Developing and Evaluating a Machine-Learning Opioid Overdose Prediction & Risk-Stratification Tool in Primary Care

Developing and Evaluating a Machine-Learning Opioid Prediction & Risk-Stratification E-Platform (DEMONSTRATE)

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.

This clinical trial evaluates the pilot implementation of a ML-driven CDS tool designed to predict opioid overdose risk within the electronic health record (EHR) system at thirteen UF Health internal medicine and family medicine clinics in Gainesville, Florida.

The implementation process involved backend and frontend development and integration of the CDS tool. For backend integration, the investigators reviewed clinical workflows, designed a data flow plan to incorporate risk scores into patient charts, and collaborated with UF Health IT and Integrated Data Repository (IDR) Research Services to address alert implementation, data flow, server specifications, and responsibilities. Risk assessments approved by UF Health IT and the institutional review board (IRB) ensured secure access to patient health information (PHI) and enabled EHR integration. For frontend development, the investigators used a user-centered design approach to create the CDS tool prototype, incorporating feedback from PCPs during formative interviews to refine the user interface and ensure timely, actionable alerts through the EPIC system without disrupting clinical workflows.

The study primarily aims to assess the usability, acceptance, and feasibility of the CDS tool six months post-implementation through mixed-method evaluations. Researchers will use semi-structured interviews and an online questionnaire to collect feedback from PCPs, focusing on alert usability, preferences, and outcomes. Quantitative analyses will evaluate alert penetration, usage patterns, and PCP actions, while qualitative analyses will explore themes and insights from override comments to guide tool optimization. Researchers will also explore secondary patient-level outcomes using EHR data such as naloxone prescriptions.

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