Planning AI requires a continuously updated model of the individual.
Without that model, care planning remains episodic. Decisions may still be informed by data, but they are made without a coherent, living representation of the person whose care is being planned.
This is the role of the Digital Twin for Health™.
The Digital Twin for Health™ (DT4H™) is the continuously evolving representation of an individual’s health state that enables individualized daily care planning.
It is not simply a record of observations. It is not a dashboard, a chart, or a repository of measurements. It is a model that maintains the current state of the individual over time.
That distinction matters.
A record tells us what has been captured. A model helps determine what it means now. The Digital Twin for Health™ brings together signals from across daily life—physiological readings, symptoms, behaviors, adherence, and context—and interprets them as part of an evolving whole.
In this way, the individual is not represented as a series of disconnected data points, but as a continuously changing state.
This state can remain stable. It can improve. It can begin to drift. The role of the Digital Twin for Health™ is to maintain that evolving picture in a form that can be used for planning.
That is what makes individualized daily care possible.
Daily care planning depends on knowing what matters now for this person, under these conditions, at this moment in time. It requires more than general protocols and more than isolated measurements. It requires an understanding of the individual’s current state and direction of change.
The Digital Twin for Health™ provides that foundation.
Planning AI uses the current health state maintained by the twin to determine what actions should be taken today. It identifies what requires reinforcement, what may need adjustment, and what can remain unchanged. In this way, daily care planning becomes responsive to the individual, rather than merely scheduled around them.
As new information arrives, the twin is updated. As the twin is updated, the plan can change.
This creates a different relationship between observation and action. Data does not simply accumulate. It becomes part of an ongoing process of interpretation and guidance.
The Digital Twin for Health™ is therefore not an abstract technical layer. It is the representation that makes continuity actionable. It is what allows Planning AI to move from general recommendations to individualized daily care.
Without a continuously evolving model of the individual, planning remains incomplete.
With it, care can be guided minute by minute, in alignment with the person’s actual state rather than an outdated snapshot.