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CAC leverages NLP to propose codes based on clinical text, but it cannot replace the coder’s application of official guidelines, payer policies, and clinical context. Best practice is a human-in-the-loop model where coders validate suggested codes, confirm principal/primary selection, verify specificity (laterality, acuity, staging), apply sequencing rules, and reconcile documentation inconsistencies. Option A over-trusts confidence scores; NLP can misinterpret negations, history vs. current, or rule-out statements. Option C risks inappropriate code acceptance without due diligence or query escalation for ambiguous documentation; coding must be based on provider documentation, not CAC output alone. Option D would prevent correction of misassigned codes, undermining accuracy and compliance. Effective CAC governance includes quality audits, feedback loops to improve NLP models, coder training, and monitoring KPIs (DNFB days, accuracy rates, denial trends). Integration with encoders and edit systems (NCCI, MUE, LCD/NCD) should complement, not replace, expert review.