Bringing Contextualized Health Data into the Diagnostic and Treatment Process

Healthcare has always relied on data.  What’s changed is the explosion of data in healthcare and the availability of this data to clinicians as well as a whole host of healthcare professionals.  Bringing context and meaning to this vast amount of data including unstructured health data is going to be key for every healthcare organization.  We sat down with Dr. Paulo Pinho, Chief Medical & Strategy Officer at Discern Health, and Dr. Tim O’Connell, Co-founder and CEO at emtelligent, to learn more about what they’re doing to contextualize data and improve processes for providers, payers, and researchers across even the most complex use cases. To illustrate this change, Dr. Pinho expects medical schools to start teaching their students how to use generative AI tools as part of their medical learning. He talks of “dually trained” clinicians who understand both the compassionate practice of medicine and the technologies that support their work. Dr. O’Connell described emtelligent’s two platforms. A “data extraction engine” reads unstructured data while understanding ambiguity, uncertainty, and context in order to perform medical tasks such as coding. Their large language model (LLM) is medically trained and can query the patient’s data to answer complex questions such as how many blood vessels are affected by a disease. Dr. Pinho explains the importance of summarizing unstructured information. Codified elements don’t ...
Source: EMR and HIPAA - Category: Information Technology Authors: Tags: AI/Machine Learning Analytics/Big Data Clinical Health IT Company Healthcare IT Hospital - Health System Discern Health emtelligent Explainable AI Healthcare AI Healthcare Data Healthcare IT Video Interviews Healthcare LLMs Healt Source Type: blogs