Skip to content

General Contact Information


Phone: 646-660-6500

Fax: 646-660-6501




Mailing Address:

Office of the Provost & Senior Vice President for Academic Affairs

Baruch College/CUNY

One Bernard Baruch Way
Box D-701

New York, NY 10010-5585


Walk-In Address:

Administrative Center

135 East 22nd Street, 7th Floor

Office of the Provost and Senior Vice President for Academic Affairs

Message Archive

Thursday April 16, 2015


This email is being sent to all members of the Baruch College faculty.

For an archive of announcements sent from the Associate Provost beginning June 2011, click here.



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.