Opioid OverdosemPROVENNCT06806163
Machine-Learning Prediction and Reducing Overdoses With EHR Nudges
A study testing EHR-Embedded Elevated-Risk Flag and EHR-Embedded Elevated-Risk Flag with Behavioral Nudges in people with Opioid Overdose and Opioid Use, run by University of Pittsburgh at 1 site in United States.
Trial 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. Read more about this study
- Who is it for?
- Ages 18 years and older
- Conditions the study is for
- Opioid Overdose, Opioid Use, Opioid Use Disorder
- What is being tested?
- EHR-Embedded Elevated-Risk Flag and EHR-Embedded Elevated-Risk Flag with Behavioral Nudges
- Type of treatment
- Lifestyle or therapy programme
- Could I receive a placebo?
- No, everyone receives an active treatment
- How many people
- 1,350 participants
- Where
- 1 siteUnited States
- Study dates
- 10 Mar 2025to May 2027
- 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
- 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
Who cannot join
- Diagnosis of malignant cancer within the past year
- Enrollment in hospice care
Trial locations and contacts
1 site in 1 country.
United States1 site
University of Pittsburgh
Pittsburgh, Pennsylvania, 15213
About this study
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.
- Conditions
- Opioid Overdose, Opioid Use, Opioid Use Disorder, Opioids
- Being tested
- EHR-Embedded Elevated-Risk Flag, EHR-Embedded Elevated-Risk Flag with Behavioral Nudges, Usual Care
- Keywords
- Machine learning, Risk prediction, Behavioral nudge, Best Practice Alert, Artificial intelligence, Primary care, Electronic Health Record Platforms, Opioid Overdose, Opioids
- Medical subject headings
- Opiate Overdose, Opioid-Related Disorders
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
EHR-Embedded Elevated-Risk FlagBehavioural
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.
EHR-Embedded Elevated-Risk Flag with Behavioral NudgesBehavioural
Also called EHR-Embedded Elevated-Risk Flag with Best Practice Alerts (BPAs)
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.
Usual CareBehavioural
Patients in the practices randomized to the Usual Care arm will receive standard care practice without change.
| Study group | Role | What they receive |
|---|---|---|
| Usual Care | Active comparator | Patients in the practices randomized to the Usual Care arm will receive standard care practice without change. Receives: Behavioral: Usual Care |
| EHR-Embedded Elevated-Risk Flag | Experimental | 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. Receives: Behavioral: EHR-Embedded Elevated-Risk Flag |
| EHR-Embedded Elevated-Risk Flag with Behavioral Nudges | Experimental | 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. Receives: Behavioral: EHR-Embedded Elevated-Risk Flag with Behavioral Nudges |
What is being measured
The primary outcome is the main question the study is designed to answer.
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:
- Naloxone prescription: Evidence of a prescription for naloxone.
- Opioid dosage < 50 morphine milligram equivalents (MME) per day: No prescriptions exceeding 50 MME/day during the measurement period.
- 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.
Measured: 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 (7)
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.
Measured: 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.)
Active Naloxone Prescription
The presence of an active naloxone prescription, determined by evidence of a naloxone order, a naloxone fill, or both.
Measured: 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.
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.
Measured: 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).
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.
Measured: 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.
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.
Measured: 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.
Emergency Department or Inpatient Visits
This outcome measures the occurrence of any emergency department (ED) or inpatient visit during the specified time period.
Measured: 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).
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.
Measured: 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).
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted3 Feb 2025
- Study start10 Mar 2025
- Primary completion31 May 2026
- Study completionMay 2027 (estimated)
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Publications (10)Papers the sponsor has linked to this study.
- Other trials for Opioid OverdoseStudies of the same condition, open and under way.
- More trials run by University of PittsburghThe sponsor's other registered studies.
- What the trial phases meanHow a treatment moves from first tests in people to approval.
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
- Randomised
- Intervention model
- Parallel groups
- Model description
- This interventional study will be a cluster randomized trial across UPMC primary care practices. Practices will be randomized into one of 3 clinician-targeted intervention groups: 1. Usual care 2. Elevated-risk flag only 3. Elevated-risk flag combined with behavioral nudge alerts. The electronic health record (EHR) based intervention will be applied in the participating practices for clinicians whose patients have been identified as elevated-risk through the risk prediction algorithm.
- Masking
- Single blind
- Who is masked
- Investigator
- Enrolment
- 1,350 participants (estimated)
Sponsor and investigators
- Lead sponsor
- University of Pittsburgh
- Sponsor type
- University, hospital or other
- Collaborators
- National Institute on Drug Abuse (NIDA)
- Responsible party
- Principal investigator, Walid Gellad, University of Pittsburgh
- Investigators
- Walid F Gellad, MD, MPH, Principal investigator, University of Pittsburgh Center for Pharmaceutical Policy and Prescribing
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT06806163
- Sponsor's protocol ID
- STUDY22040068
- Other IDs
- R01DA044985-04
- Acronym
- mPROVEN
- Status last verified
- Oct 2026
- First submitted
- 31 Jan 2025
- Last updated
- 6 Oct 2026
- Results posted
- No
- Data monitoring committee
- Yes
- FDA-regulated drug
- No
- FDA-regulated device
- No
- Individual data sharing
- Undecided
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 30901048Lo-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 32678860Lo-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 33735222Lo-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 35623798Guo 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 35315173Hulsey 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 37001323Nguyen 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 39420438Militello 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 39569464Gellad 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
Study at a glance
From the registry record, last updated by the sponsor on 6 Oct 2026. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Machine-Learning Prediction and Reducing Overdoses With EHR Nudges
- ClinicalTrials.gov ID
- NCT06806163
- Acronym / sponsor ID
- mPROVEN / STUDY22040068
- Conditions
- 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
- Study type
- Interventional
- Recruitment status
- Active, not recruiting
- Enrolment
- 1,350 participants (estimated)
- Lead sponsor
- University of PittsburghUniversity, hospital or other
- Collaborators
- National Institute on Drug Abuse (NIDA)
- Study start
- 10 Mar 2025 (actual)
- Primary completion
- 31 May 2026 (actual)
- Study completion
- May 2027 (estimated)
- First posted
- 3 Feb 2025 (actual)
- Results
- Not posted in the source record
Clinical summary
The research question as the sponsor registered it. This describes what the study plans to test, not what it found.
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.
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.
Study design
- Primary purpose
- Health services research
- Allocation
- Randomised
- Intervention model
- Parallel groups
- Masking
- Single blind (investigator)
- Number of arms
- 3
- Model description
- This interventional study will be a cluster randomized trial across UPMC primary care practices. Practices will be randomized into one of 3 clinician-targeted intervention groups: 1. Usual care 2. Elevated-risk flag only 3. Elevated-risk flag combined with behavioral nudge alerts. The electronic health record (EHR) based intervention will be applied in the participating practices for clinicians whose patients have been identified as elevated-risk through the risk prediction algorithm.
| Arm | Type | Receives |
|---|---|---|
| Usual Care | Active comparator | Behavioral: Usual CarePatients in the practices randomized to the Usual Care arm will receive standard care practice without change. |
| EHR-Embedded Elevated-Risk Flag | Experimental | Behavioral: EHR-Embedded Elevated-Risk FlagAn 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. |
| EHR-Embedded Elevated-Risk Flag with Behavioral Nudges | Experimental | Behavioral: EHR-Embedded Elevated-Risk Flag with Behavioral NudgesAn 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. |
Who may qualify
- Ages
- 18 years and older
- Sex
- All
- Healthy volunteers
- Not accepted
- Age groups
- Adult, Older adult
Key inclusion criteria (3 listed)
- 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
Key exclusion criteria (2 listed)
- Diagnosis of malignant cancer within the past year
- Enrollment in hospice care
Eligibility is decided by the study team after screening; this overview does not establish that a patient qualifies. Full inclusion and exclusion criteria.
Interventions
| Intervention | Type | Arms |
|---|---|---|
| EHR-Embedded Elevated-Risk FlagClinicians 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. | Behavioural | EHR-Embedded Elevated-Risk Flag |
| EHR-Embedded Elevated-Risk Flag with Behavioral NudgesAlso: EHR-Embedded Elevated-Risk Flag with Best Practice Alerts (BPAs)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. | Behavioural | EHR-Embedded Elevated-Risk Flag with Behavioral Nudges |
| Usual CarePatients in the practices randomized to the Usual Care arm will receive standard care practice without change. | Behavioural | Usual Care |
Primary outcome
Prescribing Practices Composite Score
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.)
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.
7 secondary outcomes
Prescribing Practices Composite Score--6 Month Measure
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.)
Active Naloxone Prescription
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.
Average Daily Opioid Dosage > 50 MME
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).
Overlapping Opioid Benzodiazepine Prescriptions
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.
Overlapping Opioid Benzodiazepine Prescriptions Where Average Daily Opioid MME > 50
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.
Emergency Department or Inpatient Visits
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).
Emergency Department or Inpatient Visits for Overdose
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).
Results and safety
This study is still under way. Results are not available in the source record yet.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 6 Oct 2026.
- Background: PubMed 30901048Lo-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.
- Background: PubMed 32678860Lo-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.
- Background: PubMed 33735222Lo-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.
- Background: PubMed 35623798Lo-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.
- Background: PubMed 35315173Guo 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.
- BackgroundHulsey 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.
- Background: PubMed 37001323Gellad 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.
- Background: PubMed 39420438Nguyen 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.
- Background: PubMed 39569464Militello 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.
- Derived from this study: PubMed 42081274Gellad 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.
- Center for Pharmaceutical Policy \& Prescribing websiteLink provided by the sponsor.
- Individual participant dataSharing planned: Undecided
Related trials for Opioid Overdose
Enrolling by invitation
PATH to Reducing Burnout Among Peers Who Deliver Overdose Prevention Services (R33 Phase Clinical Trial)
Burnout, Secondary Trauma
Active, not recruiting
Improving Access to Naloxone to Prevent Opioid Overdose Deaths (SAIA-Naloxone)
Overdose, Implementation as Usual
Recruiting
Improving Jail Response to the Overdose Crisis: A Hybrid Trial of Jail ECHO to Improve MOUD Implementation and Public Health Outcomes
Opioid Abuse (Disorder)
Not yet recruiting
Remote vs. In-Person Study Evaluation (RISE) Trials: RISE Above Opioid Overdose (Trial 3 of 3)
Remote vs. In-Person, Remote Visits
Not yet recruiting
Remote Intervention to Prevent Overdose
Accidental Overdose of Opiate
Referral and contact
AzTrials does not enrol participants or take referrals. Contact the study team or a site listed below; they decide who can take part. This study is not recruiting at the moment.
Walid F Gellad, MD, MPH principal investigator
University of Pittsburgh Center for Pharmaceutical Policy and Prescribing
1 site in 1 country: see every site and its status.
Questions about this trial
What is this clinical trial studying?
A study testing EHR-Embedded Elevated-Risk Flag and EHR-Embedded Elevated-Risk Flag with Behavioral Nudges in people with Opioid Overdose and Opioid Use, 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 3 inclusion and 2 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 6 Oct 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?
Prescribing Practices Composite Score (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.)). 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 NCT06806163, which also lists 10 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 6 Oct 2026.