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Opiate OverdoseDEMONSTRATENCT06810076

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

A study testing Machine Learning-Based Clinical Decision Support: Overdose Prevention Alert (OPA) Intervention in people with Opiate Overdose and Opioid-Related Disorders, run by University of Pittsburgh at 1 site in United States.

Active, not recruiting

Under way, not looking for participants. Everyone needed has enrolled. The study is still running.

Official record
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Trial 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. Read more about this study

Who is it for?
Ages 18 years and older
What is being tested?
Machine Learning-Based Clinical Decision Support: Overdose Prevention Alert (OPA) Intervention
Could I receive a placebo?
No, everyone receives an active treatment
How many people
674 participants
Where
1 siteUnited States
Study dates
8 Apr 2025to 7 Oct 2026
Phase
Interventional
Are there results?
Not yet: the study is under way

Key requirements

Who can take part, as listed by the sponsor. Only the study team can confirm whether you are eligible, so take this list to your doctor.

  • Ages 18 years and older
  • All sexes
  • No healthy volunteers
  • Adult, Older adult

Who may be able to join

  • 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
Show all 9Show fewer
  • 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

Who cannot join

None listed.

Trial locations and contacts

1 site in 1 country.

United States1 site

  • University of Florida Health Internal Medicine and Family Medicine

    Gainesville, Florida, 32608

Other trials in United States

About this study

Official title: 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.

Read the 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.

Being tested
Machine Learning-Based Clinical Decision Support: Overdose Prevention Alert (OPA) Intervention
Keywords
Opiate overdose, Opioid-Related Disorders, Risk Evaluation and Mitigation, Machine Learning, Medical Order Entry Systems, Decision Support Systems, Clinical
Medical subject headings
Opiate Overdose, Opioid-Related Disorders, Narcotic-Related Disorders, Substance-Related Disorders, Chemically-Induced Disorders, Mental Disorders

What participants receive

What participants receive. In a randomised study each person is assigned to one group by chance.

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

    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.

Study groups in NCT06810076
Study groupRoleWhat they receive
Overdose Prevention Alert (OPA) Intervention ArmExperimentalThe 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.
Receives: Behavioral: Machine Learning-Based Clinical Decision Support: Overdose Prevention Alert (OPA) Intervention

What is being measured

The primary outcome is the main question the study is designed to answer.

  1. 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).

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

  2. 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).

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

Secondary outcomes (6)

  1. Receipt of a naloxone order or prescription fill

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

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

  2. Absence of opioid overdose diagnoses and naloxone administration

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

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

  3. 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)

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

  4. Absence of overlapping opioid and benzodiazepine use

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

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

  5. 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).

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

  6. 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).

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

Timeline

From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.

  1. First posted5 Feb 2025
  2. Study start8 Apr 2025
  3. Primary completion7 Oct 2026 (estimated)
  4. Study completion7 Oct 2026 (estimated)

Resources

More details

For doctors and researchers: how the study is designed, who runs it, and its registry IDs.

Study design
Study type
Interventional
Phase
Phase N/A
Primary purpose
Health services research
Allocation
Not applicable
Intervention model
Single group
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.
Masking
None (open label)
Enrolment
674 participants (actual)
IDs, dates and publications
ClinicalTrials.gov ID
NCT06810076
Sponsor's protocol ID
STUDY24040038
Other IDs
R01DA050676
Acronym
DEMONSTRATE
Status last verified
Apr 2026
First submitted
27 Jan 2025
Last updated
13 Apr 2026
Results posted
No
Data monitoring committee
No
FDA-regulated drug
No
FDA-regulated device
No
Individual data sharing
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.

Publications

  • 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.

    PubMed 41375825

  • 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.

    PubMed 35623798

  • 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.

    PubMed 32678860

  • 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.

    PubMed 30901048

  • 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.

    PubMed 39569464

  • 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.

    PubMed 39420438

Questions about this trial

What is this clinical trial studying?

A study testing Machine Learning-Based Clinical Decision Support: Overdose Prevention Alert (OPA) Intervention in people with Opiate Overdose and Opioid-Related Disorders, run by University of Pittsburgh at 1 site in United States.

The full registered description is in the About this study section and the official record.

Who may be eligible to take part?

The sponsor lists 9 inclusion and 0 exclusion criteria. Eligibility is decided by the study team after screening, so read the full criteria and contact a listed site.

Is this clinical trial currently recruiting?

The record says: under way, not looking for participants, as of the sponsor's last update on 13 Apr 2026. Status can change between updates, so confirm with the study contact before making plans.

Where is the study taking place?

The record lists 1 site in United States. Whether a site is taking part right now should be confirmed with that site. See every site.

What are the possible benefits and risks?

They depend on the study and the treatment, and no study can promise a benefit. The study team explains the known risks during informed consent, before anyone joins; the record itself does not list them.

Are results available for this study?

This study is still under way. Results are not available in the source record yet.

How can a doctor refer a patient or contact the research team?

The record lists no contact. The official record names the sponsor, who can be asked directly. AzTrials does not enrol participants or take referrals.

What does the primary outcome measure?

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 study lists 2 primary outcomes in all. It is what the study was designed to measure, not a finding.

Where can I read the original study information?

On the ClinicalTrials.gov record for NCT06810076, which also lists 6 linked publications.

Does a listing mean the treatment is approved or proven to work?

No. A listing means the study is registered. It does not show that the treatment is safe, works, or is approved by a regulator.

How often is this information updated?

AzTrials copies changed records from ClinicalTrials.gov every night and reloads the whole registry every week. The sponsor last updated this record on 13 Apr 2026; AzTrials last refreshed its copy on 28 Oct 2026.

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