Using electronic health record data for substance use Screening, Brief Intervention, and Referral to Treatment among adults with type 2 diabetes: Design of a National Drug Abuse Treatment Clinical Trials Network study.
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2016-01
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BACKGROUND: The Affordable Care Act encourages healthcare systems to integrate behavioral and medical healthcare, as well as to employ electronic health records (EHRs) for health information exchange and quality improvement. Pragmatic research paradigms that employ EHRs in research are needed to produce clinical evidence in real-world medical settings for informing learning healthcare systems. Adults with comorbid diabetes and substance use disorders (SUDs) tend to use costly inpatient treatments; however, there is a lack of empirical data on implementing behavioral healthcare to reduce health risk in adults with high-risk diabetes. Given the complexity of high-risk patients' medical problems and the cost of conducting randomized trials, a feasibility project is warranted to guide practical study designs. METHODS: We describe the study design, which explores the feasibility of implementing substance use Screening, Brief Intervention, and Referral to Treatment (SBIRT) among adults with high-risk type 2 diabetes mellitus (T2DM) within a home-based primary care setting. Our study includes the development of an integrated EHR datamart to identify eligible patients and collect diabetes healthcare data, and the use of a geographic health information system to understand the social context in patients' communities. Analysis will examine recruitment, proportion of patients receiving brief intervention and/or referrals, substance use, SUD treatment use, diabetes outcomes, and retention. DISCUSSION: By capitalizing on an existing T2DM project that uses home-based primary care, our study results will provide timely clinical information to inform the designs and implementation of future SBIRT studies among adults with multiple medical conditions.
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Wu, Li-Tzy, Kathleen T Brady, Susan E Spratt, Ashley A Dunham, Brooke Heidenfelder, Bryan C Batch, Robert Lindblad, Paul VanVeldhuisen, et al. (2016). Using electronic health record data for substance use Screening, Brief Intervention, and Referral to Treatment among adults with type 2 diabetes: Design of a National Drug Abuse Treatment Clinical Trials Network study. Contemp Clin Trials, 46. pp. 30–38. 10.1016/j.cct.2015.11.009 Retrieved from https://hdl.handle.net/10161/10996.
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Li-Tzy Wu
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)
Susan Elizabeth Spratt
Bryan Courtney Batch
Type 2 Diabetes, Obesity/Overweight, Behavior change, Non-pharmacologic intervention, Health disparities
Shelley Rusincovitch
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.
Unless otherwise indicated, scholarly articles published by Duke faculty members are made available here with a CC-BY-NC (Creative Commons Attribution Non-Commercial) license, as enabled by the Duke Open Access Policy. If you wish to use the materials in ways not already permitted under CC-BY-NC, please consult the copyright owner. Other materials are made available here through the author’s grant of a non-exclusive license to make their work openly accessible.