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AI improves accuracy and completeness of hospital discharge summaries (opens in a new tab)

news-medical.net · 2026-11-02

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A source study was identified, but the study's evidence could not be prepared for claim assessment. The claims below have not been assessed.

Claim 1 of 4Not checkedA Stony Brook University study published online in Digital Medicine found that AI could help physicians prepare hospital discharge summaries more efficiently, with greater accuracy and completeness.View evidenceHide evidence

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Claim 2 of 4Not checkedPhysician reviewers preferred the AI-generated discharge summaries in 95 percent of the evaluated hospitalizations and gave them higher scores for overall quality, readability, accuracy, and completeness, with fewer missing clinical details than the human-authored summaries.View evidenceHide evidence

As stated95 percent

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Claim 3 of 4Not checkedThe study compared large language model-generated and human-authored discharge summaries for 60 Internal Medicine hospitalizations lasting 7 to 21 days at Stony Brook University Hospital, with hospitalists and primary care physicians assessing the summaries.View evidenceHide evidence

As stated60 hospitalizations; 7 to 21 days

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Claim 4 of 4Not checkedThe researchers said both types of discharge summaries contained errors, but reviewers found no statistically significant difference in estimated severity or likelihood of harm from those errors, and the authors emphasized that physician review and verification remain essential before any LLM-generated draft is used in a patient record.View evidenceHide evidence

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Clinician-centered evaluation of large language model-generated discharge summaries for longer hospitalizations

npj Digital Medicine · 2026

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Papers considered

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Crossref, Europe PMC, PubMed · 40 candidate papers

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Clinician-centered evaluation of large language model-generated discharge summaries for longer hospitalizations

Npj Digital Medicine · 2026 · Crossref

Source (opens in a new tab)

And 34 more candidates considered.