Using Audit to Improve End-of-Life Care in a Tertiary Cancer Centre
Bibliographic record
Abstract
High-quality end-of-life care (EoLC) is a critical yet often underemphasised component of oncology care. Several shortcomings in the delivery of EoLC for oncology patients in our centre during the COVID-19 pandemic were identified in our initial 2021 audit. In 2022, we introduced a care of dying patients proforma, an EoLC quality checklist, targeted education and training for staff, and an expanded end-of-life (EoL) committee. This re-audit aimed to review how these changes impacted on the care received by patients in a tertiary cancer centre. A second retrospective re-audit of patients who died between 11 July 2022 and 30 April 2023 was performed to assess quality of EoLC using the Oxford Quality indicators. A total of 72 deaths occurred over the audit period. Quality of EoLC improved significantly when compared to the initial audit (χ2 (3, n = 138) = 9.75, p = 0.021). Exploration of patients’ wishes was documented in 48.8% and referral to pastoral care was documented in 68.3%, from 24.2% and 10.6%, respectively. The proportion of patients receiving poor EoLC reduced from 21.2% to 8.3%. Our study demonstrates the benefits of simple interventions, the importance of re-audit, and the role of ongoing interdisciplinary commitment to improving EoLC for our 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 | 0.066 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".