Impact of an internal medicine nocturnist service on care of patients with cancer at a large Canadian teaching hospital: a quality-improvement study
Bibliographic record
Abstract
Background: Nocturnists (overnight hospitalists) are commonly implemented in US teaching hospitals to adhere to per-resident patient caps and improve care but are rare in Canada, where patient caps and duty hours are comparatively flexible. Our objective was to assess the impact of a newly implemented nocturnist program on perceived quality of care, code status documentation and patient outcomes. Methods: Nocturnists were phased in between June 2018 and December 2019 at Toronto General Hospital, a large academic teaching hospital in Toronto, Ontario. We performed a quality-improvement study comparing rates of code status entry into the electronic health record at admission, in-hospital mortality, the 30-day readmission rate and hospital length of stay for patients with cancer admitted by nocturnists and by residents. Surveys were administered in June 2019 to general internal medicine faculty and residents to assess their perceptions of the impact of the nocturnist program. Results: From July 2018 to June 2019, 30 nocturnists were on duty for 241/364 nights (66.5%), reducing the mean maximum overnight per-resident patient census from 40 (standard deviation [SD] 4) to 25 (SD 5) (p < 0.001). The rate of admission code status entry was 35.3% among patients admitted by residents (n = 133) and 54.9% among those admitted by nocturnists (n = 339) (p < 0.001). The mortality rate was 10.5% among patients admitted by residents and 5.6% among those admitted by nocturnists (p = 0.06), the 30-day readmission rate was 8.3% and 5.9%, respectively (p = 0.4), and the mean acute length of stay was 7.2 (SD 7.0) days and 6.4 (SD 7.8) days, respectively (p = 0.3). Surveys were completed by 15/24 faculty (response rate 62%), who perceived improvements in patient safety, efficiency and trainee education; however, only 30/102 residents (response rate 29.4%) completed the survey. Interpretation: Although implementation of a nocturnist program did not affect patient outcomes, it reduced residents’ overnight patient census, and improved faculty perceptions of quality of care and education, as well as documentation of code status. Our results support nocturnist implementation in Canadian teaching hospitals.
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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 | 0.003 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".