Human-in-the-Loop Interactive Report Generation for Chronic Disease Adherence

• URL: http://arxiv.org/abs/2601.06364v1
• Date: Sat, 10 Jan 2026 00:19:33 GMT
• Title: Human-in-the-Loop Interactive Report Generation for Chronic Disease Adherence
• Authors: Xiaotian Zhang, Jinhong Yu, Pengwei Yan, Le Jiang, Xingyi Shen, Mumo Cheng, Xiaozhong Liu,
• Abstract summary: Chronic disease management requires regular adherence feedback to prevent avoidable hospitalizations.<n>Manual authoring preserves clinical accuracy but does not scale; AI generation scales but can undermine trust in patient-facing contexts.<n>We present a clinician-in-the-loop interface that constrains AI to data organization and preserves physician oversight through recognition-based review.
• Score: 17.904419827298074
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Chronic disease management requires regular adherence feedback to prevent avoidable hospitalizations, yet clinicians lack time to produce personalized patient communications. Manual authoring preserves clinical accuracy but does not scale; AI generation scales but can undermine trust in patient-facing contexts. We present a clinician-in-the-loop interface that constrains AI to data organization and preserves physician oversight through recognition-based review. A single-page editor pairs AI-generated section drafts with time-aligned visualizations, enabling inline editing with visual evidence for each claim. This division of labor (AI organizes, clinician decides) targets both efficiency and accountability. In a pilot with three physicians reviewing 24 cases, AI successfully generated clinically personalized drafts matching physicians' manual authoring practice (overall mean 4.86/10 vs. 5.0/10 baseline), requiring minimal physician editing (mean 8.3\% content modification) with zero safety-critical issues, demonstrating effective automation of content generation. However, review time remained comparable to manual practice, revealing an accountability paradox: in high-stakes clinical contexts, professional responsibility requires complete verification regardless of AI accuracy. We contribute three interaction patterns for clinical AI collaboration: bounded generation with recognition-based review via chart-text pairing, automated urgency flagging that analyzes vital trends and adherence patterns with fail-safe escalation for missed critical monitoring tasks, and progressive disclosure controls that reduce cognitive load while maintaining oversight. These patterns indicate that clinical AI efficiency requires not only accurate models, but also mechanisms for selective verification that preserve accountability.

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