title: Predicting emergency department admissions.
abstract: OBJECTIVE
  To develop and validate models to predict emergency department (ED) presentations and hospital admissions for time and day of the year.
  
  
  METHODS
  Initial model development and validation was based on 5 years of historical data from two dissimilar hospitals, followed by subsequent validation on 27 hospitals representing 95% of the ED presentations across the state. Forecast accuracy was assessed using the mean average percentage error (MAPE) between forecasts and observed data. The study also determined a daily sample size threshold for forecasting subgroups within the data.
  
  
  RESULTS
  Presentations to the ED and subsequent admissions to hospital beds are not random and can be predicted. Forecast accuracy worsened as the forecast time intervals became smaller: when forecasting monthly admissions, the best MAPE was approximately 2%, for daily admissions, 11%; for 4-hourly admissions, 38%; and for hourly admissions, 50%. Presentations were more easily forecast than admissions (daily MAPE ∼7%). When validating accuracy at additional hospitals, forecasts for urban facilities were generally more accurate than regional forecasts (accuracy is related to sample size). Subgroups within the data with more than 10 admissions or presentations per day had forecast errors statistically similar to the entire dataset. The study also included a software implementation of the models, resulting in a data dashboard for bed managers.
  
  
  CONCLUSIONS
  Valid ED prediction tools can be generated from access to de-identified historic data, which may be used to assist elective surgery scheduling and bed management. The paper provides forecasting performance levels to guide similar studies.
authors.affiliation: CSIRO Information and Communication Technologies Centre, Level 5, UQ Health Sciences Building, Royal Brisbane and Women’s Hospital, Herston, Queensland, Australia. justin.boyle@csiro.au
authors.forename: Justin
authors.initials: J
authors.lastname: Boyle
authors.forename: Melanie
authors.initials: M
authors.lastname: Jessup
authors.forename: Julia
authors.initials: J
authors.lastname: Crilly
authors.forename: David
authors.initials: D
authors.lastname: Green
authors.forename: James
authors.initials: J
authors.lastname: Lind
authors.forename: Marianne
authors.initials: M
authors.lastname: Wallis
authors.forename: Peter
authors.initials: P
authors.lastname: Miller
authors.forename: Gerard
authors.initials: G
authors.lastname: Fitzgerald
mesh_terms: D004636:Emergency Service, Hospital / Q000706:statistics & numerical data*; D005544:Forecasting / Q000379:methods*; D006301:Health Services Needs and Demand; D006801:Humans; D015233:Models, Statistical; D008962:Models, Theoretical; D010343:Patient Admission / Q000706:statistics & numerical data*