K.E. Trinkley is associate professor, Departments of Clinical Pharmacy and Medicine and Adult and Child Consortium for Outcomes Research and Delivery Science (ACCORDS), University of Colorado Anschutz Medical Center, and clinical informaticist, Department of Clinical Informatics, UCHealth, Aurora, Colorado; ORCID: http://orcid.org/0000-0003-2041-7404 .
P.M. Ho is professor, Department of Medicine, University of Colorado Anschutz Medical Campus, and professor, VA Eastern Colorado Health Care System, Aurora, Colorado; ORCID: http://orcid.org/0000-0002-7775-6266 .
Acad Med. 2022 Oct 1;97(10):1447-1458. doi: 10.1097/ACM.0000000000004801. Epub 2022 Sep 23.
Many health systems are working to become learning health systems (LHSs), which aim to improve the value of health care by rapidly, continuously generating evidence to apply to practice. However, challenges remain to advance toward the aspirational goal of becoming a fully mature LHS. While some important challenges have been well described (i.e., building system-level supporting infrastructure and the accessibility of inclusive, integrated, and actionable data), other key challenges are underrecognized, including balancing evaluation rapidity with rigor, applying principles of health equity and classic ethics, focusing on external validity and reproducibility (generalizability), and designing for sustainability. Many LHSs focus on continuous learning cycles, but with limited consideration of issues related to the rapidity of these learning cycles, as well as the sustainability or generalizability of solutions. Some types of data have been consistently underrepresented, including patient-reported outcomes and preferences, social determinants, and behavioral and environmental data, the absence of which can exacerbate health disparities. A promising approach to addressing many challenges that LHSs face may be found in dissemination and implementation (D&I) science. With an emphasis on multilevel dynamic contextual factors, representation of implementation partner engagement, pragmatic research, sustainability, and generalizability, D&I science methods can assist in overcoming many of the challenges facing LHSs. In this article, the authors describe the current state of LHSs and challenges to becoming a mature LHS, propose solutions to current challenges, focusing on the contributions of D&I science with other methods, and propose key components and characteristics of a mature LHS model that others can use to plan and develop their LHSs.
许多医疗体系正在努力成为学习型医疗体系（LHS），旨在通过快速、持续地生成应用于实践的证据来提高医疗保健的价值。然而，要实现成为完全成熟的 LHS 的理想目标，仍然存在挑战。虽然一些重要的挑战已经得到很好的描述（即建立系统级别的支持性基础设施和包容性、综合性和可操作性数据的可及性），但其他关键挑战仍未被充分认识，包括平衡评估的快速性和严谨性、应用健康公平和经典伦理原则、关注外部有效性和可重复性（可推广性），以及为可持续性而设计。许多 LHS 专注于持续的学习周期，但对这些学习周期的快速性以及解决方案的可持续性或可推广性相关问题的考虑有限。某些类型的数据一直被严重低估，包括患者报告的结果和偏好、社会决定因素以及行为和环境数据，这些数据的缺乏可能会加剧健康差异。传播和实施（D&I）科学可能是解决 LHS 面临的许多挑战的一种有前途的方法。该方法强调多层次动态的背景因素、实施伙伴参与的代表性、实用研究、可持续性和可推广性，D&I 科学方法可以帮助克服 LHS 面临的许多挑战。在本文中，作者描述了 LHS 的现状和成为成熟 LHS 的挑战，提出了当前挑战的解决方案，重点介绍了 D&I 科学与其他方法的贡献，并提出了成熟 LHS 模型的关键组成部分和特征，其他人可以使用这些组成部分和特征来规划和开发自己的 LHS。
