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      Shaping innovations in long-term care for stroke survivors with multimorbidity through stakeholder engagement

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          Abstract

          Background

          Stroke, like many long-term conditions, tends to be managed in isolation of its associated risk factors and multimorbidity. With increasing access to clinical and research data there is the potential to combine data from a variety of sources to inform interventions to improve healthcare. A ‘Learning Health System’ (LHS) is an innovative model of care which transforms integrated data into knowledge to improve healthcare. The objective of this study is to develop a process of engaging stakeholders in the use of clinical and research data to co-produce potential solutions, informed by a LHS, to improve long-term care for stroke survivors with multimorbidity.

          Methods

          We used a stakeholder engagement study design informed by co-production principles to engage stakeholders, including service users, carers, general practitioners and other health and social care professionals, service managers, commissioners of services, policy makers, third sector representatives and researchers. Over a 10 month period we used a range of methods including stakeholder group meetings, focus groups, nominal group techniques (priority setting and consensus building) and interviews. Qualitative data were recorded, transcribed and analysed thematically.

          Results

          37 participants took part in the study. The concept of how data might drive intervention development was difficult to convey and understand. The engagement process led to four priority areas for needs for data and information being identified by stakeholders: 1) improving continuity of care; 2) improving management of mental health consequences; 3) better access to health and social care; and 4) targeting multiple risk factors. These priorities informed preliminary design interventions. The final choice of intervention was agreed by consensus, informed by consideration of the gap in evidence and local service provision, and availability of robust data. This shaped a co-produced decision support tool to improve secondary prevention after stroke for further development.

          Conclusions

          Stakeholder engagement to identify data-driven solutions is feasible but requires resources. While a number of potential interventions were identified, the final choice rested not just on stakeholder priorities but also on data availability. Further work is required to evaluate the impact and implementation of data-driven interventions for long-term stroke survivors.

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          Most cited references40

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          Managing patients with multimorbidity in primary care.

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            Interventions for improving outcomes in patients with multimorbidity in primary care and community settings.

            Many people with chronic disease have more than one chronic condition, which is referred to as multimorbidity. The term comorbidity is also used but this is now taken to mean that there is a defined index condition with other linked conditions, for example diabetes and cardiovascular disease. It is also used when there are combinations of defined conditions that commonly co-exist, for example diabetes and depression. While this is not a new phenomenon, there is greater recognition of its impact and the importance of improving outcomes for individuals affected. Research in the area to date has focused mainly on descriptive epidemiology and impact assessment. There has been limited exploration of the effectiveness of interventions to improve outcomes for people with multimorbidity.
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              Self-reported long-term needs after stroke.

              Development of interventions to manage patients with stroke after discharge from the hospital requires estimates of need. This study estimates the prevalence of self-reported need in community-dwelling stroke survivors across the United Kingdom. We conducted a survey of stroke survivors 1 to 5 years poststroke recruited through Medical Research Council General Practice Research Framework general practices and 2 population-based stroke registers. Levels and type of need were calculated with comparisons among sociodemographic groups, disability level, and cognitive status using the χ2 test or Fisher exact test, as appropriate. From 1251 participants, response rates were 60% (national sample) and 78% (population registers sample) with few differences in levels of reported need between the 2 samples. Over half (51%) reported no unmet needs; among the remainder, the median number of unmet needs was 3 (range, 1 to 13). Proportions reporting unmet clinical needs ranged from 15% to 59%; 54% reported an unmet need for stroke information; 52% reported reduction in or loss of work activities, significantly more from black ethnic groups (P=0.006); 18% reported a loss in income and 31% an increase in expenses with differences by age, ethnic group, and deprivation score. In multivariable analysis, ethnicity (P=0.032) and disability (P=0.014) were associated with total number of unmet needs. Multiple long-term clinical and social needs remain unmet long after incident stroke. Higher levels of unmet need were reported by people with disabilities, from ethnic minority groups, and from those living in the most deprived areas. Development and testing of novel methods to meet unmet needs are required.
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                Author and article information

                Contributors
                Role: Editor
                Journal
                PLoS One
                PLoS ONE
                plos
                plosone
                PLoS ONE
                Public Library of Science (San Francisco, CA USA )
                1932-6203
                5 May 2017
                2017
                : 12
                : 5
                : e0177102
                Affiliations
                [1 ]Division of Health and Social Care Research, Faculty of Life Sciences and Medicine, King’s College London, London, United Kingdom
                [2 ]King’s Improvement Science, Centre for Implementation Science, Health Service and Population Research Department, Institute of Psychiatry, Psychology and Neuroscience, King’s College London, London, United Kingdom
                [3 ]National Institute for Health Research Collaboration for Leadership in Applied Health Research and Care South London, King’s College Hospital NHS Foundation Trust and King’s College London, London, United Kingdom
                [4 ]National Institute for Health Research Biomedical Research Centre at Guy's and St Thomas' NHS Foundation Trust and King's College London, London, United Kingdom
                Centro de Neurociencias de Cuba, CUBA
                Author notes

                Competing Interests: The authors have declared that no competing interests exist.

                • Conceptualization: ES CDAW CM IM UH TP VC.

                • Data curation: ES CM TP.

                • Formal analysis: ES TP IM CM CDAW.

                • Funding acquisition: CDAW CM.

                • Investigation: ES CDAW CM IM UH TP VC.

                • Methodology: ES CM CDAW IM TP VC UH.

                • Project administration: ES TP.

                • Resources: ES CM IM TP.

                • Supervision: CM CDAW VC.

                • Validation: ES CM CDAW TP IM UH VC.

                • Visualization: ES CM CDAW IM TP VC.

                • Writing – original draft: ES CM CDAW IM TP VC UH.

                • Writing – review & editing: ES CM CDAW IM TP VC UH.

                Author information
                http://orcid.org/0000-0003-3827-224X
                Article
                PONE-D-16-42861
                10.1371/journal.pone.0177102
                5419597
                28475606
                da7bef6f-1b22-4aaa-ab74-b0256d354d81
                © 2017 Sadler et al

                This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

                History
                : 27 October 2016
                : 22 April 2017
                Page count
                Figures: 0, Tables: 1, Pages: 16
                Funding
                Funded by: National Institute for Health Research (NIHR) Collaboration for Leadership in Applied Health Research and Care South London
                Award ID: NIHR CLAHRC-2013-10022
                Award Recipient : Charles DA Wolfe
                The research was funded by the National Institute for Health Research (NIHR) Collaboration for Leadership in Applied Health Research and Care South London at King’s College Hospital NHS Foundation Trust (award number NIHR CLAHRC-2013-10022), Prof Charles DA Wolfe (stroke theme lead), http://www.nihr.ac.uk/about-us/how-we-are-managed/our-structure/infrastructure/collaborations-for-leadership-in-applied-health-research-and-care.htm. The views expressed are those of the authors and not necessarily those of the NHS, the NIHR or the Department of Health. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
                Categories
                Research Article
                Medicine and Health Sciences
                Neurology
                Cerebrovascular Diseases
                Stroke
                Medicine and Health Sciences
                Vascular Medicine
                Stroke
                Medicine and Health Sciences
                Health Care
                Long-Term Care
                Medicine and Health Sciences
                Health Care
                Health Services Research
                Medicine and Health Sciences
                Mental Health and Psychiatry
                Mood Disorders
                Depression
                Medicine and Health Sciences
                Mental Health and Psychiatry
                Medicine and Health Sciences
                Health Care
                Health Care Policy
                Computer and Information Sciences
                Data Management
                Medicine and Health Sciences
                Health Care
                Health Risk Analysis
                Custom metadata
                All relevant data are within the paper and its Supporting Information file.

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                Uncategorized

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