Transfers from intensive care unit to hospital ward: a multicentre textual analysis of physician progress notes
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
BACKGROUND: Little is known about documentation during transitions of patient care between clinical specialties. Therefore, we examined the focus, structure and purpose of physician progress notes for patients transferred from the intensive care unit (ICU) to hospital ward to identify opportunities to improve communication breaks. METHODS: This was a prospective cohort study in ten Canadian hospitals. We analyzed physician progress notes for consenting adult patients transferred from a medical-surgical ICU to hospital ward. The number, length, legibility and content of notes was counted and compared across care settings using mixed-effects linear regression models accounting for clustering within hospitals. Qualitative content analyses were conducted on a stratified random sample of 32 patients. RESULTS: A total of 447 patient medical records that included 7052 progress notes (mean 2.1 notes/patient/day 95% CI 1.9-2.3) were analyzed. Notes written by the ICU team were significantly longer than notes written by the ward team (mean lines of text 21 vs. 15, p < 0.001). There was a discrepancy between documentation of patient issues in the last ICU and first ward notes; mean agreement of patient issues was 42% [95% CI 31-53%]. Qualitative analyses identified eight themes related to focus (central point - e.g., problem list), structure (organization, - e.g., note-taking style), and purpose (intention - e.g., documentation of patient course) of the notes that varied across clinical specialties and physician seniority. CONCLUSIONS: Important gaps and variations in written documentation during transitions of patient care between ICU and hospital ward physicians are common, and include discrepancies in documentation of patient information.
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.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 it