Stanford’s ChatEHR cuts emergency physician chart review time by 40%

Stanford Health Care has deployed ChatEHR, a natural language AI tool that allows clinicians to query electronic health records using conversational prompts similar to ChatGPT. The system has already demonstrated significant efficiency gains in clinical workflows, with emergency physicians experiencing 40% faster chart review times during critical patient handoffs, helping address physician burnout while improving patient care quality.

What you should know: ChatEHR transforms how medical professionals interact with patient data by enabling natural language queries of complex medical histories.

• The tool accelerates chart reviews for emergency room admissions, streamlines patient transfer summaries, and synthesizes information from complex medical histories.
• Emergency physicians have seen 40% reduced chart review time during critical handoffs, according to Michael A. Pfeffer, Stanford’s senior vice president and chief information and digital officer.
• The system builds upon decades of healthcare digitization efforts, finally enabling the digital transformation that electronic health records promised.

Why this matters: Physicians currently spend up to 60% of their time on administrative tasks rather than direct patient care, often working extra “pajama time” hours at home to complete documentation.

• AI-powered tools like ChatEHR help reduce cognitive burnout by providing starting points for administrative tasks.
• The technology enables medical staff to focus more time on actual patient interactions, which is critical amid ongoing physician and nursing shortages.
• When combined with ambient AI scribes for note-taking, these tools are shifting healthcare toward more clinician-patient interaction time.

How it works: Stanford uses a multidisciplinary approach combining various AI technologies and secure infrastructure to support clinical workflows.

• The system can read patient portal messages and draft responses for human review and approval.
• Future agent capabilities will generate comprehensive summaries and timelines for complex cases, such as cancer treatment planning that requires reviewing entire patient records, imaging, pathology, genomic data, and clinical trial information.
• Stanford deployed SecureGPT across all of Stanford Medicine, featuring 15 different models that staff can experiment with.

The big picture: Stanford’s approach demonstrates how healthcare organizations can successfully implement AI without compromising patient data security.

• The health system uses a mix of secure private models (like Microsoft Azure), open-source models, and custom-built solutions depending on specific use cases.
• Rather than appointing a single chief AI officer, Stanford assembled a multidisciplinary team including a chief data scientist, informaticists, chief medical and nursing information officers, plus their CTO and CISO.
• “We bring together informatics, data science and traditional IT, and wrap that into the architecture; what you get is this magic group that allows you to do these very complex projects,” Pfeffer explained.

What they’re saying: Stanford leaders emphasize AI as a universal tool that should be integrated into everyday healthcare thinking.

• “It’s such an exciting time in healthcare because we’ve been spending the last 20 years digitizing healthcare data and putting it into an electronic health record, but not really transforming it,” Pfeffer said. “With the new large language model technologies, we’re actually starting to do that digital transformation.”
• “That face-to-face interaction is just priceless,” said Pfeffer. “We’re going to see AI shift more to clinician-patient interaction.”
• “The most important thing that we can do for our patients is to make sure they have appropriate care, and it takes a multidisciplinary approach,” Pfeffer noted about cancer treatment planning.