Rule-Based High-Risk Flu

August 28, 2026 updated by: Gail Rosenbaum, Geisinger Clinic

Encouraging Flu Vaccination Among High-Risk Patients Identified by a Rule-Based Additive Risk Index

Previous work by the study team has shown that informing patients of their high risk of flu and flu-related complications increases their likelihood of getting a flu shot (Rosenbaum et al., 2026). In this work, an artificial intelligence (AI)-based algorithm determined which patients were at high risk for flu. This year, the team will test whether risk messages remain effective when a non-algorithmic rule-based additive risk index (McGovern et al., 2024) is used to identify patients at high risk.

Study Overview

Status

Not yet recruiting

Conditions

Intervention / Treatment

Detailed Description

While most individuals recover from influenza without complications, certain populations-including older adults and those with underlying medical conditions-are at increased risk for severe outcomes such as pneumonia, other respiratory complications, and death. Identifying these high-risk patients is therefore important for targeting outreach and improving vaccination uptake.

Over the past three influenza seasons, Geisinger has sent as standard of care messages to patients identified as high risk for flu and flu-related complications, informing them of their risk and encouraging vaccination. These messages were implemented following a series of four randomized controlled trials conducted by the study team, which demonstrated that such messages increased influenza vaccination rates (Rosenbaum et al., 2026).

Previously, high-risk patients were identified using an AI-based model. Patients were considered high risk if they were in the top 15% of risk among Geisinger patients eligible for scoring by the model. Due to logistical and resource constraints, this model is no longer available for use. As a result, an alternative, scalable approach is needed to identify high-risk patients for upcoming influenza seasons.

Recent evidence suggests that a simple count of Center for Disease Control (CDC)-defined influenza risk factors is predictive of severe influenza outcomes (McGovern et al., 2024). This approach constructs a rule-based additive risk index by summing the number of CDC-defined high-risk conditions present in each patient's electronic health record. Results indicate that this simple index is highly informative for identifying patients at increased risk of influenza-related complications.

This study will evaluate whether a rule-based additive risk index derived from Electronic Health Record (EHR) data can be used to identify patients at high risk for influenza and influenza-related complications and support targeted outreach. Specifically, the study will assess whether informing patients with four or more CDC risk factors (representing approximately the top 15% of the distribution of scores for eligible patients) of their elevated risk increases influenza vaccination uptake.

Of the 51218 patients who met inclusion criteria, 8750 (17.1%) were randomized to the control group to allow for 80% power to detect a 1.4 percentage-point difference in vaccination rate between control and high-risk outreach groups, with a baseline of 23% and 2-tailed p<.05.

Outreach in the high-risk outreach group will be completed by the Marketing team, using filters for each study modality (mailed letter, patient portal message, text message) including filters for patient contact preferences for patients in the outreach group. In addition to a letter if eligible, patients will be sent either a portal message or a text message (not both) based on their preferences. Marketing cannot apply the same filters to patients in the control group, so to keep analyses unbiased, the primary analyses will be intent-to-treat.

If possible, the study team will attempt to rebuild the filters applied by the Marketing team, and apply them consistently to both high-risk outreach and control groups for a focused exploratory analysis among patients eligible for the messages.

Study Type

Interventional

Enrollment (Estimated)

51215

Phase

  • Not Applicable

Contacts and Locations

This section provides the contact details for those conducting the study, and information on where this study is being conducted.

Study Contact

Study Locations

  • United States
    • Pennsylvania
      • Danville, Pennsylvania, United States, 17822
        • Geisinger Health

Participation Criteria

Researchers look for people who fit a certain description, called eligibility criteria. Some examples of these criteria are a person's general health condition or prior treatments.

Eligibility Criteria

Ages Eligible for Study

  • Adult
  • Older Adult

Accepts Healthy Volunteers

No

Description

Inclusion Criteria:

  • Age 18 or older
  • Has four or more CDC risk factors for influenza reflected in EHR data
  • Has a primary care physician employed by Geisinger
  • Attended at least one appointment at Geisinger between 7/17/2024 and 7/16/2026
  • Attended at least one primary care appointment at Geisinger between 10/1/2008 and 7/16/2026

Exclusion criterion:

- Attended an appointment at Community Care between 9/1/2024 and 7/16/2026

Study Plan

This section provides details of the study plan, including how the study is designed and what the study is measuring.

How is the study designed?

Design Details

  • Primary Purpose: Prevention
  • Allocation: Randomized
  • Interventional Model: Parallel Assignment
  • Masking: None (Open Label)

Arms and Interventions

Participant Group / Arm
Intervention / Treatment
No Intervention: Control
This group will not be sent high-risk flu shot messages, but will be sent normal system messages about flu shots.
Experimental: High-risk outreach
This group will be sent messages informing them of their high risk for flu and flu-related complications, in addition to the normal system messages sent to the control group.
Behavioral: Risk reduction
Mailed letter, patient portal message and/or text message

What is the study measuring?

Primary Outcome Measures

Outcome Measure
Measure Description
Time Frame
Flu vaccination (y/n)
Time Frame: In the 29 days following the date the first messages were sent
Flu vaccine documented in the electronic health record
In the 29 days following the date the first messages were sent

Collaborators and Investigators

This is where you will find people and organizations involved with this study.

Sponsor

Geisinger Clinic

Study record dates

These dates track the progress of study record and summary results submissions to ClinicalTrials.gov. Study records and reported results are reviewed by the National Library of Medicine (NLM) to make sure they meet specific quality control standards before being posted on the public website.

Study Major Dates

Study Start (Estimated)

September 1, 2026

Primary Completion (Estimated)

September 30, 2026

Study Completion (Estimated)

September 30, 2026

Study Registration Dates

First Submitted

August 28, 2026

First Submitted That Met QC Criteria

August 28, 2026

First Posted (Actual)

September 2, 2026

Study Record Updates

Last Update Posted (Actual)

September 2, 2026

Last Update Submitted That Met QC Criteria

August 28, 2026

Last Verified

August 1, 2026

More Information

Terms related to this study

Keywords

Additional Relevant MeSH Terms

Other Study ID Numbers

  • 2026-0673

Plan for Individual participant data (IPD)

Plan to Share Individual Participant Data (IPD)?

NO

Drug and device information, study documents

Studies a U.S. FDA-regulated drug product

No

Studies a U.S. FDA-regulated device product

No

This information was retrieved directly from the website clinicaltrials.gov without any changes. If you have any requests to change, remove or update your study details, please contact register@clinicaltrials.gov. As soon as a change is implemented on clinicaltrials.gov, this will be updated automatically on our website as well.

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