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A Systematic Literature Review of Safety Culture in Hemodialysis Settings

JOURNAL OF MULTIDISCIPLINARY HEALTHCARE 2023年 16卷 1011-1022页

作者： Albreiki, Salma Alqaryuti, Alaa Alameri, Tareq Aljneibi, Amani simsekler, mecit can emre Anwar, Siddiq Lentine, Krista L. Khalifa Univ Sci & Technol Dept Ind & Syst Engn Abu Dhabi 127788 U Arab Emirates Sheikh Shakhbout Med City Abu Dhabi 10001 U Arab Emirates St Louis Univ Ctr Abdominal Transplantat St Louis MO USA Khalifa Univ Sci & Technol Dept Ind & Syst Engn POB 127788 Abu Dhabi U Arab Emirates

Background: Safety culture is an important aspect of quality in healthcare settings. There are many risks that patients can encounter in hemodialysis settings one of which is the infection risks due to the regular need to access bloodstreams using catheters and needles. Implementation of prevention guidelines, protocols and strategies that reinforce safety culture excellence are essential to mitigate risks. The objective of this study was to identify and characterize the main strategies that enhance and improve patient safety culture in hemodialysis ***: Medline (via PubMed) and Scopus were searched from 2010 to 2020 in English. Terms defining safety culture, patient safety were combined with the term hemodialysis during the search. The studies were chosen based on inclusion ***: A total of 17 articles reporting on six countries were identified that met inclusion criteria following the PRISMA statement. From the 17 papers, practices that were successfully applied to improve safety culture in hemodialysis settings included (i) training of nurses on the technologies used in hemodialysis treatment, (ii) proactive risk identification tools to prevent infections (iii) root cause analysis in evaluating the errors, (iv) hemodialysis checklist to be used by the dialysis nurses to reduce the adverse events, and (v) effective communication and mutual trust between the employee and leadership to support no-blame environment, and improve the safety ***: This systematic review provided significant insights on the strategies that healthcare safety managers and policy makers can implement to enhance safety culture in hemodialysis settings.

关键词： safety culture patient safety kidney care hemodialysis quality improvement medical error

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Advancing Postoperative Acute Kidney Injury Management through AI Modeling

Advancing Postoperative Acute Kidney Injury Management throu...

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IISE Annual Conference and Expo 2024

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

ISBN: (纸本)9781713877851

Acute Kidney Injury (AKI) following surgical procedures presents a significant challenge, impacting patient safety and increasing hospital stays and costs. Despite the potential of predictive modeling to improve postoperative AKI management, integration into clinical practice faces obstacles, including data heterogeneity and the absence of standardized analytics approaches. This paper explores the use of modern machine learning techniques to enhance AKI risk assessment, drawing on a review of recent studies to assess current models, their performance, and applicability. We identify key gaps in the literature, such as the underrepresentation of diverse patient demographics, the need for comprehensive risk factor analysis, and the importance of model validation. Our findings highlight a trend toward employing diverse algorithms and feature selection methods to improve prediction accuracy and patient care. However, further research is necessary to standardize methodologies, integrate emerging risk factors, and address implementation challenges. By advancing machine learning applications in AKI prediction, we aim to contribute to improved patient outcomes and healthcare efficiency. © IISE Annual Conference and Expo *** rights reserved.

关键词： Risk assessment

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Comprehensive review and future research agenda on discrete-event simulation and agent-based simulation of emergency departments

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SIMULATION MODELLING PRACTICE AND THEORY 2023年 129卷

作者： Ouda, Eman Sleptchenko, Andrei simsekler, mecit can emre Khalifa Univ Res Ctr Digital Supply Chain & Operat Management Dept Management Sci & Engn Abu Dhabi U Arab Emirates

With the growing demand for hospital resources worldwide, healthcare facilities, including Emergency Departments (EDs), look for opportunities to enhance patient care efficiency and streamline healthcare processes. To achieve this, simulation techniques, including Discrete Event Simulation (DES) and Agent-Based Simulation (ABS), started gaining awareness;however, little is known regarding their applications and integration with various optimization methods and scenario-generation techniques in EDs. To address this research gap, we used bibliometric analysis and the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) framework to narrow the publications to 62 articles and conduct a comprehensive review. During the evaluation of each article, three dimensions, namely the area of application, outcomes, and Key Performance Indicators (KPIs), were reviewed to understand the development and implementation of simulation techniques in EDs. The results shed light on the vital impact of process and time variations on simulation quality and the importance of involving healthcare professionals in simulation validation and verification. Furthermore, this study provided a conceptual framework that identified gaps and solutions related to the model scopes, approaches, technologies, and implementation of the DES and ABS in EDs. These findings could prove useful for healthcare researchers, practitioners, and managers in advancing the future research directions of DES and ABS and practical implications in the decision-making process.

关键词： Emergency department Healthcare Discrete-event simulation Agent-based simulation PRISMA review Bibliometric analysis

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Evaluation of the trends in jobs and skill-sets using data analytics: a case study

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JOURNAL OF BIG DATA 2022年第1期9卷 1-28页

作者： Alibasic, Armin Upadhyay, Himanshu simsekler, mecit can emre Kurfess, Thomas Woon, Wei Lee Omar, Mohammed Atif Khalifa Univ Sci & Technol Dept Ind & Syst Engn Abu Dhabi 127788 U Arab Emirates Georgia Inst Technol George W Woodruff Sch Mech Engn Atlanta GA 30332 USA Expedia Grp Dept Marketpl & Yield Data Sci Seattle WA 98119 USA

Introduction Fast-emerging technologies are making the job market dynamic, causing desirable skills to evolve continuously. It is therefore important to understand the transitions in the job market to proactively identify skill sets required. Case description A novel data-driven approach is developed to identify trending jobs through a case study in the oil and gas industry. The proposed approach leverages a range of data analytics tools, including Latent Semantic Indexing (LSI), Latent Dirichlet Allocation (LDA), Factor Analysis and Non-Negative Matrix Factorization (NMF), to study changes in the market. Further, our approach is capable of identifying disparities between skills that are covered by the educational system, and the skills that are required in the job market. Discussion and evaluation The results of the case study show that, while the jobs most likely to be replaced are generally low-skilled, some high-skilled jobs may also be at risk. In addition, mismatches are identified between skills that are imparted by the education system and the skills required in the job market. Conclusions This study presents how job market and skills required evolved over time, which can help decision-makers to prepare the workforce for highly demanding jobs and skills. Our findings are in line with the concerns that automation is decreasing the demand for certain skills. On the other hand, we also identify the new skills that are required to strengthen the need for collaboration between minds and machines.

关键词： Future of work Jobs and skills analysis Data analytics Data mining Natural language processing

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CROSS-TRAINING POLICIES FOR ENHANCED RESILIENCE IN EMERGENCY DEPARTMENTS

CROSS-TRAINING POLICIES FOR ENHANCED RESILIENCE IN EMERGENCY...

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2024 Winter Simulation Conference, WSC 2024

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

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. © 2024 IEEE.

关键词： Nursing

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Risk assessment of construction projects using Monte Carlo simulation

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INTERNATIONAL JOURNAL OF MANAGING PROJECTS IN BUSINESS 2021年第5期14卷 1202-1218页

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

Purpose This paper aims to develop a process for prioritizing project risks that integrates the decision-maker's risk attitude, uncertainty about risks both in terms of the associated probability and impact ratings, and correlations across risk assessments. Design/methodology/approach This paper adopts a Monte Carlo Simulation-based approach to capture the uncertainty associated with project risks. Risks are prioritized based on their relative expected utility values. The proposed process is operationalized through a real application in the construction industry. Findings The proposed process helped in identifying low-probability, high-impact risks that were overlooked in the conventional risk matrix-based prioritization scheme. While considering the expected risk exposure of individual risks, none of the risks were located in the high-risk exposure zone;however, the proposed Monte Carlo Simulation-based approach revealed risks with a high probability of occurrence in the high-risk exposure zone. Using the expected utility-based approach alone in prioritizing risks may lead to ignoring few critical risks, which can only be captured through a rigorous simulation-based approach. Originality/value Monte Carlo Simulation has been used to aggregate the risk matrix-based data and disaggregate and map the resulting risk profiles with underlying distributions. The proposed process supported risk prioritization based on the decision-maker's risk attitude and identified low-probability, high-impact risks and high-probability, high-impact risks.

关键词： Risk assessment Risk matrix Monte Carlo simulation Risk attitude Project risks

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Opportunities for Patient Safety Improvement using Lean

Opportunities for Patient Safety Improvement using Lean

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

作者： Alqedrah, Mohammed Marbouh, Dounia simsekler, mecit can emre Ellahham, Samer Khalifa University of Science and Technology Department of Industrial and Systems Engineering Abu Dhabi United Arab Emirates Heart and Vascular Institute and Quality and Patient Safety Institute Cleveland Clinic Abu Dhabi Abu Dhabi United Arab Emirates

ISBN: (纸本)9781665418010

Lean involves structured methods and techniques that solve problems causing waste in a system. Organizations that implement lean principles can reduce waste with ongoing process improvement. This can result in a better quality of care, reduction of medical errors, and increased patient safety. In this paper, we focus on the evaluation of applying lean in the healthcare industry to improve patient safety. The application of lean for patient safety improvement is still a relatively unexplored field. Yet, studies have shown that there lies potential in applying this concept to improve the safety and quality of care for patients. Lean can facilitate finding potential waste and patient safety risks that require quantitative analysis to classify and mitigate. However, applying lean without considering what challenges might arise and how to face such challenges may be the recipe for failure. Therefore, utilizing the benefits of lean and tailoring its opportunities to fit the healthcare industry's challenges is crucial for a successful application. This paper can be valuable to providers for learning, investigating, and correctly adopting lean in the healthcare sector to improve patient safety. © 2022 IEEE.

关键词： Accident prevention

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Assessing the impact of sustainability risks on disaster and pandemic vulnerabilities: A global perspective

GLOBAL TRANSITIONS

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GLOBAL TRANSITIONS 2025年 7卷 159-174页

作者： Qazi, Abroon Angell, Linda C. simsekler, mecit can emre Daghfous, Abdelkader Al-Mhdawi, M. K. S. Amer Univ Sharjah Sch Business Adm Sharjah U Arab Emirates Khalifa Univ Sci & Technol Dept Management Sci & Engn Abu Dhabi U Arab Emirates Teesside Univ Sch Comp Engn & Digital Technol Middlesbrough England

This study examines the impact of failing to achieve Sustainable Development Goals (SDGs) on disaster and pandemic vulnerabilities, providing a country-level perspective to inform resilience planning. The study introduces the concept of SDG-related risk, defined as the probability of not achieving the desired SDG, and classifies these risks into three categories: high, medium, and low. Using a Bayesian Belief Network (BBN) framework, two probabilistic models are developed to evaluate the influence of SDG performance on disaster risk and COVID-19 vulnerability across 165 countries. The results highlight that shortcomings in SDGs such as 'quality education', 'sustainable cities and communities', 'no poverty', and 'affordable and clean energy' significantly increase disaster and pandemic risks. Conversely, strong performance in 'peace, justice and strong institutions' and 'life on land' enhances systemic resilience. Countries with very high disaster risk are particularly exposed to deficiencies in SDGs related to 'peace, justice and strong institutions', 'sustainable cities and communities', and 'good health and well-being'. For COVID-19 risk, 'affordable and clean energy' emerges as the most critical SDG influencing high-risk exposure, whereas 'climate action' is pivotal in predicting low-risk states. These findings demonstrate the cascading risks posed by failing to achieve critical SDGs and emphasize the need for targeted interventions to mitigate vulnerabilities to disasters and pandemics, providing actionable insights for sustainable resilience strategies. (c) 2025 The Authors. Publishing services by Elsevier B.V. on behalf of KeAi Communications Co. Ltd. This is an open access article under the CC BY license (http://***/licenses/by/4.0/).

关键词： Bayesian belief network COVID-19 Disaster Resilience Risk Sustainability

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Identifying Patient Satisfaction Determinants in Hemodialysis Settings: A Systematic Review

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RISK MANAGEMENT AND HEALTHCARE POLICY 2022年 15卷 1843-1857页

作者： Al Nuairi, Arwa Bermamet, Hala Abdulla, Hind simsekler, mecit can emre Anwar, Siddiq Lentine, Krista L. Khalifa Univ Sci & Technol Dept Ind & Syst Engn Abu Dhabi 127788 U Arab Emirates Sheikh Shakhbout Med City Abu Dhabi 10001 U Arab Emirates St Louis Univ Ctr Abdominal Transplantat St Louis MO USA Khalifa Univ Sci & Technol Dept Ind & Syst Engn POB 127788 Abu Dhabi U Arab Emirates

Purpose: Patient satisfaction is a measure of care quality that assists providers in determining the effectiveness of their services while meeting patients' expectations. This study aimed to review existing studies that have focused on patients' satisfaction determinants in Hemodialysis (HD) ***: Electronic databases (PubMed, ScienceDirect, Scopus, and Google Scholar) were searched from 2000 onwards to identify studies using search terms related to patient satisfaction and hemodialysis centers. Article review was limited to studies written in English. A total of 19 articles were included by following the PRISMA statement. Data were extracted using a structured form and summarized in a tabular format to identify different determinants that showed a relationship with patient satisfaction. Determinants were classified into provider-related determinants and patient-related ***: Provider-related determinants of patient satisfaction in HD centers include staff, facility, service, and treatment. Patient-related characteristics associated with satisfaction include demographics and health status history. Based on this systematic review, key correlates of patient satisfaction in hemodialysis centers include: staff, facility, service, treatment, patient's demographics, and health ***: The findings of this study can help healthcare facilities in taking measures in line with the specified determinants to enhance patient satisfaction and improve the organizational performance of the healthcare centers. It is important to constantly study and improve these determinants based on patient feedback to improve patient satisfaction and quality of care.

关键词： hemodialysis patients patient satisfaction patient experience healthcare quality

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Leveraging artificial intelligence and decision support systems in hospital-acquired pressure injuries prediction: A comprehensive review

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ARTIFICIAL INTELLIGENCE IN MEDICINE 2023年第1期141卷 102560-102560页

作者： Toffaha, Khaled M. simsekler, mecit can emre Omar, Mohammed Atif Khalifa Univ Sci & Technol Dept Ind & Syst Engn Abu Dhabi U Arab Emirates

Background: Hospital-acquired pressure injuries (HAPIs) constitute a significant challenge harming thousands of people worldwide yearly. While various tools and methods are used to identify pressure injuries, artificial intelligence (AI) and decision support systems (DSS) can help to reduce HAPIs risks by proactively identifying patients at risk and preventing them before harming ***: This paper comprehensively reviews AI and DSS applications for HAPIs prediction using Electronic Health Records (EHR), including a systematic literature review and bibliometric ***: A systematic literature review was conducted through PRISMA and bibliometric analysis. In February 2023, the search was performed using four electronic databases: SCOPIS, PubMed, EBSCO, and PMCID. Articles on using AI and DSS in the management of PIs were ***: The search approach yielded 319 articles, 39 of which have been included and classified into 27 AI-related and 12 DSS-related categories. The years of publication varied from 2006 to 2023, with 40% of the studies taking place in the US. Most studies focused on using AI algorithms or DSS for HAPIs prediction in inpatient units using various types of data such as electronic health records, PI assessment scales, and expert knowledge-based and environmental data to identify the risk factors associated with HAPIs ***: There is insufficient evidence in the existing literature concerning the real impact of AI or DSS on making decisions for HAPIs treatment or prevention. Most studies reviewed are solely hypothetical and retrospective prediction models, with no actual application in healthcare settings. The accuracy rates, prediction results, and intervention procedures suggested based on the prediction, on the other hand, should inspire researchers to combine both approaches with larger-scale data to bring a new venue for HAPIs prevention and to investigate and adopt the suggested solutions to the e

关键词： Hospital -acquired pressure injuries HAPIs prediction Decision support systems Machine learning Systematic review Patient safety

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