An inventory of policy levers to reduce low value care: Results of a rapid scoping review
Bibliographic record
Abstract
BACKGROUND: The continued use of low-value health care consumes system resources and creates unnecessary risk. There are numerous policy levers available to improve appropriateness of care, but a supporting tool is needed to allow for characteristic and evidence comparison. OBJECTIVE: Develop an inventory which catalogues policy levers which support the reduction of low-value care, alongside their effectiveness evidence and implementation factors. METHODS: Information on relevant levers was identified through searches in Medline, Cochrane Library, and Google Scholar, with additional targeted searches. An Excel-based inventory was developed with a list of levers, their descriptions, effectiveness outcomes, and implementation considerations. Filters were developed to help identify levers based on key characteristics. The inventory was refined through presentations to and feedback from key stakeholders. RESULTS: The inventory includes 53 levers which may influence clinician or patient behaviour, service provision, fiscal policies, and populations or organizations. Levers were often used across a variety of settings, care providers, and clinical indications, though some levers addressed specific low-value care contexts. Fiscal policy levers or those influencing service provision were more restrictive, while clinician and patient behaviour levers and those aimed at populations or organizations were less restrictive. Evidence was identified for 40 levers, with 9 levers considered high impact (> 5 % change to behaviour, utilization, or cost) or consistently supported (> 10 studies, the majority reporting desired effects). CONCLUSION: This inventory can support health systems in addressing low-value care, through the ability to compare policy levers and select those applicable to the particular context.
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 | 0.151 | 0.255 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.006 | 0.013 |
| Bibliometrics | 0.046 | 0.047 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.013 | 0.011 |
| Open science | 0.003 | 0.009 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".