Dr. Lin is a Pharmacoepidemiologist in the Division of Pharmacoepidemiology and Pharmacoeconomics at the Brigham and Women’s Hospital and a practicing hospitalist at the Massachusetts General Hospital. He is an Associate Professor at Harvard Medical School and the Executive Director of the Mass General Brigham (MGB) Center for Integrated Healthcare Data Research. In this role, he has built a highly valuable research database, linking multiple health insurance claims data with a variety of clinical data from electronic health records of the MGB institutions. Leading a team of investigators, research scientists, and administrators, he has built an efficient infrastructure that makes these rich data sources readily available for researchers at Harvard Medical School to facilitate scientific discovery. This valuable database has led to multiple publications in high-impact journals and successfully competed NIH research grants.
Dr. Lin is the Director of the Program of Optimal and Safe Prescribing in the Elderly (PROSPER) with the mission to generate high-quality comparative safety and effectiveness real-world evidence for personalized prescribing and deprescribing in older adults. His research has focused on optimal prescribing in vulnerable populations using large routine-care databases and the development of analytical solutions to combat common biases in pharmacoepidemiology. As Principal Investigator (PI) of an NIH-funded R01 research project (1R01LM012594), he has developed and validated an algorithm to identify a high data-completeness cohort to reduce the information bias resulting from missing medical information recorded outside of the study electronic health record (EHR) system and demonstrated this sub-cohort is representative of the remaining population.
Dr. Lin is also the PI of an NIH-funded research project (1RF1AG063381, originally 1R01AG063381) aiming to determine the optimal anticoagulation strategy to prevent stroke in older adults with dementia and other high-risk features. He has published the first comparative effectiveness and safety study of oral anticoagulants in patients with atrial fibrillation and dementia that clearly identified the superior strategy for stroke prevention. In addition, he is the PI of an NIH-funded R01 project (1R01LM013204) to develop a data-adaptive analytical framework for reducing confounding bias in studies that leverages a large amount of patient information recorded in the free-text clinical notes and reports for confounding adjustment in comparative effectiveness research (CER). More recently, he has been leading two NIH-funded national studies about deprescribing potentially inappropriate medications in people living with dementia: 1) To establish an analytical framework to optimize post-hospitalization delirium management in people living with dementia (R01AG081412); 2) To determine the effectiveness and safety of deprescribing antipsychotics in people living with dementia and behavioral disturbance in skilled nursing facilities (R01AG081268). His long-term career goal is to establish a rigorous and generalizable framework to optimize validity and precision of comparative effectiveness research with detailed evaluation of treatment effect heterogeneity using electronic health records and insurance claims data.
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