Maximizing Value and Reducing Waste: Identifying Suboptimal Prescription Drug Dispensations in Alberta
Bibliographic record
Abstract
This dissertation represents the first application of the health technology reassessment (HTR) model for outpatient prescription drugs. The objectives of this work were to develop and apply an approach to identify candidate outpatient prescription drugs for HTR in Alberta. This thesis includes three distinct projects. First, the approach to the identification of candidate prescription drugs for HTR in Alberta was developed. The approach embodies the process attributes of data-driven, routine and replicable, actionable, enabling broad collaboration, and offering high return on investment. Initial identification of candidate technologies should include all candidate technologies and clinical areas to minimize clinician disengagement and compare outcomes of proposed changes in use. Second, funnel plots were quantitatively compared across Anatomic Therapeutic Chemical (ATC) classes at the fourth level stratified by prescriber specialty. We used variation in prescription dispensation rates between prescribers to estimate three outcomes: the number of prescribers affected, the number of patients affected, and the potential budgetary impact. Combinations of ATC class and prescriber specialty were ranked in descending order for each of these outcomes, with dispensation rates above and below the mean considered separately. The top ATC classes for each outcome showed high similarity above and below the mean, with minimal overlap between outcomes. Third, patients with dispensations of the ATC classes with the highest potential for reducing budgetary impact were compared to identify formulary management levers for policy action. Administrative data were used to evaluate the presence of variation in effective care, variation in supply sensitive care, and variation in preference sensitive care. Based on this work, suggested policy actions supported were broad, such as aggressive price negotiation, and the pursuit of alternatives such as biosimilars to reduce price. Overall, high-level assessment of variation held much promise for HTR of prescription drugs due to the low up-front efforts, but focusing on relative variation also created distance from meaningful clinical context. Other methods of technology identification based on absolute standards may be more likely to maintain clinical relevance. This would come at the cost of increased efforts during technology identification.
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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 | 0.007 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".