Clinical trial NCT05009251: Using Explainable AI Risk Predictions to Nudge Influenza Vaccine Uptake
Official title: Using Explainable AI Risk Predictions to Nudge Influenza Vaccine Uptake
Study type: INTERVENTIONAL
Overall status: COMPLETED
Phase: NA
Conditions: Influenza
Vaccination
Health Promotion
Health Behavior
Risk Reduction
Keywords: Flu Vaccine
Choice Architecture
Machine Learning
Perceived Credibility
Personalized Risk Factors
Flu Complications
Lead sponsor: National Bureau of Economic Research, Inc.
Sponsor class: OTHER
Collaborators: name: Geisinger Clinic
name: Massachusetts Institute of Technology
name: National Institute on Aging (NIA)
First posted: 2021-08-17
Enrollment: 45061
Sex: ALL
Minimum age (years): 18
Standard ages: ADULT
OLDER_ADULT
Accepts healthy volunteers: no
Allocation: RANDOMIZED
Intervention model: PARALLEL
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 Arm
Primary purpose: PREVENTION

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.

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.

Eligibility criteria: 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

Primary outcomes:
measure: Flu Vaccination at 2 Weeks After Final Outreach Date
timeFrame: Within 2 weeks of the final outreach date, 57 days (8.14 weeks) after the study start
Secondary outcomes:
measure: Flu Vaccination at 9 Weeks After Final Outreach Date
timeFrame: Within 9 weeks of the final outreach date, 106 days (15.14 weeks) after the study start
measure: Flu Diagnosis
timeFrame: 8 months (between September 9, 2021 and April 30, 2022)
measure: Flu Complications
timeFrame: 11 months (between September 9, 2021 and July 31, 2022)
measure: Healthcare Utilization
timeFrame: 11 months (between September 9, 2021 and July 31, 2022)
Other outcomes:
measure: Flu Vaccination at 2 Weeks After Final Outreach Date by Gender
timeFrame: Within 2 weeks of the final outreach date, 57 days (8.14 weeks) after the study start
measure: Flu Vaccination at 2 Weeks After Final Outreach Date by Race
timeFrame: Within 2 weeks of the final outreach date, 57 days (8.14 weeks) after the study start
measure: Flu Vaccination at 2 Weeks After Final Outreach Date by Ethnicity
timeFrame: Within 2 weeks of the final outreach date, 57 days (8.14 weeks) after the study start
Locations:
city: Danville
country: United States
facility: Geisinger Clinic
state: Pennsylvania
Study officials:
affiliation: Geisinger Clinic
name: Michelle N Meyer, PhD JD
role: PRINCIPAL_INVESTIGATOR
affiliation: Geisinger Clinic
name: Christopher F Chabris, PhD
role: PRINCIPAL_INVESTIGATOR
References: