Using Explainable AI Risk Predictions to Nudge Influenza Vaccine Uptake
NCT ID: NCT05009251
Last Updated: 2025-01-03
Study Results
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View full resultsBasic Information
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COMPLETED
NA
45061 participants
INTERVENTIONAL
2021-09-09
2022-07-31
Brief Summary
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Detailed Description
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This study will evaluate the effect of contacting patients identified as high risk with special messages to encourage vaccination. These communications will inform patients they are at high risk with either (a) no additional explanation, (b) an explanation that this determination comes from an analysis of their medical records, along with a short list of the top factors from their medical record that explain their risk, and (c) the additional explanation that an AI or ML algorithm made this determination, along with a short list of the top factors from their medical record that explain their risk.
Included in the study will be current Geisinger patients 18+ years of age with no contraindications for flu vaccine and who have been assessed by the Medial algorithm and assigned a risk score. The primary study outcomes will be the rates of flu vaccination and flu diagnosis during the 2020-21 season by targeted patients.
Conditions
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Study Design
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RANDOMIZED
PARALLEL
* No-Contact Control Arm
* Reminder Control Arm
* High Risk Only Arm
* High Risk with Explanation Based on Medical Records Arm
* High Risk with Explanation Based on Algorithm Arm
PREVENTION
SINGLE
Study Groups
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No-Contact Control
Subjects in the no-contact control arm will receive no additional pro-vaccination intervention beyond the health system's normal efforts. Although some patients are currently targeted for flu vaccination encouragement due to a conventional non-ML assessment that they are at high risk for complications, these patients are not told that they are at high risk or that they have been targeted.
No interventions assigned to this group
Reminder Control
Subjects in the reminder control arm will receive messages reminding them to get the flu shot without being advised of their risk status.
Reminder
Mailed letter, short message service (SMS) text, and/or patient portal message
High Risk Only
Subjects in this treatment arm will receive messages telling them they have been identified to be at high risk for flu complications, without specifying how or why the health system believes this to be the case.
Reminder
Mailed letter, short message service (SMS) text, and/or patient portal message
Risk reduction
Mailed letter, SMS, and/or patient portal message
High Risk with Explanation Based on Medical Records
Subjects in this treatment arm will receive messages telling them they have been identified to be at high risk for flu complications via review of their medical records and will be provided a human-understandable short list of the top factors from their medical record that explain their risk.
Reminder
Mailed letter, short message service (SMS) text, and/or patient portal message
Risk reduction
Mailed letter, SMS, and/or patient portal message
Medical records-based recommendation
Mailed letter, SMS, and/or patient portal message
High Risk with Explanation Based on Algorithm
Subjects in this treatment arm will receive messages telling them they have been identified to be at high risk for flu complications via analysis of their medical records by a computer algorithm and will be provided a human-understandable short list of the top factors from their medical record that explain their risk.
Reminder
Mailed letter, short message service (SMS) text, and/or patient portal message
Risk reduction
Mailed letter, SMS, and/or patient portal message
Medical records-based recommendation
Mailed letter, SMS, and/or patient portal message
Algorithm-based recommendation
Mailed letter, SMS, and/or patient portal message
Interventions
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Reminder
Mailed letter, short message service (SMS) text, and/or patient portal message
Risk reduction
Mailed letter, SMS, and/or patient portal message
Medical records-based recommendation
Mailed letter, SMS, and/or patient portal message
Algorithm-based recommendation
Mailed letter, SMS, and/or patient portal message
Other Intervention Names
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Eligibility Criteria
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Inclusion Criteria
* Current Geisinger patient at the time of study
* Falls in the top 10% of patients at highest risk, as identified by the flu-complication risk scores of machine learning algorithm (which operates on coded EHR data)
Exclusion Criteria
* Has opted out of receiving communications from Geisinger via all of the modalities being tested
18 Years
ALL
No
Sponsors
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Geisinger Clinic
OTHER
Massachusetts Institute of Technology
OTHER
National Institute on Aging (NIA)
NIH
National Bureau of Economic Research, Inc.
OTHER
Responsible Party
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Principal Investigators
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Michelle N Meyer, PhD JD
Role: PRINCIPAL_INVESTIGATOR
Geisinger Clinic
Christopher F Chabris, PhD
Role: PRINCIPAL_INVESTIGATOR
Geisinger Clinic
Locations
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Geisinger Clinic
Danville, Pennsylvania, United States
Countries
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Provided Documents
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Document Type: Study Protocol
Document Type: Statistical Analysis Plan
Other Identifiers
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2021-0483
Identifier Type: -
Identifier Source: org_study_id
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