AI in Modern EHR Platforms: An Overview

Major EHR platforms now provide a wide variety of native AI functionality — everything from generative AI tools that can draft prior-authorization letters to predictive models that can help detect clinical deterioration. Bolt-on tools tend to serve more specific use cases, such as generative AI tools that pre-chart for certain specialties.

But the distinction between native and bolt-on AI features in EHR platforms is a fuzzy one. That’s because many “native” features, such as AI imaging tools, require EHR vendors to integrate with third-party providers.

With any AI tool, humans should remain central to reviewing the output and making decisions about it, says Christopher Sharp, chief medical information officer at Stanford Health Care. “These features augment humans so they’re more effective.”

AI for Ambient Listening and Clinical Documentation

Traditional clinical documentation demands a lot of time and effort from healthcare providers. EHR platforms’ AI scribing tools use ambient listening and generative AI to record and summarize provider-patient interactions, then deliver those summaries back to the EHR for the providers to review, edit and finalize.

“The ability to summarize that documentation is really powerful, and that’s something generative AI does quite well,” Sharp says.

The results? Substantial time savings, reduced cognitive burden for providers and overall improved clinical documentation.

Cutting-edge ambient listening tools not only document but also can provide actionable care suggestions and make interventions. For instance, if a clinician recommends a chest X-ray to a patient, the AI tool can send that information to the EHR so the X-ray order gets made.

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Patient-Facing AI Capabilities

When patients message their providers or receive test results via the EHR portal, they must wait for their providers to find the time to answer their questions or interpret their results. Patient-facing AI capabilities enable providers to respond to their patients much more quickly.

At Stanford Health Care, providers save time by reviewing AI-generated interpretations of test results before sharing them with patients. Stanford also uses a generative AI tool that reviews patients’ medical notes to draft responses to their questions in the EHR portal. “That doesn’t change the course of care, but it does change the efficiency and burden for the clinician,” Sharp says.

Revenue Cycle Management and Administrative AI

Today’s EHRs feature AI tools that can generate documents for revenue cycle management. For instance, if a healthcare organization needs to appeal an insurance payment, it can use a generative AI tool to assess the patient’s course of care and select relevant documentation.

“AI is very helpful in identifying gaps in documentation for billing purposes,” says Sharp, who expects AI will augment or even replace manual tasks in the revenue cycle.