Implementation of a continuous admission model reduces the length of stay of patients on an internal medicine clinical teaching unit
Bibliographic record
Abstract
BACKGROUND: Optimizing hospital operations is a critical issue facing healthcare systems. Reducing unnecessary variation in patient flow is likely to improve efficiency and optimize capacity for hospital inpatients. The objective of this study was to determine whether changing admissions, from a "bolus" system to a "drip" system, would result in a smoothed daily discharge rate, and reduce the length of stay of patients on a General Internal Medicine clinical teaching unit over a period of 1 year. METHODS: We conducted a retrospective analysis of the General Internal Medicine inpatient service at Toronto General Hospital for the 6-month periods from March to August during 2 consecutive years. Length of stay distributions and daily discharge rate variations were compared between the 2 study periods. RESULTS: There were a total of 2734 discharges, 1446 occurring in the pre-change period, and 1288 in the post-change period. There was overall smoothing of the daily discharge rates, and a reduction of 0.3 days in median length of stay in the post-change period (P = 0.0065). CONCLUSIONS: Restructuring the admission system to achieve constant daily admissions to each care team resulted in a smoothing of daily discharge rates and improved operational efficiency with shorter lengths of stay.
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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 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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 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".