Designing a Substance Misuse Data Dashboard for Overdose Fatality Review Teams

Presentation Time: 08:00 AM - 08:15 AM

Abstract Keywords: Population Health, Information Visualization, User-centered Design Methods, Data Sharing, Surveys and Needs Analysis
Primary Track: Applications
Programmatic Theme: Public Health Informatics

Overdose Fatality Review (OFRs) teams use multiple, siloed data sources to understand overdose trends and inform opportunities for prevention. We sought to understand the cognitive workload of this process and then apply our findings to the design of a user-centered dashboard. The prototype dashboard was subjected to iterative assessments by a focus group of subject matter experts. Future studies will evaluate the cognitive demands of the new dashboard as compared to the incumbent workflow.

Speaker(s):
Marie Pisani, BS
University of Wisconsin, Madison, Department of Medicine

Author(s):
Marie Pisani, BS - University of Wisconsin, Madison, Department of Medicine; Madeline Oguss, MS - University of Wisconsin - Madison; Julia Dickson-Gomez, PhD - Medical College of Wisconsin; Constance Kostelac, PhD - Medical College of Wisconsin; Amy Parry, MPH - Medical College of Wisconsin; Starr Moss, MS - Wisconsin Department of Justice; Elizabeth Salisbury-Afshar, MD, MPH - University of Wisconsin-Madison; Brian Patterson, MD MPH - University of Wisconsin-Madison; Michael Spigner, MD, NRP - University of Wisconsin; Megan Gussick, MD - University of Wisconsin-Madison; Anoop Mayampurath, PhD - University of Wisconsin - Madison; Majid Afshar, MD, MSCR - University of Wisconsin - Madison;

Nudging Dentists to Prescribe Fewer Opioids through Audit and Feedback

Presentation Time: 08:15 AM - 08:30 AM

Abstract Keywords: Behavioral Change, Clinical Decision Support, Human-computer Interaction, Informatics Implementation, Patient Safety
Primary Track: Applications
Programmatic Theme: Clinical Informatics

This randomized controlled study investigated the efficacy of electronic audit and feedback (A&F) dashboards, sent monthly, in reducing opioid prescriptions by dentists. Dashboards included EHR data about a provider's own prescribing behavior and a comparison to peers. Results indicated a significant reduction in opioid prescriptions with both standard and enhanced A&F dashboards compared to controls. These findings highlight the potential of informatics approaches in mitigating unnecessary opioid prescriptions in dental care.

Speaker(s):
Muhammad Walji, PhD
UTHealth Houston McWilliams School of Biomedical Informatics

Author(s):
Muhammad Walji, PhD - UTHealth Houston McWilliams School of Biomedical Informatics; Sayali Tungare, BDS, MPH, PHD - UTHealth Houston School of Dentistry; Swaroop Gantela, MD - UTHealth Houston McWilliams School of Biomedical Informatics; Krishna Kumar Kookal, MS - UTHealth Houston School of Dentistry; Alfa-Ibrahim Yansane, PhD - UCSF School of Dentistry; Emily Sedlock, MPH - UTHealth Houston School of Dentistry; Arthur Jeske, DDS, PhD - UTHealth Houston School of Dentistry; Todd Johnson, PhD - UT Health School of Biomedical Informatics;

Clinician Perspectives on a Predictive Model for Recommending Opioid Use Disorder Treatment

Presentation Time: 08:30 AM - 08:45 AM

Abstract Keywords: Clinical Decision Support, Qualitative Methods, Surveys and Needs Analysis
Primary Track: Applications
Programmatic Theme: Clinical Informatics

Background: Predictive models that have been made available as clinical decision support systems have not always been used.

Objectives: This qualitative study aimed to identify factors that might impact the uptake of a predictive model recommending either methadone or buprenorphine as medication for opioid use disorder (MOUD) in the inpatient setting.

Methods: We conducted semi-structured interviews with clinicians who prescribe MOUD and performed a combined deductive and inductive content analysis using a socio-technical model.

Results: Thirteen clinicians were interviewed. Non-specialists trusted their specialist peers to lead MOUD decisions and claimed they would trust a tool endorsed by experts and the institution. Clinicians expected the model to follow clinical reasoning, which involves considering factors that are not well-captured by the electronic health record (e.g., housing status, access to care, facility preferences).

Conclusion: Predictive models for MOUD should be designed to foster appropriate trust given the tool’s purpose, process, limitation, and performance.

Speaker(s):
Leigh Anne Tang
Vanderbilt University Department of Biomedical Informatics

Author(s):
Leigh Anne Tang - Vanderbilt University Department of Biomedical Informatics; Michelle Gomez - Vanderbilt University; Uday Suresh, MS - Vanderbilt University Department of Biomedical Informatics; Kristopher Kast, MD - Department of Psychiatry and Behavioral Sciences, Vanderbilt University Medical Center; Robert Becker, MS - Department of Biomedical Informatics, Vanderbilt University; Thomas Reese, PharmD, PhD - Vanderbilt; Colin Walsh - Department of Biomedical Informatics, Vanderbilt University; Jessica Ancker, MPH, PhD, FACMI - Vanderbilt University Medical Center;

Web-based Interventions for Substance Use Disorders and Mental Health: Preliminary findings from a Scoping Review

Presentation Time: 08:45 AM - 09:00 AM

Abstract Keywords: Mobile Health, Personal Health Informatics, Informatics Implementation, Self-care/Management/Monitoring, Behavioral Change, Evaluation
Primary Track: Applications
Programmatic Theme: Consumer Health Informatics

This scoping review evaluated the efficacy and potential of web-based interventions for substance use disorders and mental health conditions. Forty-two studies were included, comprising randomized controlled trials, pilot trials, and effectiveness trials. Web-based interventions consistently demonstrated significant reductions in substance use, improvements in mental health outcomes (e.g., PTSD, depression, anxiety), and enhancements in emotion regulation, help-seeking, and quality of life. Several studies found web-based interventions to be non-inferior or superior to traditional face-to-face treatments. Despite limitations in the current evidence base, such as methodological issues and lack of long-term follow-up, the findings highlight the promise of web-based interventions in expanding access to evidence-based care, particularly for underserved populations. Future research should focus on refining interventions, exploring novel technologies, and evaluating long-term effectiveness and cost-effectiveness. The integration of web-based interventions into healthcare systems has the potential to significantly impact public health by increasing treatment accessibility and improving outcomes for individuals with substance use disorders and mental health conditions.

Speaker(s):
Yuri Quintana, PhD
Beth Israel Deaconess Medical Center

Author(s):