Practices of decision making in priority setting and resource allocation: a scoping review and narrative synthesis of existing frameworks
Bibliographic record
Abstract
BACKGROUND: Due to growing expenditures, health systems have been pushed to improve decision-making practices on resource allocation. This study aimed to identify which practices of priority setting and resource allocation (PSRA) have been used in healthcare systems of high-income countries. METHODS: A scoping literature review (2007-2019) was conducted to map empirical PSRA activities. A two-stage screening process was utilized to identify existing approaches and cluster similar frameworks. That was complemented with a gray literature and horizontal scanning. A narrative synthesis was carried out to make sense of the existing literature and current state of PSRA practices in healthcare. RESULTS: One thousand five hundred eighty five references were found in the peer-reviewed literature and 25 papers were selected for full-review. We identified three major types of decision-making framework in PSRA: 1) Program Budgeting and Marginal Analysis (PBMA); 2) Health Technology Assessment (HTA); and 3) Multiple-criteria value assessment. Our narrative synthesis indicates these formal frameworks of priority setting and resource allocation have been mostly implemented in episodic exercises with poor follow-up and evaluation. There seems to be growing interest for explicit robust rationales and ample stakeholder involvement, but that has not been the norm in the process of allocating resources within healthcare systems of high-income countries. CONCLUSIONS: No single dominate framework for PSRA appeared as the preferred approach across jurisdictions, but common elements exist both in terms of process and structure. Decision-makers worldwide can draw on our work in designing and implementing PSRA processes in their contexts.
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.
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 | 0.223 | 0.365 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.005 | 0.007 |
| Bibliometrics | 0.049 | 0.047 |
| Science and technology studies | 0.005 | 0.006 |
| Scholarly communication | 0.015 | 0.015 |
| Open science | 0.005 | 0.010 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".