‘Real-world’ health care priority setting using explicit decision criteria: a systematic review of the literature
Bibliographic record
Abstract
BACKGROUND: Health care decision making requires making resource allocation decisions among programs, services, and technologies that all compete for a finite resource pool. Methods of priority setting that use explicitly defined criteria can aid health care decision makers in arriving at funding decisions in a transparent and systematic way. The purpose of this paper is to review the published literature and examine the use of criteria-based methods in 'real-world' health care allocation decisions. METHODS: A systematic review of the published literature was conducted to find examples of 'real-world' priority setting exercises that used explicit criteria to guide decision-making. RESULTS: We found thirty-three examples in the peer-reviewed and grey literature, using a variety of methods and criteria. Program effectiveness, equity, affordability, cost-effectiveness, and the number of beneficiaries emerged as the most frequently-used decision criteria. The relative importance of criteria in the 'real-world' trials differed from the frequency in preference elicitation exercises. Neither the decision-making method used, nor the relative economic strength of the country in which the exercise took place, appeared to have a strong effect on the type of criteria chosen. CONCLUSIONS: Health care decisions are made based on criteria related both to the health need of the population and the organizational context of the decision. Following issues related to effectiveness and affordability, ethical issues such as equity and accessibility are commonly identified as important criteria in health care resource allocation decisions.
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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 | 0.112 | 0.324 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.008 | 0.009 |
| Bibliometrics | 0.020 | 0.019 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.004 | 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".