Machine-Learning Prediction and Reducing Overdoses with EHR Nudges
- Conditions
- Opioid OverdoseOpioid UseOpioid Use DisorderOpioids
- Registration Number
- NCT06806163
- Lead Sponsor
- University of Pittsburgh
- 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.
Recruitment & Eligibility
- Status
- RECRUITING
- Sex
- All
- Target Recruitment
- 1350
- 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
- Diagnosis of malignant cancer within the past year
- Enrollment in hospice care
Study & Design
- Study Type
- INTERVENTIONAL
- Study Design
- PARALLEL
- Primary Outcome Measures
Name Time Method Prescribing Practices Composite Score 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.) 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.
- Secondary Outcome Measures
Name Time Method Emergency Department or Inpatient Visits 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). This outcome measures the occurrence of any emergency department (ED) or inpatient visit during the specified time period.
Overlapping Opioid Benzodiazepine Prescriptions Where Average Daily Opioid MME > 50 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. This outcome evaluates the presence of overlapping opioid and benzodiazepine use where opioid MME is \> 50, defined as above.
Active Naloxone Prescription 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. The presence of an active naloxone prescription, determined by evidence of a naloxone order, a naloxone fill, or both.
Average Daily Opioid Dosage > 50 MME 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). 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.
Overlapping Opioid Benzodiazepine Prescriptions 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. 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.Emergency Department or Inpatient Visits for Overdose 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). This outcome measures the occurrence of any emergency department (ED) or inpatient visit specifically attributed to an overdose during the specified time period.
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Trial Locations
- Locations (1)
University of Pittsburgh
🇺🇸Pittsburgh, Pennsylvania, United States