Capturing Value from Health Technologies in Lean Times
Bibliographic record
Abstract
The adoption of new health technologies brings potential improvements to quality of life as well as new costs for provincial healthcare systems. An appropriate evidence-based framework for adoption decisions therefore can go a long way to improving value for money in our health systems. While making decisions to adopt new technologies is a highly politicized process, these decisions must nonetheless strive to make use of all available evidence, including economic evaluations that consider all the costs and consequences of new technologies for society as a whole, including medical, ethical, legal, social and cultural. This Commentary focuses on an emerging, evidence-based policy tool called Health Technology Assessment (HTA). HTA helps inform decisionmaking on how to balance demand and supply pressures for new technologies within a health-system budget. The overarching objective is to obtain the greatest health gains within fiscal constraints by grounding decisions in a clear, transparent and coordinated process. In Canada, the number of entities engaged in various forms of HTA has been growing, and more and more people in healthcare are becoming exposed to the techniques involved. This progress and capacity growth is encouraging. However, rather than counting on a natural slowing of healthcare costs to relieve fiscal pressures, the provinces would be better off grounding their efforts in clear and coordinated HTA processes that incorporate economic evaluation and meaningful collaborative deliberation based on available evidence. This Commentary also looks at examples of HTA experience outside Canada and highlights lessons for Ottawa and the provinces. At present, the National Institute for Health and Care Excellence in the UK is by far the most advanced example of an attempt to utilize a consistent framework for technology adoption in a government-funded healthcare system. The UK’s current transition toward “value-based pricing” is another positive example of how HTA economic evaluation can be used to inform technology adoption in a more constructive way than with traditional “yes” or “no” recommendations. Canadian HTA agencies should draw as much as possible on existing international evidence – from randomized clinical trials, post-market assessments, clinical guidelines, etc. – and maintain close relationships with their counterparts in other countries and international organizations. Finally, HTA frameworks in Canada also must aim to encourage greater stakeholder participation and relationship development.
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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 | 0.023 | 0.054 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.007 | 0.024 |
| Scholarly communication | 0.026 | 0.031 |
| Open science | 0.003 | 0.011 |
| Research integrity | 0.007 | 0.008 |
| Insufficient payload (model declined to judge) | 0.013 | 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".