Visual Explainer

Runtime intelligence evolves through signals, confidence, execution, and recalibration.

A visual explanation of how DT4H adaptive runtime flows through StateK, SETPOINT, outcomes, and confidence evolution.

Runtime flow #

SIGNALSInput streams

EHR · wearables · ArenaK · clinician · practices

→

CONFIDENCECalibration quality

Signal reliability · maturity · uncertainty

→

STATEKAdaptive state

Readiness · transition · drift · trajectory

→

SETPOINTExecution runtime

Protocols · practices · check-ins · support

→

OUTCOMESObserved response

Recovery · adherence · improvement · fatigue

↺

RECALIBRATIONAdaptive learning

Confidence evolution · Twin refinement · trajectory update

Runtime properties #

Continuous adaptation

Runtime evolves as new evidence and outcomes arrive.

Confidence visibility

Uncertainty and calibration quality remain observable.

Execution-aware learning

SETPOINT outcomes become evidence for Twin recalibration.

Longitudinal continuity

Adaptive state persists across time rather than isolated visits.

LayerArchitecture Layer

StatusActive Draft

SystemDT4H / StateK / SETPOINT

BoundaryInfrastructure, not diagnosis

System lineageDT4H→Twin→StateK→SETPOINT→Outcomes→Recalibration

Infrastructure boundaryDT4H models cohorts, Twins, calibration, and runtime state. It does not diagnose, prescribe, or replace licensed clinical judgment.

Document statusInfrastructure draft

Last updatedMay 2026

Applies toDT4H.ai / AvatarK.ai ecosystem