Problem Framing & Readiness Signal Inventory
Initial problem definition, readiness signal inventory, and data-readiness assessment for military health decision intelligence deployment. Client is a regional veterans health program managing readiness assessment across 12,000 active beneficiaries.
ANONYMIZED · REAL ENGAGEMENT · CROMTEC GROUP
Decision Environment
The program produces approximately 4,800 readiness assessment touchpoints per month across primary care, behavioral health, and occupational medicine. Current assessment methodology relies on self-reported data and episodic clinical encounters — missing the continuous signal streams that predict readiness degradation before it becomes a clinical event. No AI-assisted decision support is currently governed. Three commercial tools are in use with no audit trail, no confidence scoring, and no model versioning.
Readiness Signal Inventory
ATLAS identified six primary signal categories: (1) Biometric monitoring data — 34% capture rate, significant gap. (2) Clinical encounter records — 91% capture rate, structured. (3) Behavioral health screening results — 78% capture rate, partially structured. (4) Environmental exposure history — 18% capture rate, critical gap for ERI/HEHI modeling. (5) Physical fitness assessment records — 88% capture rate, structured. (6) Medication and treatment history — 82% capture rate, structured. The environmental exposure gap is the highest-priority remediation — it is required for NASA ERI/HEHI methodology application.
Governance Gap Analysis
Two P0 governance gaps identified: First, the three commercial AI tools in use produce no audit trail — outputs cannot be traced to the model version, input data, or confidence level that generated them. This creates liability exposure and prevents quality improvement. Second, no escalation protocol exists for AI outputs that contradict clinical judgment — there is no documented chain of authority for resolving AI-human disagreements. Both gaps must be closed before ATLAS deployment.
ATLAS Configuration Recommendation
Phase 1 deployment: ATLAS-M Readiness Intelligence Module targeting primary care and behavioral health workflows. NASA ERI/HEHI methodology applied to available environmental and biometric data. Governance layer: sealed receipts on all readiness assessments, confidence threshold at 0.75, mandatory human review for any output in the Yellow or Red tier. Environmental data enrichment program to begin in parallel — target 60% capture rate within 6 months.
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