P-65 The use of RUN-PC triage tool for community palliative care service
Bibliographic record
Abstract
Background 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). Aim To apply the RUN-PC Triage Tool to community referrals. Method 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. Results 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. Conclusion 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 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 | 0.005 | 0.043 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.044 | 0.007 |
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".