A model is only useful if it changes how decisions are made.
The Digital Twin for Health™ does not simply store information about the individual. It continuously incorporates new signals and updates the person’s current health state. That state is then used by Planning AI to guide what should happen next.
This is how continuity becomes operational.
When a new signal enters the twin, the individual’s state is updated. That signal may be physiological, behavioral, or contextual. A blood pressure reading may begin to drift upward. A medication may be missed. Sleep may become irregular. Symptoms may emerge or self-monitoring may stop.
None of these signals is meaningful in isolation. What matters is how each one changes the current state of the individual.
A missed reading may not require action if the person is otherwise stable. The same missed reading may matter a great deal if it appears alongside reduced adherence, worsening symptoms, or a pattern of recent instability. The twin maintains that context.
Each time the state changes, Planning AI is invoked.
Planning AI evaluates whether the current plan remains appropriate in light of the updated state. It determines whether stability can be maintained with the existing plan, whether reinforcement is needed, or whether the plan itself should change. If a new plan is to be considered, a proposed plan is written back into the Digital Twin for Health™.
This matters because the plan is not separate from the state of the individual. The current plan is part of the person’s active condition. Once approved by clinicians, the new plan becomes part of the twin’s representation of what is true now.
That update then triggers action.
The action may take different forms. It may be a prompt to the member. It may be a reminder to monitor. It may be a self-recovery instruction, an educational message, or a task for the care team. In some cases it may escalate to clinical review. In others, it may simply reinforce a behavior that is already working.
What matters is that action follows from planning, and planning follows from state.
This creates a continuous cycle. The twin updates state. State invokes planning. Planning updates the plan. The updated plan triggers action. Action produces new signals, and the cycle continues.
It continues until no further action is required.
This is not a one-time decision model. It is a system of continuous adjustment, where the individual’s current state and current plan remain aligned over time.
The Digital Twin for Health™ and Planning AI therefore do not operate as separate layers. They function as a coupled system. The twin maintains the current representation of the individual. Planning AI interprets that representation and recommends what should happen next. Each continuously updates the other.
That is what allows daily care to be individualized in practice.
Care is no longer a sequence of disconnected decisions. It becomes a continuous cycle of state, planning, and action.