Code Right, Get Paid: AI for Medical Billing and Documentation Automating ICD-10, CPT, and E/M Coding to Reduce Claim Denials and Capture Revenue Without Adding Staff

How much revenue is quietly slipping through your claims pipeline every month — not from fraud, not from payer cuts — but from “almost correct” coding?In today’s healthcare economy, small documentation gaps create large financial consequences. Coding inaccuracies affect up to 20% of claims. Under-coding shrinks risk-adjusted payments. Denials trigger costly rework cycles that drain staff time and delay cash flow. And traditional rule-based coding tools have reached their ceiling.Code Right, Get Paid presents a rigorously designed, evidence-backed AI architecture built specifically for medical billing and documentation. This is not a hype-driven AI pitch. It is a practical, research-supported framework for reducing denials, increasing first-pass accuracy, and protecting revenue integrity — without expanding headcount.Inside, you’ll learn:Why “almost accurate” ICD-10 and CPT coding quietly erodes marginsHow hybrid AI systems outperform both static rules engines and unconstrained generative modelsWhat benchmarks (AUC, Cohen’s κ, exact match rates) actually matter in evaluating coding AIHow to integrate AI safely within CMS, AMA, FDA, and NIST compliance boundariesPractical strategies to prevent hallucinated codes while preserving contextual intelligenceHow payer-specific denial learning models increase clean claim ratesThe real limitations of explainability, bias control, and model drift — and how to manage themThis book bridges clinical NLP research, revenue cycle operations, and regulatory accountability into one operational blueprint.If your organization is serious about measurable revenue protection — not theoretical automation — this book provides the framework.The future of revenue cycle management will be data-driven, audited, and AI-augmented. The question is whether your system will lead or lag. Read more