Healthcare has become one of the most active testbeds for AI agents. In a single week of January 2026, OpenAI launched ChatGPT Health and Anthropic launched Claude for Healthcare. Ambient scribes such as Microsoft’s Dragon Copilot are generating clinical notes in production. The 2025 CAQH Index found that more than half of health plans and a quarter of provider organizations now use AI somewhere in their workflows.

But these agents are descending on infrastructure that wasn’t made for autonomous, real-time decision-making. Data is distributed across EMRs, payers, and regional systems; policies exist in aging documents and legacy rules engines; and context about prior interactions is often trapped in a siloed format like HL7, CCDA or a proprietary interface.

With all of this in mind, it has become clear that AI agents can act faster than the underlying systems can coordinate, govern or fully understand their actions.

It’s well-documented that healthcare is slow-moving and complex to modernize, for a variety of factors. That’s also why it’s such a compelling early example of agent-driven workflows in the enterprise.

On the clinical and front-office side, we already see specialized agents emerging:

Each of these agents dramatically shorten tasks that previously took days or weeks into something near-instant.

But those impressive front-end gains come with a harsh reality on the back end: healthcare data flows are still often driven by faxes, paper files, legacy HL7 interfaces, and organization-specific standards. It’s not surprising that is where the tension shows up most clearly.

The 2025 AMA Prior Authorization Physician Survey found physicians complete an average of 40 prior authorizations per week, that practices spend 13 hours a week on them, and that 95% of physicians report the process delays patient care.

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