Evaluating health organization readiness for implementing a learning health system: a scoping review
Bibliographic record
Abstract
OBJECTIVE: Learning health systems (LHS) may improve healthcare access, innovation, coordination, continuity and quality. To ensure implementation success, healthcare organizations must be able to assess their current readiness to adopt an LHS approach; however, there is a paucity of LHS-specific readiness tools in the extant literature. Thus, the overarching aim of this study was to map the depth and breadth of LHS literature to identify the domains and items, alongside barriers, facilitators, implementation strategies and competencies relevant to include in an LHS readiness tool. METHODS: A scoping review informed by Arksey and O'Malley's framework and updates proposed by Levac et al. was employed. Scopus, MEDLINE, Embase, CINAHL, PsychINFO, Education Source and Business Source Complete were searched from inception to May 2024. English or French publications that addressed the definitions, frameworks, competencies, barriers and facilitators of an LHS were eligible. RESULTS: The bibliographic database search and screening process yielded 90 articles, published between 2007 and 2024. A total of 72 articles defined LHS, with most emphasizing continuous learning cycles, evidence integration, infrastructure and stakeholder engagement. In addition, 56 articles presented 21 frameworks (educational, logic, maturity, organizational, equity and implementation), and 50 described key domains, including the D2K-K2P-P2D cycle, core values, and leadership, governance, and data infrastructure. Barriers to implementation included limited resources, unsupportive culture, poor interoperability and ethical challenges, while facilitators were strong leadership, shared purpose, robust partnerships and supportive policies. Identified competencies spanned research, informatics, quality improvement, systems science, engagement and ethics, with educational strategies ranging from collaboratives and training programs to graduate curricula and peer learning. Readiness and maturity assessments were discussed in 28 articles, but only a few operationalized these concepts. No specific LHS readiness assessments were identified. CONCLUSIONS: Current readiness tools derived from quality-improvement contexts may be helpful but not sufficiently specific for assessing healthcare organizations' readiness to implement an LHS approach. This review identified important barriers, facilitators, and strategies related to the collective behaviour change required to implement an LHS approach that should be considered in the future development of an LHS readiness assessment.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.060 | 0.187 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.005 | 0.006 |
| Bibliometrics | 0.040 | 0.032 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.008 | 0.007 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.004 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".