Author
Muhammed Ordu
Other affiliations: University of Hertfordshire
Bio: Muhammed Ordu is an academic researcher from Osmaniye Korkut Ata University. The author has contributed to research in topics: Decision support system & Discrete event simulation. The author has an hindex of 4, co-authored 12 publications receiving 53 citations. Previous affiliations of Muhammed Ordu include University of Hertfordshire.
Papers
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TL;DR: This work developed a model that linked each and every service and specialty including A&E, and outpatient and inpatient services, with the aim of developing a linear optimization model to estimate the required bed capacity and staff needs of a mid-size hospital in England.
Abstract: 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. Incr...
35 citations
TL;DR: This study confirmed that the best demand estimates arise from different forecasting methods and forecasting periods (ie, one size does not fit all), and outperformed traditional time series forecasting methods for a number of specialties.
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 (AE ie, demand was forecasted for 38 outpatient specialties (first referrals and follow-ups), 25 inpatient specialties (elective and non-elective admissions), and for AE (b) ensure necessary resources are in place (eg, beds and staff); (c) better manage budgets, ensuring enough cash is available; and (d) reduce risk.
31 citations
TL;DR: A discrete event simulation model is developed which will enable service managers and directors of hospitals to foresee their activities in future and form a strategic plan well in advance.
Abstract: 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 ...
27 citations
30 Jul 2021
TL;DR: This paper presents a hybrid forecasting-simulation-optimisation model for an NHS Foundation Trust in the UK, developed for the first time to better plan the needs of hospitals now and into the future.
Abstract: Given 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.
10 citations
TL;DR: In this article, a data envelopment analysis (DEA) based modeling approach was developed to evaluate the effectiveness of regions (i.e., city, country or clinical commissioning groups) against the pandemic outbreak.
Abstract: BACKGROUND: The novel coronavirus is rapidly spreading over the world and puts the health systems of countries under intense pressure. High hospitalization levels due to the pandemic outbreak have caused the intensive care units to work above capacity. PURPOSE: A data envelopment analysis (DEA) based modelling approach was developed to evaluate the effectiveness of regions (i.e. city, country or clinical commissioning groups) against the pandemic outbreak. The objective is to enable related authorities better manage the struggle against the outbreak and put in place the emergency action plans immediately. METHODOLOGY/APPROACH: DEA method was used to measure the efficiency scores of countries. Super efficiency DEA method was also applied to countries based on the level of efficiencies they have achieved. Sixteen countries were selected that have been facing with Covid19 pandemic outbreak for at least 5 consecutive weeks after their 100th confirmed case. RESULTS: A total of 80 DEA models were developed, that is, 16 DEA models for each week. The percentage of efficient countries decreased dramatically over time, from 43.75% in the first week to 25% in the fifth week. Unlike most European countries, China and South Korea increased their effectiveness after first week of implementing all the necessary measures. CONCLUSION: This study sheds light into better understanding the effectiveness of policies adopted by countries and their management strategy in dealing with Covid19 pandemic. Our model will enable political leaders to identify inadequate policies as quickly as possible and learn from their peers for more effective decisions.
6 citations
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Journal Article•
TL;DR: This book by a teacher of statistics (as well as a consultant for "experimenters") is a comprehensive study of the philosophical background for the statistical design of experiment.
Abstract: THE DESIGN AND ANALYSIS OF EXPERIMENTS. By Oscar Kempthorne. New York, John Wiley and Sons, Inc., 1952. 631 pp. $8.50. This book by a teacher of statistics (as well as a consultant for \"experimenters\") is a comprehensive study of the philosophical background for the statistical design of experiment. It is necessary to have some facility with algebraic notation and manipulation to be able to use the volume intelligently. The problems are presented from the theoretical point of view, without such practical examples as would be helpful for those not acquainted with mathematics. The mathematical justification for the techniques is given. As a somewhat advanced treatment of the design and analysis of experiments, this volume will be interesting and helpful for many who approach statistics theoretically as well as practically. With emphasis on the \"why,\" and with description given broadly, the author relates the subject matter to the general theory of statistics and to the general problem of experimental inference. MARGARET J. ROBERTSON
13,333 citations
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Polytechnic University of Turin1, National Technical University of Athens2, KEDGE Business School3, University of Birmingham4, University of Mons5, Monash University6, University of Southampton7, Lancaster University8, University of Glasgow9, University of Brescia10, University of Oxford11, Zürcher Fachhochschule12, University of Reading13, University of the Algarve14, University of Lisbon15, Pontifical Catholic University of Rio de Janeiro16, Ghent University17, Nicolaus Copernicus University in Toruń18, Erasmus University Rotterdam19, SAS Institute20, University of Bath21, University of Padua22, University of Virginia23, Bocconi University24, MODUL University Vienna25, University of Maryland, College Park26, University College London27, Amazon.com28, KAIST29, Georgetown University30, Beihang University31, Miami University32, University of Skövde33, Central University of Finance and Economics34, Manchester Metropolitan University35, University of Nicosia36, United States Department of the Treasury37, George Washington University38, Durham University39, University College Dublin40, Australian National University41, University of Sydney42, University of Thessaly43, University of Valencia44, University of Bristol45, University of Castilla–La Mancha46, Technical University of Denmark47, Polytechnic Institute of Porto48, Saint Louis University49, Cardiff University50, Warsaw School of Economics51, Macquarie University52, University of Moratuwa53, University of Sri Jayewardenepura54, International Institute of Minnesota55, National and Kapodistrian University of Athens56, Norwegian Computing Center57, University of Bologna58, Duke University59, University of Duisburg-Essen60
TL;DR: A non-systematic review of the theory and the practice of forecasting, offering a wide range of theoretical, state-of-the-art models, methods, principles, and approaches to prepare, produce, organise, and evaluate forecasts.
Abstract: Forecasting has always been at the forefront of decision making and planning. The uncertainty that surrounds the future is both exciting and challenging, with individuals and organisations seeking to minimise risks and maximise utilities. The large number of forecasting applications calls for a diverse set of forecasting methods to tackle real-life challenges. This article provides a non-systematic review of the theory and the practice of forecasting. We provide an overview of a wide range of theoretical, state-of-the-art models, methods, principles, and approaches to prepare, produce, organise, and evaluate forecasts. We then demonstrate how such theoretical concepts are applied in a variety of real-life contexts.
We do not claim that this review is an exhaustive list of methods and applications. However, we wish that our encyclopedic presentation will offer a point of reference for the rich work that has been undertaken over the last decades, with some key insights for the future of forecasting theory and practice. Given its encyclopedic nature, the intended mode of reading is non-linear. We offer cross-references to allow the readers to navigate through the various topics. We complement the theoretical concepts and applications covered by large lists of free or open-source software implementations and publicly-available databases.
163 citations
TL;DR: In this paper , the authors provide an overview of a wide range of theoretical, state-of-the-art models, methods, principles, and approaches to prepare, produce, organize, and evaluate forecasts.
119 citations
TL;DR: Government statistical reports were notoriously dusty paper publications, but being able to download the actual data in electronic form from a website does make them more interesting.
Abstract: In the future there will be more old people than there are now. How do we know? Because the Government Statistical Service tells us so ( www.statistics.gov.uk/misc/sitemap.htm ). Government statistical reports were notoriously dusty paper publications, but being able to download the actual data in electronic form from a website does make them more interesting. So if …
115 citations