Belleman Tom, van Wijngaarden Jeroen D H, Kuppen Malou C P, de Groot Saskia, van der Velden Kim J M, Bosch Dianne, van Oort Inge M, Uyl-de Groot Carin A, van Deen Welmoed K
Erasmus School of Health Policy and Management, Section Health Technology Assessment Erasmus University Rotterdam Rotterdam the Netherlands.
Erasmus School of Health Policy and Management, Health Services Management and Organisation Erasmus University Rotterdam Rotterdam the Netherlands.
Learn Health Syst. 2025 Jan 16;9(3):e10476. doi: 10.1002/lrh2.10476. eCollection 2025 Jul.
Learning health systems (LHSs) are systems that seamlessly embed continuous quality improvement based on real-world data. To establish LHSs, several infrastructures need to be in place. Registries already have part(s) of this infrastructure and could therefore be leveraged to establish LHSs. This study aims to identify key factors facilitating the transition of registries into LHS to support continuous learning from real-world data.
Eleven interviews with 12 stakeholders, including medical specialists and nonmedical stakeholders, were conducted in the context of a prostate cancer registry. Findings were coded deductively based on seven previously identified facilitators for learning: complexity, relative advantage, compatibility, credibility, social impact, actionability, and resource match. These facilitators cover technical, social, and organizational aspects. An inductive phase followed to pinpoint factors for continuous learning and LHSs. Subsequently, two focus groups were conducted to ensure accurate interpretation of findings, and five expert panels to provide additional context.
Complexity within healthcare systems emerged as a significant challenge, attributed to multiple stakeholders and the rapidly changing healthcare landscape. The advantage of LHSs is the timely availability of population-based data for real-time care adjustments. Compatibility of the system with stakeholders' needs was considered pivotal requiring a relatively flexible infrastructure. Credibility of data and results was supported by creating transparent processes in which stakeholders could review data from their own patient population. Social influences, including interpersonal trust and engaged leadership, fostered collaboration within LHSs. Actionability of the findings and resource match were vital for knowledge translation and sustainability.
Our findings provide practical recommendations to support registries in transitioning towards LHSs by leveraging and expanding their infrastructure for continuous learning. We identified technical, interpersonal, and organizational factors that facilitate continuous and rapid learning using real-world data, create transparent and collaborative infrastructures, and help to navigate the complexity of the healthcare system.
学习型健康系统（LHS）是基于真实世界数据无缝嵌入持续质量改进的系统。要建立学习型健康系统，需要具备多种基础设施。注册登记系统已具备部分此类基础设施，因此可利用其来建立学习型健康系统。本研究旨在确定有助于将注册登记系统转变为学习型健康系统的关键因素，以支持从真实世界数据中持续学习。
在前列腺癌注册登记系统的背景下，对包括医学专家和非医学利益相关者在内的12名利益相关者进行了11次访谈。研究结果基于先前确定的七个学习促进因素进行演绎编码：复杂性、相对优势、兼容性、可信度、社会影响、可操作性和资源匹配。这些促进因素涵盖技术、社会和组织方面。随后进行归纳阶段，以确定持续学习和学习型健康系统的因素。随后进行了两个焦点小组讨论以确保对研究结果的准确解释，并进行了五个专家小组讨论以提供更多背景信息。
医疗保健系统的复杂性成为一项重大挑战，这归因于多个利益相关者以及快速变化的医疗保健格局。学习型健康系统的优势在于及时提供基于人群的数据以进行实时护理调整。该系统与利益相关者需求的兼容性被认为至关重要，这需要一个相对灵活的基础设施。通过创建透明的流程，让利益相关者能够审查来自其自身患者群体的数据，从而支持数据和结果的可信度。社会影响，包括人际信任和积极参与的领导力，促进了学习型健康系统内的协作。研究结果的可操作性和资源匹配对于知识转化和可持续性至关重要。
我们的研究结果提供了实用建议，以支持注册登记系统通过利用和扩展其基础设施以实现持续学习，从而向学习型健康系统转变。我们确定了技术、人际和组织因素，这些因素有助于利用真实世界数据进行持续快速学习，创建透明和协作的基础设施，并帮助应对医疗保健系统的复杂性。
