Emergency department overcrowding: analysis of the factors of renege rate.
BACKGROUND
Reneging (i.e., leaving without being seen) is an important outcome of emergency department (ED) overcrowding. The input-throughput-output conceptualization of ED patient flow is helpful in understanding and measuring the impact of various factors on this outcome.


OBJECTIVE
To quantify the impact of input and output factors on ED renege rate.


METHODS
The authors used patient-level and system-level data from multiple sources in their institution to build logistic regression models, with reneging as the dependent variable. This approach provides the impact of each input and output factor on renege rate expressed as an odds ratio (OR).


RESULTS
The OR for reneging attributable to the difference between the 80th and 20th percentile values for inpatient bed utilization is 1.05. Comparing 80th and 20th percentile values for boarded ED admits as of 7 AM, the OR is 1.73; for daily ED arrivals, the OR is 2.00; and for admission percentage, the OR is 1.12. The OR for evening versus morning patient arrival time is 3.9 and for patient arrival on a Monday versus a Sunday is 2.7. The OR for reneging for a patient presenting on Monday evening versus Sunday morning is 10.5.


CONCLUSIONS
The effects of ED input and output factors on renege rate are significant and quantifiable. At least some of the variation in these factors and subsequently their effects are predictable, suggesting that further refinement in the management of ED and inpatient resources could affect improvement in ED renege rate. Continued efforts at quantifying the effects are warranted.
Asaro
Phillip V
PV
Emergency Medicine Division, Washington University School of Medicine, St. Louis, MO, USA. asarop@msnotes.wustl.edu
Lewis
Lawrence M
LM
Boxerman
Stuart B
SB
D000328:Adult; D000368:Aged; D001509:Bed Occupancy / Q000706:statistics & numerical data*; D003441:Crowding; D004636:Emergency Service, Hospital / Q000706:statistics & numerical data*; D005260:Female; D006801:Humans; D016015:Logistic Models; D008297:Male; D010352:Patient Dropouts / Q000706:statistics & numerical data*; D012189:Retrospective Studies; D013997:Time Factors