46 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. 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 twelve year period April 2002 to March 2014. 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 underlying causes and the nature of resultant harms. 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 eight transfer incidents six suicides and five 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. Conclusions 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 | 0.032 | 0.080 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".