Mapping the landscape: A protocol for a jurisdictional scan of self-identified learning health systems

BackgroundThere is a growing movement to implement learning health systems (LHS), in which real-time evidence, informatics, patient-provider partnerships and experiences, and organizational culture are aligned to support improvements in care. However, what constitutes a LHS varies based on context and capacity, hindering standardization, scale-up, and knowledge sharing. Further, LHS often use "usual care" as the benchmark for comparing new approaches to care, but disentangling usual care from multifarious care modalities found across settings is challenging. To advance robust LHS, a comprehensive overview of existing LHS including strengths and opportunities for growth is needed. ObjectivesTo scope and identify international existing LHS to: 1) inform the global landscape of LHS, highlight common strengths, and identify opportunities for growth or improvement; and 2) identify common characteristics, emphases, assumptions, or challenges described in establishing counterfactuals in LHS. MethodsA jurisdictional scan will be conducted according to modified PRISMA guidelines. LHS will be identified through a search of peer-reviewed and grey literature using Ovid Medline, Ebsco CINAHL, Ovid Embase, Clarivate Web of Science, and PubMed Non-Medline databases and the web along with informal discussions with peer LHS experts. Self-identified LHS will be included if they are described in sufficient detail, either in literature or during informal discussions, according to [≥]4 of 10 criteria (core functionalities, analytics, use of evidence, co-design/implementation, evaluation, change management/governance structures, data sharing, knowledge sharing, training/capacity building, equity, sustainability) in an existing framework to characterize LHS. Search results will be screened, extracted, and analyzed to inform two descriptive reviews pertaining to our two main objectives. Data will be extracted according to a pre-specified extraction form and summarized descriptively. ImplicationsThis research will characterize the current landscape of worldwide LHS and provide a foundation for promoting knowledge and resource sharing, identifying next steps for the growth, improvement, and evaluation of LHS.