Improved intrahospital transport time via proximity-based staff assignments
Bibliographic record
Abstract
BACKGROUND: Intrahospital patient transport is pivotal in enabling hospital operations and facilitating safe and efficient patient movement. However, transport delays are common in hospitals, signaling a need for improvement. This study develops, implements, and evaluates a proximity-based transporter-to-request assignment system aimed at improving transport service system efficiency. MATERIALS AND METHODS: In this observational study, we used discrete-event simulation to design and optimize an enhancement to an electronic medical record's original first-in, first-out transporter-to-request assignment system, and we implemented it at a quaternary care academic medical center. Our enhancement prioritizes requests based on the proximity of available transporters within pre-specified areas. We compared transport request completion time (primary outcome) and the percentage of transports exceeding 45 minutes (secondary outcome) during control (01/2021-02/2022) and intervention (02/2022-03/2023) periods and estimated their differences using multivariate generalized linear models to adjust for confounding factors including variable workforce levels and workload. RESULTS: A total of 136 414 transport requests were included in the study. The intervention was associated with an adjusted 5.0% (95% confidence interval 1.8%-8.5%) reduction in completion times and a 16.0% (7.4%-23.9%) relative reduction in the percentage of trips exceeding the 45-minute completion time target. DISCUSSION: The intervention's improvements stem from reductions in unnecessary travel time between transport requests, common to first-in first-out assignment systems. The intervention was designed to be natively integrated into existing electronic health record systems, reducing barriers to real-world adoption. CONCLUSION: Implementing a proximity-based assignment system designed based on simulation-optimization modeling improved intrahospital patient transport efficiency without requiring additional staff.
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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 | 0.004 | 0.002 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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".