AI-Powered Identity Resolution: Privacy-Safe Member Matching in Healthcare Cloud MDM

AI Builders Global Conference 2026

Date to be announced · Time to be announced

Healthcare payer organizations are increasingly turning to cloud and AI to reconcile member identities across enrollment, claims, pharmacy, and care management systems. Identity resolution errors create asymmetric risk: false negatives fragment member histories, while false positives can expose protected health information and trigger HIPAA privacy incidents. This session introduces the Probabilistic Sentinel System (PSS), an AI-driven scoring architecture for privacy-safe member identity resolution, grounded in the Fellegi-Sunter record linkage framework and extended through differential attribute weighting, a negative SSN conflict penalty, ROC-calibrated thresholds, sensitive-condition override logic, and auditable lifecycle governance suited to cloud-native MDM platforms. Attendees will walk through the PSS scoring model, threshold routing design, audit controls, and statistical validation methodology, including precision, recall, F1 score, ROC-AUC, subgroup fairness analysis, and drift monitoring. The talk reframes identity resolution from a technical data quality task into a governed, cloud-scalable AI decision framework where every match is traceable, reviewable, and calibrated against organizational risk tolerance, delivering measurable gains in accuracy and reduced privacy exposure. Designed for healthcare data leaders, cloud and AI architects, and compliance professionals, this session offers a practical, defensible approach to identity resolution balancing automation with regulatory accountability, aligned with modernizing healthcare through digital health, cloud, and AI.

Session status: scheduled

Track: AI Industry Applications & Vertical Solutions

Level: All Levels

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