The prevalence of medical error related to end-of-life communication in Canadian hospitals: results of a multicentre observational study
Bibliographic record
Abstract
BACKGROUND: In the hospital setting, inadequate engagement between healthcare professionals and seriously ill patients and their families regarding end-of-life decisions is common. This problem may lead to medical orders for life-sustaining treatments that are inconsistent with patient preferences. The prevalence of this patient safety problem has not been previously described. METHODS: Using data from a multi-institutional audit, we quantified the mismatch between patients' and family members' expressed preferences for care and orders for life-sustaining treatments. We recruited seriously ill, elderly medical patients and/or their family members to participate in this audit. We considered it a medical error if a patient preferred not to be resuscitated and there were orders to undergo resuscitation (overtreatment), or if a patient preferred resuscitation (cardiopulmonary resuscitation, CPR) and there were orders not to be resuscitated (undertreatment). RESULTS: From 16 hospitals in Canada, 808 patients and 631 family members were included in this study. When comparing expressed preferences and documented orders for use of CPR, 37% of patients experienced a medical error. Very few patients (8, 2%) expressed a preference for CPR and had CPR withheld in their documented medical orders (Undertreatment). Of patients who preferred not to have CPR, 174 (35%) had orders to receive it (Overtreatment). There was considerable variability in overtreatment rates across sites (range: 14-82%). Patients who were frail were less likely to be overtreated; patients who did not have a participating family member were more likely to be overtreated. CONCLUSIONS: Medical errors related to the use of life-sustaining treatments are very common in internal medicine wards. Many patients are at risk of receiving inappropriate end-of-life care.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.007 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 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 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".