P-65 The use of RUN-PC triage tool for community palliative care service
Bibliographic record
Abstract
<h3>Background</h3> There is no doubting that health care providers face increasing challenges to meet the needs of the population whilst dealing with gaps in funding. Teams need to be innovative in their approach which allows for a more proactive and targeted service (Antonacci, Barrie, Baxter, et al. J Aging Res. 2020:3921245). Our hospice applies its own triage tool which has overtime created ‘front door’ pressures on the team with over 50% of all referrals viewed as amber/72 hour response. The RUN-PC tool is an evidence based triage tool which allows services to allocate resources based on complexity (Russell, Philip, Wawryk, et al. Palliat Med. 2021;35(4):759–767). It ensures equitable, efficient and transparent access to clinical services whilst managing increasing caseloads and waiting lists (Russell, Sundararajan, Hennesy-Anderson, et al. Palliat Med. 2018;32(7):1246–1254). <h3>Aim</h3> To apply the RUN-PC Triage Tool to community referrals. <h3>Method</h3> The team performed a retrospective application of the RUN score on 37 amber patients. Following the results it was proposed to have an initial trial over Quarter 1 and Quarter 2. <h3>Results</h3> The tool provided a greater breakdown of complexity of service users and allowing for the most urgent patients to be prioritised in a timely manner. Out of 37 analysed: 7 Red 24 hours. 10 amber 72 hours. 18 Green 1 week. 2 Green 2 week. <h3>Conclusion</h3> The RUN-PC was shown to provide further breakdown of patient need and the timeframes for services to respond. The initial scope reduced the number of patients needing to be seen within 72 hours by 20 which potentially frees up services to prioritise those in greatest need. Further application of the tool in Q1 and Q2 will allow further data collection and analysis.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 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 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".