Semantic Risk Scoring of Aggregated Metrics: An AI-Driven Approach for Healthcare Data Governance
Large healthcare institutions typically operate multiple business intelligence (BI) teams segmented by domain, including clinical performance, fundraising, operations, and compliance. Due to HIPAA, FERPA, and IRB restrictions, these teams face challenges in sharing patient-level data needed for analytics. To mitigate this, A metric aggregation table is proposed, which is a precomputed, privacy-compliant summary. These abstractions enable decision-making without direct access to sensitive data. However, even aggregated metrics can inadvertently lead to privacy risks if constructed without rigorous safeguards. A modular AI framework is proposed that evaluates SQL-based metric definitions fo...
| Authors | Mohammed Omer Shakeel Ahmed |
|---|---|
| Primary category | cs.LG |
| Published | 9 March 2026 |
| Last updated at source | 9 March 2026 |
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