title: Forecasting Daily Patient Outflow From a Ward Having No Real-Time Clinical Data.
abstract: OBJECTIVE
  Our study investigates different models to forecast the total number of next-day discharges from an open ward having no real-time clinical data.
  
  
  METHODS
  We compared 5 popular regression algorithms to model total next-day discharges: (1) autoregressive integrated moving average (ARIMA), (2) the autoregressive moving average with exogenous variables (ARMAX), (3) k-nearest neighbor regression, (4) random forest regression, and (5) support vector regression. Although the autoregressive integrated moving average model relied on past 3-month discharges, nearest neighbor forecasting used median of similar discharges in the past in estimating next-day discharge. In addition, the ARMAX model used the day of the week and number of patients currently in ward as exogenous variables. For the random forest and support vector regression models, we designed a predictor set of 20 patient features and 88 ward-level features.
  
  
  RESULTS
  Our data consisted of 12,141 patient visits over 1826 days. Forecasting quality was measured using mean forecast error, mean absolute error, symmetric mean absolute percentage error, and root mean square error. When compared with a moving average prediction model, all 5 models demonstrated superior performance with the random forests achieving 22.7% improvement in mean absolute error, for all days in the year 2014.
  
  
  CONCLUSIONS
  In the absence of clinical information, our study recommends using patient-level and ward-level data in predicting next-day discharges. Random forest and support vector regression models are able to use all available features from such data, resulting in superior performance over traditional autoregressive methods. An intelligent estimate of available beds in wards plays a crucial role in relieving access block in emergency departments.
authors.affiliation: Centre for Pattern Recognition and Data Analytics, Deakin University, Geelong Waurn Ponds, Australia. shivapratap@gmail.com.
authors.forename: Shivapratap
authors.identifier: http://orcid.org/0000-0002-7986-7752
authors.initials: S
authors.lastname: Gopakumar
authors.forename: Truyen
authors.identifier: http://orcid.org/0000-0001-6531-8907
authors.initials: T
authors.lastname: Tran
authors.forename: Wei
authors.identifier: http://orcid.org/0000-0002-4711-7543
authors.initials: W
authors.lastname: Luo
authors.forename: Dinh
authors.identifier: http://orcid.org/0000-0002-9977-8247
authors.initials: D
authors.lastname: Phung
authors.forename: Svetha
authors.identifier: http://orcid.org/0000-0001-8675-6631
authors.initials: S
authors.lastname: Venkatesh
keywords: discharge planning; patient flow; predictive models