AI-Powered Data Catalog Systems For Healthcare Data Discovery And Governance

Abstract

Healthcare systems store diverse clinical, operational, and research data in often-overlapping but poorly linked silos. Patient privacy and related regulations complicate data-sharing agreements and inhibit large-scale analytics. Data catalogs—the systems that enable search, browse, and discoverability of digital content across organizations—are too rarely deployed for healthcare data. AI-powered solutions have been proposed for discovery of data in many sectors. If realized, such approaches will facilitate faster access to healthcare data, compliance with regulations, and secure data-sharing agreements with proper oversight.

Data discovery in healthcare is challenging, yet essential for both clinical operations and research. The diverse roles of prospective data consumers need to be supported: data scientists, data stewards, data custodians, clinical investigators, translational faculties, and data producers. Metadata management is a critical component of successful data discovery, encompassing both population with consistent schemas and standards, and enrichment of quality, provenance, technical, and other metadata for findability. AI techniques useful for data cataloging across other sectors—including machine learning, natural language processing, and entity recognition—are applicable to the healthcare domain.