Machine-Learning Prediction and Reducing Overdoses With EHR Nudges
NCT ID: NCT06806163
Last Updated: 2025-10-10
Study Results
The study team has not published outcome measurements, participant flow, or safety data for this trial yet. Check back later for updates.
Basic Information
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RECRUITING
NA
1350 participants
INTERVENTIONAL
2025-03-10
2026-03-31
Brief Summary
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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.
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Detailed Description
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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
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Study Design
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RANDOMIZED
PARALLEL
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.
HEALTH_SERVICES_RESEARCH
SINGLE
Study Groups
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Usual Care
Patients in the practices randomized to the Usual Care arm will receive standard care practice without change.
Usual Care
Patients in the practices randomized to the Usual Care arm will receive standard care practice without change.
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.
EHR-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.
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.
EHR-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.
Interventions
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EHR-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.
EHR-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.
Usual Care
Patients in the practices randomized to the Usual Care arm will receive standard care practice without change.
Other Intervention Names
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Eligibility Criteria
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Inclusion Criteria
* 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
* Enrollment in hospice care
18 Years
ALL
No
Sponsors
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National Institute on Drug Abuse (NIDA)
NIH
University of Pittsburgh
OTHER
Responsible Party
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Walid Gellad
Professor of Medicine
Principal Investigators
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Walid F Gellad, MD, MPH
Role: PRINCIPAL_INVESTIGATOR
University of Pittsburgh Center for Pharmaceutical Policy and Prescribing
Locations
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University of Pittsburgh
Pittsburgh, Pennsylvania, United States
Countries
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Central Contacts
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References
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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.
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.
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.
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.
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.
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.
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.
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.
Related Links
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Center for Pharmaceutical Policy \& Prescribing website
Other Identifiers
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STUDY22040068
Identifier Type: -
Identifier Source: org_study_id
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