Type and use of digital technology in learning health systems: a scoping review protocol
Bibliographic record
Abstract
INTRODUCTION: Health systems in North America and Europe have been criticised for their lack of safety, efficiency and effectiveness despite rising healthcare costs. In response, healthcare leaders and researchers have articulated the need to transform current health systems into continuously and rapidly learning health systems (LHSs). While digital technology has been envisioned as providing the transformational power for LHSs by generating timely evidence and supporting best care practices, it remains to be ascertained if it is indeed playing this role in current LHS initiatives. This paper presents a protocol for a scoping review that aims at providing a comprehensive understanding of how and to what extent digital technology is used within LHSs. Results will help to identify gaps in the literature as a means to guide future research on this topic. METHODS AND ANALYSIS: Multiple databases and grey literature will be searched with terms related to learning health systems. Records selection will be done in duplicate by two reviewers applying pre-defined inclusion and exclusion criteria. Data extraction from selected records will be done by two reviewers using a piloted data charting form. Results will be synthesised through a descriptive numerical summary and a mapping of digital technology use onto types of LHSs and phases of learning within LHSs. ETHICS AND DISSEMINATION: Ethical approval is not required for this scoping review. Preliminary results will be shared with stakeholders to account for their perspectives when drawing conclusions. Final results will be disseminated through presentations at relevant conferences and publications in peer-reviewed journals.
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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 | 0.201 | 0.164 |
| Meta-epidemiology (narrow) | 0.005 | 0.006 |
| Meta-epidemiology (broad) | 0.011 | 0.010 |
| Bibliometrics | 0.023 | 0.018 |
| Science and technology studies | 0.006 | 0.008 |
| Scholarly communication | 0.010 | 0.010 |
| Open science | 0.006 | 0.008 |
| Research integrity | 0.011 | 0.007 |
| Insufficient payload (model declined to judge) | 0.054 | 0.015 |
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".