ArticleJMIR human factors2026
Sentence-Level Provenance for AI Medical Record Summarization in a Click-to-Inspect Interface: Formative Usability Evaluation.
Article in JMIR human factors, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
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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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Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background: Large language models can generate fluent summaries of longitudinal medical records, but in high-stakes clinical settings, verification burden remains a barrier to trust. Existing provenance mechanisms such as document-level citations and section references often require manual search within long, fragmented notes, limiting their usefulness during time-constrained workflows for clinicians. Objective: This study aimed to design and evaluate a sentence-level provenance interface ("click to inspect") that enables rapid verification of AI-generated longitudinal medical record summaries at the level of individual statements. Methods: Between November 2023 and January 2024, we conducted a formative usability study using remotely moderated usability sessions via Zoom to evaluate a web-based sentence-level provenance interface for AI-generated longitudinal medical record summaries. A convenience sample of clinicians was recruited through email outreach to academic and professional networks across the United States. Formative usability testing was conducted with 46 clinician interactions using synthetic longitudinal patient charts. Participants included medical students, residents, and attending physicians across multiple specialties, including internal medicine, dermatology, radiology, plastic surgery, anesthesiology, interventional radiology, obstetrics and gynecology, and family medicine. Usability was assessed using the System Usability Scale and net promoter score, alongside qualitative feedback. Results: Clinicians reported high usability (mean System Usability Scale score 86.25, SD 7.77; 95% CI 83.96-88.54 from 46 participants) and a positive overall experience (net promoter score of 35; 22/46, 47.8% promoters; 18/46, 39.1% passives; and 6/46, 13% detractors). Participants described rapid access to supporting evidence as critical for trust calibration during first-pass chart review. Qualitative feedback identified friction in traditional citation-based interfaces and supported sentence-level inspectability as a low-friction verification mechanism. Conclusions: Sentence-level provenance transforms AI-generated summaries from static narratives into interactive verification tools. An approach that enables rapid, selective inspection of individual claims during longitudinal chart review may reduce verification burden and support calibrated reliance in high-risk clinical contexts.
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