Veritas-RPM: Provenance-Guided Multi-Agent False Positive Suppression for Remote Patient Monitoring
Aswini Misro, Vikash Sharma, Shreyank N Gowda
TL;DR
Veritas-RPM introduces a provenance-guided multi-agent system for reducing false positives in remote patient monitoring, utilizing synthetic data and a layered architecture for improved decision accuracy.
Contribution
The paper proposes a novel multi-agent architecture with provenance guidance and synthetic data generation for false positive suppression in RPM.
Findings
Achieved high true suppression rates in synthetic scenarios.
Effectively routed cases to domain specialists for accurate decisions.
Reduced false escalations and indeterminate outcomes.
Abstract
We present Veritas-RPM, a provenance-guided multi-agent architecture comprising five processing layers: VeritasAgent (ground-truth assembly), SentinelLayer (anomaly detection), DirectorAgent (specialist routing), six domain Specialist Agents, and MetaSentinelAgent (conflict resolution and final decision). We construct a 98-case synthetic taxonomy of false-positive scenarios derived from documented RPM patterns. Synthetic patient epochs (n = 530) were generated directly from taxonomy parameters and processed through the pipeline. Ground-truth labels are known for all cases. Performance is reported as True Suppression Rate (TSR), False Escalation Rate (FER), and Indeterminate Rate (INDR).
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