Sadler Euan, Porat Talya, Marshall Iain, Hoang Uy, Curcin Vasa, Wolfe Charles D A, McKevitt Christopher
Division of Health and Social Care Research, Faculty of Life Sciences and Medicine, King's College London, London, United Kingdom.
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.
PLoS One. 2017 May 5;12(5):e0177102. doi: 10.1371/journal.pone.0177102. eCollection 2017.
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.
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.
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.
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.
与许多慢性病一样，中风往往在孤立于其相关风险因素和多种合并症的情况下进行管理。随着获取临床和研究数据的机会增多，有潜力整合来自各种来源的数据，为改善医疗保健的干预措施提供信息。“学习型健康系统”（LHS）是一种创新的护理模式，它将整合的数据转化为知识以改善医疗保健。本研究的目的是开发一个让利益相关者参与使用临床和研究数据的过程，以便在学习型健康系统的指导下共同产生潜在解决方案，从而改善对患有多种合并症的中风幸存者的长期护理。
我们采用了一种基于共同生产原则的利益相关者参与研究设计，以让包括服务使用者、护理人员、全科医生以及其他健康和社会护理专业人员、服务经理、服务专员、政策制定者、第三部门代表和研究人员在内的利益相关者参与进来。在10个月的时间里，我们使用了一系列方法，包括利益相关者小组会议、焦点小组、名义小组技术（确定优先事项和建立共识）以及访谈。定性数据被记录、转录并进行主题分析。
37名参与者参与了该研究。数据如何推动干预措施发展的概念难以传达和理解。参与过程使利益相关者确定了四个数据和信息需求的优先领域：1）改善护理的连续性；2）改善心理健康后果的管理；3）更好地获得健康和社会护理；4）针对多种风险因素。这些优先事项为初步设计干预措施提供了依据。最终干预措施的选择通过共识达成，这一过程考虑了证据差距和当地服务提供情况以及可靠数据的可用性。这形成了一个共同生产的决策支持工具，以改善中风后的二级预防，供进一步开发。
让利益相关者参与确定数据驱动的解决方案是可行的，但需要资源。虽然确定了一些潜在的干预措施，但最终选择不仅取决于利益相关者的优先事项，还取决于数据的可用性。需要进一步开展工作来评估数据驱动的干预措施对长期中风幸存者的影响和实施情况。
