Evaluating Priority Setting Success in Healthcare: A Pilot Study
Bibliographic record
Abstract
BACKGROUND: In healthcare today, decisions are made in the face of serious resource constraints. Healthcare managers are struggling to provide high quality care, manage resources effectively, and meet changing patient needs. Healthcare managers who are constantly making difficult resource decisions desire a way to improve their priority setting processes. Despite the wealth of existing priority setting literature (for example, program budgeting and marginal analysis, accountability for reasonableness, the 'describe-evaluate-improve' strategy) there are still no tools to evaluate how healthcare resources are prioritised. This paper describes the development and piloting of a process to evaluate priority setting in health institutions. The evaluation process was designed to examine the procedural and substantive dimensions of priority setting using a multi-methods approach, including a staff survey, decision-maker interviews, and document analysis.\nMETHODS: The evaluation process was piloted in a mid-size community hospital in Ontario, Canada while its leaders worked through their annual budgeting process. Both qualitative and quantitative methods were used to analyze the data.\nRESULTS: The evaluation process was both applicable to the context and it captured the budgeting process. In general, the pilot test provided support for our evaluation process and our definition of success, (i.e., our conceptual framework).\nCONCLUSIONS: The purpose of the evaluation process is to provide a simple, practical way for an organization to better understand what it means to achieve success in its priority setting activities and identify areas for improvement. In order for the process to be used by healthcare managers today, modification and contextualization of the process are anticipated. As the evaluation process is applied in more health care organizations or applied repeatedly in an organization, it may become more streamlined.
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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 | 0.081 | 0.099 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".