Dr. Özge Karanfil
Assistant Professor, Koç University College of Administrative Sciences and Economics, Department of Operations Management and Information Systems, Istanbul- Turkey
Sept 2018- present
Assistant Professor, Koç University School of Medicine, Department of Public Health, Istanbul- Turkey & Koç University, Coordinator for the MSc Program in Global Health
July 2020- present
Visiting Scientist, Harvard T. Chan School of Public Health, Department of Global Health and Population, 651 Huntington Avenue, Boston, MA 02115
Sept 2018 – present
I am an assistant professor at Koç University, Istanbul, at its College of Administrative Sciences and Economics, and its School of Medicine, since 2018 and 2020, respectively. I am also the coordinator of the Global Health MSc Program at Koç University, and a visiting scientist at Harvard School of Public Health since 2018. My aim is to produce policy-relevant research by integrating management, systems, and health sciences with a strong emphasis on data-driven analysis and modeling. I have BSc and MSc degrees in Industrial Engineering from Boğaziçi University, an MSc in Physiology from McGill University, and completed my Ph.D. at MIT Sloan School of Management, System Dynamics Research Group. I continued my studies as a Yerby Fellow at Harvard School of Public Health, Department of Global Health and Population, between 2016-18. With a background in management, industrial engineering, and health sciences, I am drawn to systemic problems of chronic and repetitive nature, which encompass typical constraints and approaches with important managerial implications for the societal domain, coming from medical or non-medical contexts (yet typically sharing quite similar internal structures!)
My research experience and interests can be summarized in three major, complementary areas:
i) Dynamic modeling for policy analysis (public health, medicine, environment, 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/global health research and management (such as evidence-based guideline formation, population screening, cancers, 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. Despite their importance especially in high-risk conditions, guidelines are far from optimal in practice. While there is a proliferation of modeling studies to inform CPGs, not many are addressing the actual guideline-making process itself. The scientific community also recently recognized the inherent complexity of the guideline formation process itself and invited researchers to explore the potential implications of this complexity that is inherent in complex decision-making environments (See WHO Special Issue in 2019).
There are significant gaps between practice and evidence, and at the core of it, we think two reasons need to be taken into account that is inherent in the guideline formation process: 1- Interaction of delays in various parts of the system; including delays in policy formation and implementation; and generating and assimilating scientific evidence. 2- Boundedly rational decision-making, defined by cognitive- and information limitations about the underlying system. Additional layers of complexity are added to this picture due to 3- cognitive and socio-economic factors in the decision-making environment and deviations between stakeholders’ interests, including policymakers, clinicians, academics, patients, and patient advocacy groups…
The theory and resulting models we build are grounded in empirical evidence-base, 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 (significant variations, overscreening and underscreening, gaps between policy and practice, suboptimality and fluctuations in guidelines) within the context of developed and developing countries.
Our research group also has been collecting empirical evidence for significant variations in screening trends in developing countries, and we are working on building novel models and policy decision support tools to inform and complement evidence-based guideline development. These may be categorized as “boundary objects”, or most simply, a representation—that helps different stakeholders and individuals to collaborate effectively across some boundary, who often have differences in their training, knowledge and/or objectives.
Simulation models, data and interfaces like ours can provide constructive insights and a dynamic intuition to supplement the typical empirical evidence considered to update and refine practice guidelines by the research community and policymakers; and to evaluate and test hypotheses about practice guideline formation, evidence-based training, and education, or behavior patterns in decision thresholds in various settings, and in repeated managerial contexts.
International System Dynamics Society (SDS) / 2002 – present
- Healthcare Track Chair / 2016 – 2017
- Policy Council Elected Member / 2015 – 2017
- Health Policy Special Interest Group / 2006 – present
Biomedical Special Interest Group / 2008 – present
Institute for Operations Research and the Management Sciences (INFORMS) / 2011 – present
Academy of Management (AOM)—Health Care Management Division Member / 2019 – present
Society for Medical Decision Making (SMDM) / 2014 – present
Academy Health (Academy Health) / 2015 – present
Health Systems Global (HSG) / 2017 – present