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Spitters, S. J. I., Warner, J. O. & Reed, J. (2023). Beyond Clinical Guidelines: How Care Pathways and Quality-Improvement Methods Can Support Better Allergy Care. Current Allergy and Clinical Immunology, 36(4), 226-232
Open this publication in new window or tab >>Beyond Clinical Guidelines: How Care Pathways and Quality-Improvement Methods Can Support Better Allergy Care
2023 (English)In: Current Allergy and Clinical Immunology, ISSN 1609-3607, Vol. 36, no 4, p. 226-232Article, review/survey (Refereed) Published
Abstract [en]

The increasing prevalence of allergic disease has resulted in the recognition of allergy as a global public health concern. Yet health services worldwide appear to be ill-equipped to deliver high-quality allergy care. Clinical guidelines have been developed to describe what high-quality care looks like for most allergic diseases. However, allergy guidelines do not describe how the delivery of such care is organised across clinicians and provider organisations with varying degrees of access to allergy expertise and clinical resources. In this article, we describe how care pathways can be used to improve the organisation and delivery of allergy care in accordance with the characteristics of allergic disease and local constraints in the health service. We then describe how quality-improvement methods can support the successful realisation of allergy care pathways in practice. Realising care pathways involves a highly complex process of changing the way care is practised and organised. This could involve developing a new service, clinical training or other interventions. Quality-improvement methods were developed as a guide to navigate and support the process of change and improvement. © 2023, Allergy Society of South Africa. All rights reserved.

Place, publisher, year, edition, pages
Cape Town, South Africa: Allergy Society of South Africa, 2023
Keywords
allergy, asthma, care pathways, clinical guidelines, integrated care, quality improvement
National Category
Nursing
Identifiers
urn:nbn:se:hh:diva-52318 (URN)2-s2.0-85179738110 (Scopus ID)
Note

Funding: In part by the NIHR under the CLAHRC programme for Northwest London

Available from: 2023-12-22 Created: 2023-12-22 Last updated: 2023-12-22Bibliographically approved
Antonacci, G., Whitney, J., Harris, M. & Reed, J. (2023). How do healthcare providers use national audit data for improvement?. BMC Health Services Research, 23(1), Article ID 393.
Open this publication in new window or tab >>How do healthcare providers use national audit data for improvement?
2023 (English)In: BMC Health Services Research, E-ISSN 1472-6963, Vol. 23, no 1, article id 393Article in journal (Refereed) Published
Abstract [en]

Background: Substantial resources are invested by Health Departments worldwide in introducing National Clinical Audits (NCAs). Yet, there is variable evidence on the NCAs’ effectiveness and little is known on factors underlying the successful use of NCAs to improve local practice. This study will focus on a single NCA (the National Audit of Inpatient Falls -NAIF 2017) to explore: (i) participants’ perspectives on the NCA reports, local feedback characteristics and actions undertaken following the feedback underpinning the effective use of the NCA feedback to improve local practice; (ii) reported changes in local practice following the NCA feedback in England and Wales. Methods: Front-line staff perspectives were gathered through interviews. An inductive qualitative approach was used. Eighteen participants were purposefully sampled from 7 of the 85 participating hospitals in England and Wales. Analysis was guided by constant comparative techniques. Results: Regarding the NAIF annual report, interviewees valued performance benchmarking with other hospitals, the use of visual representations and the inclusion of case studies and recommendations. Participants stated that feedback should target front-line healthcare professionals, be straightforward and focused, and be delivered through an encouraging and honest discussion. Interviewees highlighted the value of using other relevant data sources alongside NAIF feedback and the importance of continuous data monitoring. Participants reported that engagement of front-line staff in the NAIF and following improvement activities was critical. Leadership, ownership, management support and communication at different organisational levels were perceived as enablers, while staffing level and turnover, and poor quality improvement (QI) skills, were perceived as barriers to improvement. Reported changes in practice included increased awareness and attention to patient safety issues and greater involvement of patients and staff in falls prevention activities. Conclusions: There is scope to improve the use of NCAs by front-line staff. NCAs should not be seen as isolated interventions but should be fully embedded and integrated into the QI strategic and operational plans of NHS trusts. The use of NCAs could be optimised, but knowledge of them is poor and distributed unevenly across different disciplines. More research is needed to provide guidance on key elements to consider throughout the whole improvement process at different organisational levels. © 2023, The Author(s).

Place, publisher, year, edition, pages
London: BioMed Central (BMC), 2023
Keywords
Audit, Feedback, Health care improvement, Inpatient falls, National clinical audit, Quality improvement
National Category
Obstetrics, Gynecology and Reproductive Medicine
Identifiers
urn:nbn:se:hh:diva-51398 (URN)10.1186/s12913-023-09334-6 (DOI)000975950400005 ()37095495 (PubMedID)2-s2.0-85153687421 (Scopus ID)
Note

Funding: The study was funded by the Falls and Fragility Fracture Audit Programme. This report is independent research supported by the National Institute for Health and Care Research Applied Research Collaboration Northwest London. 

Available from: 2023-08-15 Created: 2023-08-15 Last updated: 2023-08-16Bibliographically approved
Petersson, L., Larsson, I., Nygren, J. M., Nilsen, P., Neher, M., Reed, J. E., . . . Svedberg, P. (2022). Challenges to implementing artificial intelligence in healthcare: a qualitative interview study with healthcare leaders in Sweden. BMC Health Services Research, 22, Article ID 850.
Open this publication in new window or tab >>Challenges to implementing artificial intelligence in healthcare: a qualitative interview study with healthcare leaders in Sweden
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2022 (English)In: BMC Health Services Research, E-ISSN 1472-6963, Vol. 22, article id 850Article in journal (Refereed) Published
Abstract [en]

Background: Artificial intelligence (AI) for healthcare presents potential solutions to some of the challenges faced by health systems around the world. However, it is well established in implementation and innovation research that novel technologies are often resisted by healthcare leaders, which contributes to their slow and variable uptake. Although research on various stakeholders’ perspectives on AI implementation has been undertaken, very few studies have investigated leaders’ perspectives on the issue of AI implementation in healthcare. It is essential to understand the perspectives of healthcare leaders, because they have a key role in the implementation process of new technologies in healthcare. The aim of this study was to explore challenges perceived by leaders in a regional Swedish healthcare setting concerning the implementation of AI in healthcare.

Methods: The study takes an explorative qualitative approach. Individual, semi-structured interviews were conducted from October 2020 to May 2021 with 26 healthcare leaders. The analysis was performed using qualitative content analysis, with an inductive approach.

Results: The analysis yielded three categories, representing three types of challenge perceived to be linked with the implementation of AI in healthcare: 1) Conditions external to the healthcare system; 2) Capacity for strategic change management; 3) Transformation of healthcare professions and healthcare practice.

Conclusions: In conclusion, healthcare leaders highlighted several implementation challenges in relation to AI within and beyond the healthcare system in general and their organisations in particular. The challenges comprised conditions external to the healthcare system, internal capacity for strategic change management, along with transformation of healthcare professions and healthcare practice. The results point to the need to develop implementation strategies across healthcare organisations to address challenges to AI-specific capacity building. Laws and policies are needed to regulate the design and execution of effective AI implementation strategies. There is a need to invest time and resources in implementation processes, with collaboration across healthcare, county councils, and industry partnerships. © The Author(s) 2022.

Place, publisher, year, edition, pages
London: BioMed Central (BMC), 2022
Keywords
Artificial intelligence, Digital transformation, Healthcare, Implementation, Healthcare leaders, Organizational change, Qualitative methods, Stakeholders
National Category
Health Care Service and Management, Health Policy and Services and Health Economy
Research subject
Health Innovation
Identifiers
urn:nbn:se:hh:diva-47654 (URN)10.1186/s12913-022-08215-8 (DOI)000819783700002 ()35778736 (PubMedID)2-s2.0-85133367171 (Scopus ID)
Funder
Vinnova, 2019-04526Knowledge Foundation, 20200208 01H
Available from: 2022-08-04 Created: 2022-08-04 Last updated: 2023-08-16Bibliographically approved
Lennox, L., Barber, S., Stillman, N., Spitters, S., Ward, E., Marvin, V. & Reed, J. (2022). Conceptualising interventions to enhance spread in complex systems: a multisite comprehensive medication review case study. BMJ Quality and Safety, 31(1), 31-44
Open this publication in new window or tab >>Conceptualising interventions to enhance spread in complex systems: a multisite comprehensive medication review case study
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2022 (English)In: BMJ Quality and Safety, ISSN 2044-5415, E-ISSN 2044-5423, Vol. 31, no 1, p. 31-44Article in journal (Refereed) Published
Abstract [en]

Background: Advancing the description and conceptualisation of interventions in complex systems is necessary to support spread, evaluation, attribution and reproducibility. Improvement teams can provide unique insight into how interventions are operationalised in practice. Capturing this 'insider knowledge' has the potential to enhance intervention descriptions.

Objectives: This exploratory study investigated the spread of a comprehensive medication review (CMR) intervention to (1) describe the work required from the improvement team perspective, (2) identify what stays the same and what changes between the different sites and why, and (3) critically appraise the 'hard core' and 'soft periphery' (HC/SP) construct as a way of conceptualising interventions.

Design: A prospective case study of a CMR initiative across five sites. Data collection included: observations, document analysis and semistructured interviews. A facilitated workshop triangulated findings and measured perceived effort invested in activities. A qualitative database was developed to conduct thematic analysis.

Results: Sites identified 16 intervention components. All were considered essential due to their interdependency. The function of components remained the same, but adaptations were made between and within sites. Components were categorised under four 'spheres of operation': Accessibility of evidence base; Process of enactment; Dependent processes and Dependent sociocultural issues. Participants reported most effort was invested on 'dependent sociocultural issues'. None of the existing HC/SP definitions fit well with the empirical data, with inconsistent classifications of components as HC or SP.

Conclusions: This study advances the conceptualisation of interventions by explicitly considering how evidence-based practices are operationalised in complex systems. We propose a new conceptualisation of 'interventions-in-systems' which describes intervention components in relation to their: proximity to the evidence base; component interdependence; component function; component adaptation and effort. © Author(s) (or their employer(s)) 2022. Re-use permitted under CC BY-NC. No commercial re-use. See rights and permissions. Published by BMJ.

Place, publisher, year, edition, pages
London: BMJ Publishing Group Ltd, 2022
Keywords
breakthrough groups, clinical practice guidelines, collaborative, complexity, healthcare quality improvement, implementation science
National Category
Cardiac and Cardiovascular Systems
Identifiers
urn:nbn:se:hh:diva-46016 (URN)10.1136/bmjqs-2020-012367 (DOI)000726951400001 ()33990462 (PubMedID)2-s2.0-85106186171 (Scopus ID)
Note

Funding: This research was funded by the National Institute for Health Research (NIHR) Collaboration for Leadership in Applied Health Research and Care Northwest London (CLAHRC NWL), now recommissioned as NIHR Applied Research Collaboration NWL (ARC NWL). The research team also acknowledges the support of the NIHR Clinical Research Network (CRN). JR was also funded by a Health Foundation Improvement Science Fellowship.

Available from: 2021-12-03 Created: 2021-12-03 Last updated: 2022-01-31Bibliographically approved
Gama, F., Tyskbo, D., Nygren, J. M., Barlow, J., Reed, J. & Svedberg, P. (2022). Implementation Frameworks for Artificial Intelligence Translation Into Health Care Practice: Scoping Review. Journal of Medical Internet Research, 24(1), Article ID e32215.
Open this publication in new window or tab >>Implementation Frameworks for Artificial Intelligence Translation Into Health Care Practice: Scoping Review
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2022 (English)In: Journal of Medical Internet Research, E-ISSN 1438-8871, Vol. 24, no 1, article id e32215Article in journal (Refereed) Published
Abstract [en]

Background: Significant efforts have been made to develop artificial intelligence (AI) solutions for health care improvement. Despite the enthusiasm, health care professionals still struggle to implement AI in their daily practice.

Objective: This paper aims to identify the implementation frameworks used to understand the application of AI in health care practice.

Methods: A scoping review was conducted using the Cochrane, Evidence Based Medicine Reviews, Embase, MEDLINE, and PsycINFO databases to identify publications that reported frameworks, models, and theories concerning AI implementation in health care. This review focused on studies published in English and investigating AI implementation in health care since 2000. A total of 2541 unique publications were retrieved from the databases and screened on titles and abstracts by 2 independent reviewers. Selected articles were thematically analyzed against the Nilsen taxonomy of implementation frameworks, and the Greenhalgh framework for the nonadoption, abandonment, scale-up, spread, and sustainability (NASSS) of health care technologies.

Results: In total, 7 articles met all eligibility criteria for inclusion in the review, and 2 articles included formal frameworks that directly addressed AI implementation, whereas the other articles provided limited descriptions of elements influencing implementation. Collectively, the 7 articles identified elements that aligned with all the NASSS domains, but no single article comprehensively considered the factors known to influence technology implementation. New domains were identified, including dependency on data input and existing processes, shared decision-making, the role of human oversight, and ethics of population impact and inequality, suggesting that existing frameworks do not fully consider the unique needs of AI implementation.

Conclusions: This literature review demonstrates that understanding how to implement AI in health care practice is still in its early stages of development. Our findings suggest that further research is needed to provide the knowledge necessary to develop implementation frameworks to guide the future implementation of AI in clinical practice and highlight the opportunity to draw on existing knowledge from the field of implementation science. ©Fábio Gama, Daniel Tyskbo, Jens Nygren, James Barlow, Julie Reed, Petra Svedberg. 

Place, publisher, year, edition, pages
Toronto, ON: JMIR Publications, 2022
Keywords
implementation framework, artificial intelligence, scoping review, service innovation
National Category
Health Care Service and Management, Health Policy and Services and Health Economy
Research subject
Health Innovation, Information driven care
Identifiers
urn:nbn:se:hh:diva-46282 (URN)10.2196/32215 (DOI)000766779500002 ()35084349 (PubMedID)2-s2.0-85123814747 (Scopus ID)
Projects
Vinnova 2019-04526Knowledge Foundation 20200208 01H
Funder
Vinnova, 2019-04526Knowledge Foundation, 20200208 01H
Available from: 2022-02-03 Created: 2022-02-03 Last updated: 2024-01-17Bibliographically approved
Nilsen, P., Reed, J., Nair, M., Savage, C., Macrae, C., Barlow, J., . . . Nygren, J. M. (2022). Realizing the potential of artificial intelligence in healthcare: Learning from intervention, innovation, implementation and improvement sciences. Frontiers in Health Services, 2, Article ID 961475.
Open this publication in new window or tab >>Realizing the potential of artificial intelligence in healthcare: Learning from intervention, innovation, implementation and improvement sciences
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2022 (English)In: Frontiers in Health Services, E-ISSN 2813-0146, Vol. 2, article id 961475Article in journal (Refereed) Published
Abstract [en]

Introduction: Artificial intelligence (AI) is widely seen as critical for tackling fundamental challenges faced by health systems. However, research is scant on the factors that influence the implementation and routine use of AI in healthcare, how AI may interact with the context in which it is implemented, and how it can contribute to wider health system goals. We propose that AI development can benefit from knowledge generated in four scientific fields: intervention, innovation, implementation and improvement sciences.

Aim: The aim of this paper is to briefly describe the four fields and to identify potentially relevant knowledge from these fields that can be utilized for understanding and/or facilitating the use of AI in healthcare. The paper is based on the authors' experience and expertise in intervention, innovation, implementation, and improvement sciences, and a selective literature review.

Utilizing knowledge from the four fields: The four fields have generated a wealth of often-overlapping knowledge, some of which we propose has considerable relevance for understanding and/or facilitating the use of AI in healthcare.

Conclusion: Knowledge derived from intervention, innovation, implementation, and improvement sciences provides a head start for research on the use of AI in healthcare, yet the extent to which this knowledge can be repurposed in AI studies cannot be taken for granted. Thus, when taking advantage of insights in the four fields, it is important to also be explorative and use inductive research approaches to generate knowledge that can contribute toward realizing the potential of AI in healthcare. © 2022 Nilsen, Reed, Nair, Savage, Macrae, Barlow, Svedberg, Larsson, Lundgren and Nygren. 

Place, publisher, year, edition, pages
Lausanne: Frontiers Media S.A., 2022
Keywords
artificial intelligence, intervention, innovation, implementation, improvement
National Category
Health Sciences
Research subject
Health Innovation, Information driven care
Identifiers
urn:nbn:se:hh:diva-48283 (URN)10.3389/frhs.2022.961475 (DOI)
Funder
Vinnova, 2019-04526Knowledge Foundation, 20200208 01H
Available from: 2022-10-06 Created: 2022-10-06 Last updated: 2024-01-23Bibliographically approved
Svedberg, P., Reed, J., Nilsen, P., Barlow, J., Macrae, C. & Nygren, J. M. (2022). Toward Successful Implementation of Artificial Intelligence in Health Care Practice: Protocol for a Research Program. JMIR Research Protocols, 11(3), Article ID e34920.
Open this publication in new window or tab >>Toward Successful Implementation of Artificial Intelligence in Health Care Practice: Protocol for a Research Program
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2022 (English)In: JMIR Research Protocols, E-ISSN 1929-0748, Vol. 11, no 3, article id e34920Article in journal (Refereed) Published
Abstract [en]

Background: The uptake of artificial intelligence (AI) in health care is at an early stage. Recent studies have shown a lack of AI-specific implementation theories, models, or frameworks that could provide guidance for how to translate the potential of AI into daily health care practices. This protocol provides an outline for the first 5 years of a research program seeking to address this knowledge-practice gap through collaboration and co-design between researchers, health care professionals, patients, and industry stakeholders.

Objective: The first part of the program focuses on two specific objectives. The first objective is to develop a theoretically informed framework for AI implementation in health care that can be applied to facilitate such implementation in routine health care practice. The second objective is to carry out empirical AI implementation studies, guided by the framework for AI implementation, and to generate learning for enhanced knowledge and operational insights to guide further refinement of the framework. The second part of the program addresses a third objective, which is to apply the developed framework in clinical practice in order to develop regional capacity to provide the practical resources, competencies, and organizational structure required for AI implementation; however, this objective is beyond the scope of this protocol.

Methods: This research program will use a logic model to structure the development of a methodological framework for planning and evaluating implementation of AI systems in health care and to support capacity building for its use in practice. The logic model is divided into time-separated stages, with a focus on theory-driven and coproduced framework development. The activities are based on both knowledge development, using existing theory and literature reviews, and method development by means of co-design and empirical investigations. The activities will involve researchers, health care professionals, and other stakeholders to create a multi-perspective understanding.

Results: The project started on July 1, 2021, with the Stage 1 activities, including model overview, literature reviews, stakeholder mapping, and impact cases; we will then proceed with Stage 2 activities. Stage 1 and 2 activities will continue until June 30, 2026.

Conclusions: There is a need to advance theory and empirical evidence on the implementation requirements of AI systems in health care, as well as an opportunity to bring together insights from research on the development, introduction, and evaluation of AI systems and existing knowledge from implementation research literature. Therefore, with this research program, we intend to build an understanding, using both theoretical and empirical approaches, of how the implementation of AI systems should be approached in order to increase the likelihood of successful and widespread application in clinical practice. © Petra Svedberg, Julie Reed, Per Nilsen, James Barlow, Carl Macrae, Jens Nygren.

Place, publisher, year, edition, pages
Toronto, ON: JMIR Publications Inc., 2022
Keywords
process evaluation, complex intervention, implementation, knowledge exchange, health policy, organizational change, capacity building, qualitative methods, framework analysis
National Category
Nursing
Research subject
Health Innovation
Identifiers
urn:nbn:se:hh:diva-46533 (URN)10.2196/34920 (DOI)000779992200013 ()35187778 (PubMedID)2-s2.0-85126448507 (Scopus ID)
Funder
VinnovaKnowledge Foundation
Available from: 2022-04-04 Created: 2022-04-04 Last updated: 2024-01-17Bibliographically approved
von Thiele Schwarz, U., Nielsen, K., Edwards, K., Hasson, H., Ipsen, C., Savage, C., . . . Reed, J. E. (2021). How to design, implement and evaluate organizational interventions for maximum impact: the Sigtuna Principles. European Journal of Work and Organizational Psychology, 30(3), 415-427
Open this publication in new window or tab >>How to design, implement and evaluate organizational interventions for maximum impact: the Sigtuna Principles
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2021 (English)In: European Journal of Work and Organizational Psychology, ISSN 1359-432X, E-ISSN 1464-0643, Vol. 30, no 3, p. 415-427Article in journal (Refereed) Published
Abstract [en]

Research on organizational interventions needs to meet the objectives of both researchers and participating organizations. This duality means that real-world impact has to be considered throughout the research process, simultaneously addressing both scientific rigour and practical relevance. This discussion paper aims to offer a set of principles, grounded in knowledge from various disciplines that can guide researchers in designing, implementing, and evaluating organizational interventions. Inspired by Mode 2 knowledge production, the principles were developed through a transdisciplinary, participatory and iterative process where practitioners and academics were invited to develop, refine and validate the principles. The process resulted in 10 principles: 1) Ensure active engagement and participation among key stakeholders; 2) Understand the situation (starting points and objectives); 3) Align the intervention with existing organizational objectives; 4) Explicate the program logic; 5) Prioritize intervention activities based on effort-gain balance; 6) Work with existing practices, processes, and mindsets; 7) Iteratively observe, reflect, and adapt; 8) Develop organizational learning capabilities; 9) Evaluate the interaction between intervention, process, and context; and 10) Transfer knowledge beyond the specific organization. The principles suggest how the design, implementation, and evaluation of organizational interventions can be researched in a way that maximizes both practical and scientific impact. © 2020 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group.

Place, publisher, year, edition, pages
Abingdon: Routledge, 2021
Keywords
Academy-practice partnership, occupational health interventions, participation, recommendations, workplace-based interventions
National Category
Public Health, Global Health, Social Medicine and Epidemiology
Identifiers
urn:nbn:se:hh:diva-46067 (URN)10.1080/1359432X.2020.1803960 (DOI)000562813600001 ()34518756 (PubMedID)2-s2.0-85089888937 (Scopus ID)
Funder
Swedish Research Council, 2016-01261
Note

Funding agency:

Joint Committee for Nordic research councils in the Humanities and Social Sciences.  Grant number: 2016-00241/NOS-HS

Swedish Research Council European Commission. Grant number:2016-01261

National Institute for Health Research (NIHR)

Health Foundation

Available from: 2021-12-08 Created: 2021-12-08 Last updated: 2021-12-08Bibliographically approved
Marston, C. A., Matthews, R., Renedo, A. & Reed, J. (2020). Working together to co-produce better health: The experience of the Collaboration for Leadership in Applied Health Research and Care for Northwest London. Journal of Health Services Research and Policy, 26(1), 28-36
Open this publication in new window or tab >>Working together to co-produce better health: The experience of the Collaboration for Leadership in Applied Health Research and Care for Northwest London
2020 (English)In: Journal of Health Services Research and Policy, ISSN 1355-8196, E-ISSN 1758-1060, Vol. 26, no 1, p. 28-36Article in journal (Refereed) Published
Abstract [en]

Objectives: To improve the provision of health care, academics can be asked to collaborate with clinicians, and clinicians with patients. Generating good evidence on health care practice depends on these collaborations working well. Yet such relationships are not the norm. We examine how social science research and health care improvement practice were linked through a programme designed to broker collaborations between clinicians, academics, and patients to improve health care – the UK National Institute for Health Research Collaboration for Leadership in Applied Health Research and Care for Northwest London. We discuss the successes and challenges of the collaboration and make suggestions on how to develop synergistic relationships that facilitate co-production of social science knowledge and its translation into practice. Methods: A qualitative approach was used, including ethnographic elements and critical, reflexive dialogue between members of the two collaborating teams. Results: Key challenges and remedies were connected with the risks associated with new ways of working. These risks included differing ideas between collaborators about the purpose, value, and expectations of research, and institutional opposition. Dialogue between collaborators did not mean absence of tensions or clashes. Risk-taking was unpopular – institutions, funders, and partners did not always support it, despite simultaneously demanding ‘innovation’ in producing research that influenced practice. Conclusions: Our path was made smoother because we had funding to support the creation of a ‘potential space’ to experiment with different ways of working. Other factors that can enhance collaboration include a shared commitment to dialogical practice, a recognition of the legitimacy of different partners’ knowledge, a long timeframe to identify and resolve problems, the maintenance of an enabling environment for collaboration, a willingness to work iteratively and reflexively, and a shared end goal. © The Author(s) 2020.

Place, publisher, year, edition, pages
Sage Publications, 2020
Keywords
adult, article, controlled study, England, expectation, funding, high risk behavior, human, leadership, medical research, patient participation, sociology, tension
National Category
Nursing Health Care Service and Management, Health Policy and Services and Health Economy
Identifiers
urn:nbn:se:hh:diva-43651 (URN)10.1177/1355819620928368 (DOI)000537815100001 ()32486987 (PubMedID)2-s2.0-85086001717 (Scopus ID)
Available from: 2020-12-08 Created: 2020-12-08 Last updated: 2024-02-07Bibliographically approved
Organisations
Identifiers
ORCID iD: ORCID iD iconorcid.org/0000-0002-9974-2017

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