Developing and Evaluating a Machine-Learning Opioid Overdose Prediction & Risk-Stratification Tool in Primary Care
Trial Parameters
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Brief Summary
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.
Eligibility Criteria
Inclusion Criteria: For PCP level outcomes assessment * PCPs * practicing in any of the 13 participating clinics (10 UF Health Family Medicine clinics and 3 UF Health Internal Medicine) in Gainesville, Florida. For patient level outcomes assessment: Inclusion criteria: Patients who seen in any of the 9 participating UF Health clinics who * are aged ≥18 years * received any opioid prescription in the past year prior to their clinic visit. * are identified as being at elevated risk for overdose by the ML algorithm. Exclusion Criteria: Patients who * had malignant cancer diagnosis or hospice care prior to study enrollment