Substance use and mental diagnoses among adults with and without type 2 diabetes: Results from electronic health records data.

Education/Training: Pre- and post-doctoral training in mental health service research, psychiatric epidemiology (NIMH T32), and addiction epidemiology (NIDA T32) from Johns Hopkins University School of Public Health (Maryland); Fellow of the NIH Summer Institute on the Design and Conduct of Randomized Clinical Trials.

Director: Duke Community Based Substance Use Disorder Research Program.

Research interests: COVID-19, Opioid misuse, Opioid overdose, Opioid use disorder, Opioid addiction prevention and treatment, Pain and addiction, Chronic diseases and substance use disorders, diabetes, pharmacy-based care models and services, medication treatment for opioid use disorder (MOUD), Drug overdose, Polysubstance use and disorders, cannabis, alcohol, tobacco, hallucinogens, stimulants, e-cigarette, SBIRT (substance use Screening, Brief Intervention, Referral to Treatment), EHR-based research and intervention, data science, psychometric analysis (IRT), epidemiology of addictions and comorbidity, behavioral health care integration, health services research (mental health disorders, substance use disorders, chronic diseases), nosology, research design, HIV risk behavior. 

FUNDED Research projects (Principal Investigator [PI], Site PI, or Sub-award PI): 
R03: Substance use/dependence (PI).
R21: Treatment use for alcohol use disorders (PI).
R21: Inhalant use & disorders (PI).
R01: MDMA/hallucinogen use/disorders (PI).
R01: Prescription pain reliever (opioids) misuse and use disorders (PI).
R01: Substance use disorders in adolescents (PI).
R21: CTN Substance use diagnoses & treatment (PI).
R33: CTN Substance use diagnoses & treatment (PI).
R01: Evolution of Psychopathology in the Population (ECA Duke site PI).
R01: Substance use disorders and treatment use among Asian Americans and Pacific Islanders (PI).
UG1: SBIRT in Primary Care (NIDA, PI).
UG1: TAPS Tool, Substance use screening tool validation in primary care (NIDA, PI).
UG1: NIDA CTN Mid-Southern Node (Clinical Trials Network, PI).
UG1: EHR Data Element Study (NIDA, PI).
UG1: Buprenorphine Physician-Pharmacist Collaboration in the Management of Patients With Opioid Use Disorder (NIDA, PI).
PCORI: INSPIRE-Integrated Health Services to Reduce Opioid Use While Managing Chronic Pain (Site PI).
CDC R01: Evaluation of state-mandated acute and post-surgical pain-specific CDC opioid prescribing (Site PI).
Pilot: Measuring Opioid Use Disorders in Secondary Electronic Health Records Data (Carolinas Collaborative Grant: Duke PI).
R21: Developing a prevention model of alcohol use disorder for Pacific Islander young adults (Subaward PI, Investigator).
UG1: Subthreshold Opioid Use Disorder Prevention Trial (NIH HEAL Initiative) (NIDA supplement, CTN-0101, Investigator).
NIDA: A Pilot Study to Permit Opioid Treatment Program Physicians to Prescribe Methadone through Community Pharmacies for their Stable Methadone Patients (NIDA/FRI: Study PI).
UG1: Integrating pharmacy-based prevention and treatment of opioid and other substance use disorders: A survey of pharmacists and stakeholder (NIH HEAL Initiative, NIDA, PI).
UG1: NorthStar Node of the Clinical Trials Network (NIDA, Site PI).
R34: Intervention Development and Pilot Study to Reduce Untreated Native Hawaiian and Pacific Islander Opioid Use Disorders (Subaward PI, Investigator).
UG1: Optimal Policies to Improve Methadone Maintenance Adherence Longterm (OPTIMMAL Study) (NIDA, Site PI).
R01: Increasing access to opioid use disorder treatment by opening pharmacy-based medication units of opioid treatment programs (NIDA, PI)

Type 2 Diabetes, Obesity/Overweight, Behavior change, Non-pharmacologic intervention, Health disparities

Michael J. Pencina, PhD
Chief Data Scientist, Duke Health
Vice Dean for Data Science
Director, Duke AI Health
Professor, Biostatistics & Bioinformatics
Duke University School of Medicine

Michael J. Pencina, PhD, is Duke Health's chief data scientist and serves as vice dean for data science, director of Duke AI Health, and professor of biostatistics and bioinformatics at the Duke University School of Medicine. His work bridges the fields of data science, health care, and AI, contributing to Duke’s national leadership in trustworthy health AI.

Dr. Pencina partners with key leaders to develop data science strategies for Duke Health that span and connect academic research and clinical care. As vice dean for data science, he develops and implements quantitative science strategies to support the School of Medicine’s missions in education and training, laboratory and clinical science, and data science.

He co-founded and co-leads the national Coalition for Health AI (CHAI), a multi-stakeholder effort whose mission is to increase trustworthiness of AI by developing guidelines to drive high-quality health care through the adoption of credible, fair, and transparent health AI systems. He also spearheaded the establishment and co-chairs Duke Health’s Algorithm-Based Clinical Decision Support (ABCDS) Oversight Committee and serves as co-director of Duke’s Collaborative to Advance Clinical Health Equity (CACHE).

Dr. Pencina is an internationally recognized authority in the evaluation of AI algorithms. Guideline groups rely on his work to advance best practices for the application of clinical decision support tools in health delivery. He interacts frequently with investigators from academic and industry institutions as well as government officials. Since 2014, he has been acknowledged annually by Thomson Reuters/Clarivate Analytics as one of the world’s "highly cited researchers" in clinical medicine and social sciences, with over 400 publications cited over 100,000 times. He serves as a deputy editor for statistics at JAMA-Cardiology.

Dr. Pencina joined the Duke University faculty in 2013, and served as director of biostatistics for the Duke Clinical Research Institute until 2018. Previously, he was an associate professor in the Department of Biostatistics at Boston University and the Framingham Heart Study, and director of statistical consulting at the Harvard Clinical Research Institute. He received his PhD in Mathematics and Statistics from Boston University in 2003 and holds master’s degrees from the University of Warsaw in actuarial mathematics and business culture.

Email: michael.pencina@duke.edu

Web Sites:  medschool.duke.edu; aihealth.duke.edu; https://scholars.duke.edu/person/michael.pencina

Phone:  919.613.9066

Address:  Duke University School of Medicine; 2424 Erwin Road, Suite 903; Durham, NC 27705

I study the meaningful use of Electronic Health Records data. My research interests sit at the intersection of biostatistics, biomedical informatics, machine learning and epidemiology. I collaborate with researchers both locally at Duke as well as nationally. I am interested in speaking with any students, methodologistis or collaborators interested in EHR data.

Please find more information at: https://sites.duke.edu/bgoldstein/

Shelley Rusincovitch, MMCi, is an informaticist and technical leader who specializes in healthcare applications of artificial intelligence and machine learning, data modeling, and data science experiential learning. She has more than 20 years of experience in clinical research including clinical trials, registries, and health system data warehousing.

Ms. Rusincovitch serves as the managing director of Duke AI Health, a multidisciplinary, campus-spanning initiative housed within the Duke University School of Medicine and designed to connect, strengthen, amplify, and grow multiple streams of theoretical and applied research on artificial intelligence and machine learning at Duke University in order to answer the most urgent and difficult challenges in medicine and population health.