Serious incidents after death: content analysis of incidents reported to a national database
Bibliographic record
Abstract
Objectives To describe serious incidents occurring in the management of patient remains after their death. Design Incidents occurring after patient deaths were analysed using content analysis to determine what happened, why it happened and the outcome. Setting The Strategic Executive Information System database of serious incidents requiring investigation occurring in the National Health Service in England. Participants All cases describing an incident that occurred following death, regardless of the age of the patient. Main outcome measures The nature of the incident, the underlying cause or causes of the incident and the outcome of the incident. Results One hundred and thirty-two incidents were analysed; these related to the storage, management or disposal of deceased patient remains. Fifty-four incidents concerned problems with the storage of bodies or body parts. Forty-three incidents concerned problems with the management of bodies, including 25 errors in postmortem examination, or postmortems on the wrong body. Thirty-one incidents related to the disposal of bodies, 25 bodies were released from the mortuary to undertakers in error; of these, nine were buried or cremated by the wrong family. The reported underlying causes were similar to those known to be associated with safety incidents occurring before death and included weaknesses in or failures to follow protocol and procedure, poor communication and informal working practices. Conclusions Serious incidents in the management of deceased patient remains have significant implications for families, hospitals and the health service more broadly. Safe mortuary care may be improved by applying lessons learned from existing patient safety work.
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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 | 0.007 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 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".