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Evidence record 7525 · automatically gathered

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. H

Record details

Published: 9 March 2026
Source: arXiv
Category: Research
Topics: Regulation · Privacy · Healthcare
Retrieved: 14 July 2026

source-onlyevidence status

These records share source-supplied organisations, an exact publisher byline, automatic topics or regions. The reason is shown on every link; related does not mean supporting, agreeing with or verifying this record.

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How to cite this record

ethics.ai (9 March 2026), “Semantic Risk Scoring of Aggregated Metrics: An AI-Driven Approach for Healthcare Data Governance,” evidence record 7525, https://ethics.ai/record/7525 (originally published by arXiv).

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