The Private Sale of Cancer Drugs in Ontario's Public Hospitals: Tough Issues at the Public/Private Interface in Health Care
Bibliographic record
Abstract
As increases in health care spending outpace economic growth, governments increasingly face tough choices. One significant cost driver is the influx of new technologies, particularly expensive drug therapies. In response, provincial governments are increasingly scrutinizing the costs and benefits of new drugs and determining that despite some therapeutic benefit, they are not sufficiently beneficial to receive public funding. These choices raise complex legal, economic, political, and ethical issues. This paper explores these issues as they pertain to Ontario's recent decision not to publicly fund three cancer drugs - Velcade, Alimta, and Zevalin. To be clear, although not considered sufficiently cost-effective to warrant public funding in Ontario these drugs are of some therapeutic benefit; indeed, a physician may strongly recommend one or more of these drugs to extend a patient's life by a few months. This is illustrated by the fact that the provinces of Quebec, Alberta and British Columbia have all elected to fund these drugs in their public hospitals. That they have chosen public funding, when Ontario has not, illustrates that no sharp distinctions can be drawn about what is medically necessary (and thus publicly funded) and what is not. Ontario's decision has resulted in pressure from patients who want to buy the drugs but have the drugs administered within public hospitals. For safety reasons, these drugs need to be provided in hospital-like settings. Patients who can afford to pay for the drug still find it difficult to access them because there is only one private cancer clinic in Ontario (downtown Toronto). The Ontario government is considering whether or not to allow private-pay drugs to be administered within public hospitals - so that people who can afford to pay for the drugs can access them more readily. We explore Ontario's dilemma in three parts. Firstly, we address how Ontario's statutory context permits or acts as a bar to the sale of drugs in public hospitals. Second, we discuss the myriad of policy concerns government faces in deciding whether to permit the sale of cancer drugs in public hospitals: fairness, equality, sustainability, compassion, safety, and the effects of such a policy on the public system. Finally, given the difficulty in safely obtaining these drugs in a private setting, we address whether the government could be compelled to allow patients access to privately purchased drugs in public hospitals via a successful challenge under s. 7 of the Canadian Charter of Rights and Freedoms.
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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 | 0.008 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.015 | 0.014 |
| Scholarly communication | 0.017 | 0.005 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.006 | 0.005 |
| Insufficient payload (model declined to judge) | 0.011 | 0.001 |
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".