Closing Ambient Clinical Documentation Gaps with Automated Provider Queries

An audit of 3,000 real clinical visits and 21,000 clarification turns found that large language models can automate the provider-query loop for clinical documentation, but roughly 9% of clarification turns hurt performance, according to a paper posted to arXiv as 2610.07502v1. The study's DAU (Draft, Ask, Update) approach builds five transcript-degradation benchmarks on public data and finds useful-question predictors are task-specific: oracle confidence dominates, note completeness needs only simple recall questions, and ICD-10 coding needs harder multi-option questions. The authors conclude deployment depends on learning when not to ask as much as what to ask.

arXiv:2610.07502v1 Announce Type: new Abstract: Provider queries are clarifying requests sent by clinical documentation specialists to physicians to close gaps in the clinical note and ensure accurate billing. Prior work automates note drafting, ICD-10 coding, and order extraction assuming a complete transcript, leaving these gaps unaddressed. We study whether an LLM can automate the query loop, termed DAU Draft, Ask, Update , across those three tasks. An audit of 3,000 real visits identifies the sources of missing documentation, from which we build five transcript-degradation benchmarks on public data. Analyzing 21k clarification turns on real conversations, we find useful-question predictors are task-specific: oracle confidence dominates, but note completeness needs only simple recall questions while ICD-10 coding needs harder, multi-option ones. About 9% of turns hurt performance, driven by redundant questions and non-answers that still trigger a rewrite. Deployment depends on learning "when not" as much as "what to" ask.