title: Regression forecasting of patient admission data.
abstract: Forecasting is an important aid in many areas of hospital management, including elective surgery scheduling, bed management, and staff resourcing. This paper describes our work in analyzing patient admission data and forecasting this data using regression techniques. Five years of Emergency Department admissions data were obtained from two hospitals with different demographic techniques. Forecasts made from regression models were compared with observed admission data over a 6-month horizon. The best method was linear regression using 11 dummy variables to model monthly variation (MAPE=1.79%). Similar performance was achieved with a 2-year average, supporting further investigation at finer time scales.
authors.affiliation: Australian E-Health Research Centre, CSIRO ICT Centre, Adelaide St., Brisbane 4000, Australia. justin.boyle@csiro.au
authors.forename: Justin
authors.initials: J
authors.lastname: Boyle
authors.forename: Marianne
authors.initials: M
authors.lastname: Wallis
authors.forename: Melanie
authors.initials: M
authors.lastname: Jessup
authors.forename: Julia
authors.initials: J
authors.lastname: Crilly
authors.forename: James
authors.initials: J
authors.lastname: Lind
authors.forename: Peter
authors.initials: P
authors.lastname: Miller
authors.forename: Gerard
authors.initials: G
authors.lastname: Fitzgerald
mesh_terms: D000465:Algorithms; D004635:Emergency Medicine / Q000379:methods / Q000458:organization & administration; D004636:Emergency Service, Hospital / Q000706:statistics & numerical data*; D005544:Forecasting; D006295:Health Resources / Q000706:statistics & numerical data*; D006302:Health Services Research / Q000379:methods; D006748:Hospital Departments / Q000706:statistics & numerical data*; D006751:Hospital Information Systems; D006761:Hospitals / Q000706:statistics & numerical data; D006801:Humans; D010561:Personnel Staffing and Scheduling; D012044:Regression Analysis; D015203:Reproducibility of Results; D013997:Time Factors