The impact of a universal admission order on health system capacity
Bibliographic record
Abstract
In our health system with multiple campuses, a universal admissions order (UAO) was introduced to further improve patient flow. We hypothesized that the UAO would more evenly distribute health system capacity, with an increase in admissions to the community affiliate sites. Inpatient and emergency department (ED) metrics were evaluated, and included total admissions, admissions to each clinical site from each ED, the time to the inpatient bed being ready to receive the ED patient, boarding times, and the left without being seen rate. After implementation of the UAO, the average time to inpatient beds being ready to accept ED patients decreased at all three clinical sites by an average of 25 minutes. Admissions were more evenly distributed amongst the three clinical sites, with 3% of all admissions admitted to a new campus. While there were likely other variables at play, there was system-wide reduction in the time to inpatient beds being ready to accept ED patients, and an improvement in boarding at the main clinical site. Our work suggests that a UAO could be a useful adjunct to central capacity management in a health system with multiple clinical campuses.
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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 | 0.012 | 0.072 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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".