Stanford clinicians trial new AI tool that lets you ‘chat’ with electronic medical records

Key Takeaways:

• Stanford Health Care has developed ChatEHR, a secure AI-powered assistant that enables clinicians to interact directly with electronic health records via natural language queries.
• ChatEHR can summarise complex patient histories, retrieve key data points, and assist with administrative tasks, freeing clinicians to focus more on patient care.
• The tool remains in pilot testing with 33 users, and its developers are actively building new features including automated clinical evaluations and in-record source citations.
  

Transforming Clinical Workflows with AI

Clinicians at Stanford Health Care are now piloting a new artificial intelligence (AI) tool, ChatEHR, which enables them to interact with patients’ electronic medical records (EMRs) in a conversational, intuitive manner – similar to how one might “chat” with a large language model such as GPT-4.

Currently in its pilot phase, ChatEHR draws upon information from a patient’s health record to respond to queries, generate summaries, and support routine clinical tasks. It is designed to be secure, context-aware, and seamlessly integrated into clinicians’ existing digital workflows.

“AI can augment the practice of physicians and other health care providers, but it’s not helpful unless it’s embedded in their workflow and the information the algorithm is using is in a medical context,” explained Nigam Shah, MBBS, PhD, Chief Data Science Officer at Stanford Health Care, who spearheaded the development of the system.

“ChatEHR is secure; it’s pulling directly from relevant medical data; and it’s built into the electronic medical record system, making it easy and accurate for clinical use.”

Origins and Development

The idea for ChatEHR took shape in 2023, when Dr Shah, Anurang Revri, Vice President and Chief Enterprise Architect for Stanford Health Care’s Technology and Digital Services, and a team of Stanford Medicine researchers recognised the clinical potential of large language models. Their aim was to develop a tool that could streamline interactions with complex patient data.

“ChatEHR opens up a new way for clinicians to interact with electronic health records in a more streamlined and efficient manner,” said Michael Pfeffer, MD, Chief Information and Digital Officer for Stanford Health Care and the School of Medicine.

“Whether that’s asking for a summary of the entire chart or retrieving specific data points relevant to the patient’s care, this is a unique instance of integrating LLM capabilities directly into clinicians’ practice and workflow. We’re thrilled to bring this to the workforce at Stanford Health Care.”

Enhancing Information Retrieval and Workflow Efficiency

Currently, the pilot programme involves 33 clinicians – including doctors, nurses, physician assistants, and nurse practitioners – who are testing the software’s performance, identifying areas for improvement, and contributing to feature development.

The interface welcomes users with:
“Hi, 👋 I’m ChatEHR! Here to help you securely chat with the patient’s medical record.”

Clinicians can then type natural-language queries such as:

• Does this person have any allergies?
• What were the results of their most recent cholesterol test?
• Have they undergone a colonoscopy? If so, were the findings normal?
  

“Making the electronic medical record more user friendly means physicians can spend less time scouring every nook and cranny of it for the information they need,” noted Dr Sneha Jain, Clinical Assistant Professor of Medicine and an early adopter of the tool.

“ChatEHR can help them get that information up front so they can spend time on what matters – talking to patients and figuring out what’s going on.”

Supporting Emergency and Transfer Care

The tool also has potential to ease the burden of clinical information gathering in emergency and transfer cases.

“It’s not just the chest pain they’re having in that moment that matters – it’s their whole story, what led up to this moment,” said Dr Jonathan Chen, MD, PhD, a hospital physician and Assistant Professor of Medicine and of Biomedical Data Sciences.

“All their prior history is relevant. What medications were they on, what side effects did they have, what surgeries took place and how did that affect them? It’s a ton of work to go back and find all of that information during a time-sensitive case, so speeding up that process would be a big help.”

In transfer cases, patients often arrive with vast documentation – sometimes hundreds of pages long. ChatEHR offers a rapid summarisation feature to distil these into concise, relevant overviews. Importantly, users can also ask follow-up questions for greater context.

Moving Beyond Queries: Automating Evaluative Tasks

The development team is expanding ChatEHR’s functionality to include “automations” – algorithmically-driven evaluations based on a patient’s clinical record.

For example, one automation helps determine whether a patient is eligible for transfer to the Sequoia Hospital patient care unit, a Stanford Medicine-affiliated facility with additional room capacity.

“That automated evaluation saves us the administrative burden of sifting through patient information and helps us quickly determine if a patient can be transferred, opening access to care here at Stanford Hospital,” Dr Shah explained.

Other automations under development aim to assist with hospice eligibility assessments and post-surgical follow-up recommendations.

Evaluating and Expanding ChatEHR

Stanford researchers are using MedHELM, an open-source framework for real-world medical AI evaluation, to systematically assess the accuracy and effectiveness of ChatEHR. Among upcoming features are citation tools, which will allow clinicians to see exactly where within a patient’s record specific pieces of information originate.

“We’re rolling this out in accordance with our responsible AI guidelines,” said Dr Shah.

“Not only ensuring accuracy and performance, but making sure we have the educational resources and technical support available to make ChatEHR usable and useful to our workforce.”

The eventual goal is to make ChatEHR available to all clinicians at Stanford who interact with patient charts, improving the speed, clarity, and usability of electronic medical records across the board.

The development of ChatEHR has been supported by Stanford’s Department of Medicine and the Center for Biomedical Informatics Research, reflecting a broader institutional commitment to integrating trustworthy AI into frontline care.