Assessing and Improving Performance: A Longitudinal Evaluation of Priority Setting and Resource Allocation in a Canadian Health Region
Bibliographic record
Abstract
BACKGROUND: In order to meet the challenges presented by increasing demand and scarcity of resources, healthcare organizations are faced with difficult decisions related to resource allocation. Tools to facilitate evaluation and improvement of these processes could enable greater transparency and more optimal distribution of resources. METHODS: The Resource Allocation Performance Assessment Tool (RAPAT) was implemented in a healthcare organization in British Columbia, Canada. Recommendations for improvement were delivered, and a follow up evaluation exercise was conducted to assess the trajectory of the organization's priority setting and resource allocation (PSRA) process 2 years post the original evaluation. RESULTS: Implementation of RAPAT in the pilot organization identified strengths and weaknesses of the organization's PSRA process at the time of the original evaluation. Strengths included the use of criteria and evidence, an ability to reallocate resources, and the involvement of frontline staff in the process. Weaknesses included training, communication, and lack of program budgeting. Although the follow up revealed a regression from a more formal PSRA process, a legacy of explicit resource allocation was reported to be providing ongoing benefit for the organization. CONCLUSION: While past studies have taken a cross-sectional approach, this paper introduces the first longitudinal evaluation of PSRA in a healthcare organization. By including the strengths, weaknesses, and evolution of one organization's journey, the authors' intend that this paper will assist other healthcare leaders in meeting the challenges of allocating scarce resources.
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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 | 0.005 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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 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".