Effect of interhospital transfer on resource utilization and outcomes at a tertiary care referral center*
Bibliographic record
Abstract
OBJECTIVE: Mortality and length of stay are two outcome variables commonly used as benchmarks in rating the performance of medical centers. Acceptance of transfer patients has been shown to affect both outcomes and the costs of health care. Our objective was to compare observed and predicted lengths of stay, observed and predicted mortality, and resource consumption between patients directly admitted and those transferred to the intensive care unit (ICU) of a large academic medical center. DESIGN: Observational cohort study. SETTING: Mixed medical/surgical ICU of a university hospital. PATIENTS: A total of 4,569 consecutive patients admitted to a tertiary care ICU from April 1, 1997, to March 30, 2000. INTERVENTIONS: None. MEASUREMENTS: Acute Physiology and Chronic Health Evaluation (APACHE) III score, actual and predicted ICU and hospital lengths of stay, actual and predicted ICU and hospital mortality, and costs per admission. MAIN RESULTS: Crude comparison of directly admitted and transfer patients revealed that transfer patients had significantly higher APACHE III scores (mean, 60.5 vs. 49.7, p < .001), ICU mortality (14% vs. 8%, p < .001), and hospital mortality (22% vs. 14%, p < .001). Transfer patients also had longer ICU lengths of stay (mean, 6.0 vs. 3.8 days, p < .001) and hospital lengths of stay (mean, 20 vs. 15.9 days, p < .001). Stratified by disease severity using the APACHE III model, there was no difference in either ICU or hospital mortality between the two populations. However, in the transfer group with the lowest predicted mortality of 0-20%, ICU and hospital lengths of stay were significantly higher. In crude cost analysis, transfer patients' costs were $9,600 higher per ICU admission compared with nontransfer patients (95% confidence interval, $6,000-$13,400). Risk stratification revealed that the higher per-patient cost was entirely confined to the transfer patients with the lowest predicted mortality. CONCLUSIONS: Patients transferred to a tertiary care ICU are generally more severely ill and consume more resources. However, they have similar adjusted mortality outcomes when compared with directly admitted patients. The difference in resource consumption is mainly attributable to the group of patients in the lowest predicted risk bracket.
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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 | 0.001 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".