TESTING MULTI-CRITERIA DECISION ANALYSIS FOR MORE TRANSPARENT RESOURCE-ALLOCATION DECISION MAKING IN COLOMBIA
Bibliographic record
Abstract
OBJECTIVES: In 2012, Colombia experienced an important institutional transformation after the establishment of the Health Technology Assessment Institute (IETS), the disbandment of the Regulatory Commission for Health and the reassignment of reimbursement decision-making powers to the Ministry of Health and Social Protection (MoHSP). These dynamic changes provided the opportunity to test Multi-Criteria Decision Analysis (MCDA) for systematic and more transparent resource-allocation decision-making. METHODS: During 2012 and 2013, the MCDA framework Evidence and Value: Impact on Decision Making (EVIDEM) was tested in Colombia. This consisted of a preparatory stage in which the investigators conducted literature searches and produced HTA reports for four interventions of interest, followed by a panel session with decision makers. This method was contrasted with a current approach used in Colombia for updating the publicly financed benefits package (POS), where narrative health technology assessment (HTA) reports are presented alongside comprehensive budget impact analyses (BIAs). RESULTS: Disease severity, size of population, and efficacy ranked at the top among fifteen preselected relevant criteria. MCDA estimates of technologies of interest ranged between 71 to 90 percent of maximum value. The ranking of technologies was sensitive to the methods used. Participants considered that a two-step approach including an MCDA template, complemented by a detailed BIA would be the best approach to assist decision-making in this context. Participants agreed that systematic priority setting should take place in Colombia. CONCLUSIONS: This work may serve as the basis to the MoHSP on its interest of setting up a systematic and more transparent process for resource-allocation decision-making.
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How this classification was reachedexpand
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 | 0.010 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".