Every day, medical providers and payers must make complex decisions based on a host of interrelated factors. Treating a patient, for example, is not just a matter of examining information from medical records, industry guidelines, and other sources. It also involves analyzing the relationships among these elements and weighing their relative importance.

Simple automated data retrieval solutions don’t offer much help in situations like these, as healthcare data is often trapped in silos and exists in a wide variety of formats.  And while large language models (LLMs) excel at handling multimodal data, they lack the deep clinical knowledge needed to unlock its value.

That’s where knowledge graphs come in. Read this white paper to learn how knowledge graphs can help healthcare organizations build AI models that are more robust, accurate, up-to-date, and efficient.