Quality improvement priorities for safer out-of-hours palliative care: Lessons from a mixed-methods analysis of a national incident-reporting database
Bibliographic record
Abstract
Background: Patients receiving palliative care are often at increased risk of unsafe care with the out-of-hours setting presenting particular challenges. The identification of improved ways of delivering palliative care outside working hours is a priority area for policymakers. Aim: To explore the nature and causes of unsafe care delivered to patients receiving palliative care from primary-care services outside normal working hours. Design: A mixed-methods cross-sectional analysis of patient safety incident reports from the National Reporting and Learning System. We characterised reports, identified by keyword searches, using codes to describe what happened, underlying causes, harm outcome, and severity. Exploratory descriptive and thematic analyses identified factors underpinning unsafe care. Setting/participants: A total of 1072 patient safety incident reports involving patients receiving sub-optimal palliative care via the out-of-hours primary-care services. Results: Incidents included issues with: medications (n = 613); access to timely care (n = 123); information transfer (n = 102), and/or non-medication-related treatment such as pressure ulcer relief or catheter care (n = 102). Almost two-thirds of reports (n = 695) described harm with outcomes such as increased pain, emotional, and psychological distress featuring highly. Commonly identified contributory factors to these incidents were a failure to follow protocol (n = 282), lack of skills/confidence of staff (n = 156), and patients requiring medication delivered via a syringe driver (n = 80). Conclusion: Healthcare systems with primary-care-led models of delivery must examine their practices to determine the prevalence of such safety issues (communication between providers; knowledge of commonly used, and access to, medications and equipment) and utilise improvement methods to achieve improvements in care.
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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 | 0.003 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| 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".