Dr. Simsekler is an Associate Professor and Associate Chair for Graduate Studies at the Department of Management Science and Engineering at Khalifa University of Science and Technology. He is also a visiting scholar at Boston Children’s Hospital (Teaching Hospital of Harvard Medical School) and University College London (UCL) School of Management.
Dr. Simsekler obtained his PhD from the University of Cambridge in 2015. During his PhD, he was a visiting researcher in the Center for Medical Innovation System at the University of Tokyo and the Center for Patient Safety and Quality Research at Boston Children’s Hospital. After completing his PhD, he worked as a Research Associate at the UCL School of Management between 2015 and 2016.
Dr Simsekler’s research interests span healthcare analytics and management to improve operational and safety outcomes and accelerate risk-based decision-making. He explores the role of systems thinking, business analytics and disruptive technologies, such as artificial intelligence and machine learning, to leverage digital and sustainable transformation in building high-value health systems.
In line with his research activities, he teaches Healthcare Analytics Management and Healthcare Operations Management courses at undergraduate and graduate levels.
Dr Simsekler is the founder and director of the Health Systems and Management Initiative (HSMI), a platform engaging stakeholders from different disciplines and exploring innovative approaches to enhance the value of healthcare delivery. He is a member of the Institute for Operations Research and Management Science (INFORMS), the Institute of Industrial and Systems Engineers (IISE), and the European Operations Management Association (EUROMA).
Dr Simsekler is the recipient of the 2022 Outstanding Global Faculty Advisor Recognition Award from the IISE, the world's largest professional society for industrial and systems engineers.
Title: Digital by Design: Leveraging AI-Driven Transformation for Patient Safety and Inclusivity in Healthcare
This study presents the significant impact of medical errors and the potential for AI-powered technologies and systems thinking to improve patient safety and inclusivity in digital transformation.
There is a growing awareness that medical errors constitute a significant challenge harming thousands of people globally every year. This study explores the potential role of AI-powered technologies, such as machine learning algorithms, point-of-care ultrasound (POCUS) devices and decision support systems, which can be utilized to proactively identify high-risk patients and facilitate risk-based decision-making in healthcare.
While patient safety is paramount in this digital transformation, we also explore the role of AI in inclusivity by personalizing healthcare services in the care process, improving accessibility, addressing bias in healthcare systems, and facilitating diagnosis and treatment for people in remote or underserved areas, or with mobility or transportation issues. The study also emphasizes the importance of incorporating system thinking into digital transformation, considering various perspectives, including system, design, people, and risk in the complex and dynamic nature of sociotechnical healthcare systems.
Title: The Role of Disruptive Technologies and Risk-Based Decision-Making in Healthcare Through Systems Thinking View of Analytics
PhD Candidate: Dima Al Absi
Supervisor(s): Mecit Can Emre Simsekler (KU), Mohammed Omar (KU)
External Collaborator(s): Dr. Siddiq Anwar (Sheikh Shakhbout Medical City)
This research aims to leverage the transformative capabilities of AI predictive models, with a particular focus on elevating patient care through early risk identification, especially concerning the widespread and severe condition of Acute Kidney Injury (AKI). The ultimate goal of this research is to bridge the chasm between AI's potential and its tangible impact on patient care, particularly concerning AKI. By shifting the paradigm from reactive healthcare practices to a proactive model of early risk identification and intervention through systems thinking, this research holds the promise of leading patient care into a new era.
This research initiative is built upon three foundational pillars:
1. Development of an ML Algorithm: A machine learning-based algorithm is designed to predict and categorize patients at high risk for AKI. This algorithm holds the potential to be a game-changer by enabling healthcare providers to identify patients in jeopardy at an earlier stage, ultimately improving their likelihood of recovery.
2. The Power of Bayesian Belief Networks: This project introduces a cutting-edge approach by constructing a Bayesian Belief Network (BBN) model coupled with an innovative Decision Support System (DSS). This integration facilitates comprehensive scenario analysis of AKI-related factors, offering a deeper understanding of the condition's intricacies and paving the way for more precise interventions.
3. Evaluating AI's Role in Healthcare Ecosystem: Beyond the technical aspects, the research explores the seamless integration of AI into healthcare systems. Utilizing the Systems Engineering Initiative for Patient Safety (SEIPS) model, this investigation explores the broader implications of AI adoption within existing healthcare infrastructures. By doing so, it addresses critical questions about the feasibility, scalability, and sustainability of AI-driven healthcare enhancements.
Title: AI-Driven Decision Support System for Early Recognition, Management, and Personalized Treatment Planning of Sepsis Evolution
PhD Candidate: Firda Rahmadani
Supervisor(s): Mecit Can Emre Simsekler (KU), Mohammed Omar (KU)
External Collaborator(s): Dr. Siddiq Anwar and Dr. Ali Mohammed Al Shidi (Sheikh Shakhbout Medical City)
In the realm of healthcare, Decision support systems (DSSs) play a crucial role in improving patient care outcomes and reducing healthcare costs by providing timely and accurate recommendations to healthcare providers. Within this context, SEPSIS, a severe medical condition characterized by the body's aberrant response to infection, presents a significant challenge. It annually affects an estimated 47-50 million individuals, with a devastating fatality rate of at least 11 million, equating to one fatality every 2.8 seconds. The complexity of SEPSIS lies in the diverse and often overlapping symptoms it shares with less severe medical conditions, rendering its diagnosis a challenging task.
This research endeavors to introduce an innovative paradigm in the realm of DSSs tailored explicitly for SEPSIS management. Its framework amalgamates system thinking with machine learning (ML) algorithms, distinguishing itself from preceding approaches that predominantly focused on clinical parameters while ignoring the impact of organizational factors in SEPSIS prediction. The proposed methodology transcends these limitations by capturing the temporal evolution of SEPSIS states, thereby facilitating the modeling of the intricate dynamics inherent to the disease.
A notable emphasis is placed on the often-underestimated role of care standardization, aiming to enhance situational awareness, foster interdisciplinary collaboration, and facilitate effective communication among healthcare professionals.
The overarching objective of this investigation is to offer insights and solutions that not only enhance SEPSIS management but also serve as a paradigm for optimizing healthcare delivery. Through this research, the potential arises to reshape the healthcare landscape, improve patient outcomes, and ultimately save lives, marking a transformative milestone in the field of medical practice.
Title: Future of Work in Healthcare for a Digitized Economy
PhD Candidate: Moustafa Magdi Abdelwanis
Supervisor(s): Mecit Can Emre Simsekler (KU), Andrei Sleptchenko (KU), Mohammed Omar (KU), Adriana Gabor (KU)
The global COVID-19 pandemic has been an unprecedented disruptor, reshaping every facet of our lives, including the workforce in healthcare. Therefore, it has become imperative for healthcare organizations and decision-makers to proactively assess and understand the evolving trends that are fundamentally altering the composition and dynamics of the workforce.
One significant consequence of the pandemic has been the integration of emerging technologies, most notably Artificial Intelligence (AI), into the healthcare sector. However, the journey towards crafting a resilient healthcare system, often referred to as Healthcare 5.0, is marked by a complex array of challenges. These challenges encompass organizational changes, intricate technological adaptations, regulatory adjustments, financial considerations, and the critical need for cultural transformations.
In response to these dynamic shifts, it is vital for decision-makers and healthcare providers to establish robust policies and protocols that place patient-centric, technology-driven healthcare systems at the forefront of their strategies. These systems are essential for overcoming the formidable barriers posed by the contemporary healthcare landscape.
This research represents a pivotal step towards equipping decision-makers with the insights needed to navigate the impending impact of emerging technologies on jobs and skill sets within the healthcare industry. Through the rigorous application of data analytics, visualization techniques, and machine learning algorithms, this study conducts a comprehensive evaluation of the job analysis framework in healthcare. The findings of this research aim to serve as a roadmap for policymakers, enabling them to guide organizational transformations that embrace digital health. By aligning workforce demands with the requisite skillsets, these shifts can be executed proactively and effectively.
Ph.D. candidates and holders with healthcare analytics and management interests are welcome to contact me to discuss potential research opportunities.
