The In-Hospital Interval: A Description of EMT Time Spent in the Emergency Department
Bibliographic record
Abstract
OBJECTIVE: We conducted a time-motion study of emergency medical technician (EMT) flow in an urban, academic emergency department (ED). Our objective was to describe the activity of the EMTs during their time in the ED. Secondary objectives included the association of time of day, age, and triage code with the various time intervals. METHODS: In this descriptive study, we combined information from two databases: prospectively collected time-motion data of EMTs presenting to one ED and an electronically collected prehospital call database of time data. The pretriage, triage, and posttriage time intervals were calculated, as well as total time spent in the ED as a proportion of total call time. Mean times with 95% confidence intervals (CIs) were reported. Analysis of variance was performed to examine the associations of time of day, age, and triage code with time intervals. RESULTS: Data were available for 152 calls. The mean pretriage interval was 8.79 (95% CI, 7.55-10.04) minutes, the mean triage interval was 5.14 (95% CI, 4.49-5.79) minutes, and the mean posttriage interval was 31.33 (95% CI, 29.08-33.58) minutes. The proportion of the total call time that was spent in the ED was 45%. Subgroup analysis showed significant differences only between total time spent in the ED in the 7:30-10:00 AM period as compared with the other periods. CONCLUSIONS: More time was spent in the pretriage and posttriage intervals as compared with the triage interval. Further time-motion studies in the ED will be necessary to plan interventions aimed at decreasing the time spent in-hospital by EMTs.
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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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 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".