The Paradox of Productivity, Technology, and Innovation in Canadian Healthcare
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Measures to raise the rate of productivity growth in the Canadian economy have been a prominent element in our economic policy debate. With healthcare now accounting for well over a tenth of GDP, the efficiency with which healthcare resources are used has a significant impact on overall productivity, and issues relating to new technology and innovation in healthcare have been attracting increasing attention. In this Commentary, we discuss how the problem of measuring the healthcare sector’s contribution to GDP has given the misleading impression that healthcare productivity growth has been slow in the past. New medical technology has helped raise both life expectancy and the average quality of life; if we had had methods to properly value these improvements, healthcare’s productivity growth would in all likelihood have looked quite impressive. But healthcare has claimed a larger share of resources over time; with our aging population this trend is likely to continue. And while the productivity of healthcare resources is higher today than in the past, our healthcare system does not compare favourably with those in many other countries. There is evidence to suggest that a substantial share of our healthcare resources essentially are wasted, being used for tests and interventions of no or little value. If ways could be found to gradually reduce this waste, productivity growth in healthcare could be boosted substantially. In looking for reasons why Canada has experienced slow aggregate productivity growth, observers have pointed to Canada’s relatively low spending on R&D, and have advocated government policies to more actively support it. We think such policies can be justified in their own right: Canada has plenty of talented researchers whose innovations could be exploited throughout the world. But we don’t think more Canadian R&D would necessarily be an effective way to increase productivity in our healthcare system. Canada is a small country, and most of the productivity-enhancing innovations and new technology that could be adopted here have been developed elsewhere. What is more important than increased R&D is that providers and managers in our system have strong incentives to adopt cost-efficient technology. To encourage this, provincial governments, with support from Ottawa, should experiment with new models of provider payment that strengthen their incentive to adopt cost-effective drugs, treatment methods, and diagnostic tests. As well, governments should work on creating a system of Health Technology Assessment (HTA) that both discourages new technology that is too costly, and is nimble enough to not impede the adoption of efficient innovations.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it