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.
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: Taryn Vian; Jeanette L Kaiser; Thandiwe Ngoma; Allison Juntunen; Kaluba K Mataka; Misheck Bwalya; Viviane I R Sakanga; Peter C Rockers; Davidson H Hamer; Godfrey Biemba; Nancy A Scott Journal: Ann Glob Health Date: 2022-05-24 Impact factor: 3.640
Authors: Lauren A Castro; Courtney D Shelley; Dave Osthus; Isaac Michaud; Jason Mitchell; Carrie A Manore; Sara Y Del Valle Journal: JMIR Public Health Surveill Date: 2021-06-09
Authors: Samuel A Cohen; Landon E Cohen; Jonathan D Tijerina; Gabriel Bouz; Rachel Lefebvre; Milan Stevanovic; Nathanael D Heckmann Journal: J Clin Transl Res Date: 2021-07-16
Authors: Manuel Tello; Eric S Reich; Jason Puckey; Rebecca Maff; Andres Garcia-Arce; Biplab Sudhin Bhattacharya; Felipe Feijoo Journal: BMC Med Inform Decis Mak Date: 2022-03-02 Impact factor: 2.796