Evaluating Health Organization Readiness for Implementing a Learning Health System: Literature Review to Inform Questionnaire Development
Bibliographic record
Abstract
Objective: Adopting a learning health system (LHS) is a promising approach to bridging knowledge-to-practice gaps. The aim of this study was to explore how LHS are defined and characterized in the literature, the barriers and facilitators health organizations may face when implementing an LHS, and what tools currently exist to help health organizations assess their readiness to implement an LHS. Methods: A literature review was conducted to identify items relevant for developing the content of an LHS readiness questionnaire. PubMed and the Learning Health Systems journal were searched from inception to December 2023. Publications that addressed the definitions, frameworks, characteristics, barriers, and facilitators of an LHS were included. Results: Of the 28 included articles, 16 provided a definition of LHS–eight of which were based on the Institute of Medicine’s definition (i.e., where science, informatics, incentives, and culture are aligned for continuous improvement and innovation). 16 articles provided domains associated with an LHS framework that informed our questionnaire. These included data to knowledge, knowledge to practice, practice to data, and core values. Barriers to adopting an LHS approach included financial constraints, time, and the complexity of the task; facilitators included financial incentives, government mandates, and consistent implementation across centres. Conclusion: Few specific LHS readiness tools have been outlined in the extant literature. Current readiness tools derived from quality improvement contexts may be helpful but not sufficiently specific for assessing healthcare organizations’ readiness to implement an LHS. A new LHS readiness questionnaire may help meet this need, but further refinement and validation is required.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.091 | 0.209 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.007 |
| Bibliometrics | 0.034 | 0.031 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.005 | 0.009 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".