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A comprehensive modelling framework to forecast the demand for all hospital services.
Int J Health Plann Manage. 2019 Apr; 34(2):e1257-e1271.IJ

Abstract

BACKGROUND

Because of increasing demand, hospitals in England are currently under intense pressure resulting in shortages of beds, nurses, clinicians, and equipment. To be able to effectively cope with this demand, the management needs to accurately find out how many patients are expected to use their services in the future. This applies not just to one service but for all hospital services.

PURPOSE

A forecasting modelling framework is developed for all hospital's acute services, including all specialties within outpatient and inpatient settings and the accident and emergency (A&E) department. The objective is to support the management to better deal with demand and plan ahead effectively.

METHODOLOGY/APPROACH

Having established a theoretical framework, we used the national episodes statistics dataset to systematically capture demand for all specialties. Three popular forecasting methodologies, namely, autoregressive integrated moving average (ARIMA), exponential smoothing, and multiple linear regression were used. A fourth technique known as the seasonal and trend decomposition using loess function (STLF) was applied for the first time within the context of health-care forecasting.

RESULTS

According to goodness of fit and forecast accuracy measures, 64 best forecasting models and periods (daily, weekly, or monthly forecasts) were selected out of 760 developed models; ie, demand was forecasted for 38 outpatient specialties (first referrals and follow-ups), 25 inpatient specialties (elective and non-elective admissions), and for A&E.

CONCLUSION

This study has confirmed that the best demand estimates arise from different forecasting methods and forecasting periods (ie, one size does not fit all). Despite the fact that the STLF method was applied for the first time, it outperformed traditional time series forecasting methods (ie, ARIMA and exponential smoothing) for a number of specialties.

PRACTISE IMPLICATIONS

Knowing the peaks and troughs of demand for an entire hospital will enable the management to (a) effectively plan ahead; (b) ensure necessary resources are in place (eg, beds and staff); (c) better manage budgets, ensuring enough cash is available; and (d) reduce risk.

Authors+Show Affiliations

University of Hertfordshire, Hertfordshire Business School, Hatfield, UK.University of Hertfordshire, Hertfordshire Business School, Hatfield, UK.University of Hertfordshire, Hertfordshire Business School, Hatfield, UK.

Pub Type(s)

Journal Article

Language

eng

PubMed ID

30901132

Citation

Ordu, Muhammed, et al. "A Comprehensive Modelling Framework to Forecast the Demand for All Hospital Services." The International Journal of Health Planning and Management, vol. 34, no. 2, 2019, pp. e1257-e1271.
Ordu M, Demir E, Tofallis C. A comprehensive modelling framework to forecast the demand for all hospital services. Int J Health Plann Manage. 2019;34(2):e1257-e1271.
Ordu, M., Demir, E., & Tofallis, C. (2019). A comprehensive modelling framework to forecast the demand for all hospital services. The International Journal of Health Planning and Management, 34(2), e1257-e1271. https://doi.org/10.1002/hpm.2771
Ordu M, Demir E, Tofallis C. A Comprehensive Modelling Framework to Forecast the Demand for All Hospital Services. Int J Health Plann Manage. 2019;34(2):e1257-e1271. PubMed PMID: 30901132.
* Article titles in AMA citation format should be in sentence-case
TY - JOUR T1 - A comprehensive modelling framework to forecast the demand for all hospital services. AU - Ordu,Muhammed, AU - Demir,Eren, AU - Tofallis,Chris, Y1 - 2019/03/22/ PY - 2019/02/20/received PY - 2019/02/21/accepted PY - 2019/3/23/pubmed PY - 2020/1/10/medline PY - 2019/3/23/entrez KW - forecasting hospital services KW - hospital demand KW - time series analysis SP - e1257 EP - e1271 JF - The International journal of health planning and management JO - Int J Health Plann Manage VL - 34 IS - 2 N2 - BACKGROUND: Because of increasing demand, hospitals in England are currently under intense pressure resulting in shortages of beds, nurses, clinicians, and equipment. To be able to effectively cope with this demand, the management needs to accurately find out how many patients are expected to use their services in the future. This applies not just to one service but for all hospital services. PURPOSE: A forecasting modelling framework is developed for all hospital's acute services, including all specialties within outpatient and inpatient settings and the accident and emergency (A&E) department. The objective is to support the management to better deal with demand and plan ahead effectively. METHODOLOGY/APPROACH: Having established a theoretical framework, we used the national episodes statistics dataset to systematically capture demand for all specialties. Three popular forecasting methodologies, namely, autoregressive integrated moving average (ARIMA), exponential smoothing, and multiple linear regression were used. A fourth technique known as the seasonal and trend decomposition using loess function (STLF) was applied for the first time within the context of health-care forecasting. RESULTS: According to goodness of fit and forecast accuracy measures, 64 best forecasting models and periods (daily, weekly, or monthly forecasts) were selected out of 760 developed models; ie, demand was forecasted for 38 outpatient specialties (first referrals and follow-ups), 25 inpatient specialties (elective and non-elective admissions), and for A&E. CONCLUSION: This study has confirmed that the best demand estimates arise from different forecasting methods and forecasting periods (ie, one size does not fit all). Despite the fact that the STLF method was applied for the first time, it outperformed traditional time series forecasting methods (ie, ARIMA and exponential smoothing) for a number of specialties. PRACTISE IMPLICATIONS: Knowing the peaks and troughs of demand for an entire hospital will enable the management to (a) effectively plan ahead; (b) ensure necessary resources are in place (eg, beds and staff); (c) better manage budgets, ensuring enough cash is available; and (d) reduce risk. SN - 1099-1751 UR - https://www.unboundmedicine.com/medline/citation/30901132/A_comprehensive_modelling_framework_to_forecast_the_demand_for_all_hospital_services_ L2 - https://doi.org/10.1002/hpm.2771 DB - PRIME DP - Unbound Medicine ER -