Healthcare organizations are finding plenty of use cases for agentic artificial intelligence, from increasing staff productivity to reducing data fragmentation.
To fulfill their aspirations through agentic AI, providers need a reliable data foundation that comes with strong governance.
Widespread adoption of healthcare data standards supports the work agentic AI can do.
Fast Healthcare Interoperability Resources (FHIR) application programming interfaces (APIs) and U.S. Core Data for Interoperability (USCDI) health data classes and data elements, along with terminologies such as SNOMED CT and LOINC, provide an essential base, says Faila Santos, senior digital health adviser at HIMSS.
In 2025, Stanford Health Care partnered with Microsoft to use the company's healthcare agent orchestrator to reduce fragmentation and produce insights from data that was challenging to search.
At Stanford Health Care, for example, its Data Science Executive Committee (DSEC) oversees governance for AI models that includes providing approvals and education on change management.
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