Patient safety in palliative care: A mixed-methods study of reports to a national database of serious incidents
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| 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 it