Adopting Multicriteria Decision Analysis in Local Health Settings: A Literature Review for Hospital Value Analysis Decision Makers
Bibliographic record
Abstract
Introduction: Hospital value analysis teams aim to scope, appraise, and procure the most cost and clinically effective alternatives, but many rely on deliberative processes and lack the use of evaluation frameworks. Multi-criteria decision analysis can complement these processes through the provision of systematic, transparent, and empirical decision support. This literature review aims to understand the applications of MCDA in local contexts. Methods: Medline (OVID), EMBASE, CINAHL, PsycINFO, and Scopus were searched, and returned 2,246 studies, of which 110 were included for full-text review, and 17 were included in the final analysis. Data relating to the context in which the study was conducted, the composition of the MCDA model used, and the reported feasibility of the use of MCDA were extracted. Results: The use of MCDA for local healthcare contexts is a recent, interprofessional, and geographically agnostic phenomenon. Diagnostics, treatment, surgical approaches, performances and preferences, education approaches, and recovery targets were the primary decision problems addressed. A combination of models was employed, and qualitative data, literature review, expert opinion, and financial measurements were used to support data requirements. Facilitating reasoning and decision-making, service quality improvement, transparency, flexibility and adaptability, participation and buy in, and feedback about MCDA were identified as key adoption characteristics. Conclusion: MCDA has numerous emerging applications to support healthcare decision makers across different decision problems and to evaluate products and processes in local settings. This review provides considerations for uptake and implementation, though further investigation into its explicit applications to hospital and perioperative value analysis is necessary to elicit the usability, feasibility, and acceptability of these models.
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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 | 0.056 | 0.170 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.006 |
| Bibliometrics | 0.032 | 0.037 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.008 | 0.008 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".