Economic implications of nighttime attending intensivist coverage in a medical intensive care unit*
Bibliographic record
Abstract
OBJECTIVE: Our objective was to assess the cost implications of changing the intensive care unit staffing model from on-demand presence to mandatory 24-hr in-house critical care specialist presence. DESIGN: A pre-post comparison was undertaken among the prospectively assessed cohorts of patients admitted to our medical intensive care unit 1 yr before and 1 yr after the change. Our data were stratified by Acute Physiology and Chronic Health Evaluation III quartile and whether a patient was admitted during the day or at night. Costs were modeled using a generalized linear model with log-link and γ-distributed errors. SETTING: A large academic center in the Midwest. PATIENTS: All patients admitted to the adult medical intensive care unit on or after January 1, 2005 and discharged on or before December 31, 2006. Patients receiving care under both staffing models were excluded. INTERVENTION: Changing the intensive care unit staffing model from on-demand presence to mandatory 24-hr in-house critical care specialist presence. MEASUREMENTS AND MAIN RESULTS: Total cost estimates of hospitalization were calculated for each patient starting from the day of intensive care unit admission to the day of hospital discharge. Adjusted mean total cost estimates were 61% lower in the post period relative to the pre period for patients admitted during night hours (7 pm to 7 am) who were in the highest Acute Physiology and Chronic Health Evaluation III quartile. No significant differences were seen at other severity levels. The unadjusted intensive care unit length of stay fell in the post period relative to the pre period (3.5 vs. 4.8) with no change in non-intensive care unit length of stay. CONCLUSIONS: We find that 24-hr intensive care unit intensivist staffing reduces lengths of stay and cost estimates for the sickest patients admitted at night. The costs of introducing such a staffing model need to be weighed against the potential total savings generated for such patients in smaller intensive care units, especially ones that predominantly care for lower-acuity patients.
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.
How this classification was reachedexpand
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 teacher head, 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".