Regression forecasting of patient admission data

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Author(s)
Boyle, Justin
Wallis, Marianne
Jessup, Melanie
Crilly, Julia
Lind, James
Miller, Peter
Fitzgerald, Gerard
Griffith University Author(s)
Year published
2008
Metadata
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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 ...
View more >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.
View less >
View more >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.
View less >
Conference Title
2008 30TH ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY, VOLS 1-8
Volume
2008
Copyright Statement
© 2008 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.
Subject
Multi-Disciplinary