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

Sponsor
Geisinger Clinic (Other)
Overall Status
Not yet recruiting
CT.gov ID
NCT05509283
Collaborator
National Bureau of Economic Research, Inc. (Other), Massachusetts Institute of Technology (Other)
52,000
5
30

Study Details

Study Description

Brief Summary

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.

Condition or Disease Intervention/Treatment Phase
  • Behavioral: Risk Reduction
N/A

Detailed 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.

Study Design

Study Type:
Interventional
Anticipated Enrollment :
52000 participants
Allocation:
Randomized
Intervention Model:
Parallel Assignment
Masking:
None (Open Label)
Masking Description:
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:
Nudging Flu Vaccination in Patients at Moderately High Risk for Flu and Flu-related Complications
Anticipated Study Start Date :
Sep 1, 2022
Anticipated Primary Completion Date :
Oct 1, 2022
Anticipated Study Completion Date :
Oct 1, 2022

Arms and Interventions

Arm Intervention/Treatment
No Intervention: Passive control

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.

Experimental: Active control

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

Behavioral: Risk Reduction
Letter, patient portal, SMS and/or another modality

Experimental: High risk only

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.

Behavioral: Risk Reduction
Letter, patient portal, SMS and/or another modality

Experimental: Risk based on medical records

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.

Behavioral: Risk Reduction
Letter, patient portal, SMS and/or another modality

Experimental: High risk based on algorithm

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.

Behavioral: Risk Reduction
Letter, patient portal, SMS and/or another modality

Outcome Measures

Primary Outcome Measures

  1. Flu vaccination [Within 2 weeks of the final message send date; patients will be monitored for approximately 6 weeks]

    Received a flu vaccination within 2 weeks of the final message send date

Other Outcome Measures

  1. High confidence flu diagnosis [Up to 8 months]

    Patient received a flu diagnosis via a positive polymerase chain reaction (PCR)/antigen/molecular test (yes/no) during the 2022-23 flu season (from the first message send date through April 30, 2023).

  2. "Likely flu" diagnosis [Up to 8 months]

    Received a "high confidence flu" diagnosis (with positive 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) (yes/no) during the 2022-23 flu season (from the first message send date through April 30, 2023). Note that "likely flu" is a superset of the "high confidence flu" diagnoses.

  3. Flu complications [Up to 11 months]

    Diagnosed with flu-related complications (yes/no) from the first message send date through July 31, 2023.

  4. ER visits [Up to 11 months]

    Number of ER visits from the first message send date through July 31, 2023.

  5. Hospitalizations [Up to 11 months]

    Number of hospitalizations from the first message send date through July 31, 2023.

  6. COVID-19 vaccination rates [Up to 8 months]

    Received at least one COVID-19 vaccination (yes/no) during the 2022-23 flu season (from the first message send date through April 30, 2023).

Eligibility Criteria

Criteria

Ages Eligible for Study:
18 Years and Older
Sexes Eligible for Study:
All
Accepts Healthy Volunteers:
No
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)

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

Contacts and Locations

Locations

No locations specified.

Sponsors and Collaborators

  • Geisinger Clinic
  • National Bureau of Economic Research, Inc.
  • Massachusetts Institute of Technology

Investigators

  • Principal Investigator: Christopher Chabris, PhD, Geisinger Clinic

Study Documents (Full-Text)

None provided.

More Information

Publications

None provided.
Responsible Party:
Christopher F Chabris, PhD, Professor, Geisinger Clinic
ClinicalTrials.gov Identifier:
NCT05509283
Other Study ID Numbers:
  • 2022-0410
First Posted:
Aug 22, 2022
Last Update Posted:
Aug 22, 2022
Last Verified:
Aug 1, 2022
Individual Participant Data (IPD) Sharing Statement:
Yes
Plan to Share IPD:
Yes
Studies a U.S. FDA-regulated Drug Product:
No
Studies a U.S. FDA-regulated Device Product:
No
Keywords provided by Christopher F Chabris, PhD, Professor, Geisinger Clinic

Study Results

No Results Posted as of Aug 22, 2022