OP150 An Inventory Of Policy Levers For Influencing Appropriate Care
Bibliographic record
Abstract
Introduction Healthcare reform through appropriate care is a current focus for many jurisdictions. A variety of policy options, or “levers,” are available to decision makers to influence appropriate care. However, these levers are not always identified in advance of developing policy recommendations, and few direct, empirical analyses are available to support their selection. An appropriate care policy lever inventory was developed for health technology assessment (HTA) users in Alberta, Canada, to support HTA scoping and policy development. Methods Relevant information was identified by a single reviewer through a scoping search of MEDLINE, forward and backward searching, and targeted gray literature searches. An Excel-based inventory was populated with a list of policy levers and their descriptions, policy effectiveness, and implementation considerations. Filters were developed to identify levers based on key characteristics. The inventory was iteratively refined through presentations to and feedback from key user groups. Results The inventory contained 53 policy levers aiming to influence service provision, clinician behavior, fiscal policies, populations or organizations, and patient behavior. The levers varied in how they restrict decision-making. Few levers were considered high impact (>5% change to behavior, utilization, or cost) or well-supported (>10 studies reporting effectiveness). Stakeholders found the inventory information useful, particularly for considering potential levers not frequently utilized within their respective programs. A user guide and case examples were also developed to help users learn to navigate the inventory. Conclusions An inventory of policy levers, which can be tailored to specific clinical areas and topics, can be of assistance to healthcare decision makers developing and utilizing HTAs to improve appropriateness of care. With limited indication-specific evidence, policy makers must utilize the broader evidence base on appropriate care policy levers to select and implement strategies that are applicable and transferable to their context. Challenges remain in systematically identifying all relevant literature given the inventory’s breadth, and in updating the inventory to reflect new evidence.
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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 | 0.020 | 0.044 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.013 | 0.014 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.008 | 0.007 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.033 | 0.005 |
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".