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In Journal of the American College of Emergency Physicians open

Objective : The objective of this study was to develop a US-representative prediction model identifying factors with a greater likelihood of patients leaving without being seen.

Methods : We conducted a retrospective cohort analysis using a 2016 nationwide emergency department (ED) sample. Patient factors considered for analysis were the following: age, sex, acuity, chronic diseases, weekend visit, quarter of presentation, median household income quartile for patient's zip code, primary/secondary insurance, total charges for the visit, and urban/rural household. Hospital factors considered were urban/rural location, trauma center/teaching hospital, and annual ED volume. Multivariable logistic regression was used to find significant predictors and their interactions. A random forest algorithm was used to determine the order of importance of factors.

Results : A total of 32,680,232 hospital-based ED visits with 466,047 incidences of leaving without being seen were included. The cohort comprised 55.5% females, with a median (IQR) age of 37 (21-58) years. Positively associating factors were male sex (odds ratio [OR], 1.22; 99% confidence interval [CI], 1.17-1.26), lower acuity (P < 0.001), and annual ED visits ≥60,000 (OR, 1.44; 99% CI, 1.21-1.7) versus <20,000. Negatively associating factors were primary insurance being Medicare/Tricare or private insurance (P < 0.001); weekend presentations (OR, 0.87; 99% CI, 0.85-0.89); age >64 or <18 years (P < 0.001); and higher median household income for patient's zip code second (OR, 0.86; 99% CI, 0.77-0.97), third (OR, 0.8; 99% CI, 0.7-0.91), and fourth (OR, 0.7; 99% CI, 0.6-0.8) quartiles versus the first quartile. Significant interactions existed between age, acuity, primary insurance, and chronic conditions. Primary insurance was the most predictive.

Conclusion : Our derivation model reiterated several modifiable and non-modifiable risk factors for leaving without being seen established previously while rejecting the importance of others.

Sheraton Mack, Gooch Christopher, Kashyap Rahul


ED wait times, LWBS, NEDS Database, emergency department, health services research, machine learning, prediction model