Validation Roadmap

DT4H validation progresses from infrastructure QA to longitudinal adaptive evidence.

The roadmap separates infrastructure stability, workflow pilots, runtime validation, clinician review, longitudinal evidence, and future research-grade publication.

Validation maturity roadmap#

Current phasePilot validation
Runtime focusConfidence + drift
Operational layerSoCal + Warangal
Future targetResearch publication
01 · Infrastructure QATechnical stability

Validate routes, runtime pages, metadata integrity, responsive behavior, docs workspace stability, and explainability surfaces.

02 · Workflow pilotsOperational fit

Use SoCal and Warangal workflows to test usability, clinician fit, data continuity, and operational boundaries.

03 · Runtime validationAdaptive continuity

Track signal freshness, Twin confidence, recalibration behavior, StateK transitions, and SETPOINT execution continuity.

04 · Clinician reviewInterpretation alignment

Compare modeled runtime state against clinician longitudinal interpretation and observational continuity.

05 · Research packagingPublication readiness

Prepare reproducibility evidence, assumptions, limitations, methodology artifacts, and future study design packages.

Validation boundaries#

Infrastructure QATechnical readiness

Confirms site, APIs, navigation, metadata integrity, mobile behavior, and runtime explainability stability.

Pilot workflowsOperational evidence

Confirms longitudinal usability, clinician fit, data continuity, and workflow sustainability.

Research validationEvidence development

Confirms reproducibility, bounded assumptions, confidence behavior, and methodology transparency.

Clinical governanceBounded interpretation

Diagnosis and treatment remain clinician-governed and outside autonomous DT4H outputs.

Longitudinal validation direction#

01

Twin continuity studies

Evaluate whether Twin trajectories remain longitudinally stable across changing evidence conditions.

02

ArenaK behavioral validation

Measure repeatability and resilience interpretation under adaptive gameplay conditions.

03

Reference-human alignment

Study how cohort priors and personal runtime evidence converge over time.

04

SETPOINT execution outcomes

Evaluate whether adaptive execution loops improve continuity and adherence behavior.

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