Overview
Dr. Tenenbaum is a faculty member in the Division of Translational Biomedical Informatics in the Department of Biostatistics and Bioinformatics. Her primary research interests are 1. Informatics to enable whole person health, including mental health and social determinants of health (SDOH); 2. Infrastructure and data governance to enable collaboration and integrative data analysis; and 3. Ethical, legal, and social issues in biomedicine. She is also Special Advisor for Research Data Innovation in Duke's Office of Research & Innovation.
From 2019 to 2024, Dr. Tenenbaum served as Chief Data Officer for North Carolina's Department of Health and Human Services (NC DHHS). She continues to consult with state government, serving as Senior Data Strategist for NC HealthConnex, North Carolina's statewide Health Information Exchange (HIE).
Nationally Dr. Tenenbaum has served as an Associate Editor for the Journal of Biomedical Informatics, an elected member of AMIA's Board of Directors, and on the Board of Scientific Counselors for the National Library of Medicine. She is also an elected Fellow and President-Elect of the American College of Medical Informatics.
Current Appointments & Affiliations
Recent Publications
Accelerating a learning public health system: Opportunities, obstacles, and a call to action.
Journal Article Learn Health Syst · October 2024 INTRODUCTION: Public health systems worldwide face increasing challenges in addressing complex health issues and improving population health outcomes. This experience report introduces the concept of a Learning Public Health System (LPHS) as a potential so ... Full text Link to item CiteHealth Care Cost Reductions with Machine Learning-Directed Evaluations during Radiation Therapy - An Economic Analysis of a Randomized Controlled Study.
Journal Article NEJM AI · April 2024 BACKGROUND: Machine learning (ML) may cost-effectively direct health care by identifying patients most likely to benefit from preventative interventions to avoid negative and expensive outcomes. System for High-Intensity Evaluation During Radiation Therapy ... Full text Link to item CiteHealthcare provider evaluation of machine learning-directed care: reactions to deployment on a randomised controlled study.
Journal Article BMJ Health Care Inform · February 2023 OBJECTIVES: Clinical artificial intelligence and machine learning (ML) face barriers related to implementation and trust. There have been few prospective opportunities to evaluate these concerns. System for High Intensity EvaLuation During Radiotherapy (NC ... Full text Link to item CiteRecent Grants
NC Medicaid Value-Based Payment Implementation
Public ServiceAdvisor · Awarded by NC Medicaid · 2022 - 2026RADx-UP CDCC
ResearchCo Investigator · Awarded by National Institutes of Health · 2020 - 2025Gut Liver Brain Biochemical Axis in Alzheimer's Disease
ResearchCo Investigator · Awarded by National Institutes of Health · 2018 - 2023View All Grants