Health care priority setting: principles, practice and challenges
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
BACKGROUND: Health organizations the world over are required to set priorities and allocate resources within the constraint of limited funding. However, decision makers may not be well equipped to make explicit rationing decisions and as such often rely on historical or political resource allocation processes. One economic approach to priority setting which has gained momentum in practice over the last three decades is program budgeting and marginal analysis (PBMA). METHODS: This paper presents a detailed step by step guide for carrying out a priority setting process based on the PBMA framework. This guide is based on the authors' experience in using this approach primarily in the UK and Canada, but as well draws on a growing literature of PBMA studies in various countries. RESULTS: At the core of the PBMA approach is an advisory panel charged with making recommendations for resource re-allocation. The process can be supported by a range of 'hard' and 'soft' evidence, and requires that decision making criteria are defined and weighted in an explicit manner. Evaluating the process of PBMA using an ethical framework, and noting important challenges to such activity including that of organizational behavior, are shown to be important aspects of developing a comprehensive approach to priority setting in health care. CONCLUSION: Although not without challenges, international experience with PBMA over the last three decades would indicate that this approach has the potential to make substantial improvement on commonly relied upon historical and political decision making processes. In setting out a step by step guide for PBMA, as is done in this paper, implementation by decision makers should be facilitated.
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 | 0.011 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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 it