AI-Powered Identity Resolution: Privacy-Safe Member Matching in Healthcare Cloud MDM
About this session
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.
Speaker
Key takeaways
- Match errors aren't symmetric risks. A missed match fragments a member's record and hurts operations; a false match can leak PHI and trigger a HIPAA incident. Treating both as equally bad misses the real cost structure — the scoring model has to weight them differently.
- Statistical rigor makes automation defensible. ROC-calibrated thresholds, precision/recall/F1 tracking, subgroup fairness checks, and drift monitoring turn "the algorithm decided" into a decision that can be audited, explained, and defended to compliance and regulators.
- Governance has to be built into the pipeline, not bolted on after. Features like the negative SSN conflict penalty and sensitive-condition override logic show that privacy-safety and auditability work best when they're part of the scoring architecture itself, not a manual review layer added later.