Joan M Culley1, Sara Donevant2, Jean Craig3, Jane Richter2, Abbas S Tavakoli2, Erik Svendsen4, Salvatore DiNardi2. 1. Associate Professor, College of Nursing, University of South Carolina, Columbia, South Carolina. 2. College of Nursing, University of South Carolina, Columbia, South Carolina. 3. Database Architect, Honest Broker, Office of Biomedical Informatics Center, Medical University of South Carolina, Charleston, South Carolina. 4. Department of Public Health Sciences, Medical University of South Carolina, Charleston, South Carolina.
Abstract
OBJECTIVE: Our objective was to validate a novel irritant gas syndrome agent (IGSA) triage algorithm for use in an emergency department (ED). We assessed efficiency, accuracy, and precision of our IGSA triage algorithm based on signs/symptoms of actual patients. DESIGN: After characterizing the signs/symptoms of an actual IGSA exposure event, we developed and validated the IGSA triage algorithm using a simulated computer exercise to compare the IGSA triage algorithm to the preferred hospital triage algorithm, the Emergency Severity Index (ESI). SETTING: This study was a simulated computer exercise using surveys developed in Research Electronic Data Capture software. Nurse volunteers simulated triaging 298 patients. PARTICIPANTS: Patient data included 146 patients treated during the disaster as well as 152 unexposed patients. Twenty-six nurse volunteers were assigned to triage the patients using one of the algorithms in the simulated computer exercise. MAIN OUTCOME MEASURE(S): The precision of the IGSA triage algorithm was 0.82 (confidence interval [CI] 0.78-0.85) and ESI 0.73 (CI 0.69-0.77). Weighted κ for ESI and IGSA accuracy for exposed patients was 0.32 (95% CI 0.26-0.37) and 0.81 (95% CI 0.77-0.85), respectively. RESULTS: The IGSA triage algorithm was more accurate and precise than the ESI algorithm for triaging patients exposed to an irritant gas. CONCLUSIONS: This study validates the IGSA triage algorithm as the basis for the development of a prototype software application to quickly identify victims of a chemical disaster and triage patients efficiently and accurately with the potential to dramatically improve the processing of patients in EDs.
OBJECTIVE: Our objective was to validate a novel irritant gas syndrome agent (IGSA) triage algorithm for use in an emergency department (ED). We assessed efficiency, accuracy, and precision of our IGSA triage algorithm based on signs/symptoms of actual patients. DESIGN: After characterizing the signs/symptoms of an actual IGSA exposure event, we developed and validated the IGSA triage algorithm using a simulated computer exercise to compare the IGSA triage algorithm to the preferred hospital triage algorithm, the Emergency Severity Index (ESI). SETTING: This study was a simulated computer exercise using surveys developed in Research Electronic Data Capture software. Nurse volunteers simulated triaging 298 patients. PARTICIPANTS: Patient data included 146 patients treated during the disaster as well as 152 unexposed patients. Twenty-six nurse volunteers were assigned to triage the patients using one of the algorithms in the simulated computer exercise. MAIN OUTCOME MEASURE(S): The precision of the IGSA triage algorithm was 0.82 (confidence interval [CI] 0.78-0.85) and ESI 0.73 (CI 0.69-0.77). Weighted κ for ESI and IGSA accuracy for exposed patients was 0.32 (95% CI 0.26-0.37) and 0.81 (95% CI 0.77-0.85), respectively. RESULTS: The IGSA triage algorithm was more accurate and precise than the ESI algorithm for triaging patients exposed to an irritant gas. CONCLUSIONS: This study validates the IGSA triage algorithm as the basis for the development of a prototype software application to quickly identify victims of a chemical disaster and triage patients efficiently and accurately with the potential to dramatically improve the processing of patients in EDs.
Authors: Paul A Harris; Robert Taylor; Robert Thielke; Jonathon Payne; Nathaniel Gonzalez; Jose G Conde Journal: J Biomed Inform Date: 2008-09-30 Impact factor: 6.317
Authors: Sara B Donevant; Erik R Svendsen; Jane V Richter; Abbas S Tavakoli; Jean B R Craig; Nicholas D Boltin; Homayoun Valafar; Salvatore Robert DiNardi; Joan M Culley Journal: J Am Med Inform Assoc Date: 2019-10-01 Impact factor: 4.497