AI Talks

In HL7's AI Talks podcast series, hear from leading experts as they explore the potential of AI to detect fraud, enhance collaboration between stakeholders and streamline payment processes, all while supporting transparency and trust in healthcare data.

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AI to Combat Healthcare Fraud and Improve Payment Integrity

Listen as industry leaders explore how AI can address the $900 billion lost annually in the U.S. due to healthcare fraud, waste and abuse. The podcast underscores the importance of interoperability and responsible data sharing, emphasizing that AI, supported by standards like HL7 FHIR, can transform payment integrity and reduce fraud in the healthcare system.

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HL7 Terminology Update for AI Use Cases

Listen to a succinct 6 minute summary of the latest updates to HL7 Terminology (THO), a central asset for HL7-managed terminology supporting all HL7 products like FHIR, CDA, and V2, introduce new content specifically for Artificial Intelligence (AI) use cases in release 6.0.2. These enhancements include new Purpose of Use Codes, such as MLTRAINING for machine learning training and PMTDS for decision support assisted payment decisions, which define reasons for accessing or disclosing health data and are relevant for FHIR Consent Resources, security policies, and audit tracking. Additionally, the Provenance Participant Type has been enhanced with new codes like "transformer," describing roles for agents—including non-human participants—in data lineage, particularly when AI converts data from one format to another (e.g., CDA to FHIR). New Provenance Security Codes, such as AIAST (Artificial Intelligence asserted) and DICTAST (Dictation asserted), are also introduced to indicate security observation metadata within FHIR Provenance and other FHIR resources. 

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