Scholarship and Biography
Moaven Razavi, PhD is a Scientist in the Schneider Institutes for Health Policy at the Heller School of Brandeis University.
He served as the Director of the Bureau for Research at the Institute for Management and Planning Studies (IMPS) in Tehran, Iran from 2003 to 2005. He was also consulting to the World Bank and the WHO-EMRO with their health sector reform portfolio of research that focused on healthcare financing models in the EMRO region from 2003 to 2006.
In 2005, Dr. Razavi was awarded an International Ford Foundation Fellowship in International Health Policy and Management at Brandeis University, and one year later he was awarded the Heller School Scholarship for the PhD program in Health Policy. He officially joined the Schneider Institutes for Health Policy in 2008. At the Schneider Institutes, he worked with the Institute for Global Health as well as the Institutes on Health Care Systems. Outside Brandeis he was a co-recipient of the Massachusetts General Hospital’s Clinical Innovation Award (2008) and presented his research to the Massachusetts Medical Society in competition for the MMS Information Technology Award 2009.
He served as the task lead for Identification of Inefficiencies in Health Care Spending for State of Vermont and served as co-investigator of the Medicare Health Plan Value Methodology, and played key roles in other health policy research projects including membership in the Technical Assistance Team to the Beacon Community Medicare Claims Data Performance Measurement project for CMS. The project Episode Grouper for Medicare is another federally funded project in which Dr. Razavi had significant contributions in particular in risk adjustment models and the algorithms of attribution of episodes to multiple providers.
Gaining from a multidisciplinary training in electronic engineering and computer science, economics, healthcare management, and health policy, in the era of healthcare big data, Dr. Razavi remains an active player in healthcare data science research and education. His most recent academic and research activities include design of new courses in healthcare data science and analytics to be offered in Fall 2018, serving as the PI for the project “Applications of Text Analytics and Natural Language Processing (NLP) in Healthcare Unstructured Big Data” and involving in certain type of research that require state of the art knowledge of data and analytics. He currently sits on the technical advisory committee sponsored by the Tufts CTSI Informatics Strategy where the data scientists from Tufts, Brandeis, MIT, and Northeastern come together to support the development of an environment that facilitates working with analytic and predictive models, merge language and approaches around doing things that will have an impact on health, utilizing statistics, informatics, data base access, data coordinating center, etc. In this advisory role Dr. Razavi provides input to design and build the Tufts Analytics Platform TAP, a platform-as-service tool that will support new applications of data science in translational research. The Tufts Analytic Platform is strategically aligned with NIH/NCATS vision for Informatics to support Clinical and Translational Science by developing novel approaches to aggregate, standardize and analyze data to improve health, prevent and detect disease at an earlier stage, and personalize interventions. The other mission of this advisory committee is to support efforts in answering unmet need for integrated informatics and analytics infrastructure as well as tools and methods. In this line of research he has published scholarly articles in predictive models including a pharma sponsored predictive model for post surgical procedures infections and complications among Medicare beneficiaries using large insurance claim databases.
Dr. Razavi has received research support from diverse institutions such as Center for Medicare and Medicaid (CMS), states of Vermont and Mississippi, the Bill and Melinda Gates Foundation, GSK pharmaceutical Co., Biogen Inc., Change Healthcare, The World Bank, World Health Organization, and NIH through multiple sources including Tufts CTSI. He advises MS and PhD students and teaches courses in health economics, research method, and healthcare data science and data analytics, and advanced topics in predictive analytics and applications of machine learning and artificial intelligence using complex healthcare data.