Applied to patient records, imaging, genetics and real-time biometric data, AI can identify at-risk individuals and trigger early interventions before symptoms escalate.
A vivid example includes how ambient sensing, AI-driven triage and real-time risk analysis can transform the emergency room into a space of high-functioning calm. Notably, these tools reduce moral injury and burnout by taking pressure off human decision processes during critical, high-pressure moments.
Patients with diabetes or heart disease can receive daily nudges and support via digital companions that adjust to their behavior and biometrics. Instead of generic checklists, patients receive what works for them when and where they need it.
Another example that illustrates this concept well is an AI-powered health twin that learns continuously, guides decision-making and adjusts care dynamically. It doesn't replace the physician, it makes care more precise and timelier, especially for complex or chronic cases.
AI only works if it has the right data. Right now, too much healthcare data is trapped in silos.
To deliver proactive care, we need secure, interoperable data systems where information follows patients across settings. That includes longitudinal patient records, consent-driven data sharing, and privacy and compliance baked into every layer.
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