Associate Professor of Medicine
Brigham and Women's Hospital
Partners Healthcare System
399 Revolution Drive, Suite 1315
Somerville MA 02145
Best Paper Selection for the Yearbook of Medical Informatics
Distinguished Paper Award, Medinfo / World Congress on Medical and Health Informatics
Care Coordination for Improved Outcomes Challenge, Health 2.0 Developer Challenge
Best Paper Award Nomination, Medinfo / World Congress on Medical and Health Informatics
Partners in Excellence - Leadership and Innovation
Innovation Discovery Award
Partners in Excellence - Quality Treatment and Service
Elected Fellow, American College of Medical Informatics
Faculty Pillar Award - Research Innovation
Available: 01/17/20, Expires: 01/28/22
Severe cutaneous adverse reactions (SCARs) result in substantial morbidity, long term disability, health care burden and a mortality of 10-50%. To advance the science of clinical and genetic risk factor identification for antibiotic SCAR, we will leverage large electronic health record (EHR) data and advanced informatics technology. Through case validation we will create an informatics roadmap for other institutions with similar EHR data to identify SCAR cases and we will establish a data sharing platform, including an online electronic phenotype and patient registry, that can be used to enlarge SCAR cohorts for future large-scale genomics studies.
The student will assist with research activities that include chart review, case identification, data analysis and manuscript presentation.
Available: 02/03/20, Expires: 01/01/21
Health care today is increasingly delivered in the outpatient setting supported by the use of electronic health records (EHRs) with clinical decision support. However, despite the growth in EHR adoption, results are mixed about the impact of these changes on the safety of patient care, including medication safety. Approaches to identifying adverse drug events (ADEs) have improved, but there is no standard approach that is used across multiple organizations to track harm in the aggregate, leaving organizations struggling to know how they are doing with respect to safety. EHRs and computational methods for analyzing EHRs (e.g., natural language processing [NLP]) can be used to both decrease the burden of measuring safety issues and improve reliability of identifying adverse events. The overall goal of this project is to evaluate the performance of artificial intelligence methods in detecting ADEs in ambulatory primary care clinical notes. The student will be involved in diverse research activities, including chart review, method development and implementation, data analysis and manuscript writing.
The research activities and funding listed below are automatically derived from
NIH ExPORTER and other sources, which might result in incorrect or missing items.
to make corrections and additions.
Sep 16, 2019 - Aug 31, 2023
Clinical Informatics to Advance Epidemiology and Pharmacogenetics of Serious Cutaneous Adverse Drug Reactions
Role: Principal Investigator
Jul 1, 2018 - Jun 30, 2020
Similar-cAses Finder for Risk Reduction – the SAFRR system
Jul 1, 2018 - Jun 30, 2019
Computerized Support for Malpractice Auditing and Coding
May 7, 2018 - Apr 30, 2022
Improving Allergy Documentation and Clinical Decision Support in the EHR
Oct 1, 2017 - Sep 30, 2018
Brigham Care Redesign Incubator and Startup Program (BCRISP)
Finding Needles in the Haystack: Using Clinical Data and Machine Learning to Identify Patients for Early Palliative Care Communication Interventions in Inpatient
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