Clinical trial NCT06806163: Machine-Learning Prediction and Reducing Overdoses With EHR Nudges
Official title: Machine-Learning Prediction and Reducing Overdoses With EHR Nudges
Study type: INTERVENTIONAL
Overall status: RECRUITING
Phase: NA
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
Opioids
Lead sponsor: University of Pittsburgh
Sponsor class: OTHER
Collaborators: name: National Institute on Drug Abuse (NIDA)
First posted: 2025-02-03
Enrollment: 1350
Sex: ALL
Minimum age (years): 18
Standard ages: ADULT
OLDER_ADULT
Accepts healthy volunteers: no
Allocation: RANDOMIZED
Intervention model: PARALLEL
Intervention 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.
Primary purpose: HEALTH_SERVICES_RESEARCH

Brief 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.

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.

Eligibility criteria: 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:

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

Primary outcomes:
measure: Prescribing Practices Composite Score
timeFrame: 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.)
Secondary outcomes:
measure: Active Naloxone Prescription
timeFrame: Assessed at 6 months after study enrollment by reviewing data from the 12 months preceding the index date (i.e., at 6 months after enrollment). An active naloxone prescription is recorded if one exists at any point during the year before the index date.
measure: Average Daily Opioid Dosage > 50 MME
timeFrame: Assessed at 6 months after study enrollment, based on the average daily MME calculated over the 7 days preceding the index date (i.e., 6 months after enrollment).
measure: Overlapping Opioid Benzodiazepine Prescriptions
timeFrame: Assessed at six months after study enrollment, based on overlap occurring on the index date (i.e., 6 months after enrollment) or within the 28 days preceding the index date.
measure: Overlapping Opioid Benzodiazepine Prescriptions Where Average Daily Opioid MME > 50
timeFrame: Assessed at six months after study enrollment, based on overlap occurring on the index date (i.e., 6 months after enrollment) or within the 28 days preceding the index date.
measure: Emergency Department or Inpatient Visits
timeFrame: Assessed at six months after study enrollment, based on visits occurring within the 30 days prior to the index date (i.e., 6 months after enrollment).
measure: Emergency Department or Inpatient Visits for Overdose
timeFrame: Assessed at six months after study enrollment, based on visits occurring within the 30 days prior to the index date (i.e., 6 months after enrollment).
Other outcomes:
Locations:
city: Pittsburgh
country: United States
facility: University of Pittsburgh
state: Pennsylvania
Study officials:
affiliation: University of Pittsburgh Center for Pharmaceutical Policy and Prescribing
name: Walid F Gellad, MD, MPH
role: PRINCIPAL_INVESTIGATOR
References:
citation: 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.
citation: 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.
citation: 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.
citation: 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.
citation: 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.
citation: 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.
citation: 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.
citation: 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.
citation: 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.