Communication Errors in Dispatch of Air Medical Transport
Bibliographic record
Abstract
BACKGROUND: Communication errors are a source of preventable medical errors. In high-risk health care settings, identifying the source and addressing root causes can reduce error and improve patient safety. While air medical transport is a high-risk setting, its sources and rates of error have been investigated only within the last several years. OBJECTIVES: This investigation examined the rate and types of communication errors during call booking of interfacility air medical transports. The primary objective was to determine the incidence and type of errors when the initial requests for transfer took place between the sending facility and transport medicine communication center. The secondary objective was to identify potential underlying causes of these errors. METHODS: Requests for urgent and emergent interfacility air medical transfers were examined prospectively during a consecutive two-week period. As the first step in call booking, sending facility staff speak directly to communication center staff and are asked for administrative, demographic, and medical details to determine patient acuity and call priority. After this information was captured, investigators contacted the sending facility to verify the information and identify any communication errors. Errors were classified as major (potentially impacting care) or minor (unlikely to impact care) and as errors of omission or commission. Common error types were presented to a management focus group to identify potential contributing causes for these errors. RESULTS: One hundred twelve calls were randomly selected during the study period, with 98 meeting study criteria. Of those, 41 (42%) calls contained a total of 65 errors. Eleven were classified as major, including five errors of omission and six errors of commission. The most common major errors were recording "no drug allergies" when a drug allergy was present (n = 4), incorrect diagnosis (n = 2), and failure to record that patients were intubated or required mechanical ventilation (n = 2 each). There were 54 minor errors, including 41 omission errors and 13 commission errors. Nearly half the errors were attributed to procedures and software. No identified error resulted in patient harm or an adverse outcome. CONCLUSIONS: Communication-based errors are common in the initial phases of call booking in air medical transport. Human and process-driven errors contribute equally to these errors.
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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 | 0.004 | 0.054 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".