N/A N=117,649 Randomized Double-blind Prevention

Encouraging Flu Vaccination Among High-Risk Patients Identified by ML

Influenza · Vaccination · Health Promotion · Health Behavior · Risk Reduction

Bottom Line

View on ClinicalTrials.gov: NCT04323137 ↗

Enrolled (actual)

117,649

Serious AEs

—

Results posted

Nov 2022

Primary outcome: Primary: Flu Vaccination Rate — 4901; 5042; 5087; 5090 Participants — p=0.0035

Study Design & Population

Study type: Interventional
Phase: N/A
Interventions: Risk reduction (Behavioral); Medical records-based recommendation (Behavioral); Algorithm-based recommendation (Behavioral)
Age: Pediatric, Adult, Older Adult · 17+ yrs
Sex: All
Sponsor: Geisinger Clinic
Primary completion: May 2021

Outcome Measures

Outcome	Result	p-value
PRIMARY Flu Vaccination Rate	4901; 5042; 5087; 5090	0.0035 sig
PRIMARY Flu Vaccination Rate by Risk Level	1485; 1536; 1513; 1537; 1658; 1749	.181
PRIMARY High Confidence Flu Diagnosis Rate	—	—
SECONDARY "Likely Flu" Diagnosis Rate	—	—
SECONDARY Flu Complications Rate	—	—
SECONDARY Change in ER Visits From Pre- to Post-intervention	—	—
SECONDARY Change in Hospitalizations From Pre- to Post-intervention	—	—
SECONDARY Flu Vaccination Among Fellow Household Members	2136; 2207; 2165; 2175	—
SECONDARY High Confidence Flu Diagnosis Among Fellow Household Members	—	—
SECONDARY "Likely Flu" Diagnosis Among Fellow Household Members	—	—
SECONDARY Flu Complications Among Fellow Household Members	—	—
SECONDARY Flu Vaccination Among Those at Sub-threshold Risk	18268	—
SECONDARY High Confidence Flu Diagnosis Among Those at Sub-threshold Risk	—	—
SECONDARY "Likely Flu" Diagnosis Among Those at Sub-threshold Risk	—	—
SECONDARY Flu Complications Among Those at Sub-threshold Risk	—	—

Summary

The purpose of the current study is to test different interventions to determine the most effective way to promote flu vaccine uptake in a high-risk population identified by an "artificial intelligence" (AI) or machine learning (ML) algorithm. The specific aims are: 1. Evaluate the effect on flu vaccination rates of informing health-system patients who are identified by an ML analysis of EHR data to be at high risk for flu complications that 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, and (c) the additional explanation that an AI or ML algorithm made this determination. 2. Evaluate the effects of the same three interventions on diagnoses of flu in the same patients.

Eligibility Criteria

Inclusion Criteria

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 Medial's machine learning algorithm (which operates on coded EHR data)
May limit inclusion to patients that are under Geisinger primary care, depending on algorithm performance of patients who have non-Geisinger PCPs

Exclusion Criteria

Has contraindications for flu vaccination
Has opted out of receiving communications from Geisinger via all of the modalities being tested

View full record on ClinicalTrials.gov →

Data sourced from ClinicalTrials.gov (NCT04323137). Outcome figures and adverse-event rates are extracted automatically from the registry's posted results and are provided for clinician reference, not as a substitute for the primary publication.