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Faculty Research Seminar on Business Analytics
Monday, April 20, 12:30-2pm, NVC 14-266
Optimizing Intervention and Prevention Policies in the Health Care System
Margrét Vilborg Bjarnadóttir
Dr. Margrét Vilborg Bjarnadóttir, is an Assistant Professor of Management Science and Statistics in the DO&IT group. Dr. Bjarnadóttir graduated from MIT's Operations Research Center in 2008, defending her thesis titled "Data Driven Approach to Health Care, Application Using Claims Data." Dr. Bjarnadóttir specializes in operations research methods using large scale data. Her work spans applications ranging from analyzing nation-wide cross-ownership patterns and systemic risk in finance to drug surveillance and practice patterns in health care. She has consulted with both health care start-ups on risk modeling using health care data as well as governmental agencies such as a central bank on data-driven fraud detection algorithms.
Risk prediction models are increasingly common in the health care system. Models of the risk of a patient being readmitted to the hospital within 30 days, developing disease complications, or not attending a medical appointment are just a few examples. The application of these models in clinical settings includes a choice of which intervention programs to offer and to whom in order to improve outcomes. These decisions involve considering the costs and benefits of each program and the risk characteristics of the population. Because more data on individuals are now available in real time, it is possible to base decisions such as intervention program enrollment on individualized risk scores. In this study, we propose a methodology for combining prediction models and optimization to select which intervention program(s) to run and which patients to enroll. As a real world example, we apply our methodology to an outpatient clinic whose goal is to reduce appointment cancellations. We present empirical insights into risk factors for appointment cancellations.