Vanderhout Shelley, Bird Marissa, Giannarakos Antonia, Panesar Balpreet, Whitmore Carly
Institute for Better Health, Trillium Health Partners, Mississauga, ON, Canada.
Institute of Health Policy, Management, and Evaluation, University of Toronto, Toronto, ON, Canada.
JMIR Res Protoc. 2024 Dec 6;13:e57929. doi: 10.2196/57929.
In learning health systems (LHSs), real-time evidence, informatics, patient-provider partnerships and experiences, and organizational culture are combined to conduct "learning cycles" that support improvements in care. Although the concept of LHSs is fairly well established in the literature, evaluation methods, mechanisms, and indicators are less consistently described. Furthermore, LHSs often use "usual care" or "status quo" as a benchmark for comparing new approaches to care, but disentangling usual care from multifarious care modalities found across settings is challenging. There is a need to identify which evaluation methods are used within LHSs, describe how LHS growth and maturity are conceptualized, and determine what tools and measures are being used to evaluate LHSs at the system level.
This study aimed to (1) identify international examples of LHSs and describe their evaluation approaches, frameworks, indicators, and outcomes; and (2) describe common characteristics, emphases, assumptions, or challenges in establishing counterfactuals in LHSs.
A jurisdictional scan, which is a method used to explore, understand, and assess how problems have been framed by others in a given field, will be conducted according to modified PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines. LHSs will be identified through a search of peer-reviewed and gray literature using Ovid MEDLINE, EBSCO CINAHL, Ovid Embase, Clarivate Web of Science, PubMed non-MEDLINE databases, and the web. We will describe evaluation approaches used both at the LHS learning cycle and system levels. To gain a comprehensive understanding of each LHS, including details specific to evaluation, self-identified LHSs will be included if they are described according to at least 4 of 11 prespecified criteria (core functionalities, analytics, use of evidence, co-design or implementation, evaluation, change management or governance structures, data sharing, knowledge sharing, training or capacity building, equity, and sustainability). Search results will be screened, extracted, and analyzed to inform a descriptive review pertaining to our main objectives. Evaluation methods and approaches, both within learning cycles and at the system level, as well as frameworks, indicators, and target outcomes, will be identified and summarized descriptively. Across evaluations, common challenges, assumptions, contextual factors, and mechanisms will be described.
As of October 2024, the database searches described above yielded 3503 citations after duplicate removal. Full-text screening of 117 articles is complete, and 49 articles are under analysis. Results are expected in early 2025.
This research will characterize the current landscape of LHS evaluation approaches and provide a foundation for developing consistent and scalable metrics of LHS growth, maturity, and success. This work will also serve to identify opportunities for improving the alignment of current evaluation approaches and metrics with population health needs, community priorities, equity, and health system strategic aims.
Open Science Framework b5u7e; https://osf.io/b5u7e.
INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/57929.
在学习型卫生系统(LHS)中,实时证据、信息学、患者与提供者的伙伴关系及体验,以及组织文化相结合,以开展支持改善医疗服务的“学习循环”。尽管LHS的概念在文献中已相当成熟,但评估方法、机制和指标的描述却不太一致。此外,LHS通常将“常规护理”或“现状”作为比较新护理方法的基准,但在各种环境中从多种护理模式中区分出常规护理具有挑战性。有必要确定LHS中使用了哪些评估方法,描述LHS的发展和成熟是如何概念化的,并确定在系统层面用于评估LHS的工具和措施。
本研究旨在(1)确定LHS的国际范例,并描述其评估方法、框架、指标和结果;(2)描述在LHS中建立反事实时的共同特征、重点、假设或挑战。
将根据修改后的PRISMA(系统评价和Meta分析的首选报告项目)指南进行辖区扫描,这是一种用于探索、理解和评估给定领域中其他人如何构建问题的方法。将通过使用Ovid MEDLINE、EBSCO CINAHL、Ovid Embase、Clarivate Web of Science、PubMed非MEDLINE数据库和网络搜索同行评审文献和灰色文献来识别LHS。我们将描述在LHS学习循环和系统层面使用的评估方法。为了全面了解每个LHS,包括评估的具体细节,如果根据11个预先指定的标准(核心功能、分析、证据使用、共同设计或实施、评估、变革管理或治理结构、数据共享、知识共享、培训或能力建设、公平性和可持续性)中的至少4个进行了描述,自我认定的LHS将被纳入。将对搜索结果进行筛选、提取和分析,以形成与我们的主要目标相关的描述性综述。将描述学习循环内和系统层面的评估方法和途径,以及框架、指标和目标结果,并进行描述性总结。在各项评估中,将描述共同的挑战、假设、背景因素和机制。
截至2024年10月,上述数据库搜索在去除重复项后产生了3503条引文。117篇文章的全文筛选已完成,49篇文章正在分析中。预计2025年初得出结果。
本研究将描述LHS评估方法的当前状况,并为制定LHS发展、成熟和成功的一致且可扩展的指标提供基础。这项工作还将有助于确定改进当前评估方法和指标与人群健康需求、社区优先事项、公平性和卫生系统战略目标一致性的机会。
开放科学框架b5u7e;https://osf.io/b5u7e。
国际注册报告识别码(IRRID):DERR1-10.2196/57929。