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

A novel healthcare resource allocation decision support tool: A forecasting-simulation-optimization approach

© 2020 Informa UK Limited, trading as Taylor & Francis Group. This is an accepted manuscript of an article published by Taylor & Francis in Journal of the Operational Research Society on 03 Feb 2020, available online: https://doi.org/10.1080/01605682.2019.1700186.The increasing pressures on the healthcare system in the UK are well documented. The solution lies in making best use of existing resources (e.g. beds), as additional funding is not available. Increasing demand and capacity shortages are experienced across all specialties and services in hospitals. Modelling at this level of detail is a necessity, as all the services are interconnected, and cannot be assumed to be independent of each other. Our review of the literature revealed two facts; First an entire hospital model is rare, and second, use of multiple OR techniques are applied more frequently in recent years. Hybrid models which combine forecasting, simulation and optimization are becoming more popular. We developed a model that linked each and every service and specialty including A&E, and outpatient and inpatient services, with the aim of, (1) forecasting demand for all the specialties, (2) capturing all the uncertainties of patient pathway within a hospital setting using discrete event simulation, and (3) developing a linear optimization model to estimate the required bed capacity and staff needs of a mid-size hospital in England (using essential outputs from simulation). These results will bring a different perspective to key decision makers with a decision support tool for short and long term strategic planning to make rational and realistic plans, and highlight the benefits of hybrid models.Peer reviewe

Analysis of River Water Quality Via Geographic Information Systems: A Case Study

In today's world, it has become necessary to deal with problems regarding water resources through studies to be conducted on a local even basin basis. In this study, considering the water quality as the limiting factor for the Ergene Basin, we have considered it necessary to make an analysis of the change of the current water quality by years in the basin, designed and stored in computer environment an environmental data base including information, such as topography, administrative boundaries, settlement areas streams, polluting facilities, forest cover of the basin in the form of a geographic information system in order to display the current situation of the basin in computer environment, and aimed to identify the classification of the water quality by using a software package (MapInfo) on data

Determination of land data of Ergene Basin (Turkey) by planning geographic information systems

Geographic Information Systems (GIS) provide to gather environmental, legal and positional whole data pertaining to land. GIS is used to make future plans with the maps formed by digitizing informations gathered from land data. In the study, positional data pertaining to Ergene Basin where is chosen as the area of study are first processed into GIS medium and then thematic maps of the region are formed. It is aimed with the maps prepared within the context of the study to form a base in making further plans for basins. © 2009 Asian Network for Scientific Information

Determination of Water Quality of Ergene River By Planning Environmental Information System

Today, it has become necessary to deal with the water resources problems by studies on regional basis or even on the basis of river basin. In this study, considering the water quality as a limiting factor for the Ergene Basin, it was decided that an analysis of the variation of water quality with respect to years is necessary, and therefore, the identification of water quality classes was aimed at using a software package program (MapInfo). Upon queries, it is determined that the river water at spring section is I. quality water, whereas the river water at downstream side can be classified as IV.-V. quality water as irrigation water

Stereochemical Investigations of Diastereomeric N-[2-(Aryl)-5-methyl-4-oxo-1,3-thiazolidine-3-yl]-pyridine-3-carboxamides by Nuclear Magnetic Resonance Spectroscopy (1D and 2D)

Some new N-[2-(aryl)-5-methyl-4-oxo-1,3-thiazolidine-3-yl]-pyridine-3-carboxamides were synthesized and their structures were investigated by IR, NMR(H-1, C-13, and 2D), and mass spectra. The presence of C-2 and C-5 stereogenic centers on the thiazolidinone ring resulted in diastereoisomeric pairs. The configurations of two stereogenic centers were assigned based upon H-1 NMR analysis of coupling constants and 2D nuclear overhauser enhancement spectroscopy (NOESY) experiment. Resolution of the diastereoisomers was performed by high performance liquid chromatography (HPLC) using a chiral stationary phase

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

A hybrid analytical model for an entire hospital resource optimisation

© The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2021. This is the accepted manuscript version of an article which has been published in final form at https://doi.org/10.1007/s00500-021-06072-xGiven the escalating healthcare costs around the world (more than 10% of the world's GDP) and increasing demand hospitals are under constant scrutiny in terms of managing services with limited resources and tighter budgets. Hospitals endeavour to find sustainable solutions for a variety of challenges ranging from productivity enhancements to resource allocation. For instance, in the UK, evidence suggests that hospitals are struggling due to increased delayed transfers of care, bed-occupancy rates well above the recommended levels of 85% and unmet A&E performance targets. In this paper, we present a hybrid forecasting-simulation-optimisation model for an NHS Foundation Trust in the UK. Using the Hospital Episode Statistics dataset for A&E, outpatient and inpatient services, we estimate the future patient demands for each speciality and model how it behaves with the forecasted activity in the future. Discrete event simulation is used to capture the entire hospital within a simulation environment, where the outputs is used as inputs into a multi-period integer linear programming (MILP) model to predict three vital resource requirements (on a monthly basis over a 1-year period), namely beds, physicians and nurses. We further carry out a sensitivity analysis to establish the robustness of solutions to changes in parameters, such as nurse-to-bed ratio. This type of modelling framework is developed for the first time to better plan the needs of hospitals now and into the future.Peer reviewe