A decision support system for demand and capacity modelling of an accident and emergency department

© 2019 Operational Research Society.Accident and emergency (A&E) departments in England have been struggling against severe capacity constraints. In addition, A&E demands have been increasing year on year. In this study, our aim was to develop a decision support system combining discrete event simulation and comparative forecasting techniques for the better management of the Princess Alexandra Hospital in England. We used the national hospital episodes statistics data-set including period April, 2009 – January, 2013. Two demand conditions are considered: the expected demand condition is based on A&E demands estimated by comparing forecasting methods, and the unexpected demand is based on the closure of a nearby A&E department due to budgeting constraints. We developed a discrete event simulation model to measure a number of key performance metrics. This paper presents a crucial study which will enable service managers and directors of hospitals to foresee their activities in future and form a strategic plan well in advance.Peer reviewe

Usage of enterprise resource planning systems in higher education institutions in Pakistan

Copyright and all rights therein are retained by the authors. All persons copying this information are expected to adhere to the terms and conditions invoked by each author's copyright. These works may not be re-posted without the explicit permission of the copyright holdersThe main objective of the study was to identify the factors contributing to the usage of enterprise resource planning systems at the organisational layer, the departmental layer and the end-user layer in Higher Education Institutions (HEIs) in Pakistan. The conceptual framework of this study is based on the Unified Theory of Acceptance and Use of Technology (UTAUT) developed by Venkatesh, Morris, Davis, & Davis (2003). The multi-level conceptual model developed for the study was tested empirically using three distinct questionnaires for analytical layers. Primary data was collected from 18 higher education institutions in Pakistan; 86 responses from the organisational layer, 143 from the departmental layer and 1088 from the end-user layer. Structural equations were formulated to investigate the effect of factors at three layers contributing to the usage of Enterprise Resource Planning Systems (ERPS). Organisational training was found to be the only factor not making a significant contribution to the usage of enterprise resource planning systems while all other factors included in the conceptual framework were proved to be significant. The model formulation and application of SEM techniques to investigate the determinants of usage of ERPS in HEIs in Pakistan is the unique contribution of this studyFinal Published versio

A comprehensive modelling framework to forecast the demand for all hospital services

© 2019 John Wiley & Sons, Ltd.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.Peer reviewe