When complexity science meets implementation science: a theoretical and empirical analysis of systems change
When complexity science meets implementation science: a theoretical and empirical analysis of systems change
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DOI：
10.1186/s12916-018-1057-z
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发表时间：
2018-04-30
期刊：
影响因子：
9.3
通讯作者：
Herkes, Jessica
中科院分区：
文献类型：
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作者：
Braithwaite, Jeffrey;Churruca, Kate;Herkes, Jessica
背景：实施科学有一个核心目标-将证据付诸实践。在循证医学运动的早期，这一任务被解释为线性术语，其中知识管道从实验室创建的证据转移到临床试验，最后通过新的测试，药物，设备或程序进入临床实践。我们现在知道，这种直线思维充其量是天真的，只不过是一种理想化，在管道中出现了多个裂缝。讨论：知识管道源于机械和线性的科学方法，虽然在过去的两个世纪里为医学带来了巨大的进步，但其应用局限于医疗保健等复杂的社会系统。相反，复杂性科学，一种理解主体之间相互联系以及它们如何产生紧急，动态，系统级行为的理论方法，代表了一个越来越有用的变革概念框架。在这里，我们讨论了实施科学可以从复杂性科学中学习到什么，并梳理出医疗保健系统的一些属性，这些属性可以实现或限制我们为更好，更有效，更循证的医疗保健所制定的目标。澳大利亚的两个例子，一个主要是自上而下的，基于在全国范围内应用新标准，另一个主要是自下而上的，在200多家医院采用医疗急救团队，为基于复杂性的实施方法提供了实证支持。主要的经验教训是，可以通过许多方式促进变革，但需要一个触发机制，如立法或利益攸关方的广泛协议;反馈循环对保持变革势头至关重要;涉及时间的延长，通常比开始时认为的要长得多;考虑到现有网络和社会技术特点，采取系统知情的复杂性方法是有益的。将医疗保健视为一个复杂的自适应系统意味着通过逐步模型将证据纳入常规实践是不可行的。复杂性科学迫使我们考虑系统的动态特性和深深陷入社会实践中的各种特征，同时指出，任何变化过程都必须考虑多种力量，变量和影响，并且不可预测性和不确定性是多部分复杂系统的正常特性。
Background: Implementation science has a core aim - to get evidence into practice. Early in the evidence-based medicine movement, this task was construed in linear terms, wherein the knowledge pipeline moved from evidence created in the laboratory through to clinical trials and, finally, via new tests, drugs, equipment, or procedures, into clinical practice. We now know that this straight-line thinking was naive at best, and little more than an idealization, with multiple fractures appearing in the pipeline.Discussion: The knowledge pipeline derives from a mechanistic and linear approach to science, which, while delivering huge advances in medicine over the last two centuries, is limited in its application to complex social systems such as healthcare. Instead, complexity science, a theoretical approach to understanding interconnections among agents and how they give rise to emergent, dynamic, systems-level behaviors, represents an increasingly useful conceptual framework for change. Herein, we discuss what implementation science can learn from complexity science, and tease out some of the properties of healthcare systems that enable or constrain the goals we have for better, more effective, more evidence-based care. Two Australian examples, one largely top-down, predicated on applying new standards across the country, and the other largely bottom-up, adopting medical emergency teams in over 200 hospitals, provide empirical support for a complexity-informed approach to implementation. The key lessons are that change can be stimulated in many ways, but a triggering mechanism is needed, such as legislation or widespread stakeholder agreement; that feedback loops are crucial to continue change momentum; that extended sweeps of time are involved, typically much longer than believed at the outset; and that taking a systems-informed, complexity approach, having regard for existing networks and socio-technical characteristics, is beneficial.Conclusion: Construing healthcare as a complex adaptive system implies that getting evidence into routine practice through a step-by-step model is not feasible. Complexity science forces us to consider the dynamic properties of systems and the varying characteristics that are deeply enmeshed in social practices, whilst indicating that multiple forces, variables, and influences must be factored into any change process, and that unpredictability and uncertainty are normal properties of multi-part, intricate systems.