Nudging Flu Vaccination in Patients at Moderately High Risk for Flu and Flu-related Complications

Geisinger Health

Status: Completed

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

Treatments: Behavioral: Risk Reduction

Study type: Interventional

Funder types: Other

Identifiers

NCT05509283

2022-0410

Details and patient eligibility

About

This study will test the relative efficacy of high-risk messages in increasing flu shot rates in patients at moderately high risk for flu and complications (those in the top 11-20% of risk). 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 a flu vaccine.

Full description

Almost everyone age 6 months or older can benefit from the vaccine, which can reduce illnesses, missed work, hospitalizations, and death by reducing the likelihood of contracting influenza. Flu shots are particularly important for patients at high risk of experiencing severe outcomes.

In the 2020-21 and 2021-22 flu seasons, the study team sent messages to Geisinger patients in the top 10% of risk for flu and complications according to an artificial intelligence algorithm. Messages that disclosed patients' risk status significantly increased flu vaccination rates. Additionally, messages that included risk information were most effective in patients at relatively lower risk (those in the top 4-10%) compared with those at the highest risk (top 3%).

The present work will test the effectiveness of high-risk messages in patients who are in the top 11-20% of risk, at high risk but lower than previous studies. 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, or (c) the additional explanation that an AI or ML algorithm made this determination.

Enrollment: 40,671 patients

Sex: All

Ages: 18+ years old

Volunteers: No Healthy Volunteers

Inclusion criteria

• Included on a list of active Geisinger patients (all patients on this list attended at least one primary care appointment at Geisinger between 10/1/2008 and 4/13/2022, and either had a Geisinger primary care provider assigned as of April 2022, or were in the Electronic Health Record [EHR] since at least September 2021 and had at least one encounter in 2020-2022)
• Aged 18 or older
• In the top 11-20% of risk for flu and flu complications, according to Medial's flu complications machine learning algorithm (which operates on coded EHR data)
• Has a Geisinger PCP assigned as of August 2022
• Has had an encounter in the last 2 years as of August 2022

Exclusion criteria

• Cannot be contacted via any of the communication modalities (e.g., letter, patient portal, SMS) being used in the study, either due to insufficient/missing contact information in the EHR or because they opted out of all modalities

Trial design

Primary purpose: Prevention

Allocation: Randomized

Interventional model: Parallel Assignment

Masking: Single Blind

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40,671 participants in 5 patient groups

Passive control

No Intervention group

Description:

Patients in the passive 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.

Active control

Experimental group

Description:

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

Treatment:

Behavioral: Risk Reduction

High risk only

Experimental group

Description:

Patients 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

Risk based on medical records

Experimental group

Description:

Patients 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.

Treatment:

Behavioral: Risk Reduction

High risk based on algorithm

Experimental group

Description:

Patients 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.

Treatment:

Behavioral: Risk Reduction