From Transactional EHRs to Longitudinal Twin Infrastructure

Electronic Health Records preserve transactional clinical history, including encounters, diagnoses, labs, medications, procedures, and notes. However, EHRs do not inherently preserve the longitudinal interpretive model that experienced clinicians develop across years of observing patients, families, adherence patterns, referrals, outcomes, and local population context. This paper proposes Digital Twin for Health infrastructure as a complementary modeling layer that transforms transactional history into cohort-aware, calibrated, longitudinal Twin state.

We introduce a DT4H architecture in which EHR-derived history, clinician mental models, cohort context, reference-human priors, and outcome feedback are used to initialize and recalibrate individualized Twins. StateK computes current state, confidence, trajectory, and transition readiness, while SETPOINT provides bounded execution loops through protocols, practices, check-ins, and outcomes. The system is explicitly framed as infrastructure support rather than diagnostic authority.

The proposed validation pathway focuses on runtime observability, signal freshness, confidence evolution, calibration stability, workflow fit, and clinician-guided review. This approach may preserve longitudinal context during provider transitions, specialty referrals, and care-continuity gaps while maintaining clear clinical governance boundaries.