Tenzin Dechen is a biostatistician with experience in healthcare delivery and data science. She holds an MPH in epidemiology from Columbia University’s Mailman School of Public Health. Her work focuses on applying data analytics to understand clinical workflows, support care coordination, and inform health system improvement.
Tenzin spent nearly a decade at the Center for Healthcare Delivery Science at Beth Israel Deaconess Medical Center (BIDMC), Boston, where she worked with diverse health and population datasets, including electronic health record (EHR) data, to evaluate care delivery, assess quality, and support data-informed decision-making. Her work comprised statistical modeling, causal inference, and predictive analytics, with applications in population health and health equity. During the COVID-19 pandemic, she contributed to the development of forecasting models to help anticipate patient demand and guide resource planning. She also codeveloped a mobility-based Business Risk Index using location data to examine patterns in transmission risk and support hospital operations.
Currently on a self-directed sabbatical, Tenzin is focusing on global health systems and digital health infrastructure. Her work emphasizes the use of EHR systems to improve data quality, workflow efficiency, and patient care in resource-limited settings.
Tenzin Dechen’s Fulbright-Nehru research project is exploring how an EHR system can support patient care and clinic operations at Sera Mey Health Center, located in a Tibetan settlement in southern India and serving both Tibetan and neighboring rural populations. Using historical paper records and newly digitized data, the project is examining patterns in patient care, identifying gaps in follow-up, and evaluating how workflow factors such as patient volume and wait times shape service delivery. By generating practical insights and tools tailored to the center’s needs, the project aims to support clinical workflows, improve efficiency, and enable more patient-centered healthcare in the resource-limited setting.