Bibliographic record
Abstract
e18295 Background: How can health technology assessments be deployed in a market-based healthcare system to improve value and sustainability? Methods: The pan-Canadian Oncology Drug Review (pCODR) is an evidence-based, cancer drug review process that guides formulary decision-making. As of 1/1/17, data from all 98 reviews and economic guidances were abstracted for price of medication, total health care cost per patient, cost-utility provided by the submitter and re-analysis by pCODR. Regression analysis identified correlations. Expected use of therapy was estimated employing data from the Canadian Cancer Society. An optimal formulary was developed, optimizing value for money. Results: Of the 98 reviews, 13 were not finalized, 3 were withdrawn, 1 was suspended. 4 reviews were excluded since the base-case was ambiguous. The median drug price per 28-day cycle was $7,567 (range $2,800-$18,435), with no annual difference from 2012-2016 (p = 0.49). The median best-estimate of cost-utility was $190,858/QALY (IQR $125,585/QALY) with a median net increase in health system cost of $62,771/patient (IQR $89,260) and 0.48 LYG/patient (range 0.04-2.43). Cost per 28-day cycle was a weak predictor of value (R² = 0.02, p < 0.01), and not of health system cost (R² = 0.14, p = 0.06). Funding all efficacious medications by a single payer insurance plan in Canada would require $5.91 billion producing 31,705 QALYs, annually. 26% of the cumulative budget would buy 41% of the health benefit, 56% of the budget would buy 70% of the effect. Once a budget is determined, new medication would replace drugs of higher cost per QALY. Employing this method increased QALY yield of the budget by 67%, 21%, 15%, and 14% at $1B, $2B, $3B, and $4B, respectively. The formulary turnover would be 66%, 44%, 37%, and 22% at each respective budget level. Conclusions: An optimized formulary requires practical deployment of HTA, the ability to shift resources across budgets, and the ability to continuously renegotiate prices based on incremental value. Future work is needed on publically acceptable divestment methods for lower value pharmaceuticals.
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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 | 0.014 | 0.049 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.032 | 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".