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Clinical notes, problem lists, medication orders, and billing codes are core components of the electronic health record. When these components conflict, the record may become less reliable for care delivery, quality measurement, and reimbursement. Current inconsistency detection is largely manual, episodic, and dependent on documentation audits. This approach is difficult to scale across encounters, specialties, and longitudinal records. This article proposes a deep learning NLP model for detecting contradictions among clinical notes, problem lists, medication orders, and billing codes. The goal is to support continuous documentation integrity surveillance. The proposed model uses transformer-based encoders for clinical text and embedding layers for structured coded fields. Cross-attention mechanisms align concepts across EHR modules before classifying consistency relationships. Conceptually, the model could surface discrepancies such as a diagnosis documented in a note but absent from the problem list, or a billing code unsupported by physician documentation. Its output would include an inconsistency category and an interpretable explanation for clinician review. A unified NLP model for cross-module inconsistency detection could improve EHR trustworthiness, documentation quality, and clinical audit workflows. Such a system should be evaluated prospectively before operational deployment.

Clinical documentation is central to continuity of care, coding, billing, compliance, and quality measurement, yet it remains a major source of administrative burden for physicians. Artificial intelligence, especially natural language processing, has been proposed as a way to improve documentation quality, coding accuracy, billing support, and clinical workflow efficiency. This systematic review synthesised evidence from 2017 to 2023 on natural language processing methods applied to clinical documentation improvement. The review focused on automated clinical coding, note quality, billing support, computer-assisted physician documentation, and physician workflow efficiency. A PRISMA 2020-compliant search strategy was applied to PubMed, Scopus, IEEE Xplore, and Web of Science for publications from 1 January 2017 to 31 December 2023. Screening was performed in duplicate, and eligible studies were narratively synthesised by documentation domain, model type, deployment maturity, and evaluation approach. The evidence base expanded rapidly during the review period, especially in automated coding and clinical note generation. Several studies reported technically promising systems for ICD coding, documentation summarisation, and speech-derived notes, whereas billing outcomes and sustained workflow effects were less commonly evaluated. Natural language processing for clinical documentation improvement appears most mature for automated coding and note analysis. Evidence for billing support and physician workflow transformation remains less developed, particularly in prospective clinical environments.