学习型卫生系统的分析方法:输送系统科学。

Michael Stoto, Gareth Parry, Lucy Savitz
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引用次数: 7

摘要

作为关于学习型卫生系统如何使用常规收集的电子卫生数据(EHD)来推进知识和支持持续学习的四篇系列论文中的最后一篇,本综述描述了交付系统科学如何提供一种系统手段来回答在将复杂干预措施转化为其他实践环境时出现的问题。当重点放在创新的翻译和传播上时,问题就不同于评价性研究了。因果推理不是主要问题,但人们必须问:干预是如何以及为什么起作用的?什么对谁有效,在什么情况下有效?如何修改模型以适应新的环境?在这些情况下,组织因素和设计、基础设施、政策和支付机制都会影响干预的成功,因此需要一种理论驱动的形成性评估方法,该方法考虑了从活动到参与者参与的干预的完整路径,并改变了他们的行为方式,从而改变了临床过程和结果的预期变化。这需要一种科学的质量改进方法,其特点是有理论基础;迭代测试;明确、可衡量的过程和结果目标;合适的分析方法;并记录了结果。为了更好地回答交付系统科学中出现的问题,本文介绍了一些可以应用于学习型卫生系统的标准定性研究方法:Pawson和Tilley的“现实主义评估”,基于理论的评估方法,混合方法和案例研究方法,以及“积极偏差”方法。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

Analytical Methods for a Learning Health System: 4. Delivery System Science.

Analytical Methods for a Learning Health System: 4. Delivery System Science.

Analytical Methods for a Learning Health System: 4. Delivery System Science.

Analytical Methods for a Learning Health System: 4. Delivery System Science.

The last in a series of four papers on how learning health systems can use routinely collected electronic health data (EHD) to advance knowledge and support continuous learning, this review describes how delivery system science provides a systematic means to answer questions that arise in translating complex interventions to other practice settings. When the focus is on translation and spread of innovations, the questions are different than in evaluative research. Causal inference is not the main issue, but rather one must ask: How and why does the intervention work? What works for whom and in what contexts? How can a model be amended to work in new settings? In these settings, organizational factors and design, infrastructure, policies, and payment mechanisms all influence an intervention's success, so a theory-driven formative evaluation approach that considers the full path of the intervention from activities to engage participants and change how they act to the expected changes in clinical processes and outcomes is needed. This requires a scientific approach to quality improvement that is characterized by a basis in theory; iterative testing; clear, measurable process and outcomes goals; appropriate analytic methods; and documented results. To better answer the questions that arise in delivery system science, this paper introduces a number of standard qualitative research approaches that can be applied in a learning health system: Pawson and Tilley's "realist evaluation," theory-based evaluation approaches, mixed-methods and case study research approaches, and the "positive deviance" approach.

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