Literature DB >> 16801284

The in-hospital interval: a description of EMT time spent in the emergency department.

Eli Segal1, Vedat Verter, Antoinette Colacone, Marc Afilalo.   

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.

Mesh:

Year:  2006        PMID: 16801284     DOI: 10.1080/10903120600725884

Source DB:  PubMed          Journal:  Prehosp Emerg Care        ISSN: 1090-3127            Impact factor:   3.077


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4.  Comparison of pre-hospital emergency services time intervals in patients with heart attack in Arak, Iran.

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5.  Machine learning-based forecasting of firemen ambulances' turnaround time in hospitals, considering the COVID-19 impact.

Authors:  Selene Cerna; Héber H Arcolezi; Christophe Guyeux; Guillaume Royer-Fey; Céline Chevallier
Journal:  Appl Soft Comput       Date:  2021-06-04       Impact factor: 6.725

  5 in total

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