UCLA researchers have developed an AI system that turns fragmented electronic health records (EHR) normally in tables into readable narratives, allowing artificial intelligence to make sense of complex patient histories and use these narratives to perform clinical decision support with high accuracy. The Multimodal Embedding Model for EHR (MEME) transforms tabular health data into "pseudonotes" that mirror clinical documentation, allowing AI models designed for text to analyze patient information more effectively. 

Electronic health records contain vast amounts of patient information that could help doctors make faster, more accurate decisions in emergency situations. However, most cutting-edge AI models work with text, while hospital data is stored in complex tables with numbers, codes, and categories. This mismatch has prevented healthcare systems from fully leveraging advanced AI capabilities. Emergency departments, where quick decisions can be critical, particularly need tools that can rapidly process comprehensive patient histories to predict outcomes and guide treatment decisions.

Researchers created a novel approach that converts tabular electronic health record data into text-based "pseudonotes" using medical documentation shortcuts commonly used by healthcare providers. On other words, instead of treating the EHR as a collection of codes, pseudonotes creates a story composed of multiple narratives. The system breaks patient data into concept-specific blocks (medications, triage vitals, diagnostics, etc.), transforming each into text using simple templates, and then encodes each one separately using language models. It essentially emulates a form of medical reasoning.

Source: https://www.uclahealth.org/news/release/ai-model-converts-hospital-records-text-better-emergency