Extended Discharge Timeline for Older Adult Trauma Patients: An Increasing Threat to the Efficiency of Trauma Centers
Bibliographic record
Abstract
BACKGROUND: Loss of community dwelling status is frequent after hospitalization for traumatic injuries in older adults. Arranging placement to a skilled nursing facility or an inpatient rehabilitation facility often causes a delay in discharge after the patient is medically stable, exacerbating bed capacity issues in health systems and unnecessarily exposing patients to hospital-related complications. This study aimed to identify characteristics associated with delayed discharge and to quantify temporal trends in discharge delays at a high-volume trauma center. STUDY DESIGN: We retrospectively analyzed all trauma patients from 2018 to 2023 in our Level I trauma registry, excluding in-hospital mortalities. Patients living greater than 70 miles from our trauma center were excluded due to the need for long-distance transport. Patients were stratified by delayed discharge time vs discharge at medical readiness. RESULTS: Of 17,886 patients, 1,091 (6.1%) had a delay in discharge, waiting an average of 4.9 days after medical readiness. Patients with delayed discharge were more likely to be older (65.1 vs 53.2 years, p < 0.001), female (50.1% vs 38.8%, p < 0.001), have a higher Injury Severity Score (12.4 vs 8.6, p < 0.001), and blunt mechanism (92.7% vs 80.4%, p < 0.001). In a multivariate analysis, Medicare beneficiary status and skilled nursing facility placement remained associated with delayed discharge. The time of the week that the patient was medically ready for discharge did not significantly affect the discharge delay duration. CONCLUSIONS: Trauma patients with Medicare experience significant delays in discharge. Currently, an average of 5 patients await disposition in our trauma center, which has increased yearly from a low of 0.5 patients in 2018. Further investigation is needed to determine causes of increased delays, including insurance denial of inpatient rehabilitation.
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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 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".