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Prioritizing Indicators for Rapid Response in Global Health Security:A Bayesian Network Approach

International Journal of Disaster Risk Science 2024年第4期15卷 536-551页

作者： Abroon Qazi mecit can emre simsekler M.K.S.Al‑Mhdawi School of Business Administration American University of SharjahSharjah 26666UAE Department of Management Science and Engineering Khalifa University of Science&TechnologyAbu Dhabi 127788UAE School of Computing Engineering&Digital TechnologiesTeesside UniversityMiddlesbrough TS13BXUK

This study explored a Bayesian belief networks(BBNs)approach,developing two distinct models for prioritizing the seven indicators related to the“rapid response to and mitigation of the spread of an epidemic”category within the context of both the specifc category and the Global Health Security Index(GHS index).Utilizing data from the 2021 GHS index,the methodology involves rigorous preprocessing,the application of the augmented naive Bayes algorithm for structural learning,and k-fold *** fndings show unique perspectives in both BBN *** the mutual value of information analysis,“linking public health and security authorities”emerged as the key predictor for the“rapid response to and mitigation of the spread of an epidemic”category,while“emergency preparedness and response planning”assumed precedence for the GHS *** analysis highlighted the critical role of“emergency preparedness and response planning”and“linking public health and security authorities”in extreme performance states,with“access to communications infrastructure”and“trade and travel restrictions”exhibiting varied *** BBN models exhibit high predictive accuracy,achieving 83.3%and 82.3%accuracy for extreme states in“rapid response to and mitigation of the spread of an epidemic”and the GHS index,*** study contributes to the literature on GHS by modeling the dependencies among various indicators of the rapid response dimension of the GHS index and highlighting their relative importance based on the mutual value of information and sensitivity analyses.

关键词： Bayesian belief networks Global health security Indicators Mitigation Policy implications Rapid response

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Exploring the risks of automation bias in healthcare artificial intelligence applications:A Bowtie analysis

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Journal of Safety Science and Resilience 2024年第4期5卷 460-469页

作者： Moustafa Abdelwanis Hamdan Khalaf Alarafati Maram Muhanad Saleh Tammam mecit can emre simsekler Department of Management Science and Engineering Khalifa University of Science and TechnologyAbu DhabiUnited Arab Emirates

This study conducts an in-depth review and Bowtie analysis of automation bias in AI-driven Clinical Decision Support Systems (CDSSs) within healthcare settings. Automation bias, the tendency of human operators to over-rely on automated systems, poses a critical challenge in implementing AI-driven technologies. To address this challenge, Bowtie analysis is employed to examine the causes and consequences of automation bias affected by over-reliance on AI-driven systems in healthcare. Furthermore, this study proposes preventive measures to address automation bias during the design phase of AI model development for CDSSs, along with effective mitigation strategies post-deployment. The findings highlight the imperative role of a systems approach, integrating technological advancements, regulatory frameworks, and collaborative endeavors between AI developers and healthcare practitioners to diminish automation bias in AI-driven CDSSs. We further identify future research directions, proposing quantitative evaluations of the mitigation and preventative measures.

关键词： Artificial intelligence Automation bias Decision support systems Medical errors Patient safety Bowtie analysis

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A Path Towards Human-AI Decision-Making in Sepsis Care through Human-Centered Systems-Based Design Approach

A Path Towards Human-AI Decision-Making in Sepsis Care throu...

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IEEE International Technology Management Conference

作者： Firda Rahmadani mecit can emre simsekler Department of Management Science and Engineering Khalifa University of Science and Technology Abu Dhabi United Arab Emirates

ISBN: (数字)9798350367355

ISBN: (纸本)9798350367362

Sepsis is a critical condition when the body's response to infection triggers widespread inflammation, leading to severe health complications. Despite advancements in medical care through AI-based prediction models and clinical decision support systems, sepsis remains a leading cause of death in hospitals, highlighting the urgent need for improved diagnostic methods, treatment protocols, and public awareness to prevent this severe condition effectively. Further, the ethical implications and risks associated with AI implementation necessitate careful consideration of diversity, equity, and inclusion in designing and deploying human-AI decision support. To consider such challenges, we propose a human-centered systems-based (HCSB) design approach to AI implementation that prioritizes user needs systematically and ensures fair and equitable treatment across diverse patient populations, addressing disparities that may arise from socio-economic factors, geographic location, or cultural differences. Key considerations include the development of inclusive datasets that reflect diverse patient demographics, designing transparent and interpretable algorithms, and establishing protocols for continuous monitoring and evaluation to detect and mitigate biases. Moreover, collaboration among interdisciplinary teams, including healthcare professionals, AI engineers, ethicists, and community representatives, is essential to embedding an HCSB design approach throughout the decision support lifecycle.

关键词： Ethics Protocols Technology management Hospitals Predictive models Sepsis Cultural differences Artificial intelligence Socioeconomics Monitoring

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Optimizing Emergency Department Operations: A Conceptual Framework for Enhancing Patient Flow

Optimizing Emergency Department Operations: A Conceptual Fra...

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IEEE International Technology Management Conference

作者： Eman Ouda Andrei Sleptchenko mecit can emre simsekler Management Science & Engineering Khalifa University of Science & Technology Abu Dhabi United Arab Emirates

ISBN: (数字)9798350367355

ISBN: (纸本)9798350367362

Overcrowding in emergency departments (EDs) poses significant challenges to healthcare systems worldwide, leading to delays in patient care, increased morbidity, and decreased patient satisfaction. This paper proposes a conceptual framework for the patient flow to address these challenges by incorporating key factors that contribute to overcrowding and optimizing resource utilization. By integrating factors into input, throughput, and output components, the framework offers a comprehensive understanding of the parameters that influence patient flow and ED capacity. This model helps determine when operations research (OR) techniques should be used to match supply with demand. The application of the framework is discussed in the context of managing surge demand and developing disruptive and innovative technologies to improve emergency care.

关键词： Uncertainty Technology management Operations research Hospitals Throughput Emergency services Real-time systems Resource management Surges Predictive analytics

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Review of Hospital Outpatient No-Show Explainable Prediction Using Machine Learning

Review of Hospital Outpatient No-Show Explainable Prediction...

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IEEE International Technology Management Conference

作者： Khaled Toffaha mecit can emre simsekler Mohammed Omar Management Science and Engineering Khalifa University Abu Dhabi UAE

ISBN: (数字)9798350367355

ISBN: (纸本)9798350367362

Patient no-shows present significant challenges and costs to healthcare systems, prompting a focused exploration of hospital outpatient no-show prediction using explainable machine learning (ML) in this review. The systematic methodology aligns with Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, ensuring transparency, replicability, and thoroughness. A comprehensive search across major literature databases yielded 92 potential studies on ML models for outpatient no-show prediction. Following screening and eligibility assessments, 51 studies met inclusion criteria, emphasizing outpatient no-shows, leveraging explainable ML, featuring predictive modeling, and reporting quantitative metrics. Examining the publication distribution from 2010 to 2024 reveals a discernible upward linear trend, indicating the growing relevance of this research domain. The preference for more interpretable ML models over time, such as regression, decision trees, and ensemble methods, underscores their transparency and actionability. Despite challenges like limited model generalizability and clinician hesitation, proposed solutions, including collaborative learning and feature engineering pipelines, aim to enhance the reliability and applicability of outpatient noshow prediction models. This comprehensive strategy addresses existing challenges, facilitating the effective integration of explainable ML in healthcare settings and contributing to improved outpatient appointment attendance. In conclusion, this systematic review provides a robust foundation for understanding and implementing explainable ML approaches in predicting hospital outpatient no-shows, offering valuable insights for healthcare practitioners and researchers alike.

关键词： Accuracy Technology management Systematics Hospitals Predictive models Data models Ensemble learning Regression tree analysis Replicability Systematic literature review

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An integrative risk assessment approach to enhancing patient safety in Continuous Renal Replacement Therapy(CRRT)

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Journal of Safety Science and Resilience 2024年第3期5卷 344-354页

作者： Ahmed Saad Juman Alsadi Dima Tareq Al-Absi Muhra Almulla mecit can emre simsekler Ahmed Adel Sadeq Fahad Omar Mazhar Basha Islam Khatab Noha Abu Khater Andrea Molesi Siddiq Anwar Departnent of Management Science and Engineering Khalifa University of Science&TechnologyAbu DhabiUAE Department of Pharmacy Sheikh Shakhbout Medical CiyAbu DhabiUAE Departnent of Critical Care Sheikh Shakhbout Medical CityAbu DhabiUAE Departnent of Intermal Medicine Sheikh Shakhbout Medical CityAbu DhabiUAE

Continuous Renal Replacement Therapy(CRRT)serves as an intervention strategy for the management of acute kidney injury(AKl)in critically ill ***,owing to its complex nature and the potential for com-plications,the implementation of CRRT demands continuous monitoring to prevent patient safety *** study aims to identify and validate prevalent risks linked to CRRT within a real-world clinical setting,intending to propose preventive measures grounded in expert *** systematically categorize and visually depict the risks,their consequences,preventive measures,and recovery controls,our study employed the Bowtie method in conjunction with the Systems Engineering Initiative for Patient Safety(SEIPS)*** addition to considering patient-related factors that exhibit variability among critically ill individuals,our key findings showed that the most influential risks impacting the effective delivery of CRRT are incidents of clotted filters,bleeding risks arising from the necessity of anticoagulation for filter efficacy,vascular catheter-related bloodstream infections,variations in proficiency levels among healthcare professionals regarding CRRT modalities,especially in oper-ating the CRRT machines,high nursing workload,frequent nursing turnover,occurrences of hypophosphatemia,variability in CRRT prescribing patterns,and issues related to communication among *** research sheds light on the primary risks associated with CRRT and provides practical and viable strategies for effective ***,the Bowtie diagram developed as part of this study serves as a valuable tool for visually representing the healthcare system and facilitating the identification of system-related risks within healthcare settings.

关键词： Patient safety Riskmanagement CRRT Continuous Renal Replacement Therapy Acutekidneyinjury,Bowtiemethod SEIPS

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Measuring Emergency Department Resilience to Demand Surge: A Discrete-Event Simulation Framework 23

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

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Proceedings of the Winter Simulation Conference

作者： Eman Ouda Andrei Sleptchenko mecit can emre simsekler Ghada R El-Eid Department of Management Science and Engineering Khalifa University Abu Dhabi UNITED ARAB EMIRATES Emergency Department Sheikh Shakhbout Medical City 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.

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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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IEEE International Technology Management Conference

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

ISBN: (数字)9798350367355

ISBN: (纸本)9798350367362

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.

关键词： Radio frequency Costs Sensitivity Technology management Surgery Medical services Predictive models Prediction algorithms Resource management Kidney

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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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Simulation Winter Conference

作者： Eman Ouda Andrei Sleptchenko mecit can emre simsekler Ghada R El-Eid Department of Management Science and Engineering Khalifa University Abu Dhabi United Arab Emirates Emergency Department Sheikh Shakhbout Medical City Abu Dhabi United Arab Emirates

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.

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Cross-Training Policies for Enhanced Resilience in Emergency Departments 24

Cross-Training Policies for Enhanced Resilience in Emergency...

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Proceedings of the Winter Simulation Conference

作者： Moustafa Abdelwanis Eman Ouda Andrei Sleptchenko Adriana F. Gabor mecit can emre simsekler Mohammed Omar Department of Management Science and Engineering Khalifa University Abu Dhabi UAE Department of Mathematics Khalifa University Abu Dhabi UAE

ISBN: (纸本)9798331534202

This paper investigates cross-training policies to enhance emergency department resilience in managing patient demand surges. We evaluated operational performance under varying patient flows using a simulation model based on an emergency department in Abu Dhabi, UAE. Results demonstrated a significant increase in patient length of stay in the triage room due to a shortage of available nurses during surges. Conversely, nurses in other areas (adult zone, pediatrics, and fast-track) were less affected. Two cross-training policies were investigated: pooling nurses from the triage and adult zones and expanding the pool to include the pediatrics section. The first policy reduced patient length of stay in the triage by 91.71%. The second policy further improved flexibility, leading to reductions in length of stay. Additional experiments revealed the limitations of the second policy when subjected to higher surge levels; therefore, further research is needed to explore a broader range of policies and contexts.

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