Los Angeles-based UCLA Health has developed an AI model that transforms electronic medical records into text to improve emergency care decisions.
The Multimodal Embedding Model for EHR platform converts tabular data into “pseudonotes” that look like clinical documentation, enabling AI tools designed for text, as well as emergency medicine providers, to analyze the information faster and more effectively. The researchers published their findings July 2 in npj Digital Medicine.
“This bridges a critical gap between the most powerful AI models available today and the complex reality of healthcare data,” said Simon Lee, PhD student at UCLA Computational Medicine, in a July 2 news release. “By converting hospital records into a format that advanced language models can understand, we’re unlocking capabilities that were previously inaccessible to healthcare providers. The fact that this approach is more portable and adaptable than existing healthcare AI systems could make it particularly valuable for institutions working with different data standards.”
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