Considering patient perspectives in economic evaluations of health interventions
Bibliographic record
Abstract
Current guidelines for evaluating the cost-effectiveness of health interventions commonly recommend the use of a payer and/or a societal perspective. This raises the concern that the resulting reimbursement decision may overlook the full spectrum of impacts and equity considerations. In this paper, we argue that a potential solution is to supplement a societal- or payer-perspective economic evaluation with an additional evaluation accounting for exclusively the patient perspective. We present five categories of health interventions for which a patient-perspective analysis may be informative including those (1) that cross the definitional boundary between drugs and non-drug technologies; (2) affect patient adherence to protocol; (3) represent revolutionary treatments for genetic disorders; (4) with an incremental cost-effectiveness ratio involving slightly less effective, but substantially less costly, than the current standard; and (5) have been previously approved for funding but now being targeted for potential delisting or disinvestment. Real-world examples are discussed in detail. Lived experience individuals were invited to provide vignettes. Discussions are provided regarding how to incorporate patient inputs to improve patient-centered decision-making.
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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 | 0.304 | 0.439 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.011 | 0.011 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.005 | 0.008 |
| Insufficient payload (model declined to judge) | 0.009 | 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".