Ontario’s approach to tackling drug funding sustainability.
Bibliographic record
Abstract
38 Background: One of the challenges facing Ontario relates to managing the rising costs of cancer drug treatments. The annual growth rate of cancer drug spending has increased by 10-20% since 2010, exceeding other therapeutic categories and is expected to continue to grow significantly faster than expenditures in other areas. Paradoxically, the price of a drug seems to have little relation to its demonstrated efficacy ( http://www.asco.org/practice-research/cancer-care-america-2015/focus-cost ). The Cancer Quality Council of Ontario (CQCO) and Cancer Care Ontario (CCO) embarked on a journey to systematically address this challenge. Methods: The CQCO and CCO focused on identifying and reviewing the critical success factors of a sustainable drug reimbursement program with international, pan-Canadian and internal input. Recognizing that drug funding sustainability is a challenge faced by health systems worldwide, the scope of this work was broadened from a provincial focus to one that was relevant across the Canadian context. Results: Ultimately, this work resulted in CQCO providing a core set of recommendations for CCO that may also be relevant to other reimbursement programs, in order to maximize the effectiveness of cancer drug use and support overall system sustainability in a patient-centred way. The recommendations to address drug funding sustainability included: (1) Transparency in drug funding decisions; (2) development of process to incorporate current best evidence to support system sustainability; (3) development of a consistent approach to gathering and analyzing real world evidence (RWE); (4) development of a consistent process for disinvestment and renegotiation of prices with buy-in from public, patients and clinicians; (5) development of a provincial process to maximize harmonization in cancer drug funding coverage decisions; (6) refinement of the algorithm and priority setting for review of drug submissions; and (7) incorporating RWE into funding decisions and downstream re-evaluation. Conclusions: CCO is determining an action plan based on the above recommendations and developing partnerships to support successful implementation to improve sustainability in regards to cancer drug funding.
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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 | 0.046 | 0.071 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.008 | 0.009 |
| Science and technology studies | 0.011 | 0.011 |
| Scholarly communication | 0.017 | 0.007 |
| Open science | 0.004 | 0.010 |
| Research integrity | 0.009 | 0.008 |
| Insufficient payload (model declined to judge) | 0.014 | 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".