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| ID | Type | Description | Link |
|---|---|---|---|
| R01DA050676 | U.S. NIH Grant/Contract | View source |
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| Name | Class |
|---|---|
| National Institute on Drug Abuse (NIDA) | NIH |
| Applied Decision Science | UNKNOWN |
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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.
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| Label | Type | Description | Intervention Names |
|---|---|---|---|
| Overdose Prevention Alert (OPA) Intervention Arm | Experimental | 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. |
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| Name | Type | Description | Arm Group Labels | Other Names |
|---|---|---|---|---|
| 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. |
| Measure | Description | Time Frame |
|---|---|---|
| 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:
| 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:
Mean scores (with standard deviations [SD]) will be calculated across all items, as well as individual average scores (SD). | From enrollment and up to 7 months post implementation of the OPA |
| Measure | Description | Time Frame |
|---|---|---|
| Receipt of a naloxone order or prescription fill | Proportion of patients receiving alert who have 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) |
| Absence of opioid overdose diagnoses and naloxone administration |
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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
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| Name | Affiliation | Role |
|---|---|---|
| Wei-Hsuan Lo-Ciganic, PhD | Division of General Internal Medicine, School of Medicine, University of Pittsburgh, Pittsburgh, PA | Principal Investigator |
| Facility | Status | City | State | ZIP | Country | Contacts |
|---|---|---|---|---|---|---|
| University of Florida Health Internal Medicine and Family Medicine | Gainesville | Florida | 32608 | United States |
| PubMed Identifier | Type | Citation | Retractions |
|---|---|---|---|
| 41375825 | Background | Hong JJ, Wilson DL, Nguyen K, Gellad WF, Diiulio J, Militello L, Yan S, Harle CA, Nelson D, Rosenberg EI, Schmidt S, Chang CH, Cochran G, Wu Y, Staras SAS, Kuza C, Lo-Ciganic WH. Protocol for a Single-Arm Pilot Clinical Trial: Developing and Evaluating a Machine Learning Opioid Prediction & Risk-Stratification E-Platform (DEMONSTRATE). J Clin Med. 2025 Dec 1;14(23):8522. doi: 10.3390/jcm14238522. | |
| 35623798 | Background | Lo-Ciganic WH, Donohue JM, Yang Q, Huang JL, Chang CY, Weiss JC, Guo J, Zhang HH, Cochran G, Gordon AJ, Malone DC, Kwoh CK, Wilson DL, Kuza CC, Gellad WF. Developing and validating a machine-learning algorithm to predict opioid overdose in Medicaid beneficiaries in two US states: a prognostic modelling study. Lancet Digit Health. 2022 Jun;4(6):e455-e465. doi: 10.1016/S2589-7500(22)00062-0. |
| Label | URL |
|---|---|
| Center for Pharmaceutical Policy \& Prescribing website | View source |
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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.
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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.
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|
Proportion of patients receiving alert who do not have an opioid overdose diagnoses and naloxone administration |
| 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) | 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 | 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). | 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). | From enrollment and up to 12 months (3, 6, 12 months) post implementation |
| 32678860 | Background | Lo-Ciganic WH, Huang JL, Zhang HH, Weiss JC, Kwoh CK, Donohue JM, Gordon AJ, Cochran G, Malone DC, Kuza CC, Gellad WF. Using machine learning to predict risk of incident opioid use disorder among fee-for-service Medicare beneficiaries: A prognostic study. PLoS One. 2020 Jul 17;15(7):e0235981. doi: 10.1371/journal.pone.0235981. eCollection 2020. |
| 30901048 | Background | Lo-Ciganic WH, Huang JL, Zhang HH, Weiss JC, Wu Y, Kwoh CK, Donohue JM, Cochran G, Gordon AJ, Malone DC, Kuza CC, Gellad WF. Evaluation of Machine-Learning Algorithms for Predicting Opioid Overdose Risk Among Medicare Beneficiaries With Opioid Prescriptions. JAMA Netw Open. 2019 Mar 1;2(3):e190968. doi: 10.1001/jamanetworkopen.2019.0968. |
| 39569464 | Background | Militello LG, Diiulio J, Wilson DL, Nguyen KA, Harle CA, Gellad W, Lo-Ciganic WH. Using human factors methods to mitigate bias in artificial intelligence-based clinical decision support. J Am Med Inform Assoc. 2025 Feb 1;32(2):398-403. doi: 10.1093/jamia/ocae291. |
| 39420438 | Background | Nguyen K, Wilson DL, Diiulio J, Hall B, Militello L, Gellad WF, Harle CA, Lewis M, Schmidt S, Rosenberg EI, Nelson D, He X, Wu Y, Bian J, Staras SAS, Gordon AJ, Cochran J, Kuza C, Yang S, Lo-Ciganic W. Design and development of a machine-learning-driven opioid overdose risk prediction tool integrated in electronic health records in primary care settings. Bioelectron Med. 2024 Oct 18;10(1):24. doi: 10.1186/s42234-024-00156-3. |
| ID | Term |
|---|---|
| D000083682 | Opiate Overdose |
| D009293 | Opioid-Related Disorders |
| D000079524 | Narcotic-Related Disorders |
| D019966 | Substance-Related Disorders |
| D064419 | Chemically-Induced Disorders |
| D001523 | Mental Disorders |
| ID | Term |
|---|---|
| D062787 | Drug Overdose |
| D063487 | Prescription Drug Misuse |
| D000076064 | Drug Misuse |
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| ID | Term |
|---|---|
| D008722 | Methods |
| ID | Term |
|---|---|
| D008919 | Investigative Techniques |
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