Using Explainable AI Risk Predictions to Nudge Influenza Vaccine Uptake

National Bureau of Economic Research, Inc.

Status: Completed

Conditions: Health Promotion; Influenza; Vaccination; Risk Reduction; Health Behavior

Treatments: Behavioral: Reminder; Behavioral: Algorithm-based recommendation; Behavioral: Medical records-based recommendation; Behavioral: Risk reduction

Study type: Interventional

Funder types: Other; NIH

Identifiers

NCT05009251

2021-0483

P30AG034532 (U.S. NIH Grant/Contract)

Details and patient eligibility

About

The study team previously demonstrated that patients are more likely to receive flu vaccine after learning that they are at high risk for flu complications. Building on this past work, the present study will explore whether providing reasons that patients are considered high risk for flu complications (a) further increases the likelihood they will receive flu vaccine and (b) decreases the likelihood that they receive diagnoses of flu and/or flu-like symptoms in the ensuing flu season. It will also examine whether informing patients that their high-risk status was determined by analyzing their medical records or by an artificial intelligence (AI) / machine-learning (ML) algorithm analyzing their medical records will affect the likelihood of receiving the flu vaccine or diagnoses of flu and/or flu-like symptoms.

Full description

Geisinger has partnered with Medial EarlySign and developed an ML algorithm to identify patients at risk for serious (moderate to severe) flu-associated complications on the basis of their existing electronic health record (EHR) data. Geisinger will apply this algorithm to current patients during the 2021-22 flu season.

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.

Enrollment: 45,061 patients

Sex: All

Ages: 18+ years old

Volunteers: No Healthy Volunteers

Inclusion criteria

• Aged 18 or older
• 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 contraindications for flu vaccination
• Has opted out of receiving communications from Geisinger via all of the modalities being tested

Trial design

Primary purpose: Prevention

Allocation: Randomized

Interventional model: Parallel Assignment

Masking: Single Blind

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45,061 participants in 5 patient groups

No-Contact Control

No Intervention group

Description:

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.

Reminder Control

Experimental group

Description:

Subjects in the reminder control arm will receive messages reminding them to get the flu shot without being advised of their risk status.

Treatment:

Behavioral: Reminder

High Risk Only

Experimental group

Description:

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.

Treatment:

Behavioral: Risk reduction

Behavioral: Reminder

High Risk with Explanation Based on Medical Records

Experimental group

Description:

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.

Treatment:

Behavioral: Risk reduction

Behavioral: Medical records-based recommendation

Behavioral: Reminder

High Risk with Explanation Based on Algorithm

Experimental group

Description:

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.

Treatment:

Behavioral: Risk reduction

Behavioral: Medical records-based recommendation

Behavioral: Algorithm-based recommendation

Behavioral: Reminder