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Quality assessment of enterprise risk management programs

JOURNAL OF RISK RESEARCH 2021年第1期25卷 92-112页

作者： Qazi, Abroon simsekler, mecit can emre Amer Univ Sharjah Sch Business Adm Sharjah U Arab Emirates Khalifa Univ Sci & Technol Dept Ind & Syst Engn Abu Dhabi U Arab Emirates

Enterprise risk management (ERM) is an important process for organizations to manage risks and identify opportunities in a holistic manner. Earlier studies have explored antecedents and consequences of ERM programs considering organizational, procedural, technical, and other related factors. However, limited evidence is available regarding the effectiveness of ERM programs. Using semi-structured interviews with ERM experts, this paper explores how experts assess the efficacy and quality of ERM programs and whether they adopt project management (PM) and total quality management (TQM) tools for implementing and improving the ERM program. Further, the Interpretive Structural Modeling (ISM) technique is used to explore causality among factors influencing the efficacy of an ERM program. The findings of this study provide useful insights to both practitioners and researchers to better understand the importance of assessing and enhancing the quality of ERM programs, and establish cause-effect relationships across factors influencing the efficacy of such programs.

关键词： Enterprise risk management total quality management project management interpretive structural modeling semi-structured interviews

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Efficacy of early warning systems in assessing country-level risk exposure to COVID-19

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GEOMATICS NATURAL HAZARDS & RISK 2021年第1期12卷 2352-2366页

作者： Qazi, Abroon simsekler, mecit can emre Akram, Muhammad Amer Univ Sharjah Sch Business Adm Sharjah U Arab Emirates Khalifa Univ Sci & Technol Dept Ind & Syst Engn Abu Dhabi U Arab Emirates Abertay Univ Sch Business Law & Social Sci Dundee Scotland

COVID-19 has evolved as a pandemic causing unprecedented damages and disruptions to all spheres of life including healthcare, transportation, supply chains, education, and economy, among others. Pandemics are very low-probability events associated with deep uncertainty about the timing of such events and ensuing damages. National policy-makers generally rely on a set of risk indices associated with natural disasters and pandemics to assess the country's vulnerability and strategy formulation for such rare events. This paper explores the efficacy of early warning systems (disasters and epidemics-based risk ratings) in predicting the country-level exposure to COVID-19. Utilizing three real data-sets reflecting the risk exposure of individual countries to disasters, epidemics, and COVID-19, we explore relations among the associated risk dimensions, namely hazard and exposure, vulnerability, and lack of coping capacity. A comprehensive methodology integrating Pearson's correlation, ANOVA, and Bayesian Belief Networks-based techniques is adopted to explore and triangulate relations among the three risk indices. Results show that the risk ratings associated with epidemic risk and COVID-19 risk are statistically strongly correlated. However, only the vulnerability dimension of epidemic risk significantly influences the two risks.

关键词： COVID-19 risk Bayesian Belief Networks disasters epidemics pandemic healthcare vulnerability ANOVA

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Value-Driven Healthcare: Cost-Benefit ML Approach to AKI Management in Cardiac Surgery

Value-Driven Healthcare: Cost-Benefit ML Approach to AKI Man...

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2024 IEEE International Conference on Technology Management, Operations and Decisions, ICTMOD 2024

作者： Al-Absi, Dima Tareq simsekler, mecit can emre Anwar, Siddiq Omar, Mohammed Atif Khalifa University Department of Management Science and Engineering Abu Dhabi United Arab Emirates Sheikh Shakhbout Medical City Department of Medicine Abu Dhabi United Arab Emirates

ISBN: (纸本)9798350367355

Acute Kidney Injury (AKI) following cardiac surgery is a significant complication that impacts patient outcomes and healthcare costs. This study introduces a cost-sensitive predictive model integrating Random Forest (RF) and eXtreme Gradient Boosting (XGB) algorithms to enhance the identification of AKI risk. Our model achieved a recall of 96%, demonstrating its high sensitivity in identifying at-risk patients, which is critical for minimizing missed diagnoses and improving early intervention strategies. By incorporating cost considerations into the machine learning framework, the model ensures a balance between clinical and economic outcomes, leading to an estimated net savings of $8,101,676 through optimized resource allocation and reduced complications. This work highlights the potential of integrating cost-sensitive methodologies with predictive modeling to promote value-driven healthcare and improve decision-making in clinical settings. © 2024 IEEE.

关键词： Cardiovascular surgery

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Comparative Analysis of Machine Learning Algorithms for Hepatitis C Virus (HCV) Prediction 50

Comparative Analysis of Machine Learning Algorithms for Hepa...

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50th International Conference on Computers and Industrial Engineering: Sustainable Digital Transformation, CIE 2023

作者： Toffaha, Khaled M. simsekler, mecit can emre Omar, Mohammed Atif Department of Management Science and Engineering Khalifa University of Science and Technology Abu Dhabi United Arab Emirates

ISBN: (纸本)9781713886952

Hepatitis C is a prevalent disease, with an estimated 3-4 million new cases annually. This poses a significant public health concern, necessitating effective identification and treatment strategies. This study compares the performance of multi and binary-class labels using the same dataset, considering various evaluation metrics and tool comparisons. Furthermore, the study seeks to utilize Machine learning (ML) algorithms to identify key features in predicting the Hepatitis C Virus (HCV) using an Egyptian patient dataset. The results reveal that the Support Vector Machine (SVM) achieved the highest accuracy rate of 28.45% for the multi-class label. At the same time, the random forest attained an accuracy of 75.05% for the binary class label. Notably, the binary class performance outperformed the multi-class label. Additionally, multi-feature selection methods improved convergence speed and yielded better accuracies and precisions. Normalization and data scaling techniques also played a vital role in improving the results. Moreover, the study employed Bayesian Networks (BNs) and the Shapley Additive Explanations (SHAP) method to gain insights into the predictions made by the ML model. These techniques provided valuable explanations for the model's decisions, enhancing interpretability and aiding in understanding the factors driving the predictions. Overall, this study contributes to HCV prediction by comparing performance between different label types and exploring feature selection methods. The findings underscore the importance of accurate prediction and highlight the potential of advanced techniques such as BNs and SHAP for improved interpretability in ML models. © 2023 Computers and Industrial Engineering. All rights reserved.

关键词： Forecasting

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Blockchain-Based Management for Organ Donation and Transplantation

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IEEE ACCESS 2022年 10卷 59013-59025页

作者： Hawashin, Diana Jayaraman, Raja Salah, Khaled Yaqoob, Ibrar simsekler, mecit can emre Ellahham, Samer Khalifa Univ Sci & Technol Dept Ind & Syst Engn Abu Dhabi U Arab Emirates Khalifa Univ Sci & Technol Dept Elect Engn & Comp Sci Abu Dhabi U Arab Emirates Cleveland Clin Heart & Vasc Inst Abu Dhabi U Arab Emirates

Today's organ donation and transplantation systems pose different requirements and challenges in terms of registration, donor-recipient matching, organ removal, organ delivery, and transplantation with legal, clinical, ethical, and technical constraints. Therefore, an end-to-end organ donation and transplantation system is required to guarantee a fair and efficient process to enhance patient experience and trust. In this paper, we propose a private Ethereum blockchain-based solution to enable organ donation and transplantation management in a manner that is fully decentralized, secure, traceable, auditable, private, and trustworthy. We develop smart contracts and present six algorithms along with their implementation, testing, and validation details. We evaluate the performance of the proposed solution by performing privacy, security, and confidentiality analyses as well as comparing our solution with the existing solutions. We make the smart contract code publicly available on Github.

关键词： Organ transplantation Blockchains Smart contracts Surgery Hospitals Kidney Biological systems Blockchain ethereum organ donation organ transplantation smart contracts traceability

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A critical review of implementing lean and simulation to improve resource utilization and patient experience in outpatient clinics

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TQM JOURNAL 2023年第3期35卷 734-758页

作者： Al-Kaf, Abdulqader Jayaraman, Raja Demirli, Kudret simsekler, mecit can emre Ghalib, Hussam Quraini, Dima Tuzcu, Murat Khalifa Univ Dept Ind & Syst Engn Abu Dhabi U Arab Emirates Concordia Univ Dept Mech Ind & Aerosp Engn Montreal PQ Canada Cleveland Clin Abu Dhabi Inst Heart & Vasc Abu Dhabi U Arab Emirates Cleveland Clin Abu Dhabi Dept Cardiovasc Med Abu Dhabi U Arab Emirates

Purpose The purpose of this paper is to explore and critically review the existing literature on applications of Lean Methodology (LM) and Discrete-Event Simulation (DES) to improve resource utilization and patient experience in outpatient clinics. In doing, it is aimed to identify how to implement LM in outpatient clinics and discuss the advantages of integrating both lean and simulation tools towards achieving the desired outpatient clinics outcomes. Design/methodology/approach A theoretical background of LM and DES to define a proper implementation approach is developed. The search strategy of available literature on LM and DES used to improve outpatient clinic operations is discussed. Bibliometric analysis to identify patterns in the literature including trends, associated frameworks, DES software used, and objective and solutions implemented are presented. Next, an analysis of the identified work offering critical insights to improve the implementation of LM and DES in outpatient clinics is presented. Findings Critical analysis of the literature on LM and DES reveals three main obstacles hindering the successful implementation of LM and DES. To address the obstacles, a framework that integrates DES with LM has been recommended and proposed. The paper provides an example of such a framework and identifies the role of LM and DES towards improving the performance of their implementation in outpatient clinics. Originality/value This study provides a critical review and analysis of the existing implementation of LM and DES. The current roadblocks hindering LM and DES from achieving their expected potential has been identified. In addition, this study demonstrates how LM with DES combined to achieve the desired outpatient clinic objectives.

关键词： Lean Simulation Appointment scheduling Patient flow Utilization Outpatient clinics

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Data technologies and analytics for policy and governance: a landscape review

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DATA & POLICY 2025年 7卷 e25-e25页

作者： Asensio, Omar Isaac Moore, Catherine E. Ulibarri, Nicola simsekler, mecit can emre Lan, Tian Rivero, Gonzalo Georgia Inst Technol Sch Publ Policy Atlanta GA 30332 USA Georgia Inst Technol Inst Data Engn & Sci IDEaS Atlanta GA 30332 USA Univ Calif Irvine Dept Urban Planning & Publ Policy Irvine CA USA Khalifa Univ Sci & Technol Dept Management Sci & Engn Abu Dhabi U Arab Emirates Univ London Sch Management London England UCL Dept Geog London England Wuhan Univ Sch Resource & Environm Sci Wuhan Peoples R China

Data for Policy (***), a trans-disciplinary community of research and practice, has emerged around the application and evaluation of data technologies and analytics for policy and governance. Research in this area has involved cross-sector collaborations, but the areas of emphasis have previously been unclear. Within the Data for Policy framework of six focus areas, this report offers a landscape review of Focus Area 2: Technologies and Analytics. Taking stock of recent advancements and challenges can help shape research priorities for this community. We highlight four commonly used technologies for prediction and inference that leverage datasets from the digital environment: machine learning (ML) and artificial intelligence systems, the internet-of-things, digital twins, and distributed ledger systems. We review innovations in research evaluation and discuss future directions for policy decision-making.

关键词： digital data digital twins distributed ledger systems internet of things machine learning and artificial intelligence

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Measuring Emergency Department Resilience to Demand Surge: A Discrete-Event Simulation Framework

Measuring Emergency Department Resilience to Demand Surge: A...

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2023 Winter Simulation Conference, WSC 2023

作者： Ouda, Eman Sleptchenko, Andrei emre simsekler, mecit can El-Eid, Ghada R Khalifa University Department of Management Science and Engineering Shakhbout Bin Sultan St - Hadbat Al Za'faranah Abu Dhabi United Arab Emirates Sheikh Shakhbout Medical City Emergency Department Al Mafraq Abu Dhabi United Arab Emirates

ISBN: (纸本)9798350369663

This research explores the resilience components in emergency departments (EDs) during surges through discrete-event simulation (DES). By focusing on the resistance and recoverability components, the resilience of the ED is analyzed, as well as the flow of the patient and the resources required at each step. A simulation is developed to model an ED in the UAE and validated through collected timestamps. The results demonstrate the ordinary conditions of the ED and its calculated resilience, recoverability, and resistance, as well as its strength under conditions of surge demand. To investigate the impact of resources on the ED's resilience, the resilience triangle is analyzed, and different interventions are applied by adding physicians, nurses, and beds and their effects. The methodology and simulation model provide significant insights to ED managers to evaluate and improve their department's resilience during surges and emergencies. © 2023 IEEE.

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Nexus between drivers of COVID-19 and country risks

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SOCIO-ECONOMIC PLANNING SCIENCES 2023年 85卷 101276页

作者： Qazi, Abroon simsekler, mecit can emre Amer Univ Sharjah Sch Business Adm Sharjah U Arab Emirates Khalifa Univ Sci & Technol Dept Ind & Syst Engn Abu Dhabi U Arab Emirates

COVID-19 has disrupted all spheres of life, including country risk regarding the exposure of economies to multi -dimensional risk drivers. However, it remains unexplored how COVID-19 has impacted different drivers of country risk in a probabilistic network setting. This paper uses two datasets on country-level COVID-19 and country risks to explore dependencies among associated drivers using a Bayesian Belief Network model. The drivers of COVID-19 risk, considered in this paper, are hazard and exposure, vulnerability and lack of coping capacity, whereas country risk drivers are economic, financing, political, business environment and commercial risks. The results show that business environment risk is significantly influenced by COVID-19 risk, whereas commercial risk (demand disruptions) is the least important factor driving COVID-19 and country risks. Further, country risk is mainly influenced by financing, political and economic risks. The contribution of this study is to explore the impact of various drivers associated with the country-level COVID-19 and country risks in a unified probabilistic network setting, which can help policy-makers prioritize drivers for managing the two risks.

关键词： COVID-19 risk Country risk Vulnerability Business environment risk Commercial risk Bayesian Belief Network

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A Review on Data Analytics in Patient Safety

A Review on Data Analytics in Patient Safety

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2022 Advances in Science and Engineering Technology International Conferences, ASET 2022

作者： Ummar, Mumtaz Ramadan, Mariam simsekler, mecit can emre Khalifa University of Science and Technology Department of Industrial and Systems Engineering Abu Dhabi United Arab Emirates

ISBN: (纸本)9781665418010

The increased use of health information technology combined with digitizing records and planning systems has created massive volumes of data. With the help of innovative approaches, analytical tools and disruptive technologies, such data has been recently leveraged to improve healthcare outcomes, including patient safety. This paper entails a review of various healthcare data analytic tools and techniques used to prevent false positives, missed diagnoses, reduce patient falls and other patient safety incidents. © 2022 IEEE.

关键词： Data Analytics

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