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As AI floods the EHR, a quieter question is arising: what leads to EHR AI features disabled, rather than embraced, by the clinicians who use them daily? Every major EHR vendor is in the same race right now. Epic has more than 150 AI features and enhancements in development for 2026, spanning conversational search, autonomous coding and AI-assisted charting, on top of ambient tools already in use. Oracle Health is expanding its Clinical AI Agent to automate clinical orders, including labs, imaging, prescriptions and referrals.
Why Embedded AI Doesn’t Guarantee Success
CIOs have told Becker’s that embedding AI directly into the EHR could finally give clinicians meaningful time back. “AI that isn’t embedded in the EHR is like having a capable assistant who sits outside the room,” Muhammad Siddiqui, CIO of Reid Health in Richmond, Indiana, told Becker’s in February.
A Caution Against Assuming Embedded Means Better
But Siddiqui also cautioned against assuming embedded automatically means better. “If the AI is noisy, inconsistent or hard to govern, it can quietly undermine trust,” he said. “Clinicians are quick to disengage when tools feel unreliable or create rework.” That noisy, unreliable version of embedded AI is what leads to EHR AI features disabled at health systems already walking back on underperforming tools.
Chart Summarization Tops the List for EHR AI Features Disabled
According to Deepti Pandita, MD, vice president of clinical informatics, chief medical informatics and AI officer, and associate professor of medicine at UCI Health in Orange, California, chart and visit summarization is one of the tools not meeting expectations. “Talking to my provider peers, the one feature they don’t like, despite seemingly initially being excited about it, is chart summary AI, whether released as an EHR feature or from an ambient scribe company,” Pandita told Becker’s.
Why Summaries Fall Short
“The patient-level clinical summary is often not useful because, even when it reflects recentness, it is typically generated primarily from aggregated chart content, e.g., problem list/history and narrative note text, rather than from the structured encounter-level decision outputs clinicians rely on: medication changes, newly placed orders/referrals, and the specific follow-up/return plan,” Pandita said. The result is a summary that sounds plausible and context-light but omits or dilutes the “what changed today” and “what we decided today” elements, meaning clinicians still have to reread the actual visit documentation to find the missing plan, history of present illness, and assessment nuances. This turns the summary “into extra work instead of a time-saver,” she said, making chart summarization a prime candidate among EHR AI features disabled for underperformance.
Alert Fatigue Drives More EHR AI Features Disabled
AI-driven alerts that contribute to alert fatigue are another feature Usman Akhtar, MD, associate vice president and chief medical informatics officer at VHC Health in Arlington, Virginia, told Becker’s he would turn off. Alert fatigue in the EHR predates generative AI by well over a decade, and layering AI-generated recommendations on top of that existing alert burden, without redesigning when and how those alerts fire, is precisely the failure mode, according to Akhtar.
Intelligence Shouldn’t Mean Interruption
“If I could eliminate one AI-driven EHR feature, it would be any tool that turns ‘intelligence’ into an interruption,” he said. “Too often, broad AI-generated alerts or recommendations surface at the wrong time, with too little context, and add noise instead of clarity. Clinicians do not need more digital nudges; they need technology that quietly removes friction, reduces clicks, and gives them time back. AI should feel like a trusted assistant, not another pop-up demanding attention.”
The Real Problem Behind EHR AI Features Disabled
Neither complaint is really about AI failing at what it was built to do. A summary that faithfully reflects the chart is doing its job; an alert that fires reliably is doing its job. The problem both chief medical informatics officers describe is a mismatch between what the tool outputs and what the clinician actually needs in that specific moment: the one line that changed since yesterday’s note, or a recommendation that shows up mid-task instead of at the point of decision.
What This Means for Health IT Leaders
As vendors continue racing to add more AI capabilities, these examples of EHR AI features disabled suggest that success won’t be measured by feature count alone, but by how precisely each tool aligns with the specific moment and context in which a clinician needs it. Health systems evaluating new AI tools may benefit from asking not just whether a feature works technically, but whether it delivers the exact piece of information or action a clinician needs at that precise point in their workflow.
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