Chemically-Induced Disorders, Mental Disorders, Narcotic-Related Disorders, Opiate Overdose, Opioid-Related Disorders, Substance-related Disorders
Conditions
Keywords
Opiate overdose, Opioid-Related Disorders, Risk Evaluation and Mitigation, Machine Learning, Medical Order Entry Systems, Decision Support Systems, Clinical
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.
Detailed description
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.
Interventions
BEHAVIORALMachine 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.
Sponsors
University of Pittsburgh
National Institute on Drug Abuse (NIDA)
Applied Decision Science
Study design
Allocation
NA
Intervention model
SINGLE_GROUP
Primary purpose
HEALTH_SERVICES_RESEARCH
Masking
NONE
Intervention model description
This single-arm clinical trial employs a pre- and post-implementation pilot evaluation design to assess the usability, acceptability, and feasibility of implementing a ML-driven overdose CDS tool across thirteen UF Health primary care clinics (3 internal medicine and 6 family medicine clinics in Gainesville, Florida). The CDS tool will generate an Overdose Prevention Alert (OPA) when a PCP signs an opioid order in Epic® for patients at elevated risk of opioid overdose identified by ML algorithm.
Eligibility
Sex/Gender
ALL
Age
18 Years to No maximum
Healthy volunteers
No
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
Design outcomes
Primary
| Measure | Time frame | Description |
|---|---|---|
| Composite patient-level outcomes related to opioids | 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: 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). |
| PCP's use feedback of the Overdose Prevention Alert (OPA) | 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: 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). |
Secondary
| Measure | Time frame | Description |
|---|---|---|
| Receipt of a naloxone order or prescription fill | 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 |
| Absence of opioid overdose diagnoses and naloxone administration | 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 |
| Absence of ED visits or hospitalizations due to opioid overdose or OUD | 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) |
| Absence of overlapping opioid and benzodiazepine use | 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 |
| Absence of high-dose opioid use (average daily morphine milligram equivalent ≥50) | 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). |
| Receipt of referrals to non-pharmacological pain management (e.g., physical therapy, chiropractic care | 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). |
Countries
United States
Contacts
PRINCIPAL_INVESTIGATORWei-Hsuan Lo-Ciganic, PhD
Division of General Internal Medicine, School of Medicine, University of Pittsburgh, Pittsburgh, PA
Outcome results
None listed