AI is quickly becoming standard across EHRs. But as healthcare organizations move from experimenting with individual AI tools to deploying them more broadly, the question is shifting: Is the AI simply connected to the EHR, or is it built into the platform itself?

Whether AI sits alongside the EHR or is built natively has real implications for clinical workflows. Standalone tools can introduce new data boundaries, governance processes, integrations, and workflow interruptions. AI-native capabilities can instead work from the same data, permissions, workflows, and clinical context as the rest of the platform.

That difference becomes increasingly important as organizations scale AI. Here are three things healthcare leaders should consider:

A clinical encounter starts well before the patient enters the exam room. Clinicians may need to review years of notes, medications, labs, treatments, and previous encounters to understand the full picture. AI native to the EHR can help synthesize that longitudinal record and carry relevant context from preparation into the visit itself.

Before the visit: AI-native tools built inside the workflow can automatically generate problem-oriented summaries of relevant patient history, while other chart-related tools empower clinicians with the information needed at point-of-care by asking natural-language questions directly within the encounter workflow.

During the visit: Ambient note tools can discretely, safely, and accurately capture visit notes, proactively reflecting the patient-provider conversation without requiring a separate application or login.

After the visit: Because documentation is captured during the encounter, clinicians can review and refine the record rather than spend valuable time reconstructing the visit afterward.

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