Study shows AI may improve patient discharge summaries
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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.
The main finding of the study revealed that the physician reviewers preferred the AI-generated discharge summaries in 95% of the evaluated hospitalizations. Additionally, the reviewers gave the summaries higher scores for overall quality, readability, accuracy and completeness. They also identified fewer missing clinical details compared to the human-authored summaries.
According to the authors, primary care physicians also found the AI-generated (LLM) summaries easier to understand, easier to explain to patients and caregivers, and more useful for follow-up care.
"These results provide evidence that artificial intelligence can support discharge documentation for complex hospital stays," summarizes Rachel Wong, co-corresponding author and clinical associate professor in the departments of Internal Medicine and Biomedical Informatics in the Renaissance School of Medicine (RSOM) at Stony Brook University.
The researchers also found that both types of discharge summaries contained errors. Yet, physician reviewers found no statistically significant difference in the estimated severity or likelihood of harm associated with those errors. The authors stress that physician review and verification remain essential before any LLM-generated discharge draft is used for a patient record.
Fusheng Wang, co-corresponding author and a professor in the Department of Biomedical Informatics and Computer Science at Stony Brook University, says the next steps in the research are to continue developing the LLM for complex patient discharge summaries with a specific emphasis on assessing accuracy, safety, actual time savings for physicians and how it would fit into clinical workflows. He also says a pilot study to evaluate the tool in everyday clinical practice at Stony Brook Medicine is being considered.
Publication details
Tyler Osborne et al., Clinician-centered evaluation of large language model-generated discharge summaries for longer hospitalizations, npj Digital Medicine (2026). DOI: 10.1038/s41746-026-03320-y
Journal information: npj Digital Medicine
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Lisa Lock
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