LLM-DQR: Large language model-based automated generation of data quality rules for electronic health records

To develop and evaluate LLM-DQR, an automated approach using large language models to generate electronic health record data quality rules, addressing the limitations of current manual and automated methods that suffer from low efficiency, limited flexibility, and inadequate coverage of complex business logic.

We designed a comprehensive pipeline with three core components: (1) standardized input processing integrating database schemas, natural language requirements, and sample data; (2) Chain-of-Thought prompt engineering for guided rule generation; and (3) closed-loop validation with deduplication, sandbox execution, and iterative debugging. The approach was evaluated on two distinct, publicly available datasets: the Paediatric Intensive Care (PIC) dataset and the Medical Information Mart for Intensive Care (MIMIC-IV) dataset. Performance was compared against manual expert construction (expert-DQR) and clinical information model-based generation (CIM-DQR).

LLM-DQR demonstrated higher performance across all evaluation metrics. The GPT implementation achieved overall coverage rates of 97.1% on the PIC dataset and 99.6% on the MIMIC-IV dataset, outperforming CIM-DQR. Performance was particularly strong for complex dimensions: achieving 100% coverage for Consistency rules on both datasets, whereas CIM-DQR achieved 0%. Construction time was reduced by over 10-fold compared to manual methods. Additionally, on the PIC dataset, LLM-DQR generated 89 extra, expert-validated rules.

The stronger performance demonstrates LLMs' capability to understand complex EHR data patterns and assessment requirements, functioning as data quality analysis assistants with domain knowledge and logical reasoning capabilities.

LLM-DQR provides an efficient, scalable solution for automated data quality rule generation in clinical settings, offering considerable improvements over traditional approaches.