Supporting dispatch decisions in interfacility medical transfers: Understanding the roles of uncertainty and reliability
Bibliographic record
Abstract
Efficient transfer of patients between facilities is a key component of regionalized critical care systems, but interfacility transfers pose added risks to patients. We created a decision support tool for dispatchers in medical transportation systems which provides estimates of the time to definitive care, the time between receiving the transfer request and patient handoff, which is a key component of dispatch decisions. This tool provides point-estimates of transfer times that are more accurate than the dispatchers own estimates, to support resource allocation and medical triage decisions. However, additional information about the uncertainty of these estimates may further improve dispatcher decision making. This paper describes an observational study conducted with 2 expert and 2 novice dispatchers in a medical transportation service on usage of our prototype decision support tool. In particular, we focused on whether these dispatchers use uncertainty information in their decision process, and how this information should be presented in a tool. We found that uncertainty, as represented by of the spread of possible transfer times (i.e., variability of times), is not currently considered by dispatchers. However, our observations suggest that reliability information (i.e., the trustworthiness of the tool's estimates) would be used. Expert dispatchers would use information about the reliability of estimates to build their trust in the system, while novices should use reliability information to calibrate their trust.
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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 | 0.017 | 0.113 |
| 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 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".