Patient safety in palliative care: A mixed-methods study of reports to a national database of serious incidents
Bibliographic record
Abstract
BACKGROUND: Patients receiving palliative care are vulnerable to patient safety incidents but little is known about the extent of harm caused or the origins of unsafe care in this population. AIM: To quantify and qualitatively analyse serious incident reports in order to understand the causes and impact of unsafe care in a population receiving palliative care. DESIGN: A mixed-methods approach was used. Following quantification of type of incidents and their location, a qualitative analysis using a modified framework method was used to interpret themes in reports to examine the underlying causes and the nature of resultant harms. SETTING AND PARTICIPANTS: Reports to a national database of 'serious incidents requiring investigation' involving patients receiving palliative care in the National Health Service (NHS) in England during the 12-year period, April 2002 to March 2014. RESULTS: A total of 475 reports were identified: 266 related to pressure ulcers, 91 to medication errors, 46 to falls, 21 to healthcare-associated infections (HCAIs), 18 were other instances of disturbed dying, 14 were allegations against health professions, 8 transfer incidents, 6 suicides and 5 other concerns. The frequency of report types differed according to the care setting. Underlying causes included lack of palliative care experience, under-resourcing and poor service coordination. Resultant harms included worsened symptoms, disrupted dying, serious injury and hastened death. CONCLUSION: Unsafe care presents a risk of significant harm to patients receiving palliative care. Improvements in the coordination of care delivery alongside wider availability of specialist palliative care support may reduce this risk.
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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 | 0.045 | 0.115 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.001 |
| 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".