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Abstract ObjectivesTo develop CigStopper, a real-time, automated medical billing prototype designed to identify eligible tobacco cessation care codes, thereby reducing administrative workload while improving billing accuracy. Materials and MethodsChatGPT prompt engineering generated a synthetic corpus of physician-style clinical notes categorized for CPT codes 99406/99407. Practicing clinicians annotated the dataset to train multiple machine learning (ML) models focused on accurately predicting billing code eligibility. ResultsDecision tree and random forest models performed best. Mean performance across all models: PRC AUC = 0.857, F1 score = 0.835. Generalizability testing on deidentified notes confirmed that tree-based models performed best. DiscussionCigStopper shows promise for streamlining manual billing inefficiencies that hinder tobacco cessation care. ML methods lay the groundwork for clinical implementation based on good performance using synthetic data. Automating high-volume, low-value tasks simplify complexities in a multi-payer system and promote financial sustainability for healthcare practices. ConclusionCigStopper validates foundational methods for automating the discernment of appropriate billing codes for eligible smoking cessation counseling care.