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Active, not recruitingNCT06806163mPROVENUpdated Oct 6, 2026

Machine-Learning Prediction and Reducing Overdoses With EHR Nudges

An interventional study of EHR-Embedded Elevated-Risk Flag and EHR-Embedded Elevated-Risk Flag with Behavioral Nudges in Opioid Overdose, Opioid Use and Opioid Use Disorder, 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 2026-10-06.

Sponsored by University of Pittsburgh · Not applicable, Interventional, and Health services research

Updated Oct 6, 2026Now Active, not recruitingPrimary completion moved+1 moreGo to Updates ↓
Phase
Not applicable
Study type
Interventional
Enrollment
1,350
Allocation
Randomized
Ages
18 Years and older
Sex
All
01

Study summary

The goal of this cluster randomized clinical trial is to test a clinician-targeted behavioral nudge intervention in the Electronic Health Record (EHR) for patients who are identified by a machine-learning based risk prediction model as having an elevated risk for an opioid overdose.

The clinical trial will evaluate the effectiveness of providing a flag in the EHR to identify individuals at elevated risk with and without behavioral nudges/best practice alerts (BPAs) as compared to usual care by primary care clinicians.

The primary goals of the study are to improve opioid prescribing safety and reduce overdose risk.

Read the detailed description

In response to the opioid overdose crisis, health systems have instituted multiple interventions to reduce patient risk, including decreasing unsafe opioid prescribing among high-risk patients and dispensing naloxone. However, these interventions face two key challenges. First, there are limited and poorly performing tools to identify who is truly at risk of overdose, leading to burdensome interventions targeting an overly broad population or missing key high-risk individuals. Second, even with more accurate identification of high-risk patients, highly effective strategies to change clinician behavior remain limited. Common cognitive biases may underlie clinicians' lack of response to risk factors for overdose.

This project aims to address both of these limitations by combining more accurate risk prediction tools to identify those at elevated risk of opioid overdose with novel "nudge" interventions based on principles of behavioral economics that have been shown to address cognitive biases and change prescribing behavior. The primary hypothesis is that high-risk patients in primary care practices randomized to the elevated-risk flag + nudge intervention will have safer prescribing compared to usual care.

02

Conditions studied

  • Opioid Overdose
  • Opioid Use
  • Opioid Use Disorder
  • Opioids

Keywords

  • Machine learning
  • Risk prediction
  • Behavioral nudge
  • Best Practice Alert
  • Artificial intelligence
  • Primary care
  • Electronic Health Record Platforms
  • Opioid Overdose
  • Opioids
03

In context

Opiate Overdose

70 studies on the registry are indexed under Opiate Overdose; 11 are open to participants now.

This study's planned enrollment of 1,350 is above the median of 184 across 48 interventional studies indexed under Opiate Overdose.

Browse Opiate Overdose studies →

Lead sponsor

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.

04

Who can participate

Ages eligible
18 Years and older
Sexes eligible
All
Accepts healthy volunteers
No

Go through the criteria one by one — yes, no or not sure — and take the list to your doctor or the study team. Kept in this browser only.

Inclusion criteria

  • Received an opioid prescription within the past year
  • Age 18 years or older at the time of the opioid prescription
  • At least one visit to an internal medicine or family care practice within the past year

Exclusion criteria

Exclusion Criteria:

  • Diagnosis of malignant cancer within the past year
  • Enrollment in hospice care
05

Study design

Phase
Not applicable
Primary purpose
Health services research
Allocation
Randomized
Intervention model
Parallel assignment
Masking
Single (Investigator)
Enrollment
1,350 participants (estimated)

Study arms

  • Active comparator
    Usual Care

    Patients in the practices randomized to the Usual Care arm will receive standard care practice without change.

    Behavioral: Usual Care

  • Experimental
    EHR-Embedded Elevated-Risk Flag

    An elevated-risk flag will be embedded in the EHR and prominently displayed in the chart during encounters for patients identified as having elevated-risk for opioid overdose.

    Behavioral: EHR-Embedded Elevated-Risk Flag

  • Experimental
    EHR-Embedded Elevated-Risk Flag with Behavioral Nudges

    An elevated-risk flag will be embedded in the EHR and prominently displayed in the chart during encounters for patients identified as having elevated-risk for opioid overdose. This flag will be combined with a set of best practice alerts/behavioral nudges that will trigger when certain conditions are met during encounters with elevated-risk patients.

    Behavioral: EHR-Embedded Elevated-Risk Flag with Behavioral Nudges

Interventions

  • BehavioralEHR-Embedded Elevated-Risk Flag

    Clinicians seeing patients at elevated predicted risk will see a flag on the EHR 'storyboard' during in person or telephone encounters indicating the patient is at elevated predicted risk of opioid overdose. The clinician will have the option of including this information into their decision-making process when providing care. There will be no best practice alerts/behavioral nudges in this arm.

  • BehavioralEHR-Embedded Elevated-Risk Flag with Behavioral Nudges

    Clinicians seeing patients at elevated predicted risk for opioid overdose will see a flag on the EHR storyboard indicating that the patient is at elevated predicted risk. Clinicians will also receive up to 4 best practice alerts/behavioral nudges during an in-person or telephone primary care encounter with elevated risk patients when certain requirements are met: 1) if the patient does not have an active naloxone prescription on their medication list, the clinicians will receive an active choice alert during any medication ordering to encourage naloxone prescription; 2) if the patient's opioid dosage is \>50 MME, OR they are ordered a new opioid prescription, OR they have an overlapping opioid and benzodiazepine prescription order, the clinicians will receive an accountable justification alert when the relevant order is entered.

    Also known as: EHR-Embedded Elevated-Risk Flag with Best Practice Alerts (BPAs)

  • BehavioralUsual Care

    Patients in the practices randomized to the Usual Care arm will receive standard care practice without change.

06

What researchers measure

Primary outcomes

  1. Prescribing Practices Composite Score

    This outcome measures a composite of three prescribing practices associated with a reduced risk of opioid overdose. Each of the three is assigned a value of one point, with the composite score ranging from 0 to 3: 1. Naloxone prescription: Evidence of a prescription for naloxone. 2. Opioid dosage \< 50 morphine milligram equivalents (MME) per day: No prescriptions exceeding 50 MME/day during the measurement period. 3. No opioid and benzodiazepine overlap: No concurrent prescriptions for opioids and benzodiazepines. The composite score will be treated as a 3-point ordinal measure reflecting adherence to these prescribing practices.

    Time frame: Assessed at 4 months following study enrollment (i.e., at 4 months after the first encounter in the study period. An encounter refers to the 1st primary care visit for a patient enrolled in the study.)

Secondary outcomes

  1. Prescribing Practices Composite Score--6 Month Measure

    This outcome measures a composite of three prescribing practices associated with a reduced risk of opioid overdose. Each of the three is assigned a value of one point, with the composite score ranging from 0 to 3: 1. Naloxone prescription: Evidence of a prescription for naloxone. 2. Opioid dosage \< 50 morphine milligram equivalents (MME) per day: No prescriptions exceeding 50 MME/day during the measurement period. 3. No opioid and benzodiazepine overlap: No concurrent prescriptions for opioids and benzodiazepines. The composite score will be treated as a 3-point ordinal measure reflecting adherence to these prescribing practices.

    Time frame: Assessed at 6 months following study enrollment (i.e., at 6 months after the first encounter in the study period. An encounter refers to the 1st primary care visit for a patient enrolled in the study.)

  2. Active Naloxone Prescription

    The presence of an active naloxone prescription, determined by evidence of a naloxone order, a naloxone fill, or both.

    Time frame: Assessed at 4 & 6 months after study enrollment by reviewing data from 12 months preceding index date (i.e., at 4 & 6 months after enrollment). An active naloxone prescription is recorded if one exists at any point during the year before the index date.

  3. Average Daily Opioid Dosage > 50 MME

    The average daily opioid dosage, calculated in morphine milligram equivalents (MME), is evaluated using prescription fills data as the primary source. If fills data are unavailable, prescription orders data will be used. This measure reflects whether the average daily MME exceeds 50 during the specified time period.

    Time frame: Assessed at 4 and 6 months after study enrollment, based on the average daily MME calculated over the 7 days preceding the index date (i.e., 4 and 6 months after enrollment).

  4. Overlapping Opioid Benzodiazepine Prescriptions

    This outcome evaluates overlapping opioid and benzodiazepine use, determined by the following criteria: (A) Active overlap on the index date: Both an opioid and a benzodiazepine prescription fill are active on the index date. (B) Historical overlap: At least one opioid and one benzodiazepine prescription order within the past 28 days. Overlap is defined as meeting any of the above.

    Time frame: Assessed at 4 and 6 months after study enrollment, based on overlap occurring on the index date (i.e., 4 and 6 months after enrollment) or within the 28 days preceding the index date.

  5. Overlapping Opioid Benzodiazepine Prescriptions Where Average Daily Opioid MME > 50

    This outcome evaluates the presence of overlapping opioid and benzodiazepine use where opioid MME is \> 50, defined as above.

    Time frame: Assessed at 4 and 6 months after study enrollment, based on overlap occurring on the index date (i.e., 4 and 6 months after enrollment) or within the 28 days preceding the index date.

  6. Emergency Department or Inpatient Visits

    This outcome measures the occurrence of any emergency department (ED) or inpatient visit during the specified time period.

    Time frame: Assessed at 4 and 6 months after study enrollment, based on visits occurring within the 30 days prior to the index date (i.e., 4 and 6 months after enrollment).

  7. Emergency Department or Inpatient Visits for Overdose

    This outcome measures the occurrence of any emergency department (ED) or inpatient visit specifically attributed to an overdose during the specified time period.

    Time frame: Assessed at 4 and 6 months after study enrollment, based on visits occurring within the 30 days prior to the index date (i.e., 4 and 6 months after enrollment).

07

Study locations

1 site
08

References and documents

Publications

  • 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 ↗
  • 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, Donohue JM, Hulsey EG, Barnes S, Li Y, Kuza CC, Yang Q, Buchanich J, Huang JL, Mair C, Wilson DL, Gellad WF. Integrating human services and criminal justice data with claims data to predict risk of opioid overdose among Medicaid beneficiaries: A machine-learning approach. PLoS One. 2021 Mar 18;16(3):e0248360. doi: 10.1371/journal.pone.0248360. eCollection 2021. PubMed 33735222 ↗
  • 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 ↗
  • Guo J, Gellad WF, Yang Q, Weiss JC, Donohue JM, Cochran G, Gordon AJ, Malone DC, Kwoh CK, Kuza CC, Wilson DL, Lo-Ciganic WH. Changes in predicted opioid overdose risk over time in a state Medicaid program: a group-based trajectory modeling analysis. Addiction. 2022 Aug;117(8):2254-2263. doi: 10.1111/add.15878. Epub 2022 Apr 3. PubMed 35315173 ↗
  • Hulsey E, Hershey TB, Parker LS, Kuza C, Fedro-Byrom S, Gellad WF. Overdose Risk Prediction Algorithms: The Need for a Comprehensive Legal Framework. Health Affairs Forefront. 2022 November 22. doi: 10.1377/forefront.20221118.549875.
  • Gellad WF, Yang Q, Adamson KM, Kuza CC, Buchanich JM, Bolton AL, Murzynski SM, Goetz CT, Washington T, Lann MF, Chang CH, Suda KJ, Tang L. Development and validation of an overdose risk prediction tool using prescription drug monitoring program data. Drug Alcohol Depend. 2023 May 1;246:109856. doi: 10.1016/j.drugalcdep.2023.109856. Epub 2023 Mar 27. PubMed 37001323 ↗
  • 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 ↗
  • 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 ↗
  • Gellad WF, Chen YF, Park TW, Yang Q, Arnold JD, Kuza CC, Fedro-Byrom SN, Diiulio J, Militello LG, Whitlock M, Sadhu EM, Visweswaran S, Fine MJ, Abebe KZ, Suda KJ, Lo-Ciganic WH. Machine Learning Prediction and Reducing Overdoses With Electronic Health Record Nudges (mPROVEN) in the Primary Care Setting: Protocol for a Cluster Randomized Controlled Trial. JMIR Res Protoc. 2026 May 4;15:e94007. doi: 10.2196/94007. PubMed 42081274 ↗

Individual participant data

Plan to share: Undecided

09

Timeline

1 registry update since Sep 25, 2026
ElapsedEstimatedEstimated, not yet reachedDate before it movedWhere the date was before it movedSimilar studies: half by the dot; the band is the middle halfHalf of similar studies reached it by the dot; the band is the middle half, from a quarter to three quarters

Ran from Mar 2025 to primary completion in May 2026, 14 months.

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 14 months is below that.

Since we started tracking on Sep 25, 2026, the dates moved once, on Oct 6, 2026: primary completion Oct 2026 → May 2026 and completion Feb 2027 → May 2027.

Earlier changes are on the record history on ClinicalTrials.gov ↗

How these figures are worked out, and which studies are left out of them.

Registered
Jan 31, 2025
First posted
Feb 3, 2025
StartActual
Mar 10, 2025
Primary completionActual
May 31, 2026was Oct 2026
CompletionEst.
May 2027was Feb 2027
Last update
Oct 6, 2026
What these dates mean
Start.
When the first participant enrolled; while estimated, when the study expects to open to recruitment.
Primary completion.
When the last participant was examined or treated to collect data for the study's main question, its primary outcome. A study that stopped early gives the date its data collection stopped.
Completion.
When the last participant's last visit took place, for every outcome, secondary ones and side effects included: the end of the study. Often later than primary completion, sometimes the same day.
Actual and Est.
Actual means the registry says the date has happened. Est. (estimated) is a date the sponsor plans or expects. It can still move, and the sponsor is expected to change it to actual once it has passed.
Status
Recruiting→Active, not recruiting
changed Oct 6, 2026
Primary completion
Oct 2026→May 31, 2026 (actual)
Oct 6, 2026
Study completion
Feb 2027→May 2027
Oct 6, 2026
Show all 1 update
  1. Oct 6, 2026
    Recruiting→Active, not recruiting
    Primary completion Oct 2026→May 31, 2026 (now actual)
    Study completion Feb 2027→May 2027
    + 3 other changes: verification date, contact details and site details

From the registry record's own update history. This site started tracking changes on Sep 25, 2026; for anything earlier, see the record history on ClinicalTrials.gov ↗

10

Registry details

Key details

Study ID
NCT06806163
Responsible party
Walid Gellad (Professor of Medicine, University of Pittsburgh) — Principal investigator
First posted
Feb 3, 2025
Start date
Mar 10, 2025
Primary completion
May 31, 2026
Completion
May 2027 (estimated)
Last update
Oct 6, 2026

Study contacts

Walid F Gellad, MD, MPH
principal investigator · University of Pittsburgh Center for Pharmaceutical Policy and Prescribing

Oversight

Data monitoring committee
Yes
FDA-regulated drug
No
FDA-regulated device
No
View the source record on ClinicalTrials.gov ↗

Not currently enrolling

This study is active, not recruiting, as verified in Oct 2026. You cannot join it, but the record below documents what was studied.

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