TY - JOUR
T1 - Forecasting Emergency Department Crowding
T2 - A Prospective, Real-time Evaluation
AU - Hoot, Nathan R.
AU - LeBlanc, Larry J.
AU - Jones, Ian
AU - Levin, Scott R.
AU - Zhou, Chuan
AU - Gadd, Cynthia S.
AU - Aronsky, Dominik
N1 - Funding Information:
Dr. Hoot was supported by training grants from the National Library of Medicine (LM07450) and the National Institute of General Medical Studies (T32 GM07347). This research was also supported in part by a grant from the National Library of Medicine (R21 LM009002-01). The authors thank Keith Wrenn, MD and Chris Bunick, PhD of Vanderbilt University Medical Center for providing valuable feedback on the manuscript draft. The authors declare no financial or commercial conflicts of interest pertaining to the work.
PY - 2009/5
Y1 - 2009/5
N2 - Objective: Emergency department crowding threatens quality and access to health care, and a method of accurately forecasting near-future crowding should enable novel ways to alleviate the problem. The authors sought to implement and validate the previously developed ForecastED discrete event simulation for real-time forecasting of emergency department crowding. Design and Measurements: The authors conducted a prospective observational study during a three-month period (5/1/07-8/1/07) in the adult emergency department of a tertiary care medical center. The authors connected the forecasting tool to existing information systems to obtain real-time forecasts of operational data, updated every 10 minutes. The outcome measures included the emergency department waiting count, waiting time, occupancy level, length of stay, boarding count, boarding time, and ambulance diversion; each forecast 2, 4, 6, and 8 hours into the future. Results: The authors obtained crowding forecasts at 13,239 10-minute intervals, out of 13,248 possible (99.9%). The R2 values for predicting operational data 8 hours into the future, with 95% confidence intervals, were 0.27 (0.26, 0.29) for waiting count, 0.11 (0.10, 0.12) for waiting time, 0.57 (0.55, 0.58) for occupancy level, 0.69 (0.68, 0.70) for length of stay, 0.61 (0.59, 0.62) for boarding count, and 0.53 (0.51, 0.54) for boarding time. The area under the receiver operating characteristic curve for predicting ambulance diversion 8 hours into the future, with 95% confidence intervals, was 0.85 (0.84, 0.86). Conclusions: The ForecastED tool provides accurate forecasts of several input, throughput, and output measures of crowding up to 8 hours into the future. The real-time deployment of the system should be feasible at other emergency departments that have six patient-level variables available through information systems.
AB - Objective: Emergency department crowding threatens quality and access to health care, and a method of accurately forecasting near-future crowding should enable novel ways to alleviate the problem. The authors sought to implement and validate the previously developed ForecastED discrete event simulation for real-time forecasting of emergency department crowding. Design and Measurements: The authors conducted a prospective observational study during a three-month period (5/1/07-8/1/07) in the adult emergency department of a tertiary care medical center. The authors connected the forecasting tool to existing information systems to obtain real-time forecasts of operational data, updated every 10 minutes. The outcome measures included the emergency department waiting count, waiting time, occupancy level, length of stay, boarding count, boarding time, and ambulance diversion; each forecast 2, 4, 6, and 8 hours into the future. Results: The authors obtained crowding forecasts at 13,239 10-minute intervals, out of 13,248 possible (99.9%). The R2 values for predicting operational data 8 hours into the future, with 95% confidence intervals, were 0.27 (0.26, 0.29) for waiting count, 0.11 (0.10, 0.12) for waiting time, 0.57 (0.55, 0.58) for occupancy level, 0.69 (0.68, 0.70) for length of stay, 0.61 (0.59, 0.62) for boarding count, and 0.53 (0.51, 0.54) for boarding time. The area under the receiver operating characteristic curve for predicting ambulance diversion 8 hours into the future, with 95% confidence intervals, was 0.85 (0.84, 0.86). Conclusions: The ForecastED tool provides accurate forecasts of several input, throughput, and output measures of crowding up to 8 hours into the future. The real-time deployment of the system should be feasible at other emergency departments that have six patient-level variables available through information systems.
UR - http://www.scopus.com/inward/record.url?scp=65349128949&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=65349128949&partnerID=8YFLogxK
U2 - 10.1197/jamia.M2772
DO - 10.1197/jamia.M2772
M3 - Article
C2 - 19261948
AN - SCOPUS:65349128949
SN - 1067-5027
VL - 16
SP - 338
EP - 345
JO - Journal of the American Medical Informatics Association
JF - Journal of the American Medical Informatics Association
IS - 3
ER -