What Changes When AI Can Read All of the Electronic Health Record?

Every oncologist knows the electronic health record is where the cancer story goes to hide. Outside records arrive by fax, get scanned into the media tab under the wrong name, and pile up to hundreds of documents per patient from dozens of institutions, written in coding languages that never quite translate. Screening one chart for a clinical trial takes an hour. Tumor registries run months behind. Drugs get denied because a performance status was in the note but not in the request. No wonder "data" can feel like a four-letter word. This episode is about what changes when AI can finally read all of it. In this episode of AI and Healthcare, oncologists Sanjay Juneja, MD and Doug Flora, MD sit down with Mika Newton, CEO of xCures, to unpack a new AI in Precision Oncology paper (https://journals.sagepub.com/doi/10.1177/2993091X261492372) on using large language models to turn hundreds of medical records per cancer patient into structured, verifiable healthcare data. In this episode: • Why a cancer patient's medical records can run to 231 documents from dozens of institutions, and why most of it is still not in discrete fields • The many "languages" of healthcare data (OMOP, SNOMED, ICD, CPT) and why the crosswalks are never one-to-one • Learning the structure of a document before extracting from it, and why 95% of the work is organizing rows and columns • The three buckets: what to automate, where a human belongs in the loop, and when to give up on an unreadable page • How to vet a data vendor: dual human extraction, model drift, a 17-page buyer's guide, and whether it pays for itself • Interoperability in healthcare: TEFCA, Individual Access Services, CMS's Kill the Clipboard program, and what changes when patients own their own health data • The disparity question nobody has solved yet Mika Newton is Chief Executive Officer of xCures, which has applied AI to organizing and extracting structured data from medical records since 2018. He is a founder of this podcast and a member of the editorial board of AI in Precision Oncology, the journal publishing the paper discussed in this episode. Doug Flora, MD is the journal's Editor-in-Chief; the paper went through independent peer review. Read the paper: https://journals.sagepub.com/doi/10.1177/2993091X261492372 xCures Buyers Guide: https://xcures.com/interactive-buyers-guide/ Kill the Clipboard: https://www.cms.gov/health-tech-ecosystem/early-adopters/kill-the-clipboard Subscribe for conversations about artificial intelligence, oncology, clinical medicine, and the future of healthcare. Hosts: Sanjay Juneja, MD: https://www.linkedin.com/in/sanjayjunejamd Douglas Flora, MD: https://www.linkedin.com/in/douglasflora Follow TensorBlack on X: https://x.com/tensorblack Follow TensorBlack on Instagram: https://www.instagram.com/tensorblack Follow TensorBlack on LinkedIn: https://www.linkedin.com/company/tensorblack/ Follow TensorBlack on Facebook: https://www.facebook.com/tensorblack 00:00 AI and Healthcare 00:30 Introduction 01:59 COI 03:01 AI and clinical documentation 04:40 Medicine runs on documents 06:04 OMOP, SNOMED, ICD, CPT: healthcare data languages 08:52 Fax machines and HIPAA 10:04 The media tab problem 11:12 How xCures started 12:03 Document structure before extraction 14:46 231 documents per cancer patient 16:19 Data science is 95% data prep 17:07 Clinical trial screening: an hour per chart 19:14 LLM extraction: enrichment and inference 20:48 AI and healthcare labor 21:58 Human in the loop: the three buckets 24:13 Why the journal published the paper 26:09 AI hallucinations: measure, don't worry 27:17 How to vet a healthcare data vendor 28:05 Dual extraction, model drift, buyer's guide 30:39 Healthcare AI ROI 31:50 EHR integrations vs. patient apps 32:31 Healthcare interoperability, solved in 2023 33:34 TEFCA, Individual Access Services, Kill the Clipboard 35:17 Patient-owned health data 36:00 Health records outside HIPAA 38:22 Digital pathology and health disparities 40:56 Every medical record is a patient 42:47 Data that changes a patient's life #AIinHealthcare #MedicalRecords #CancerCare