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Active, Not Recruiting

NCT Number: NCT06810076

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

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.

Active, Not Recruiting

This study is active but is not currently recruiting participants.

Key information

Conditions

Opiate Overdose Chemically-Induced Disorders Drug Misuse Drug Overdose Mental Disorders Narcotic-Related Disorders Opioid-Related Disorders Prescription Drug Misuse Substance-Related Disorders

Age range

18 year and older

Sex eligibility

All sexes

Study type

Interventional

Phase

Not applicable

Primary location

University of Florida Health Internal Medicine and Family Medicine

Gainesville, Florida, 32608, United States

About this study

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.

Who can participate

Healthy volunteers accepted: No

Only the study team can determine whether someone qualifies for participation.

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

Treatment and study plan

Machine Learning-Based Clinical Decision Support: Overdose Prevention Alert (OPA) Intervention

Behavioral

In this study, researchers will pilot test an interruptive, ML CDS tool for opioid overdose risk across thirteen primary care clinics at the UF Health in Gainesville, FL. When a patient is identified by the ML algorithm as having an elevated overdose risk and a PCP signs an opioid prescription for the patient, an Opioid Prevention Alert (OPA) will be triggered. The alert will include the rationale for the patient's elevated risk status and provide three risk mitigation recommendations: optimizing pain treatment and mental health support, reviewing and discussing risks with the patient, and offering naloxone annually if no prior naloxone order is found in the patient's record. PCPs can also select an override reason, such as the patient already has naloxone, declined the intervention, is not present/it is not the right time, or the alert is not relevant/other comments, when appropriate.

Primary outcomes

  1. Composite patient-level outcomes related to opioids

    Time frame: From enrollment and up to 12 months (3, 6, 12 months) post implementation of the OPA

    The CDS tool will generate an Overdose Prevention Alert (OPA) when a PCP signs an opioid order in Epic®. To evaluate the tool's effectiveness, researchers will conduct within-clinic comparisons (pre- vs. post-implementation) and examine a composite of patient-level outcomes post-implementation, including the proportion of patients having any of the following 6 outcomes:

    • receipt of a naloxone order or prescription fill;
    • absence of opioid overdose diagnoses and naloxone administration;
    • absence of ED visits or hospitalizations due to opioid overdose or OUD;
    • absence of overlapping opioid and benzodiazepine use;
    • absence of high-dose opioid use (average daily morphine milligram equivalent ≥50);
    • receipt of referrals to non-pharmacological pain management (e.g., physical therapy, chiropractic care).

  2. PCP's use feedback of the Overdose Prevention Alert (OPA)

    Time frame: From enrollment and up to 7 months post implementation of the OPA

    An online questionnaire for PCPs who interacted with OPA includes 12 Likert-scale items (4-point scale: 1 = Strongly Disagree to 4 = Strongly Agree) assessing OPA's acceptability, appropriateness, and feasibility:

    • OPA's information was clear.
    • OPA was easy to use.
    • OPA helps identify patients at increased overdose risk.
    • OPA helps understand patient's overdose risk.
    • OPA provides risk management recommendations.
    • OPA identifies the right patients with elevated overdose risk.
    • OPA notifies the correct healthcare team member (i.e., PCPs).
    • A pop-up alert is an appropriate notification approach.
    • Signing an opioid order is the right time for OPA.
    • Alert frequency is appropriate.
    • I prefer OPA over the legacy naloxone alert (see picture).
    • I want this OPA to continue to operate in my EHR.

    Mean scores (with standard deviations [SD]) will be calculated across all items, as well as individual average scores (SD).

Secondary outcomes

  1. Receipt of a naloxone order or prescription fill

    Time frame: From enrollment and up to 12 months (3, 6, 12 months) post implementation of the Overdose Prevention Alert (OPA)

    Proportion of patients receiving alert who have a naloxone order or prescription fill

  2. Absence of opioid overdose diagnoses and naloxone administration

    Time frame: From enrollment and up to 12 months (3, 6, 12 months) post implementation

    Proportion of patients receiving alert who do not have an opioid overdose diagnoses and naloxone administration

  3. Absence of ED visits or hospitalizations due to opioid overdose or OUD

    Time frame: From enrollment and up to 12 months (3, 6, 12 months) post implementation

    Proportion of patients receiving alert who do not have ED visits or hospitalizations due to opioid overdose or opioid use disorder (OUD)

  4. Absence of overlapping opioid and benzodiazepine use

    Time frame: From enrollment and up to 12 months (3, 6, 12 months) post implementation

    Proportion of patients receiving alert who do not have overlapping opioid and benzodiazepine use

  5. Absence of high-dose opioid use (average daily morphine milligram equivalent ≥50)

    Time frame: From enrollment and up to 12 months (3, 6, 12 months) post implementation

    Proportion of patients receiving alert who do not have high-dose opioid use (average daily morphine milligram equivalent ≥50).

  6. Receipt of referrals to non-pharmacological pain management (e.g., physical therapy, chiropractic care

    Time frame: From enrollment and up to 12 months (3, 6, 12 months) post implementation

    Proportion of patients receiving alert who have referrals to non-pharmacological pain management (e.g., physical therapy, chiropractic care).

Sponsors and collaborators

Lead sponsor

University of Pittsburgh

Other

Collaborators

  • Applied Decision Science
  • National Institute on Drug Abuse (NIDA)

Registry information

Official study title

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

Acronym: DEMONSTRATE

Important dates

Study start
2025
Primary completion
2026
Study completion
2026
First posted
Feb 5, 2025
Registry last updated
Apr 13, 2026

OpenTrials presents study information sourced from ClinicalTrials.gov. The official registry record should be consulted for the latest information.

View the official ClinicalTrials.gov record (opens in a new tab)

This listing is for discovery and informational purposes only. It is not medical advice, does not guarantee that a study is recruiting, and does not determine eligibility. Contact the study team and a qualified healthcare professional when considering participation.

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