Bridging Health Technology Assessment (HTA) and Efficient Health Care Decision Making with Multicriteria Decision Analysis (MCDA)
Bibliographic record
Abstract
BACKGROUND: Health care decision making is complex and requires efficient and explicit processes to ensure transparency and consistency of factors considered. OBJECTIVES: To pilot an adaptable decision-making framework incorporating multicriteria decision analysis (MCDA) in health technology assessment (HTA) with a pan-Canadian group of policy and clinical decision makers and researchers appraising 10 medicines covering 6 therapeutic areas. METHODS: An appraisal group was convened and participants were asked to express their individual perspectives, independently of the medicines, by assigning weights to each criterion of the MCDA core model: disease severity, size of population, current practice and unmet needs, intervention outcomes (efficacy, safety, patient reported), type of health benefit, economics, and quality of evidence. Participants then assigned performance scores for each medicine using available evidence synthesized in a "by-criterion" HTA report covering each of the MCDA CORE model criteria. MCDA estimates of perceived value were calculated by combining normalized weights and scores. Feedback on the approach was collected through structured discussion. RESULTS: Relative weights on criteria varied widely, reflecting the diverse perspectives of participants. Scores for each criterion provided a performance measure, highlighting strengths and weaknesses of each medicine. MCDA estimates of perceived value ranged from 0.42 to 0.64 across medicines, providing comprehensive measures incorporating a large spectrum of criteria. Participants reported that the framework provided an efficient approach to systematic consideration in a pragmatic format of the multiple elements guiding decision, including criteria and values (MCDA core model) and evidence (HTA "by-criterion" report). CONCLUSIONS: This proof-of-concept study demonstrated the usefulness of incorporating MCDA in HTA to support transparent and systematic appraisal of health care interventions. Further research is needed to advance MCDA-based approaches to more effective healthcare 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 | 0.246 | 0.289 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.005 | 0.004 |
| Bibliometrics | 0.011 | 0.009 |
| Science and technology studies | 0.005 | 0.012 |
| Scholarly communication | 0.013 | 0.008 |
| Open science | 0.006 | 0.017 |
| Research integrity | 0.005 | 0.007 |
| 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; 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".