Using Explainable AI Risk Predictions to Nudge Influenza Vaccine Uptake

Sponsor

National Bureau of Economic Research, Inc. (Other)

Overall Status

Active, not recruiting

CT.gov ID

NCT05009251

Collaborator

Geisinger Clinic (Other), Massachusetts Institute of Technology (Other), National Institute on Aging (NIA) (NIH)

45,061

Enrollment

Location

Arms

10.7

Anticipated Duration (Months)

4220.1

Patients Per Site Per Month

Study Details

Study Description

Brief Summary

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.

Condition or Disease	Intervention/Treatment	Phase
Influenza Vaccination Health Promotion Health Behavior Risk Reduction	Behavioral: Reminder Behavioral: Risk reduction Behavioral: Medical records-based recommendation Behavioral: Algorithm-based recommendation	N/A

Detailed 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.

Study Design

Study Type:

Interventional

Actual Enrollment :

45061 participants

Allocation:

Randomized

Intervention Model:

Parallel Assignment

Intervention Model Description:

Patients from the high-risk sample will be randomly assigned to one of five arms: 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 ArmPatients from the high-risk sample will be randomly assigned to one of five arms: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

Masking:

Single (Care Provider)

Masking Description:

Providers who prescribe vaccination and diagnose conditions will not be randomized to study arms or informed of patient assignment. Although patients will not be explicitly informed which arm they have been randomized to, they will be aware of the messages they receive.

Primary Purpose:

Prevention

Official Title:

Using Explainable AI Risk Predictions to Nudge Influenza Vaccine Uptake

Actual Study Start Date :

Sep 9, 2021

Actual Primary Completion Date :

Nov 5, 2021

Anticipated Study Completion Date :

Jul 31, 2022

Arms and Interventions

Arm	Intervention/Treatment
No Intervention: 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.
Experimental: 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.	Behavioral: Reminder Mailed letter, short message service (SMS) text, and/or patient portal message
Experimental: 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.	Behavioral: Reminder Mailed letter, short message service (SMS) text, and/or patient portal message Behavioral: Risk reduction Mailed letter, SMS, and/or patient portal message
Experimental: 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.	Behavioral: Reminder Mailed letter, short message service (SMS) text, and/or patient portal message Behavioral: Risk reduction Mailed letter, SMS, and/or patient portal message Behavioral: Medical records-based recommendation Mailed letter, SMS, and/or patient portal message Other Names: Credibility
Experimental: 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.	Behavioral: Reminder Mailed letter, short message service (SMS) text, and/or patient portal message Behavioral: Risk reduction Mailed letter, SMS, and/or patient portal message Behavioral: Medical records-based recommendation Mailed letter, SMS, and/or patient portal message Other Names: Credibility Behavioral: Algorithm-based recommendation Mailed letter, SMS, and/or patient portal message Other Names: Credibility

Outcome Measures

Primary Outcome Measures

Flu vaccination at 2 weeks after final outreach date [Within 2 weeks of the final outreach date, 57 days (8.14 weeks) after the study start]
Received a flu vaccination

Secondary Outcome Measures

Flu vaccination at 9 weeks after final outreach date [Within 9 weeks of the final outreach date, 106 days (15.14 weeks) after the study start]
Received a flu vaccination
Flu diagnosis [8 months (between September 9, 2021 and April 30, 2022)]
Received a "high confidence flu" diagnosis (with positive polymerase chain reaction [PCR]/antigen/molecular test) and/or "likely flu" diagnosis (as assessed via International Classification of Disease [ICD] codes or Tamiflu administration or positive PCR/antigen/molecular test) Note that "likely flu" is a superset of the "high confidence flu" diagnoses.
Flu complications [11 months (between September 9, 2021 and July 31, 2022)]
Diagnosed with flu-related complications
Healthcare utilization [11 months (between September 9, 2021 and July 31, 2022)]
Visited ER, was hospitalized, or had flu-related insurance claims

Eligibility Criteria

Criteria

Ages Eligible for Study:

18 Years and Older

Sexes Eligible for Study:

All

Accepts 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)