Examination of Impact of After-Hours Admissions on Hospital Resource Use, Patient Outcomes, and Costs
Bibliographic record
Abstract
Background. Nighttime and weekends in hospital and intensive care unit (ICU) contexts are thought to present a greater risk for adverse events than daytime admissions. Although some studies exist comparing admission time with patient outcomes, the results are contradictory. No studies currently exist comparing costs with the time of admission. We investigated the differences in-hospital mortality, ICU length of stay, ICU mortality, and cost between daytime and nighttime admissions. Methods. All adult patients (≥18 years of age) admitted to a large academic medical-surgical ICU between 2011 and 2015 were included. Admission cohorts were defined as daytime (8:00–16:59) or nighttime (17:00–07:59). Student’s t-tests and chi-squared tests were used to test for associations between days spent in the ICU, days on mechanical ventilation, comorbidities, diagnoses, and cohort membership. Regression analysis was used to test for associations between patient and hospitalization characteristics and in-hospital mortality and total ICU costs. Results. The majority of admissions occurred during nighttime hours (69.5%) with no difference in the overall Elixhauser comorbidity score between groups ( p = 0.22 ). Overall ICU length of stay was 7.96 days for daytime admissions compared to 7.07 days ( p = 0.001 ) for patients admitted during nighttime hours. Overall mortality was significantly higher in daytime admissions (22.5% vs 20.6, p = 0.012 ); however, ICU mortality was not different. The average MODS was 2.9 with those admitted during the daytime having a significantly higher MODS (3.0, p = 0.046 ). Total ICU cost was significantly higher for daytime admissions ( p = 0.003 ). Adjusted ICU mortality was similar in both groups despite an increased rate of adverse events for nighttime admissions. Daytime admissions were associated with increased cost. There was no difference in all hospital total cost or all hospital direct cost between groups. These findings are likely due to the higher severity of illness in daytime admissions. Conclusion. Daytime admissions were associated with a higher severity of illness, mortality rate, and ICU cost. To further account for the effect of staffing differences during off-hours, it may be beneficial to compare weekday and weeknight admission times with associated mortality rates.
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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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".