Supporting Air Versus Ground Vehicle Decisions for Interfacility Medical Transport Using Historical Data
Bibliographic record
Abstract
Patients undergoing interfacility transfers are at potentially greater risk of adverse or critical events than those in hospital, and efficient transfers play a significant role in reducing mortality and morbidity. Medical dispatchers rely on accurate estimations of transfer time in determining the most appropriate method of transportation, often either a helicopter and/or land ambulance, in situations that are characterized by high time pressure and uncertainty. In this paper, we propose the design of a data-driven decision support tool to improve dispatcher transport mode decision making. We studied the dispatch process of the air and land medical transport system in Ontario, Canada through onsite observations and developed a tool which generates transfer time estimates based on historical data. We found that dispatchers have large estimation errors, and are biased toward higher degrees of underestimation for air transfers compared with land transfers. In contrast, the proposed tool produced estimates that had significantly less error than dispatcher estimates. The estimation error for the tool was on the average 21 min less: a practically significant difference in urgent patient care. Through onsite observations and the relevant literature, we also identified factors that may influence the collaboration between the dispatcher and the tool. This research is a first attempt to study how decisions are made for interfacility medical transfers and for evaluating the accuracy of human operator estimates of these transfer times. It is also the first to demonstrate a tool's utility in comparison to existing procedures for estimating transfer times.
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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 | 0.003 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".