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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. 

A wide variety of mechanisms, such as alert triggers and auditing routines, have been developed to notify administra- tors about types of suspicious activities in the daily use of large databases of personal and sensitive information. However, such mechanisms are limited in that: 1) the volume of such alerts is often substantially greater than the capabilities of resource- constrained organizations and 2) strategic attackers may disguise their actions or carefully choose which records they touch, thus evading auditing routines. To address these problems, we introduce a novel approach to database auditing that explicitly accounts for adversarial behavior by 1) prioritizing the order in which types of alerts are investigated and 2) providing an upper bound on how much resource to allocate for auditing each alert type. We model the interaction between a database auditor and potential attackers as a Stackelberg game in which the auditor chooses an auditing policy and attackers choose which records in a database to target. We further introduce an efficient approach that combines linear programming, column generation, and heuristic search to derive an auditing policy, in the form of a mixed strategy. We assess the performance of the policy selection method using a publicly available credit card application dataset, the results of which indicate that our method produces high-quality database audit policies, significantly outperforming baselines that are not based in a game theoretic framing.