A prebill control plane for record integrity in inpatient artificial intelligence-enabled clinical documentation workflows

Ambient artificial intelligence (AI) scribes and other electronic health record (EHR)-integrated generative AI tools are entering clinical documentation workflows because they offer measurable, if still modest, gains in documentation efficiency and clinician experience. At the same time, the medical record remains a high-consequence informatics object that supports clinical communication, continuity, documentation clarification, coding, payment, and downstream organizational reporting. As AI-generated content moves through drafting, summarization, clarification, and coding-adjacent workflows, the central challenge is no longer simply whether a model can generate fluent text, but whether health systems can preserve record integrity as documentation moves downstream. We examine this problem in United States inpatient claim-linked workflows. This Perspective proposes a prebill control plane as a candidate governance model for that problem. We describe three recurring modes of record-integrity failure that motivate it: unsupported addition, clinically meaningful loss, and propagation across encounters and systems. We then outline five operational components of a minimum viable control plane: physician-led governance and oversight, evidence visibility, change control, monitoring, and auditability. The framework is intended to support safer adoption by protecting patient safety, clinician safety, and record integrity under real-world inpatient and mid-revenue-cycle workflow pressure.