] five techniques for analysis, we adopted pattern-matching, seeking rival explanations, linking data to propositions, and explanation building. Exploring rival explanations is an attempt to scan the data to provide an alternate explanation of a phenomenon. To support this, a number of analytical questions were developed to underpin the propositions and our aims, and aid consistency of analytical focus among our evaluation partners:

What seems to work with what outcomes when making improvements to integrated care?

What are the explanations for succeeding and improving integrated care initiatives?

What are the explanations for NOT succeeding and improving integrated care initiatives?

Are there any factors that can be seen as having an impact on integrated care improvements?

What factors can be identified that could apply to integrated care improvements across the EU, and be transferable?

Once each site analysis was completed, an overarching cross case synthesis took place. Overall, the evidence created from this type of analysis is considered robust and reliable [ 28 ].

Discussion: addressing the challenges

The discussion will critically review the SUSTAIN design and its appropriateness as an approach to integrated care evaluation. It will firstly discuss the overarching evaluation approach that incorporated the EIT and case study design, follow with a critical reflection on the development of a core set of indicators, and debate the choice of our design in the context of other suitable approaches, namely realistic evaluation.

Reflecting on the EIT, it proved to be highly suitable as a framework for implementation and evaluation for SUSTAIN, in its applicability and use in real-life contexts. A key feature was its practical ability to support a participative environment through the steering group meetings. Here, the framework promoted engagement through its ability to portray a logical and straightforward approach to implementation and evaluation, enabling members to proactively deal with contextual and hence evaluation challenges. It also enabled a level of knowledge exchange and action between the researchers and stakeholders. Other studies are similarly incorporating ‘fit for purpose’ research designs that are placed within the EIT framework. Carrieri et al [ 29 ] for example, are undertaking a realist review of interventions to tackle doctors’ mental health, using the EIT to convene a stakeholder group with experts in which research can inform practical decision-making and dissemination of messages. Also Resnik et al [ 30 ] are testing the EIT for implementation of interventions to manage behavioural and psychological symptoms associated with dementia, incorporating a pragmatic trial.

Similarly, case study design with its inherent flexibility provided a sound basis for harmonising the disparities between sites and provided a platform to test our core indicators. Yin [ 21 ] and Cresswell [ 31 ] promote the usefulness of this embedded multi-method design for its ability to add or remove data sources without detriment to the overall analysis. Case study design also gave us a solid data analysis strategy that could accommodate and make comparable and meaningful discrepancies across our partner countries. Case studies have been used in clinical practice and research for a number of decades in complex settings including integrated care [ 27 , 32 ], as well as within an implementation science approach [ 33 ] and in EU studies [ 34 ].

In addition, the incorporation of case study design was a significant addition to theory building opportunities (which is somewhat lacking in EIT – see later discussion), going some way towards assembling a deeper theoretical understanding of integrated care. Eisenhardt & Graebner (2007) [ 35 ] suggest that theory is emergent, situated in and developed by recognising patterns of relationships among constructs within and across cases. The use of replication logic assisted by pattern matching assists with theory building, in that multiple cases serve as replications, contrasts and extension to the emerging theories. Within SUSTAIN, case study design supported the development of our propositions and consequent explanatory models. Theories embodied within the propositions could be tested and expounded, ultimately leading to theory building, in relation to our central concepts of person-centredness, prevention orientation, safety and efficiency in care delivery, and what seems to ‘work’ in integrated care improvements (see SUSTAIN final report De Bruin et al. 2018) [ 36 ].

However our implementation research approach could be described as overly simplistic and lacking clear steps to achieve certain EIT goals, which leaves it open to interpretation. In addition, more guidance is needed regarding how each triangle component relate to each other, as well as how the evidence and stakeholders’ input connect to the triangle individually and as a whole. Importantly, it does not describe sufficiently well how the different context levels should be situated within the triangle and how, within a constantly changing environment, it misses out consideration of sustainability of the intervention. But, the simplicity of the EIT could be described as a strength, in that it is understandable and accessible by participants in the real world, vital in the highly participative stance of the framework. Given the variability within our projects and integrated care interventions generally, the lack of clear process information generated better ‘bottom up’ plans about how the components would work together and relate to the intervention as a whole. Indeed, Glasgow et al. (2012) see other knowledge translation models as too complicated, academic or time consuming for those who wish to use the evidence. In contrast, they purport the EIT to be applicable and usable in a variety of situations, as we found.

With respect to the three key elements within the EIT framework, the practical measures aspect will now be discussed in more detail as it affected the methodological approach. The two other elements, namely evidence-based interventions and the participatory implementation process (including stakeholder involvement), are more concerned with the intervention development and roll out and are reported elsewhere [ 16 , 17 , 37 ]. It is worth, however, mentioning briefly here some experiences with stakeholder involvement as they affected the methodology. In the face of universal health and social care resource constraints, considerable commitment was required for stakeholders not only to develop and implement the improvements with research teams at sites, but also to take part in interviews and assist with obtaining quantitative indicators. Our partnership approach fostered through the EIT approach enabled sustained buy-in to a large extent. However during the course of the implementation plan roll-out, two sites withdrew due to competing priorities and a diversion of resources away from the SUSTAIN initiative. We were able to gather valuable data on the context and reasons for this withdrawal to supplement out analysis. Again, the adoption of case study design overcame these flexes during the data collection period and, overall, helped to create useful and transferable results [ 31 , 38 ].

Moving now to the practical measures, the extent to which we were able to develop a core set of applicable measures needs consideration. Given the difficulties with integrated care evaluation, we made efforts in our design to select meaningful and pragmatic instruments through a wide literature search, particularly with respect to measuring service user impact. A number of considerations resulted in a contraction of suitable instruments; for example, they had to be applicable to each of the very different integrated care improvements set up within the 14 SUSTAIN sites; they had to be suitable for administration to frail older people; and our central concepts of person-centredness, prevention orientation, safety needed to be reflected in the instruments. Authors have usefully illumined on the evaluation of integrated care and the utility of associated instruments, many of which were considered during the selection process [ 39 , 40 ]. However, it became clear early on that several existing and validated indicators for frail older people with multimorbidity would be unsuitable. With quality of life measures for example, this was due to the high possibility that relatively short interventions would have little impact; and recommended instruments such as PACIC (Vrijhoef et al. 2009) were not ‘hitting’ all of our considerations sufficiently closely. We therefore narrowed our focus onto an examination of improvements to care and the personal impact of care delivery, which included degrees of person-centredness, experiences of co-ordination, and perceived control and independence.

With this in mind and after much deliberation within the SUSTAIN consortium, we selected the P3CEQ [ 41 , 42 ], and the PCHC [ 43 ], the latter validated for our population group. At the time of selection, the P3CEQ was relatively new but seemed suitable for administration across all sites and intervention types. We did experience, however, some repetition between these two questionnaires, and in some sites there were significant problems with recruitment and fatigue of older people. In response to this, the PCHC was withdrawn, as the P3CEQ seemed more tuned to the SUSTAIN themes and also included items on control and independence in health and social care. Case study design accommodated this adaptation. The data collection and analysis relating to the P3CEQ was not without its challenges during the course of SUSTAIN however. We found it needed essential preconditions (eg. face-to-face administration, collections of reasons for non-response) and administration and coding guidelines (eg where informal carers support service users to answer questions) [ 44 ]. We conclude through our experiences, that establishing a solid and standard cross-country measure of older service user experiences for integrated care still remains fraught with complexity and somewhat elusive.

Obstacles were more apparent with obtaining quantitative indicators due to the availability, accessibility and reliability of appropriate health and social care data across partner countries. This is due for example to differences in what and how data is collected, variations in the geographical representation of data, and the general lack of social care data, and these problems are persistent. For example, across Europe, data is scattered across systems, is not interoperable, and there are privacy concerns and technical challenges that block effective data recording and sharing at local, national and European levels [ 18 ]. In addressing this somewhat ‘hostile’ environment, we co-created a core list with professionals and managers at the sites of what could be obtained from either routine service level data, clinical notes, care plans or other sources (see Efficiency data in Table 1 for indicators that were deemed common across sites).

Collecting directly from clinical data and care plans had the potential to be a rich source of data [ 45 ] and could overcome the problem of aggregated measures, such as hospital admission data, and their sensitivity to projects where the population group is small and widely dispersed. Similarly, with the cost data, very few sites were able to extract specific costs related directly to the improvement interventions, but an estimate of staff hours was deemed possible, to give some indication of resource use. However using both clinical notes, care plans and staff hours were dependent upon the accurate recording of these events by busy practitioners and managers, which could not be assured, an aspect also acknowledged by Jefferies et al [ 46 ] With clinical notes, this recording was variable and unless prompted, did not always yield the information required. Care plans were not always completed or available; other researchers have had similar experiences and list causes as staff time pressures, poor document construction and communication difficulties with service users, recognised in other studies [ 7 ]. With staff hours, although diaries/templates were made available at sites, staff worked across initiatives and were not always able to separate and accurately record specific hours dedicated to the improvement initiatives. So, in most cases this was estimated, and thus the ability to give a sound cost analysis was greatly reduced.

With this last point, difficulties with the measurement of cost in integrated care is the subject of much debate within the literature. Lack of standardised outcomes and continuous changes in care delivery, for example, render the employment of traditional economic models unusable [ 47 ]. While SUSTAIN was keen to avoid health economic methods that have a poor fit with the nature of integrated care, it was clear that our more pragmatic approach was also not optimal, and the search for a more reliable and attributable method should continue.

Any deficits within quantitative data were however compensated by the richness of our qualitative data sources. As well as service user and carer interviews, we obtained professional, managerial and other stakeholder viewpoints, alongside documentary evidence from care plans (where available), steering group meetings and field notes. These perspectives provided valuable insights into personal impacts of the intervention, contextual influences and more nuanced information about if, how and why improvements made a difference (see De Bruin et al. [ 36 ]).

Having reviewed the relative mertis of the EIT framework, the discussion moves on to a critique of case study design. One of the most commonly cited disadvantages of case studies is that findings can lack generalisability and scientific credibility because replication is difficult [ 37 ]. However, external validity can be stronger in multiple case study designs, which was the choice in SUSTAIN, and can be weak in more highly ranked randomised control trials. Such weaknesses in RCT design have been exposed in a number of systematic reviews and secondary analyses [ 48 ].

In practical terms, there are further difficulties that researchers can encounter. For example, there can be a tendency to become overwhelmed with data and the process can be very time consuming, particularly with regard to developing and blending thematic statements from the analysed data sources. This occurs particularly when propositions are lacking and there has been no attempt to link the data collection with the aims of the study in a focused way, or create some boundaries to data collection [ 26 ]. In SUSTAIN, we established clear objectives and propositions, protocols for every aspect of data collection and management, analytical templates for ensuring consistency with data analysis, and a shared quality-controlled database. Difficulties still arose however, so to supplement this and optimise uniformity of our evaluation approach, we arranged regular one-to-one progress and ‘trouble-shooting’ calls with research teams and devoted space at six monthly consortium meetings to deal with methods issues.

The discussion now moves finally to a consideration of why we selected implementation research over other methods such as realist evaluation. For SUSTAIN, the importance of gaining a consistent and understandable method across different institutions and contexts, as well as involving stakeholders not wholly conversant with research, was paramount. While our approach was not fault-free, realist methods also has its challenges regarding its complexity. For example, Greenhalgh et al. (2009) [ 10 ] noted that a set of more or less well-defined ‘mechanisms of change’ in reality can prove difficult to nail, and the process of developing CMO configurations is an interpretive task, achieved through much negotiation and dispute. In addition, the authors add that while realist evaluation can draw useful lessons about how particular preconditions make certain outcomes more likely, it cannot produce a simple recipe for success. Given that this latter aspect was a significant factor for our aim of promoting good knowledge transfer, the applicability of realist approaches to our design was limited, with implementation research seemingly more suitable.

Nevertheless, similarities are evident between these different evaluation approaches. While realist uses the development of CMO configurations, implementation research also investigates equally important factors affecting implementation (geographical, cultural beliefs, poverty), the processes of implementation themselves (multi-disciplinary working, local resource distribution) and the end product or outcome of the implementation [ 14 ]. Implementation research does not however link the components so strongly, circumnavigating the lengthy interpretation tendency of realist approaches. Nor does it, particularly in the case of EIT, lend itself to so readily to theory generation, unlike realist approaches. Hence combining the EIT framework with case study design as we did in SUSTAIN offered stronger opportunities for theory testing and development, as previously outlined.

Conclusions: lessons learned and recommendations

Overall, in the strive to seek out the answers to ‘what works’ in integrated care provision, SUSTAIN has enabled the identification of different ways to advance integrated care evaluation locally, nationally and across Europe, that fundamentally recognises its status as a complex intervention, and as a process. Operating within this conceptual and theoretical understanding, we were able to apply pre-emptive consideration to the challenges in the evaluation design, obtaining a good pragmatic fit with the objectives of evaluating improvements. It is clear that difficulties with health data continue, which impacted on our ability to provide a robust transferable set of core indicators, highlighting the continuing challenges. However, instruments within this set still are anticipated to be of value and more meaningful to what integrated care should aim to achieve. Integrated care evaluation continues to challenge, and our approach in SUSTAIN was not without its own challenges. However out intention with this paper is to support researchers by adding to the international methodological repertoire of evaluation approaches that encourage a goodness of contextual fit.

The following are key lessons learned and recommendations:

Without doubt, we would advocate a participatory approach to evaluation designs and one set within implementation research. This recognises the dynamic nature of integrated care implementation and keeps pace with its ebbs and flows, thereby strengthening the evaluation approach and potential for knowledge transfer.

Case study design also proved to be highly useful and adaptable to the changes in evaluation requirements, variations between sites, and is pertinent to cross-European comparative research.

With respect to the target group of older people, there is a clear need to employ more innovative data collection techniques that step aside from traditional survey and interview approaches, towards methods that are interactive, engaging and experiential and take account of ageing. Talking Mats, a tested and validated vehicle to support older people to communicate about things that matter to them, is gathering momentum as a research tool [ 49 ] and may be a way forward.

Further research is needed to better understand and measure the relationship between resource and cost changes and integrated care. In keeping with growing opinion, the focus must move away from traditional health economic models towards a more realistic and pragmatic perspective of what can be measured. Rephrasing of cost objectives towards investigating a ‘better use of resources’ within the integrated care environment may be a start.

Availability of data and materials

Researchers can apply for data by submitting a proposal to [email protected] . After agreement of the proposal analysis by the SUSTAIN steering committee, and after ethics approval and a data transfer agreement, collaborative researchers can receive data for a specific research question. Fees will be dependent upon the amount of work needed for data extraction.

Abbreviations

Evidence Integration Triangle

European Union

Person-centred experiences of co-ordinated care questionnaire

Perceived control of health care

Team climate inventory short version

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Acknowledgements

The content of this article reflects only the SUSTAIN consortium members’ views. The European Union is not liable for any use that may be made of the information contained herein. The authors would like to acknowledge the contributions of the SUSTAIN consortium as a whole to the methods development.

The SUSTAIN project was funded under Horizon 2020 – the Framework Programme for Research and Innovation (2014–2020) from the European Commission under grant agreement No. 634144. The funding body had no role in the design of the study, data collection, analysis, and interpretation of data, nor in the writing of this manuscript.

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Contributions

The authors have read and approved the manuscript. JB was responsible for and led the selection of the methodological approach, its development and the responses to methods challenges as the project evolved. JB also selected suitable instruments and wrote all the internal study protocols and guidelines associated with conducting the evaluation. This was consistently and strongly supported by SdB, and guided by GN and CB. The SUSTAIN consortium contributed through discussion and feedback. All authors contributed towards drafts of the articles.

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Correspondence to Jenny Billings .

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All seven countries participating in SUSTAIN obtained ethical approval according to their own governance procedures. Regarding consent to participate, written informed consent was obtained from all participants in all countries. The details of the institutions seeking approval and their respective Ethics Committees are as follows:

Austria . Institution: Osterreichishe Plattform fur Interdisziplinare Alternsfragen. Ethics committee of Vienna.

Catalonia, Spain . Institution: Agencia de Qualitat/Avaluacio Sanitaries de Catalunya. Ethics Committee: Comite Etica D’Investigacio Clinica 2015888.

Estonia . Institution: Sihtasutus Poliitikauuringute Keskus Praxis. Ethics committee: Meditsiiniuuringute Eetikakomitee 1187.

Germany . Institution: Stiftung Gesundheit. Ethics committees: Medical Association of Berlin and Medical Association of Brandenburg.

The Netherlands . Institution: Rijksinstituut voor Volksgezondheit en Milieu Centrum voor Voedin. Ethics committee: Medische Ethische Toetsingscommissie VU Medisch Centrum 2016.507.

Norway . Institution: University of Olso. Ethics Committee: REK Regionale Komiteer for Medisinsk og Helsefaglig Forskningsetikk 2016/2004/REK.

UK . Institution: University of Kent. Health Research Authority approval ref. 16/1EC08/0045.

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Billings, J., de Bruin, S.R., Baan, C. et al. Advancing integrated care evaluation in shifting contexts: blending implementation research with case study design in project SUSTAIN. BMC Health Serv Res 20 , 971 (2020). https://doi.org/10.1186/s12913-020-05775-5

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Qualitative Research Designs

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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• Mixed Methods Research | Definition, Guide & Examples

Mixed Methods Research | Definition, Guide & Examples

Published on August 13, 2021 by Tegan George . Revised on June 22, 2023.

Mixed methods research combines elements of quantitative research and qualitative research in order to answer your research question . Mixed methods can help you gain a more complete picture than a standalone quantitative or qualitative study, as it integrates benefits of both methods.

Mixed methods research is often used in the behavioral, health, and social sciences, especially in multidisciplinary settings and complex situational or societal research.

• To what extent does the frequency of traffic accidents ( quantitative ) reflect cyclist perceptions of road safety ( qualitative ) in Amsterdam?
• How do student perceptions of their school environment ( qualitative ) relate to differences in test scores ( quantitative ) ?
• How do interviews about job satisfaction at Company X ( qualitative ) help explain year-over-year sales performance and other KPIs ( quantitative ) ?
• How can voter and non-voter beliefs about democracy ( qualitative ) help explain election turnout patterns ( quantitative ) in Town X?
• How do average hospital salary measurements over time (quantitative) help to explain nurse testimonials about job satisfaction (qualitative) ?

Table of contents

When to use mixed methods research, mixed methods research designs, advantages of mixed methods research, disadvantages of mixed methods research, other interesting articles, frequently asked questions.

Mixed methods research may be the right choice if your research process suggests that quantitative or qualitative data alone will not sufficiently answer your research question. There are several common reasons for using mixed methods research:

• Generalizability : Qualitative research usually has a smaller sample size , and thus is not generalizable. In mixed methods research, this comparative weakness is mitigated by the comparative strength of “large N,” externally valid quantitative research.
• Contextualization: Mixing methods allows you to put findings in context and add richer detail to your conclusions. Using qualitative data to illustrate quantitative findings can help “put meat on the bones” of your analysis.
• Credibility: Using different methods to collect data on the same subject can make your results more credible. If the qualitative and quantitative data converge, this strengthens the validity of your conclusions. This process is called triangulation .

As you formulate your research question , try to directly address how qualitative and quantitative methods will be combined in your study. If your research question can be sufficiently answered via standalone quantitative or qualitative analysis, a mixed methods approach may not be the right fit.

But mixed methods might be a good choice if you want to meaningfully integrate both of these questions in one research study.

Keep in mind that mixed methods research doesn’t just mean collecting both types of data; you need to carefully consider the relationship between the two and how you’ll integrate them into coherent conclusions.

Mixed methods can be very challenging to put into practice, and comes with the same risk of research biases as standalone studies, so it’s a less common choice than standalone qualitative or qualitative research.

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There are different types of mixed methods research designs . The differences between them relate to the aim of the research, the timing of the data collection , and the importance given to each data type.

As you design your mixed methods study, also keep in mind:

• Your research approach ( inductive vs deductive )
• Your research questions
• What kind of data is already available for you to use
• What kind of data you’re able to collect yourself.

Here are a few of the most common mixed methods designs.

Convergent parallel

In a convergent parallel design, you collect quantitative and qualitative data at the same time and analyze them separately. After both analyses are complete, compare your results to draw overall conclusions.

• On the qualitative side, you analyze cyclist complaints via the city’s database and on social media to find out which areas are perceived as dangerous and why.
• On the quantitative side, you analyze accident reports in the city’s database to find out how frequently accidents occur in different areas of the city.

In an embedded design, you collect and analyze both types of data at the same time, but within a larger quantitative or qualitative design. One type of data is secondary to the other.

This is a good approach to take if you have limited time or resources. You can use an embedded design to strengthen or supplement your conclusions from the primary type of research design.

Explanatory sequential

In an explanatory sequential design, your quantitative data collection and analysis occurs first, followed by qualitative data collection and analysis.

You should use this design if you think your qualitative data will explain and contextualize your quantitative findings.

Exploratory sequential

In an exploratory sequential design, qualitative data collection and analysis occurs first, followed by quantitative data collection and analysis.

You can use this design to first explore initial questions and develop hypotheses . Then you can use the quantitative data to test or confirm your qualitative findings.

“Best of both worlds” analysis

Combining the two types of data means you benefit from both the detailed, contextualized insights of qualitative data and the generalizable , externally valid insights of quantitative data. The strengths of one type of data often mitigate the weaknesses of the other.

For example, solely quantitative studies often struggle to incorporate the lived experiences of your participants, so adding qualitative data deepens and enriches your quantitative results.

Solely qualitative studies are often not very generalizable, only reflecting the experiences of your participants, so adding quantitative data can validate your qualitative findings.

Method flexibility

Mixed methods are less tied to disciplines and established research paradigms. They offer more flexibility in designing your research, allowing you to combine aspects of different types of studies to distill the most informative results.

Mixed methods research can also combine theory generation and hypothesis testing within a single study, which is unusual for standalone qualitative or quantitative studies.

Mixed methods research is very labor-intensive. Collecting, analyzing, and synthesizing two types of data into one research product takes a lot of time and effort, and often involves interdisciplinary teams of researchers rather than individuals. For this reason, mixed methods research has the potential to cost much more than standalone studies.

Differing or conflicting results

If your analysis yields conflicting results, it can be very challenging to know how to interpret them in a mixed methods study. If the quantitative and qualitative results do not agree or you are concerned you may have confounding variables , it can be unclear how to proceed.

Due to the fact that quantitative and qualitative data take two vastly different forms, it can also be difficult to find ways to systematically compare the results, putting your data at risk for bias in the interpretation stage.

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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.

• Degrees of freedom
• Null hypothesis
• Discourse analysis
• Control groups
• Non-probability sampling
• Quantitative research
• Inclusion and exclusion criteria

Research bias

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

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.

In mixed methods research , you use both qualitative and quantitative data collection and analysis methods to answer your research question .

Data collection is the systematic process by which observations or measurements are gathered in research. It is used in many different contexts by academics, governments, businesses, and other organizations.

Triangulation in research means using multiple datasets, methods, theories and/or investigators to address a research question. It’s a research strategy that can help you enhance the validity and credibility of your findings.

Triangulation is mainly used in qualitative research , but it’s also commonly applied in quantitative research . Mixed methods research always uses triangulation.

These are four of the most common mixed methods designs :

• Convergent parallel: Quantitative and qualitative data are collected at the same time and analyzed separately. After both analyses are complete, compare your results to draw overall conclusions. 
• Embedded: Quantitative and qualitative data are collected at the same time, but within a larger quantitative or qualitative design. One type of data is secondary to the other.
• Explanatory sequential: Quantitative data is collected and analyzed first, followed by qualitative data. You can use this design if you think your qualitative data will explain and contextualize your quantitative findings.
• Exploratory sequential: Qualitative data is collected and analyzed first, followed by quantitative data. You can use this design if you think the quantitative data will confirm or validate your qualitative findings.

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Single Case Research Design

• Stefan Hunziker 3 &
• Michael Blankenagel 3  
• First Online: 04 January 2024

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This chapter addresses single-case research designs’ peculiarities, characteristics, and significant fallacies. A single case research design is a collective term for an in-depth analysis of a small non-random sample. The focus of this design is in-depth. This characteristic distinguishes the case study research from other research designs that understand the individual case as a relatively insignificant and interchangeable aspect of a population or sample. Also, researchers find relevant information on writing a single case research design paper and learn about typical methods used for this research design. The chapter closes by referring to overlapping and adjacent research designs.

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Hunziker, S., Blankenagel, M. (2024). Single Case Research Design. In: Research Design in Business and Management. Springer Gabler, Wiesbaden. https://doi.org/10.1007/978-3-658-42739-9_8

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ORIGINAL RESEARCH article

This article is part of the research topic.

Smart and Sustainable Planning for Europe and Beyond

Heat and the planning system: how can local authorities encourage deployment of low and zero carbon heating? Provisionally Accepted

• 1 University of Leeds, United Kingdom

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There is widespread recognition of the need for new homes to feature only low or zero carbon (LZC) heating. However, residential developers continue to choose conventional high-carbon options such as natural gas boilers over Net Zero compatible alternatives. This study explores how UK Local Authorities (LAs) within the English planning system can encourage residential developers to deploy LZC heating systems within their projects. We adopt an embedded case study design and analyse 30 residential project proposals within two LA areas. Through documentary analysis and expert interviews with local stakeholders, we explore local planning policies adopted and interactions between developers and LA officers, along with the resultant outcomes. We find that LAs can encourage developers to adopt LZC heating technologies above and beyond that which is required nationally. The conditions for this to occur are: (1) a planning policy which restricts allowable heating technology options, (2) empowering LA officers to enforce policies, (3) advice and support for developers to consider alternatives, and where necessary, (4) political backing to challenge unwilling developers.

Keywords: ASHP, air source heat pump, CHP, combined heat and power, Comm., communal, DH, district heating, GSHP, ground source heat pump, Ind., individual, kWh, Kilowatt-hour, LA, local authority

Received: 16 Nov 2023; Accepted: 02 Apr 2024.

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

* Correspondence: Dr. David Barns, University of Leeds, Leeds, United Kingdom

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