Data-driven laboratory stewardship: an implementation science perspective
Bibliographic record
Abstract
Abstract: When we hear the phrase ‘data-driven laboratory stewardship’, we often think of this as referring to using data on test use (e.g., test volumes or costs) to highlight opportunities for improvement. While these data are undoubtedly essential, there are many other potential data sources that can inform laboratory stewardship initiatives. Such data sources can be identified by drawing on key lessons from the rapidly developing field of implementation science (the scientific study of methods to facilitate the uptake of best practices into routine, everyday healthcare). Here, we introduce this field and outline some of its key lessons about relevant data to support (I) developing an understanding of the factors influencing over- or under-use of tests and enablers of change/barriers impeding change; and (II) selection of improvement strategies that are most suited to disrupting current patterns of test use and capitalizing on enablers of change/breaking down barriers impeding change. We also provide suggestions for how laboratory stewardship teams can put these lessons into practice as part of stewardship initiative development, and tools that can support these activities. The key lessons are couched within an over-arching framework that can be used to guide the development, implementation, and evaluation of laboratory stewardship initiatives as part of continuous improvement activities embedded within a learning health system.
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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 | 0.342 | 0.321 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.009 | 0.008 |
| Science and technology studies | 0.007 | 0.049 |
| Scholarly communication | 0.037 | 0.032 |
| Open science | 0.010 | 0.018 |
| Research integrity | 0.018 | 0.034 |
| Insufficient payload (model declined to judge) | 0.009 | 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".