title: Prospective validation of a new model for evaluating emergency medical services systems by in-field observation of specific time intervals in prehospital care.
abstract: OBJECTIVE
  To develop and validate a new time interval model for evaluating operational and patient care issues in emergency medical service (EMS) systems. DESIGN/SETTING/TYPE OF PARTICIPANT: Prospective analysis of 300 EMS responses among 20 advanced life support agencies throughout an entire state by direct, in-field observation.
  
  
  RESULTS
  Mean times (minutes) were response, 6.8; patient access, 1.0; initial assessment, 3.3; scene treatment, 4.4; patient removal, 5.5; transport, 11.7; delivery, 3.5; and recovery, 22.9. The largest component of the on-scene interval was patient removal. Scene treatment accounted for only 31.0% of the on-scene interval, whereas accessing and removing patients took nearly half of the on-scene interval (45.8%). Operational problems (eg, communications, equipment, uncooperative patient) increased patient removal (6.4 versus 4.5; P = .004), recovery (25.4 versus 20.2; P = .03), and out-of-service (43.0 versus 30.1; P = .007) intervals. Rural agencies had longer response (9.9 versus 6.4; P = .014), transport (21.9 versus 10.3; P < .0005), and recovery (29.8 versus 22.1; P = .049) interval than nonrural. The total on-scene interval was longer if an IV line was attempted at the scene (17.2 versus 12.2; P < .0001). This reflected an increase in scene treatment (9.2 versus 2.8; P < .0001), while patient access and patient removal remained unchanged. However, the time spent attempting IV lines at the scene accounted for only a small part of scene treatment (1.3 minutes; 14.1%) and an even smaller portion of the overall on-scene interval (7.6%). Most of the increase in scene treatment was accounted for by other activities than the IV line attempts.
  
  
  CONCLUSIONS
  A new model reported and studied prospectively is useful as an evaluative research tool for EMS systems and is broadly applicable to many settings in a demographically diverse state. This model can provide accurate information to system researchers, medical directors, and administrators for altering and improving EMS systems.
authors.affiliation: Arizona Emergency Medicine Research Center, College of Medicine, University of Arizona, Tucson.
authors.forename: D W
authors.initials: DW
authors.lastname: Spaite
authors.forename: T D
authors.initials: TD
authors.lastname: Valenzuela
authors.forename: H W
authors.initials: HW
authors.lastname: Meislin
authors.forename: E A
authors.initials: EA
authors.lastname: Criss
authors.forename: P
authors.initials: P
authors.lastname: Hinsberg
mesh_terms: D001130:Arizona; D004632:Emergency Medical Services / Q000458:organization & administration / Q000706:statistics & numerical data*; D006801:Humans; D017146:Models, Organizational; D013997:Time Factors