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 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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".