title: Forecasting emergency department crowding: a prospective, real-time evaluation.
abstract: 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.
  
  
  METHODS
  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 R(2) 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.
authors.affiliation: Vanderbilt University Medical Center, Nashville, TN 37232, USA. nathan.hoot@vanderbilt.edu
authors.forename: Nathan R
authors.initials: NR
authors.lastname: Hoot
authors.forename: Larry J
authors.initials: LJ
authors.lastname: Leblanc
authors.forename: Ian
authors.initials: I
authors.lastname: Jones
authors.forename: Scott R
authors.initials: SR
authors.lastname: Levin
authors.forename: Chuan
authors.initials: C
authors.lastname: Zhou
authors.forename: Cynthia S
authors.initials: CS
authors.lastname: Gadd
authors.forename: Dominik
authors.initials: D
authors.lastname: Aronsky
mesh_terms: D000328:Adult; D001509:Bed Occupancy; D003196:Computer Graphics; D003198:Computer Simulation; D003441:Crowding; D004636:Emergency Service, Hospital / Q000706:statistics & numerical data*; D005544:Forecasting; D006801:Humans; D007902:Length of Stay; D017146:Models, Organizational; D015233:Models, Statistical; D019370:Observation; D009874:Operations Research; D011446:Prospective Studies; D012372:ROC Curve; D013997:Time Factors