Adopting Multicriteria Decision Analysis in Local Health Settings: A Literature Review for Hospital Value Analysis Decision Makers
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
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.006 | 0.016 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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