ArticleJournal of the American Medical Informatics Association : JAMIA2026
Generative artificial intelligence for inpatient documentation summarization: mixed-methods quality assessment and early real-world experience.
Article in Journal of the American Medical Informatics Association : JAMIA, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
1 citing paper in PubMed.
- PAUSE-Agents: A Clinician-in-the-Loop Multi-Agent AI Pipeline for ICU-to-Ward Handoff Briefs.medRxiv : the preprint server for health sciences · 2026Article
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Authors and funding
6 authors.
Funding
Abstract
objectiveWe evaluated the quality and adoption of a large language model (LLM)-based summarization tool for ongoing hospital care. MATERIALS AND
methodsAn AI summarization tool was created to provide a "Patient Story", specialty-specific "Recent Notes", and "Recent Events" over the previous 24 or 72 hours. We conducted a pragmatic mixed-methods quality assessment at three tertiary-care academic hospitals utilizing (1) Provider Documentation Summarization Quality Instrument (PDSQI-9), (2) utilization analytics, and (3) qualitative end user feedback. The PDSQI-9 included whether summaries were accurate, cited, comprehensible, organized, succinct, non-stigmatizing, synthesized, thorough, and useful. 512 users were given access, from whom 52 participants submitted 205 surveys (10.2% response rate).
results52 respondents submitted an average of 4.3 surveys (range 1-8). Users rated the tool favorably across all PDSQI-9 domains, with a combined average score of 4.68 (range 4.56-4.78, S.D. 0.67) across the eight domains scored on a 5-point modified Likert scale. Utilization metrics demonstrated strong uptake with frequent views. Positive qualitative feedback revealed cognitive offloading for complex patients and effective summarization of medical problems. Critical feedback showed a need to cross-reference narrative notes to current-state data and lack of detailed specialty-specific summarization. DISCUSSION: Generative artificial intelligence has emerged as a potentially transformative technology for generating succinct, verifiable summaries of ongoing care. In this pragmatic implementation, end-users indicated high perceived quality across PDSQI-9 domains.
conclusionsAn LLM-based summarization tool for ongoing hospitalization care was rated of high quality by diverse clinicians in real-world settings, demonstrated a favorable safety profile, and showed sustained utilization.
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Registered trials
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