Why Do Health Economists Promote Technology Adoption Rather Than the Search for Efficiency? A Proposal for a Change in Our Approach to Economic Evaluation in Health Care
Bibliographic record
Abstract
At a time of intense pressure on health care budgets, the technology management challenge is for disinvestment in low-value technologies and reinvestment in higher value alternatives. The aim of this article is to explore ways in which health economists might begin to redress the observed imbalance between the evaluation of new and existing in-use technologies. The argument is not against evaluating new technologies but in favor of the "search for efficiency," where the ultimate objective is to identify reallocations that improve population health in the face of resource scarcity. We explore why in-use technologies may be of low value and consider how economic evaluation analysts might embrace a broader efficiency lens, first through "technology management" (a process of analysis and evidence-informed decision making throughout a technology's life cycle) and progressing through "pathway management" (the search for efficiency gains across entire clinical care pathways). A number of model-based examples are used to illustrate the approaches.
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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 | 0.193 | 0.302 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.004 | 0.050 |
| Scholarly communication | 0.019 | 0.035 |
| Open science | 0.004 | 0.007 |
| Research integrity | 0.016 | 0.025 |
| Insufficient payload (model declined to judge) | 0.004 | 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".