Ozge Karanfil
I am aiming to produce policy-relevant research by integrating management, complex systems and health sciences with a strong emphasis on data-driven analysis and modeling. I have BSc and MSc degrees in Industrial Engineering from Bogazici University, an MSc in Physiology from McGill University, and completed my PhD at MIT Sloan School of Management, System Dynamics Research Group. With a background in industrial engineering, management and health sciences, I am drawn to systemic problems of chronic nature, which encompass typical constraints and approaches with important managerial implications for the societal domain, coming from medical or non-medical contexts. My training is multi-disciplinary, and I have both collaborated and published with experts in management, systems sciences, health sciences, and engineering to solve complex managerial problems with important policy implications for the societal domain. My research experience and interests can be summarized in three major, complementary areas:
i) Dynamic modeling for policy analysis (public health, medicine, environment, sustainability, or any other complex system problem)
ii) Specific application areas in health policy and management as an overarching theme at various levels (micro, mezzo, macro), relevant to clinical/public health research and management (such as evidence-based guideline formation, cancer screening, chronic and cardiovascular disease management, disease biomarkers, physiologically oriented disease modeling, NCDs, cardiovascular diseases and obesity)
iii) The underlying theoretical and empirical methods to cultivate research in the first two domains
One of my main lines of research is the investigation of the universal problem of evidence-based development of sound and reliable clinical practice guidelines. The theory and resulting models I build are grounded in empirical evidence-base and use a mix of quantitative and qualitative methods and data, a dynamic modeling approach to complex systems, statistical data analysis, and other decision-analysis tools to explain long-term trends in population screening and related problems within the context of developed countries. I have collected evidence for significant variations in screening trends in developed countries, and have been working on building novel models and policy decision support tools to inform and complement evidence-based clinical practice guidelines (CPGs).
Please contact me for possible collaborations and available positions in my research group.
Email: [email protected] and [email protected]
Supervisors: John D. Sterman, Yaman Barlas
Address: OZGE KARANFIL, PhD / Assistant Professor
Dept. of Operations Management and Information Systems, College of Administrative Sciences and Economics (CASE)
School of Medicine, Koç University, Istanbul
Research Affiliate, MIT Sloan School of Management
Research Affiliate, KU Translational Medicine (KUTTAM)
Web: http://www.karanfillab.com/
i) Dynamic modeling for policy analysis (public health, medicine, environment, sustainability, or any other complex system problem)
ii) Specific application areas in health policy and management as an overarching theme at various levels (micro, mezzo, macro), relevant to clinical/public health research and management (such as evidence-based guideline formation, cancer screening, chronic and cardiovascular disease management, disease biomarkers, physiologically oriented disease modeling, NCDs, cardiovascular diseases and obesity)
iii) The underlying theoretical and empirical methods to cultivate research in the first two domains
One of my main lines of research is the investigation of the universal problem of evidence-based development of sound and reliable clinical practice guidelines. The theory and resulting models I build are grounded in empirical evidence-base and use a mix of quantitative and qualitative methods and data, a dynamic modeling approach to complex systems, statistical data analysis, and other decision-analysis tools to explain long-term trends in population screening and related problems within the context of developed countries. I have collected evidence for significant variations in screening trends in developed countries, and have been working on building novel models and policy decision support tools to inform and complement evidence-based clinical practice guidelines (CPGs).
Please contact me for possible collaborations and available positions in my research group.
Email: [email protected] and [email protected]
Supervisors: John D. Sterman, Yaman Barlas
Address: OZGE KARANFIL, PhD / Assistant Professor
Dept. of Operations Management and Information Systems, College of Administrative Sciences and Economics (CASE)
School of Medicine, Koç University, Istanbul
Research Affiliate, MIT Sloan School of Management
Research Affiliate, KU Translational Medicine (KUTTAM)
Web: http://www.karanfillab.com/
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Papers by Ozge Karanfil
a natural disease history and a realistic yet generic structure that allows keeping track of critical stocks that have been generally overlooked in previous modeling studies. Our model is specific to prostate-specific antigen (PSA) screening for prostate cancer (PCa), including the natural progression of the disease, respective changes in population size and composition, clinical detection, adoption of the PSA screening test by medical professionals, and the dissemination of the screening test. The key outcome measures for the model are selected to show the fundamental tradeoff between the main harms and benefits of screening, with the main harms including (i) overdiagnosis, (ii) unnecessary biopsies, and (iii) false positives. The focus of this study is on building the most reliable and flexible model structure for medical screening and keeping track of its main harms and benefits. We show the importance of some metrics which are not readily measured or considered by existing medical
literature and modeling studies. While the model is not primarily designed for making inferences about optimal screening policies or scenarios, we aim to inform modelers and policymakers about potential levers in the system and provide a reliable model structure for medical screening that may complement other modeling studies designed for cancer interventions. Our simulation model can offer a formal means to improve the development and implementation of evidence-based screening,
and its future iterations can be employed to design policy recommendations to address important policy areas, such as the increasing pool of cancer survivors or healthcare spending in the U.S.
a natural disease history and a realistic yet generic structure that allows keeping track of critical stocks that have been generally overlooked in previous modeling studies. Our model is specific to prostate-specific antigen (PSA) screening for prostate cancer (PCa), including the natural progression of the disease, respective changes in population size and composition, clinical detection, adoption of the PSA screening test by medical professionals, and the dissemination of the screening test. The key outcome measures for the model are selected to show the fundamental tradeoff between the main harms and benefits of screening, with the main harms including (i) overdiagnosis, (ii) unnecessary biopsies, and (iii) false positives. The focus of this study is on building the most reliable and flexible model structure for medical screening and keeping track of its main harms and benefits. We show the importance of some metrics which are not readily measured or considered by existing medical
literature and modeling studies. While the model is not primarily designed for making inferences about optimal screening policies or scenarios, we aim to inform modelers and policymakers about potential levers in the system and provide a reliable model structure for medical screening that may complement other modeling studies designed for cancer interventions. Our simulation model can offer a formal means to improve the development and implementation of evidence-based screening,
and its future iterations can be employed to design policy recommendations to address important policy areas, such as the increasing pool of cancer survivors or healthcare spending in the U.S.