Andrew D McRae1,2, Brian H Rowe3, Iram Usman4, Eddy S Lang5,6, Grant D Innes5,6, Michael J Schull7, Rhonda Rosychuk4. 1. Department of Emergency Medicine, Rm C231 Foothills Medical Centre, University of Calgary, 1403 29 St NW, Calgary, AB, T2N 2T9, Canada. Amcrae@ucalgary.ca. 2. Department of Community Health Sciences, University of Calgary, Calgary, AB, Canada. Amcrae@ucalgary.ca. 3. Department of Emergency Medicine, University of Alberta, Edmonton, AB, Canada. 4. Department of Pediatrics, University of Alberta, Edmonton, AB, Canada. 5. Department of Emergency Medicine, Rm C231 Foothills Medical Centre, University of Calgary, 1403 29 St NW, Calgary, AB, T2N 2T9, Canada. 6. Department of Community Health Sciences, University of Calgary, Calgary, AB, Canada. 7. Institute for Clinical and Evaluative Sciences and Sunnybrook Health Sciences Centre, Toronto, ON, Canada.
Abstract
OBJECTIVE: We sought to compare strengths of association among multiple emergency department (ED) input, throughput and output metrics and the outcome of 72-h ED re-visits. METHODS: This database analysis used healthcare administrative data from three urban, university-affiliated EDs in Calgary, Canada, calendar years 2010-2014. We used data from all patients presenting to participating EDs during the study period, and the primary analysis was performed on patients discharged from the ED. Regression models quantified the association between input, throughput and output metrics and the risk of return ED visit within 72 h of discharge from the index ED encounter. Strength of association between the crowding metrics and 72-h ED re-visits was compared using Akaike's Information Criterion. RESULTS: The findings of this study are based on data from 845,588 patient encounters ending in discharge. The input metric with the strongest association with 72-h re-visits was median ED waiting time. The throughput metric with the strongest association with 72-h re-visits was the ED occupancy. The output metric with the strongest association with 72-h re-visits was the median inpatient boarding time. CONCLUSION: Input, throughput and output metrics are all associated with 72-h re-visits. Delays in any of these operational phases have detrimental effects on patient outcomes. ED waiting time, ED occupancy, and boarding times are the most meaningful input, throughput and output metrics. These should be the preferred metrics for quantifying ED crowding in research and quality improvement efforts, and for clinicians to monitor ED crowding in real time.
OBJECTIVE: We sought to compare strengths of association among multiple emergency department (ED) input, throughput and output metrics and the outcome of 72-h ED re-visits. METHODS: This database analysis used healthcare administrative data from three urban, university-affiliated EDs in Calgary, Canada, calendar years 2010-2014. We used data from all patients presenting to participating EDs during the study period, and the primary analysis was performed on patients discharged from the ED. Regression models quantified the association between input, throughput and output metrics and the risk of return ED visit within 72 h of discharge from the index ED encounter. Strength of association between the crowding metrics and 72-h ED re-visits was compared using Akaike's Information Criterion. RESULTS: The findings of this study are based on data from 845,588 patient encounters ending in discharge. The input metric with the strongest association with 72-h re-visits was median ED waiting time. The throughput metric with the strongest association with 72-h re-visits was the ED occupancy. The output metric with the strongest association with 72-h re-visits was the median inpatient boarding time. CONCLUSION: Input, throughput and output metrics are all associated with 72-h re-visits. Delays in any of these operational phases have detrimental effects on patient outcomes. ED waiting time, ED occupancy, and boarding times are the most meaningful input, throughput and output metrics. These should be the preferred metrics for quantifying ED crowding in research and quality improvement efforts, and for clinicians to monitor ED crowding in real time.
Authors: E W Steyerberg; F E Harrell; G J Borsboom; M J Eijkemans; Y Vergouwe; J D Habbema Journal: J Clin Epidemiol Date: 2001-08 Impact factor: 6.437