Transfers from intensive care unit to hospital ward: a multicentre textual analysis of physician progress notes
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.
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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 | 0.009 | 0.048 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| 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 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".