Enhancing palliative care occupancy and efficiency: a quality improvement project that uses a healthcare pathway for service integration and policy development
Bibliographic record
Abstract
This article described our experience in implementing a quality improvement project to overcome the bed overcapacity problem at a comprehensive cancer centre in a tertiary care centre. We formed a multidisciplinary team including a representative from patient and family support (six members), hospice care and home care services (four members), multidisciplinary team development (four members) and the national lead. The primary responsibility of the formulated team was implementing measures to optimise and manage patient flow. We used the plan-do-study-act cycle to engage all stakeholders from all service layers, test some interventions in simplified pilots and develop a more detailed plan and business case for further implementation and roll-out, which was used as a problem-solving approach in our project for refining a process or implementing changes. As a result, we observed a significant reduction in bed capacity from 35% in 2017 to 13.8% in 2018. While the original length of stay (LOS) was 28 days, the average LOS was 19 days in 2017 (including the time before and after the intervention), 10.8 days in 2018 (after the intervention was implemented), 10.1 days in 2019 and 16 days in 2020. The increase in 2020 parameters was caused by the COVID-19 pandemic, since many patients did not enrol in our new care model. Using a systematic care delivery approach by a multidisciplinary team improves significantly reduced bed occupancy and reduces LOS for palliative care patients.
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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 | 0.024 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".