Patient Choice in the Selection of Hospitals by 9-1-1 Emergency Medical Services Providers in Trauma Systems
Bibliographic record
Abstract
OBJECTIVES: Reasons for undertriage (transporting seriously injured patients to nontrauma centers) and the apparent lack of benefit of trauma centers among older adults remain unclear; understanding emergency medical services (EMS) provider reasons for selecting certain hospitals in trauma systems may provide insight to these issues. In this study, the authors evaluated reasons cited by EMS providers for selecting specific hospital destinations for injured patients, stratified by age, injury severity, field triage status, and prognosis. METHODS: This was a retrospective cohort study of injured children and adults transported by 61 EMS agencies to 93 hospitals (trauma and nontrauma centers) in five regions of the western United States from 2006 through 2008. Hospital records were probabilistically linked to EMS records using trauma registries, state discharge data, and emergency department data. The seven standardized reasons cited by EMS providers for selecting hospital destinations included closest facility, ambulance diversion, physician choice, law enforcement choice, patient or family choice, specialty resource center, and other. "Serious injury" was defined as an Injury Severity Score (ISS) ≥ 16, and unadjusted in-hospital mortality was considered as a marker of prognosis. All analyses were stratified by age in 10-year increments, and descriptive statistics were used to characterize the findings. RESULTS: A total of 176,981 injured patients were evaluated and transported by EMS over the 3-year period, of whom 5,752 (3.3%) had ISS ≥ 16 and 2,773 (1.6%) died. Patient or family choice (50.6%), closest facility (20.7%), and specialty resource center (15.2%) were the most common reasons indicated by EMS providers for selecting destination hospitals; these frequencies varied substantially by patient age. The frequency of patient or family choice increased with increasing age, from 36.4% among 21- to 30-year-olds to 75.8% among those older than 90 years. This trend paralleled undertriage rates and persisted when restricted to patients with serious injuries. Older patients with the worst prognoses were preferentially transported to major trauma centers, a finding that was not explained by field triage protocols. CONCLUSIONS: Emergency medical services transport patterns among injured patients are not random, even after accounting for field triage protocols. The selection of hospitals appears to be heavily influenced by patient or family choice, which increases with patient age and involves inherent differences in patient prognosis.
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".