A method to identify prescription drug targets for health technology reassessment
Bibliographic record
Abstract
INTRODUCTION: The simultaneous existence of low-value health care and underutilization of high-value care are global problems. Health technology reassessment (HTR) aims to optimize the value for money of technologies already in use within health care. Identifying candidate interventions for HTR remains challenging. Therefore, we tested a novel method to identify candidate outpatient prescription drugs for HTR through practice variation. METHODS: We used administrative data for all publicly funded outpatient prescriptions dispensed to persons aged 65 or older in Alberta in 2023. Through quantitative comparison of funnel plots for Anatomic Therapeutic Chemical (ATC) classes at the fourth level stratified by prescriber specialty, variation in prescription dispensation rates between prescribers was used to estimate three outcomes: the number of prescribers affected, the number of patients affected, and the potential budgetary impact. We ranked combinations of ATC class and prescriber specialty in descending order for each outcome, with use above and below the mean considered separately. RESULTS: We analyzed data on 17.5 million dispensations, encompassing more than 8,000 prescribers and approximately 600,000 patients. The top ATC class-prescriber specialty combinations for each outcome showed high similarity above and below control limits while exhibiting minimal overlap between outcomes. CONCLUSIONS: Our method successfully identified ATC class-prescriber specialty combinations with marked variation in use, for potential advancement through the HTR process. Depending on the perspective of those undertaking HTR of prescription drugs, different outcomes may be useful in technology prioritization. To make the ATC class-prescriber specialty combinations actionable, future efforts should focus on exploring the patients affected.
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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 | 0.017 | 0.097 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.009 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 0.002 |
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".