Improving timely transfers from acute care to the local palliative care unit for patients at the end of life
Bibliographic record
Abstract
BACKGROUND: Despite evidence showing that nearly two thirds of the Canadian population prefer to die at home, the majority die in hospital. Honoring a patient's wish for their preferred location of death is an essential component in end-of-life care. Therefore, for those patients admitted to acute care whose choice is to transfer to a palliative care unit for end-of-life care, it is imperative that this occurs in a safe and timely manner. The General Internal Medicine ward at this local tertiary care academic center, did not have a standardized process for transferring patients at the end-of-life to the local palliative care unit. With bed calls made between Monday to Saturday at 8 am, weekday and weekend transfer times ranged between 1 to 6 hours. The aim of this project was to establish a standardized, safe and efficient patient transfer from acute care to the palliative care unit for a daily standard arrival time. METHODS: A multidisciplinary quality improvement team was formed to analyze the transfer process. Several Plan Do Study Act cycles were tested, targeting all steps of the transfer process and turnaround time. An outcome measure aiming for a turnaround time of two hours was set as the target. RESULTS: A total of fourteen patient transfers were included. Average transfer time during the weekday was reduced from a baseline average of 180.2 to 128.3 min. This change was found to be statistically significant and sustained (P<0.003). The average transfer time on weekends remained stable at 234 min. The outcome target of a 10:00 am arrival time to the palliative care unit was achieved 42% of the time. CONCLUSIONS: This project remains on-going and early data is encouraging as it met the targeted transfer time 42% of the time. Fidelity in the process measures helped to meet the targeted turnaround time of two hours for a safe and efficient transfer to the palliative care unit and ensured patients got to their preferred location for end of life care. The goal is to expand this project to other general internal medicine wards across the organization.
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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 | 0.008 | 0.032 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".