This clinical trial aims to evaluate the pilot implementation of a machine-learning (ML)-driven clinical decision support (CDS) tool designed to predict opioid overdose risk within the electronic health record (EHR) system at UF Health Internal Medicine and Family Medicine clinics in Gainesville, Florida. The study will use a pre- versus post-implementation design to compare outcomes within clinics, focusing on measures such as naloxone prescribing rates and opioid overdose occurrences. Researchers will also assess the usability, acceptability, and feasibility of the CDS tool through qualitative interviews with primary care clinicians (PCPs) in the participating clinics.
Opiate Overdose, Opioid-Related Disorders, Narcotic-Related Disorders, Substance-related Disorders, Chemically-Induced Disorders, Mental Disorders
This clinical trial aims to evaluate the pilot implementation of a machine-learning (ML)-driven clinical decision support (CDS) tool designed to predict opioid overdose risk within the electronic health record (EHR) system at UF Health Internal Medicine and Family Medicine clinics in Gainesville, Florida. The study will use a pre- versus post-implementation design to compare outcomes within clinics, focusing on measures such as naloxone prescribing rates and opioid overdose occurrences. Researchers will also assess the usability, acceptability, and feasibility of the CDS tool through qualitative interviews with primary care clinicians (PCPs) in the participating clinics.
Developing and Evaluating a Machine-Learning Opioid Overdose Prediction & Risk-Stratification Tool in Primary Care
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University of Florida Health Internal Medicine and Family Medicine, Gainesville, Florida, United States, 32608
Researchers look for people who fit a certain description, called eligibility criteria. Some examples of these criteria are a person's general health condition or prior treatments.
For general information about clinical research, read Learn About Studies.
18 Years to
ALL
No
University of Pittsburgh,
Wei-Hsuan Lo-Ciganic, PhD, PRINCIPAL_INVESTIGATOR, Division of General Internal Medicine, School of Medicine, University of Pittsburgh, Pittsburgh, PA
2026-10-02