P-38 Hospice community palliative care team – a multi-faceted approach to improve responsiveness
Bibliographic record
Abstract
<h3>Background</h3> Demand for our community service has exceeded available capacity for years, leading to longer waiting times and some referred patients dying before being seen. A successful business case allowed team expansion, together with a focus on working more efficiently. <h3>Global aim</h3> All referred patients to have a clinical assessment prior to death, and to see 50% more patients. <h3>Methods/actions</h3> Adopted continuous improvement methodologies with whole team ownership. We identified a range of actions: To support recruitment, we established Clinical Nurse Specialist (CNS) development posts, supported through a structured competency and mentorship programme to become CNSs. A new community consultant post. Every patient receives an IPOS questionnaire prior to the first assessment, focussing assessments. Reviewed holistic assessment documentation. Streamlined processes for patients discharged from hospital under rapid discharge pathway including individualised end of life care assessment template. Established an urgent response team. Established a new cluster structure; developed in the footprints of the Primary Care Networks, to support collaborative working. Embedded new triaging structure based on complexity and urgency, managed within clusters. Continued to provide the hospice 24/7 advice line: new documentation and processes. <h3>Results/impact</h3> Community consultant recruited and two team members have now achieved CNS status with a further two in development. During Quarter 3 2022/23 the number of new patient assessments completed increased by 61% compared with 2021/22, with January and February 2023 increasing by 59% and 73% respectively. Deaths before assessment were lower for each quarter 2022/23 compared to the year before despite increased referral numbers; 44% reduction overall. <h3>Conclusions/future</h3> Continue to utilise continuous improvement methodologies, learning from experience and data. Further work needed to reduce number of those dying before assessment. Complete review of 2022 referrals and their timeliness. Projects will focus on ensuring referrals are received earlier, e.g., a collaborative project proposed with our renal team to establish a best supportive care renal clinic.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 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".