This study was funded in part by grant, 1K01HS017957-01, which Dr. McCarthy received from the Agency for Healthcare Research and Quality.
Characterizing Waiting Room Time, Treatment Time, and Boarding Time in the Emergency Department Using Quantile Regression
Article first published online: 29 JUL 2010
© 2010 by the Society for Academic Emergency Medicine
Academic Emergency Medicine
Volume 17, Issue 8, pages 813–823, August 2010
How to Cite
Ding, R., McCarthy, M. L., Desmond, J. S., Lee, J. S., Aronsky, D. and Zeger, S. L. (2010), Characterizing Waiting Room Time, Treatment Time, and Boarding Time in the Emergency Department Using Quantile Regression. Academic Emergency Medicine, 17: 813–823. doi: 10.1111/j.1553-2712.2010.00812.x
Supervising Editor: James E. Olson, PhD.
- Issue published online: 29 JUL 2010
- Article first published online: 29 JUL 2010
- Received November 5, 2009; revisions received January 21 and February 17, 2010; accepted February 19, 2010.
- emergency department;
- length of stay;
- quantile regression;
ACADEMIC EMERGENCY MEDICINE 2010; 17:813–823 © 2010 by the Society for Academic Emergency Medicine
Objectives: The objective was to characterize service completion times by patient, clinical, temporal, and crowding factors for different phases of emergency care using quantile regression (QR).
Methods: A retrospective cohort study was conducted on 1-year visit data from four academic emergency departments (EDs; N = 48,896–58,316). From each ED’s clinical information system, the authors extracted electronic service information (date and time of registration; bed placement, initial contact with physician, disposition decision, ED discharge, and disposition status; inpatient medicine bed occupancy rate); patient demographics (age, sex, insurance status, and mode of arrival); and clinical characteristics (acuity level and chief complaint) and then used the service information to calculate patients’ waiting room time, treatment time, and boarding time, as well as the ED occupancy rate. The 10th, 50th, and 90th percentiles of each phase of care were estimated as a function of patient, clinical, temporal, and crowding factors using multivariate QR. Accuracy of models was assessed by comparing observed and predicted service completion times and the proportion of observations that fell below the predicted 10th, 50th, and 90th percentiles.
Results: At the 90th percentile, patients experienced long waiting room times (105–222 minutes), treatment times (393–616 minutes), and boarding times (381–1,228 minutes) across the EDs. We observed a strong interaction effect between acuity level and temporal factors (i.e., time of day and day of week) on waiting room time at all four sites. Acuity level 3 patients waited the longest across the four sites, and their waiting room times were most influenced by temporal factors compared to other acuity level patients. Acuity level and chief complaint were important predictors of all phases of care, and there was a significant interaction effect between acuity and chief complaint. Patients with a psychiatric problem experienced the longest treatment times, regardless of acuity level. Patients who presented with an injury did not wait as long for an ED or inpatient bed. Temporal factors were strong predictors of service completion time, particularly waiting room time. Mode of arrival was the only patient characteristic that substantially affected waiting room time and treatment time. Patients who arrived by ambulance had shorter wait times but longer treatment times compared to those who did not arrive by ambulance. There was close agreement between observed and predicted service completion times at the 10th, 50th, and 90th percentile distributions across the four EDs.
Conclusions: Service completion times varied significantly across the four academic EDs. QR proved to be a useful method for estimating the service completion experience of not only typical ED patients, but also the experience of those who waited much shorter or longer. Building accurate models of ED service completion times is a critical first step needed to identify barriers to patient flow, begin the process of reengineering the system to reduce variability, and improve the timeliness of care provided.