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The use of artificial intelligence (AI) could help physicians prepare hospital patient discharge summaries more efficiently and with greater accuracy and completeness. This is the conclusion of a Stony Brook University study published online in npj Digital Medicine.

Discharge summaries provide essential information for patients when they leave a hospital. The data includes essential information such as a patient's treatment, medication needs, changes in medication, unresolved medical issues and follow-up needs as an outpatient. Primary care physicians rely on discharge summaries to continue their care of patients, and discharge summaries help to provide a patient care roadmap going forward.

This study focused on longer, complex hospital stays, where there is a larger volume of medical information to summarize. Such in-house scenarios can be a significant burden for providers and add to their time spent on discharge documentation.

The method centered on an approach that used a large language model (LLM)—a form of AI that can process and generate text—to develop discharge summaries for complex patient cases. Clinicians helped design the approach based on the information physicians need after patient discharge. The LLM process organized patient medical records and other information to create a patient summary that provided the core information for a patient discharge summary—all within a few minutes.

The research team compared LLM-generated and human-authored discharge summaries for 60 internal medicine hospitalizations at Stony Brook University Hospital lasting seven to 21 days, with paired assessment by hospitalists and primary care physicians.

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