Abstract Background: Ambient artificial intelligence (AI) clinical documentation tools have gained rapid adoption in healthcare to address physician burnout from documentation burden. However, current implementations primarily rely on real-time audio capture without systematically incorporating longitudinal patient data, potentially limiting documentation completeness for chronic disease management. Objective: To compare documentation completeness between ambient audio-only workflows and those augmented with historical clinical data from electronic health records (EHRs) for type 2 diabetes and hypertension encounters in primary care. Methods: We conducted a retrospective, paired, cross-sectional study of 354 primary care encounters in which diabetes mellitus (DM, n=119) and/or hypertension (HTN, n=281) were treated. Each condition instance was analysed twice to compare two methods of automated documentation: using only physician-patient conversation transcripts (termed "ambient only") compared with consolidated automated documentation that includes historical clinical data in addition to the ambient conversation (ambient + history; termed "consolidated"). Documentation completeness was assessed using the "assessment" subset of the QNOTE clinical documentation quality measurement instrument, evaluating four domains: completeness, clinical coherence, clarity, conciseness. Scoring was automated using an LLM pipeline with physician validation on a 20% sample. Results: Consolidated documentation achieved significantly higher mean total assessment composite score compared to ambient-only (94.8 vs. 80.1 on a scale of 0-100; difference 14.6 points; 95% CI 13.4-15.8; P<0.001). The largest improvements was observed in the completeness domain (difference 42.5 points; P<0.001). DM and HTN both showed similar performance of consolidated documentation vs. ambient only. Conclusions: Augmenting ambient AI documentation with historical EHR data significantly improves documentation completeness for chronic disease management in primary care. These preliminary findings challenge the prevailing audio-first implementation paradigm and suggest that bidirectional EHR integration may be essential for comprehensive AI-assisted documentation, particularly for conditions requiring synthesis of longitudinal clinical data.
Neural manifolds that orchestrate walking and stopping
Walking, stopping and maintaining posture are essential motor behaviors, yet the underlying neural processes remain poorly understood. Here, we investigate neural activity behind locomotion and