Prospective validation of a new model for evaluating emergency medical services systems by in-field observation of specific time intervals in prehospital care

Daniel W Spaite, Terence D Valenzuela, Harvey W Meislin, Elizabeth A. Criss, Paul Hinsberg

Research output: Contribution to journalArticle

102 Citations (Scopus)

Abstract

Study 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, communi-cations, 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) intervals 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. Conclusion: 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.

Original languageEnglish (US)
Pages (from-to)638-645
Number of pages8
JournalAnnals of Emergency Medicine
Volume22
Issue number4
DOIs
StatePublished - 1993

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Emergency Medical Services
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Research

Keywords

  • emergency medical service systems
  • emergency medical services
  • evaluation
  • prehospital care

ASJC Scopus subject areas

  • Emergency Medicine

Cite this

@article{e26d7e8bd8224a9cbd4588034636fc29,
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 = "Study 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, communi-cations, 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) intervals 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. Conclusion: 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.",
keywords = "emergency medical service systems, emergency medical services, evaluation, prehospital care",
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T1 - Prospective validation of a new model for evaluating emergency medical services systems by in-field observation of specific time intervals in prehospital care

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AU - Valenzuela, Terence D

AU - Meislin, Harvey W

AU - Criss, Elizabeth A.

AU - Hinsberg, Paul

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N2 - Study 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, communi-cations, 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) intervals 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. Conclusion: 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.

AB - Study 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, communi-cations, 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) intervals 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. Conclusion: 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.

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