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Simple rules for evidence translation in complex systems: A qualitative study

机译:复杂系统中证据翻译的简单规则:定性研究

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Abstract BackgroundEnsuring patients benefit from the latest medical and technical advances remains a major challenge, with rational-linear and reductionist approaches to translating evidence into practice proving inefficient and ineffective. Complexity thinking, which emphasises interconnectedness and unpredictability, offers insights to inform evidence translation theories and strategies. Drawing on detailed insights into complex micro-systems, this research aimed to advance empirical and theoretical understanding of the reality of making and sustaining improvements in complex healthcare systems.MethodsUsing analytical auto-ethnography, including documentary analysis and literature review, we assimilated learning from 5?years of observation of 22 evidence translation projects (UK). We used a grounded theory approach to develop substantive theory and a conceptual framework. Results were interpreted using complexity theory and ‘simple rules’ were identified reflecting the practical strategies that enhanced project progress.ResultsThe framework for Successful Healthcare Improvement From Translating Evidence in complex systems (SHIFT-Evidence) positions the challenge of evidence translation within the dynamic context of the health system. SHIFT-Evidence is summarised by three strategic principles, namely (1) ‘act scientifically and pragmatically’ – knowledge of existing evidence needs to be combined with knowledge of the unique initial conditions of a system, and interventions need to adapt as the complex system responds and learning emerges about unpredictable effects; (2) ‘embrace complexity’ –?evidence-based interventions only work if related practices and processes of care within the complex system are functional, and evidence-translation efforts need to identify and address any problems with usual care, recognising that this typically includes a range of interdependent parts of the system; and (3) ‘engage and empower’ – evidence translation and system navigation requires commitment and insights from staff and patients with experience of the local system, and changes need to align with their motivations and concerns. Twelve associated ‘simple rules’ are presented to provide actionable guidance to support evidence translation and improvement in complex systems.ConclusionBy recognising how agency, interconnectedness and unpredictability influences evidence translation in complex systems, SHIFT-Evidence provides a tool to guide practice and research. The ‘simple rules’ have potential to provide a common platform for academics, practitioners, patients and policymakers to collaborate when intervening to achieve improvements in healthcare.
机译:摘要背景要确保患者从最新的医学和技术进步中受益,仍然是一项重大挑战,采用理性线性和归约论方法将证据转化为实践证明是无效的和无效的。强调相互联系和不可预测性的复杂性思维提供了见解,可以为证据翻译理论和策略提供依据。本研究以对复杂微系统的详细见解为基础,旨在增进对复杂医疗系统进行和持续改进的现实的经验和理论理解。方法使用分析性人种志学,包括文献分析和文献综述,我们从5中吸取了学习经验多年对22个证据翻译项目的观察(英国)。我们使用扎根的理论方法来发展实体理论和概念框架。使用复杂性理论解释结果,并确定“简单规则”以反映可增强项目进展的实际策略。结果从复杂系统中的证据转换成功实现医疗保健成功改进的框架(SHIFT-Evidence)将证据转换的挑战置于动态环境中。卫生系统。 SHIFT证据通过三个战略原则进行了总结,即(1)“科学而务实地行动” –现有证据的知识需要与系统独特的初始条件的知识相结合,干预措施需要随着复杂系统的响应而适应关于不可预期的影响的学习应运而生; (2)“拥抱复杂性”-基于证据的干预措施只有在复杂系统中相关的护理实践和流程起作用的情况下才有效,并且证据转换工作需要确定并解决常规护理中的任何问题,并认识到这通常包括系统的一系列相互依存的部分; (3)“参与和授权”-证据翻译和系统导航需要具有本地系统经验的工作人员和患者的承诺和见解,而变化也需要与其动机和关注点保持一致。提出了十二个相关的“简单规则”以提供可操作的指导,以支持复杂系统中的证据翻译和改进。结论通过认识代理,相互联系和不可预测性如何影响复杂系统中的证据翻译,SHIFT-Evidence提供了一种指导实践和研究的工具。 “简单规则”有可能为学者,从业人员,患者和政策制定者提供一个通用平台,以便他们介入以改善医疗保健。

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