Study Results
Outcome measurements, participant flow, baseline characteristics, and adverse events have been published for this study.
View full resultsBasic Information
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COMPLETED
NA
80452 participants
INTERVENTIONAL
2022-09-01
2023-04-01
Brief Summary
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Detailed Description
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Geisinger partnered with Medial EarlySign (Medial) to develop a machine learning (ML) algorithm to help identify people at risk for serious flu-associated complications based on existing electronic health record data. Eligible at-risk patients will be randomized to an active control group (clinician will be shown a standard flu alert) or one of two experimental groups (clinician will be shown an alert indicating patient's high risk, with or without describing the patient's factors contributing to that risk).
Conditions
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Keywords
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Study Design
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RANDOMIZED
PARALLEL
PREVENTION
SINGLE
Study Groups
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Standard Alert
Standard flu alert
Alert
Non-interruptive best practice alert in the electronic health record
High-risk Alert
Flu alert that indicates patient is at high risk for flu and its complications
Alert
Non-interruptive best practice alert in the electronic health record
Salient alert features
Larger alert header and body font size, use of different font colors and boldface
High-risk Text
Alert header indicates patient is at high risk for flu and its complications; alert body indicates the percentage of risk (e.g., in the top 3% of risk)
High-risk Alert with Risk Factors
Flu alert that indicates patient is at high risk for flu and its complications and presents the factors contributing to this high risk
Alert
Non-interruptive best practice alert in the electronic health record
Salient alert features
Larger alert header and body font size, use of different font colors and boldface
High-risk Text
Alert header indicates patient is at high risk for flu and its complications; alert body indicates the percentage of risk (e.g., in the top 3% of risk)
Risk factors
Alert body indicates the top 3 factors contributing to the high risk
Interventions
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Alert
Non-interruptive best practice alert in the electronic health record
Salient alert features
Larger alert header and body font size, use of different font colors and boldface
High-risk Text
Alert header indicates patient is at high risk for flu and its complications; alert body indicates the percentage of risk (e.g., in the top 3% of risk)
Risk factors
Alert body indicates the top 3 factors contributing to the high risk
Eligibility Criteria
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Inclusion Criteria
* Have been determined to be in the top 20% of risk through Medial's ML algorithm
* Attend an appointment where the flu alert fires (Geisinger sets when flu alerts start and end--between \~9/1/2022 and \~4/30/2023, as well as the trigger conditions for the alert, which includes valid departments and visits and excludes contraindications like Guillain-Barre syndrome)
* Any Geisinger clinician who sees patient-participants in our study for an appointment where their flu shot alert fires
18 Years
ALL
No
Sponsors
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Massachusetts Institute of Technology
OTHER
National Institute on Aging (NIA)
NIH
National Bureau of Economic Research, Inc.
OTHER
Geisinger Clinic
OTHER
Responsible Party
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Christopher F Chabris, PhD
Professor
Principal Investigators
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Christopher F Chabris, PhD
Role: PRINCIPAL_INVESTIGATOR
Geisinger Clinic
Locations
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Geisinger Clinic
Danville, Pennsylvania, United States
Countries
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Provided Documents
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Document Type: Study Protocol
Document Type: Statistical Analysis Plan
Other Identifiers
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2022-0502
Identifier Type: -
Identifier Source: org_study_id