London Transfer Project: improving handover documentation from long-term care homes to hospital emergency departments
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
About one-quarter of all long-term care (LTC) residents are transferred to an emergency department (ED) every 6 months in Ontario, Canada. When residents are unable to describe their health issues, ED staff rely on LTC transfer reports to make informed decisions. However, transfer information gaps are common, and may contribute to unnecessary tests, unwanted treatments and longer ED length of stay. London Health Sciences Centre, an academic hospital system in London, Ontario, partnered with 10 LTC homes to improve emergency reporting of their residents' reason for transfer and baseline cognition. After conducting a root cause analysis, 7 of 10 homes implemented a standard minimum set of currently available transfer forms, including a computer-generated summary of resident's most recent interRAI functional assessment. Results were analysed using statistical process control charts and data were posted on a public website (LondonTransferProject.com). The documentation rate of 'reason for transfer' improved from 61% to 84%, and 'baseline cognitive status' improved from 4% to 56% across all 10 homes. These results suggest that transfer communication can be improved by codesigning and implementing solutions with ED and LTC staff, which build upon current reporting practices shared across multiple LTC organisations.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 it