Modern medicine has traditionally relied on linear cause-and-effect models: a symptom appears, a diagnosis follows, and treatment is applied. This approach has saved countless lives and it took a short time between “cause” and “disease.”
Prevention in those “acute diseases” was often easier—a vaccine. But when it comes to prevention of chronic diseases, it is increasingly insufficient.
While modern medicine has extended lifespan, it has been less successful at extending healthspan, by leaving out preventive measures. Medical science has not done as well with either treatment or prevention for “chronic” diseases with long durations of onset, multiple interacting causes, and which requires following patients' lived experience for decades to learn who benefits the most from life style changes or combinations of medications or novel vaccines added to already approved cures. Building a combination “prevention prescription” is at the heart of concierge longevity.
Today’s technologies offer new opportunities for prevention by identifying subtle, multifactorial signals and helping doctors and patients sort out personalized preventive pathways. Evity thinks we can make these “prevention prescriptions" to increase years of healthy lives not just for those who can pay for concierge programs, but for everyone.
Research in aging biology and network medicine shows that health and disease arise from systems of interaction rather than single variables. Molecular pathways, organ systems, and external exposures influence one another dynamically across the life course.2, 3
Large-scale longitudinal studies now demonstrate that:
Yet most clinical workflows are not designed to integrate these signals. Clinicians face fragmented data, evolving evidence, and limited time. As a result, preventive decision-making often relies on simplified risk scores or population averages that obscure individual trajectories.
Systems-level analyses have shown that modest changes across multiple domains—such as physical activity, metabolic health, inflammation, and sleep—can interact to significantly alter long-term cardiovascular and metabolic risk.5
Environmental exposures further compound biological vulnerability. Long-term exposure to fine particulate air pollution (PM2.5), for example, is associated with increased cardiovascular and all-cause mortality, particularly when layered onto existing metabolic or inflammatory risk.6
These relationships are rarely obvious in isolation. They become visible only when examined together, over time.
Artificial intelligence enables the analysis of complex, multivariate data at a scale no individual clinician could manage alone. Properly designed systems can surface non-obvious correlations, track changes over time, and connect evolving research to individual patient contexts.
As Dr. Larry Brilliant emphasizes:
“It seems like every day we learn of life saving and healthspan extending plans that were discovered by accident. Combining immunotherapy for cancer with certain vaccinations doubles healthy life expectancy for example. These combinations and correlations may have remained “not obvious” to humans, but they are often "obvious" to artificial intelligence systems. Artificial intelligence gives us the ability to see patterns in health and disease that no human could ever detect alone. But its true value lies in how it supports human judgment, compassion, and shared decision-making.”
Importantly, this is not about automating care. It is about revealing information that enables better human decisions, earlier.
Correlation alone does not improve outcomes. Translation matters.
Preventive insights must be:
When correlations are surfaced responsibly, they allow clinicians and patients to align around earlier, more personalized interventions, shifting care upstream—before decline becomes irreversible.
The future of prevention is not prediction for its own sake. It is earlier understanding, grounded in evidence, that enables more timely and meaningful care.
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