A wave of hospitals is putting large language model chatbots to work on patient charts. The tools search and summarize records, aiming to surface details buried in ever-bloating electronic health files.
STAT’s Katie Palmer reports on the trend, which spans homegrown and vendor-built tools. The case for them: clinicians routinely struggle to find the details they need, because modern EHRs have grown so dense that relevant history gets lost in the noise.
A Stanford case shows the upside. Pathologists had stained a lymph node biopsy 70 times without identifying the patient’s cancer. A doctor trying ChatEHR, an LLM tool for records, asked whether the patient had ever had skin lesions. The system pulled up an earlier diagnosis of sarcomatoid squamous cell carcinoma made at another hospital, which accounted for the biopsy findings, the physician wrote in feedback.
Solving diagnostic mysteries, however, is the least of the tools’ selling points, according to the article. The bigger prize is giving clinicians a fast, reliable way to query years of records during routine care. Persistent monitoring, the piece argues, is the key to deploying these tools safely, since errors in retrieval can propagate into decisions without continuous human oversight.
