title: Short-term forecasting of emergency inpatient flow.
abstract: Hospital managers have to manage resources effectively, while maintaining a high quality of care. For hospitals where admissions from the emergency department to the wards represent a large proportion of admissions, the ability to forecast these admissions and the resultant ward occupancy is especially useful for resource planning purposes. Since emergency admissions often compete with planned elective admissions, modeling emergency demand may result in improved elective planning as well. We compare several models for forecasting daily emergency inpatient admissions and occupancy. The models are applied to three years of daily data. By measuring their mean square error in a cross-validation framework, we find that emergency admissions are largely random, and hence, unpredictable, whereas emergency occupancy can be forecasted using a model combining regression and autoregressive integrated moving average (ARIMA) model, or a seasonal ARIMA model, for up to one week ahead. Faced with variable admissions and occupancy, hospitals must prepare a reserve capacity of beds and staff. Our approach allows estimation of the required reserve capacity.
authors.affiliation: Department of Mathematics and Statistics, Universityof Melbourne, Parkville, Vic. 3010, Australia. gabraham@csse.unimelb.edu.au
authors.forename: Gad
authors.initials: G
authors.lastname: Abraham
authors.forename: Graham B
authors.initials: GB
authors.lastname: Byrnes
authors.forename: Christopher A
authors.initials: CA
authors.lastname: Bain
mesh_terms: D001509:Bed Occupancy / Q000706:statistics & numerical data*; D004636:Emergency Service, Hospital / Q000706:statistics & numerical data*; D005544:Forecasting; D006784:Hospitals, Teaching; D006801:Humans; D007902:Length of Stay / Q000706:statistics & numerical data; D015233:Models, Statistical; D010343:Patient Admission / Q000706:statistics & numerical data*; D010561:Personnel Staffing and Scheduling / Q000706:statistics & numerical data