Health Ahead Comparative Effectiveness Study
Health Ahead: Sequential Comparative-Effectiveness Studies Toward Automated, Universally Deployable Preventive Health Screening
William Brandenburg, MD
1,000,000 participants
Jun 9, 2026
INTERVENTIONAL
Conditions
Summary
The Health Ahead Comparative Effectiveness Study is a pragmatic, parallel-arm interventional platform that systematically compares successive changes to preventive health screening on the path toward a fully automated screening system deployable in any environment, including the most isolated and resource-limited communities. Each change is isolated as a single variable against current practice, and every comparison is evaluated with a common set of engagement, behavior-change, experience, cost, and longitudinal outcome measures, so results accumulate on a consistent yardstick across the life of the platform. The first comparison evaluates AI-assisted versus human-only interpretation ("reads") of screening data. Subsequent pre-planned comparisons, added by protocol amendment, evaluate static versus interactive personalized report delivery; mobile community versus fixed laboratory screening; and a hybrid medical-droid-plus-human delivery model versus human-only screening.
Eligibility
Plain Language Summary
Simplified for easier understanding
This summary was AI-generated to explain the trial in plain language. It is not medical advice. Always discuss eligibility with your doctor before enrolling in a clinical trial.
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Interventions
An AI-generated read of the acquired screening battery is produced and presented to the physician of record as a clinical aid. All models used are third-party models, executed unmodified on Longevity Metrics clinical infrastructure. The physician reviews each read, records a structured judgment before sign-off, and then produces and signs the released interpretation, which may adopt, modify, or reject any read.
All acquired screening data, images, tracings, and laboratory results are interpreted by the physician of record and qualified clinical staff under physician supervision, by standard practice and without AI decision support. This is the current standard of care and the active comparator.
Locations(1)
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NCT07669168