Pilot Validation

DT4H pilots validate infrastructure behavior before clinical claims.

Pilot validation focuses on longitudinal modeling quality, cohort-aware inference, calibration behavior, runtime observability, and SETPOINT feedback loops.

Validation flow #

01Observe

Collect bounded longitudinal signals and workflow context.

02Model

Initialize cohorts, reference humans, Twins, and calibration state.

03Validate

Review confidence, drift, reproducibility, and runtime behavior.

04Recalibrate

Use outcomes and feedback to refine the model.

Pilot metrics #

01

Signal freshness

How current and complete the runtime evidence is.

02

Twin confidence

How mature the individualized model becomes over time.

03

Calibration stability

Whether recalibration improves state quality without false precision.

04

Reference drift

Whether cohort/reference priors remain appropriate.

05

Outcome feedback

Whether SETPOINT outcomes improve model refinement.

06

Governance safety

Whether outputs remain bounded and non-diagnostic.

Pilot boundaries #

Validation Scope

What pilots can validate

  • Runtime observability.
  • Cohort-aware initialization.
  • Twin confidence progression.
  • Calibration and drift behavior.
  • SETPOINT feedback loop quality.
Boundary

What pilots do not claim

  • Not diagnostic validation.
  • Not treatment efficacy claims.
  • Not autonomous clinical authority.
  • Not regulated deployment approval.
  • Not replacement for clinician judgment.
LayerGovernance Framework
StatusActive Draft
SystemDT4H / StateK / SETPOINT
BoundaryInfrastructure, not diagnosis
System lineageDT4HTwinStateKSETPOINTOutcomesRecalibration
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