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Short-term forecasting of emergency inpatient flow.
IEEE Trans Inf Technol Biomed. 2009 May; 13(3):380-8.IT

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+Show Affiliations

Department of Mathematics and Statistics, Universityof Melbourne, Parkville, Vic. 3010, Australia. gabraham@csse.unimelb.edu.auNo affiliation info availableNo affiliation info available

Pub Type(s)

Journal Article
Research Support, Non-U.S. Gov't

Language

eng

PubMed ID

19244023

Citation

Abraham, Gad, et al. "Short-term Forecasting of Emergency Inpatient Flow." IEEE Transactions On Information Technology in Biomedicine : a Publication of the IEEE Engineering in Medicine and Biology Society, vol. 13, no. 3, 2009, pp. 380-8.
Abraham G, Byrnes GB, Bain CA. Short-term forecasting of emergency inpatient flow. IEEE Trans Inf Technol Biomed. 2009;13(3):380-8.
Abraham, G., Byrnes, G. B., & Bain, C. A. (2009). Short-term forecasting of emergency inpatient flow. IEEE Transactions On Information Technology in Biomedicine : a Publication of the IEEE Engineering in Medicine and Biology Society, 13(3), 380-8. https://doi.org/10.1109/TITB.2009.2014565
Abraham G, Byrnes GB, Bain CA. Short-term Forecasting of Emergency Inpatient Flow. IEEE Trans Inf Technol Biomed. 2009;13(3):380-8. PubMed PMID: 19244023.
* Article titles in AMA citation format should be in sentence-case
TY - JOUR T1 - Short-term forecasting of emergency inpatient flow. AU - Abraham,Gad, AU - Byrnes,Graham B, AU - Bain,Christopher A, Y1 - 2009/02/24/ PY - 2009/2/27/entrez PY - 2009/2/27/pubmed PY - 2009/9/9/medline SP - 380 EP - 8 JF - IEEE transactions on information technology in biomedicine : a publication of the IEEE Engineering in Medicine and Biology Society JO - IEEE Trans Inf Technol Biomed VL - 13 IS - 3 N2 - 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. SN - 1558-0032 UR - https://www.unboundmedicine.com/medline/citation/19244023/Short_term_forecasting_of_emergency_inpatient_flow_ L2 - https://dx.doi.org/10.1109/TITB.2009.2014565 DB - PRIME DP - Unbound Medicine ER -