STAT+: How health systems are embracing chatbots to query and summarize patient records

The pathologists were stumped. Six people had tried to identify a patient’s cancer based on a recent lymph node biopsy. They’d stained the cells 70 times to try to draw out more distinguishing features — but still, nobody had an answer.

At Stanford, a physician called onto the case was trying out a new tool called ChatEHR, one of several large language model-powered tools being deployed by health systems to summarize patients’ often-extensive medical records. It got a question: Did the patient have any history of skin lesions? After some back-and-forth, from the depths of the patient’s history, ChatEHR delivered an answer: In a different health system, the patient had previously been diagnosed with sarcomatoid squamous cell carcinoma.

It “completely explained the findings in the lymph node,” wrote the happy doctor in their feedback for the chatbot. “If that doesn’t prove the value of ChatEHR, I don’t know what does!” 

This was the kind of needle in a haystack doctors hoped to find when health systems first started experimenting with generative AI tools like ChatEHR to search and synthesize patients’ health records. Clinicians often struggle to find the information they need to care for patients, because modern electronic health records have gotten so bloated. Today, a number of health systems are moving toward broad implementation of chatbots for EHRs, both homegrown and vendor-built. And it turns out that solving diagnostic mysteries is the least of their selling points. 

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