A Modular Evaluation of AI-Assisted Clinical Documentation
TL;DR
The results support the use of this modular AI-assisted clinical documentation pipeline as a human-supervised draft-generation tool that still requires clinician review, local workflow evaluation, and prospective clinical validation before broader deployment.
Abstract
Clinical documentation in Electronic Health Records (EHRs) remains a substantial source of administrative burden for clinicians. In this study, we evaluate a modular AI-assisted clinical documentation pipeline using two complementary approaches: (1) a controlled benchmark based on multilingual synthetic clinical dialogues, and (2) an observational analysis of real-world usage traces from routine deployments. The benchmark enables systematic comparison of ASR–LLM configurations under fully controlled conditions, using metrics for transcription accuracy (Word Error Rate and Medical WER), report-generation quality, and modeled processing cost. Within this benchmark setting, Voxtral showed the strongest ASR performance among the evaluated models, while GPT-4o and Gemini 1.5 Pro showed the strongest report-generation performance under the automated evaluation used in this study. The real-world trace analysis should be interpreted as descriptive evidence of operational use, not as prospective clinical validation or as a direct evaluation of any single benchmarked configuration. Taken together, the results support the use of this pipeline as a human-supervised draft-generation tool that still requires clinician review, local workflow evaluation, and prospective clinical validation before broader deployment.