Data quality is the AI strategy

Summary

NYU Langone Health’s AI strategy starts with data quality, arguing that healthcare AI cannot be reliable when source data is fragmented or inconsistent. The institution standardized on common transactional platforms, including one electronic health record and one ERP system, established authoritative data sources, and fixes data at the source rather than mapping it in the warehouse layer. Its Databricks-based unified data and AI platform, with Unity Catalog, supports clinicians, analysts, scientists, and corporate users across care, operations, and research, while real-time feeds power emergency-room decision-support models. Mherabi also describes a three-layer analytics model: structured visualizations, conversational tools such as Genie, and answers delivered in formats suited to the user. The stated conclusion is that upstream data discipline, governance, literacy, and adaptable platforms provide the foundation for trustworthy AI and timely clinical insight.

Context

NYU Langone Health recognized in 2017 that realizing AI’s potential would require modernizing its data stack and making data quality, reliability, and accessibility foundational to patient care, research, operations, and future real-time clinical decision support.

Approach / What changed

NYU Langone standardized common transactional platforms, defined authoritative master data sources, fixed quality issues upstream instead of mapping them in the warehouse, and built a Databricks-based unified data and AI platform with Unity Catalog. It also expanded platform access through governance, access controls, literacy programs, and training, while supporting real-time feeds and layered analytics experiences.

Takeaways

• NYU Langone uses one electronic health record and one ERP system to reduce fragmentation and establish common transactional foundations as practices and hospitals join the system.
• Unity Catalog is treated as a foundational governance layer: the organization defines master data sources, assigns ownership, and helps users find trusted data without duplicating work.
• The analytics model combines structured visualizations, conversational tools such as Genie, and responses delivered as facts, visualizations, or compact numerical displays depending on user needs.

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