Perspective review: Will generative AI make common data models obsolete in future analyses of distributed data networks?

Abstract

Integrating real-world healthcare data is challenging due to diverse formats and terminologies, making standardization resource-intensive. While Common Data Models (CDMs) facilitate interoperability, they often cause information loss, exhibit semantic inconsistencies, and are labor-intensive to implement and update. We explore how generative artificial intelligence (GenAI), especially large language models (LLMs), could make CDMs obsolete in quantitative healthcare data analysis by interpreting natural language queries and generating code, enabling direct interaction with raw data. Knowledge graphs (KGs) standardize relationships and semantics across heterogeneous data, preserving integrity. This perspective review proposes a fourth generation of distributed data network analysis, building on previous generations categorized by their approach to data standardization and utilization. It emphasizes the potential of GenAI to overcome the limitations CDMs with GenAI-enabled access, KGs, and automatic code generation. A data commons may further enhance this capability, and KGs may well be needed to enable effective GenAI. Addressing privacy, security, and governance is critical; any new method must ensure protections comparable to CDM-based models. Our approach would aim to enable efficient, real-time analyses across diverse datasets and enhance patient safety. We recommend prioritizing research to assess how GenAI can transform quantitative healthcare data analysis by overcoming current limitations.

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