Generative AI in Admission Notes and Diagnostic Completeness: A Pilot Study

Background Admission history and physical (H&P) notes influence inpatient documentation, risk adjustment, and reimbursement. In high-acuity settings, time constraints and fragmented chart data contribute to under-documentation of comorbidities. Generative AI systems that draft admission notes from existing electronic health record data may improve documentation completeness, but real-world inpatient performance remains insufficiently characterized. Objective To evaluate whether AI-generated admission notes identify documentation-relevant diagnoses supported by the medical record but not explicitly captured in provider-authored admission notes. Methods In this single-center retrospective pilot study, we reviewed 22 matched pairs of AI-generated and provider-authored admission H&P notes at a large academic medical center. We assessed principal diagnosis concordance and identified net-new secondary diagnoses. Secondary diagnoses identified by the AI but absent from provider-authored notes were adjudicated by a Clinical Documentation Improvement (CDI) team using standard institutional criteria. Results AI-generated notes aligned with the provider-authored principal diagnosis in 91% of cases (20/22). The CDI team adjudicated 104 AI-identified secondary diagnoses, of which 97% (101/104) were supported for documentation. Ninety-four diagnoses were net-new, quality-relevant conditions not documented in provider-authored notes (median 4.5 per admission). Net-new diagnoses varied across provider types-advanced practice providers (median 6), residents (5), and staff physicians (3)-without statistically significant differences (p = 0.36). Discussion Despite its modest sample size, this pilot demonstrated that AI-generated drafts achieved high principal diagnosis concordance and identified additional CDI-supported diagnoses, though narrative quality and factual accuracy were not evaluated. These patterns suggest a potential role for AI in supporting inpatient documentation completeness. Conclusion These findings highlight the potential of generative AI to surface documentation-relevant information at admission and underscore the importance of human oversight in AI-assisted documentation workflows. Larger multi-center studies are needed to assess generalizability and safety.