How Large Language Models are Helping Crack the Code of Electronic Health Records

How Large Language Models are Helping Crack the Code of Electronic Health Records

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xCures’ Mark Shapiro explores how Large Language Models can extract structured data from fragmented and unstructured clinical notes, increase the completeness of medical records, uncover missing treatments, and make critical details easier to find. AI isn’t replacing doctors—it’s giving them the information they need, when they need it.
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xCuresC3318-12Founded in 2018, xCures operates a real-time, healthcare data platform that automatically retrieves medical records from any US care site and normalizes and structures them into a searchable database. Assisted by artificial intelligence, the platform auto-extracts source-verifiable, configurable checklists and natural language patient summaries to give fast and easy access to the right clinical information needed to deliver care.