A Guide to Common AI Features for EHR Platforms

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.

Clinical Decision Support and Predictive Analytics

On its EHR, BJC can access an array of clinical decision support tools that leverage predictive analytics. For example, a machine learning-driven early warning system helps identify and reduce sepsis. Another tool pulls various data from medical records to predict length of hospital stay and risk of readmission, helping providers improve discharge planning.

“The value case for predictive analytics tools has been well proved,” Payne says.

Intelligent Search and Data Access

EHR platforms use search and chatbot tools that help staff easily navigate the massive amounts of data within the EHR.

“The challenge with every EHR is just the sheer volume of information, so information retrieval can be a very challenging task,” Payne says. “We try to help people spend less time pointing and clicking and more time consuming information.”

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How To Evaluate AI Features for Your Organization

To determine which AI-powered EHR features to use, every healthcare organization needs a level of governance that includes a broad group of leaders, not just technology leaders, Sharp says. The use of AI in EHRs should be part of strategic discussions that consider and monitor the technology’s value and benefits.

When deciding whether to adopt an AI tool from a third-party provider, healthcare leaders must determine if it’s better for their organization to wait for the EHR platforms to develop the feature or to become an early adopter, Payne says. Leaders also must weigh the likely development timeline for EHR platforms against the maturity of third-party companies.

The central question that Payne asks of any AI tool is, “How do the tools improve both patient experience and provider satisfaction?” By reducing provider burden and improving provider-patient communications, the technology is enhancing the human experience of healthcare, he says. And providers are realizing they can use AI to give care both more efficiently and more effectively.

“This is the first time in my career I’ve ever seen a suite of technologies that our providers actually want and ask for,” Payne says.