How to Test AI Agents in Healthcare: What to Check Before Go-Live

Healthcare AI agents should undergo a pre-go-live testing gate that evaluates both their conversations and their actions in clinical systems, with safety-critical failures such as missed emergency cues, unverified PHI disclosures, wrong-patient EHR access, or unconfirmed writes blocking release regardless of average performance. Recommended testing covers red-flag escalation during routine tasks, identity verification and minimum-necessary disclosure, FHIR and EHR tool-call accuracy in synthetic-data sandboxes, unsupported clinical claims and scope boundaries, fairness across patient personas, and phone-specific issues including accents, noise, interruptions, and transfer failures. The guidance recommends predefined pass criteria, named owners, retained transcripts and recordings, configuration details, and documentation of failed or unverifiable criteria for clinical, privacy, and QA sign-off. It links these practices to HIPAA, Section 1557, ONC HTI-1, FDA, NIST, and WHO guidance while noting that applicability requires legal and compliance review. It also stresses rerunning the full suite after any prompt, model, knowledge-base, or tool change, and describes TestMu AI products as tools for testing conversation quality, phone interactions, and verified system effects.

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