An interventional study of Machine Learning-Based Clinical Decision Support: Overdose Prevention Alert (OPA) Intervention in Opiate Overdose, Opioid-Related Disorders and Narcotic-Related Disorders, sponsored by University of Pittsburgh. Active, not recruiting at 1 site in United States. Open to participants aged 18 Years and older. Per ClinicalTrials.gov, last updated 2026-04-13.
Sponsored by University of Pittsburgh · Not applicable, Interventional, and Health services research
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.
70 studies on the registry are indexed under Opiate Overdose; 11 are open to participants now.
This study's enrollment of 674 is above the median of 184 across 48 interventional studies indexed under Opiate Overdose.
Browse Opiate Overdose studies →University of Pittsburgh is the lead sponsor of 1,419 studies on the registry; 168 are open to participants now.
Of its 9 completed or terminated interventional studies of FDA-regulated products, 4 (44%) have results posted.
Counted across the registry records on this site, refreshed daily.
Inclusion Criteria:
For PCP level outcomes assessment
For patient level outcomes assessment:
Inclusion criteria: Patients who seen in any of the 9 participating UF Health clinics who
The intervention arm will receive a ML CDS tool that provides interruptive alerts for patients at elevated risk of opioid overdose, triggered when a clinician signs an opioid order.
Behavioral: Machine Learning-Based Clinical Decision Support: Overdose Prevention Alert (OPA) Intervention
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.
Composite patient-level outcomes related to opioids
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: 1. receipt of a naloxone order or prescription fill; 2. absence of opioid overdose diagnoses and naloxone administration; 3. absence of ED visits or hospitalizations due to opioid overdose or OUD; 4. absence of overlapping opioid and benzodiazepine use; 5. absence of 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 of the OPA
PCP's use feedback of the Overdose Prevention Alert (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: 1. OPA's information was clear. 2. OPA was easy to use. 3. OPA helps identify patients at increased overdose risk. 4. OPA helps understand patient's overdose risk. 5. OPA provides risk management recommendations. 6. OPA identifies the right patients with elevated overdose risk. 7. OPA notifies the correct healthcare team member (i.e., PCPs). 8. A pop-up alert is an appropriate notification approach. 9. Signing an opioid order is the right time for OPA. 10. Alert frequency is appropriate. 11. I prefer OPA over the legacy naloxone alert (see picture). 12. 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).
Time frame: From enrollment and up to 7 months post implementation of the OPA
Receipt of a naloxone order or prescription fill
Proportion of patients receiving alert who have 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)
Absence of opioid overdose diagnoses and naloxone administration
Proportion of patients receiving alert who do not have an opioid overdose diagnoses and naloxone administration
Time frame: From enrollment and up to 12 months (3, 6, 12 months) post implementation
Absence of ED visits or hospitalizations due to opioid overdose or OUD
Proportion of patients receiving alert who do not have ED visits or hospitalizations due to opioid overdose or opioid use disorder (OUD)
Time frame: From enrollment and up to 12 months (3, 6, 12 months) post implementation
Absence of overlapping opioid and benzodiazepine use
Proportion of patients receiving alert who do not have overlapping opioid and benzodiazepine use
Time frame: From enrollment and up to 12 months (3, 6, 12 months) post implementation
Absence of high-dose opioid use (average daily morphine milligram equivalent ≥50)
Proportion of patients receiving alert who do not have 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
Receipt of referrals to non-pharmacological pain management (e.g., physical therapy, chiropractic care
Proportion of patients receiving alert who have 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
Plan to share: No — While transparency and data sharing are critical to advancing clinical research, researchers are unable to share individual participant data (IPD) derived from UF Health EHR due to institutional and legal constraints. The data are governed by strict privacy regulations, including HIPAA, which mandate the protection of patient confidentiality. Additionally, UF Health's policies restrict the dissemination of EHR data to ensure compliance with these regulations and safeguard against the risk of re-identification. As a result, while researchers can report aggregated findings, sharing raw participant-level data is not permissible under current regulatory and institutional frameworks.
Started Apr 2025 (actual). Primary completion is estimated for Oct 2026, 17 months after the start.
Of 28 interventional studies with no phase under Opiate Overdose, half reached primary completion within 26 months of starting (the median), counting those still running; a quarter within 14 and three quarters within 41. This study's planned 17 months is below that.
How these figures are worked out, and which studies are left out of them.
This study is active, not recruiting, as verified in Apr 2026. You cannot join it, but the record below documents what was studied.
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