Comparing Adherence with Best Practices in End-of-Life Care After Implementing the End-of-Life Order Set: A Quality Improvement Project in an Ottawa Academic Hospital
Bibliographic record
Abstract
Background: Physicians in acute care require tools to assist them in transitioning patients from a "life prolonging" approach to "end-of-life care," and standardized order sets can be a useful strategy. The end-of-life order set (EOLOS) was developed and implemented in the medical wards of a community academic hospital. Objective: To compare adherence with best practices in end-of-life care after implementing the EOLOS. Methods: We conducted a retrospective chart review of admitted patients with expected deaths in the year preceding EOLOS implementation ("before EOLOS" group), and in the 12 to 24 months following EOLOS implementation ("after EOLOS" group). Results: A total of 295 charts were included: 139 (47%) in the "before EOLOS" group and 156 (53%) in the "after EOLOS" group, of which 117/156 charts (75%) had a completed EOLOS. The "after EOLOS" group demonstrated more "do not resuscitate" orders and more written communication to team members about comfort goals of care. There was a decrease in nonbeneficial interventions in the last 24 hours of life in the "after EOLOS" group: high-flow oxygen, intravenous antibiotics, and deep vein thrombosis/venous thromboembolism prophylaxis. The "after EOLOS" group demonstrated increased prescription of all common end-of-life medications, except for opioids, which had a high preexisting rate of prescription. Patients in the "after EOLOS" group showed a higher rate of spiritual care and palliative care consult team consultation. Conclusion: Findings support standardized order sets as a good framework allowing generalist hospital staff to improve adherence to established palliative care principles and improve end-of-life care of hospital inpatients.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 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".