What implementation strategies and outcome measures are used to transform healthcare organizations into learning health systems? A mixed-methods review protocol
Bibliographic record
Abstract
Abstract Background A learning health system (LHS) framework provides an opportunity for health system restructuring to provide value-based healthcare. However, there is little evidence showing how to effectively implement a LHS in practice. Objective A mixed-methods review is proposed to identify and synthesize the existing evidence on effective implementation strategies and outcomes of LHS in an international context. Methods A mixed-methods systematic review will be conducted following methodological guidance from Joanna Briggs Institute (JBI) and PRISMA reporting guidelines. Six databases (CINAHL, Embase, MEDLINE, PAIS, Scopus and Nursing & Allied Health Database) will be searched for terms related to LHS, implementation and evaluation measures. Three reviewers will independently screen the titles, abstracts and full texts of retrieved articles. Studies will be included if they report on the implementation of a LHS in any healthcare setting. Qualitative, quantitative or mixed-methods study designs will be considered for inclusion. No restrictions will be placed on language or date of publication. Grey literature will be considered for inclusion but reviews and protocol papers will be excluded. Data will be extracted from included studies using a standardized extraction form. One reviewer will extract all data and a second will verify. Critical appraisal of all included studies will be conducted by two reviewers. A convergent integration approach to data synthesis will be used, where qualitative and quantitative data will be synthesized separately and then integrated to present overarching findings. Data will be presented in tables and narratively. Conclusion This review will address a gap in the literature related to implementation of LHS. The findings from this review will provide researchers with a better understanding of how to design and implement LHS interventions. This systematic review was registered in PROSPERO (CRD42022293348).
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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 | 0.256 | 0.251 |
| Meta-epidemiology (narrow) | 0.006 | 0.006 |
| Meta-epidemiology (broad) | 0.016 | 0.023 |
| Bibliometrics | 0.023 | 0.016 |
| Science and technology studies | 0.006 | 0.006 |
| Scholarly communication | 0.011 | 0.012 |
| Open science | 0.009 | 0.007 |
| Research integrity | 0.013 | 0.007 |
| Insufficient payload (model declined to judge) | 0.058 | 0.012 |
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".