Medical Coding AI — Part 3: Generative AI, Compliance & Production Deployment
This course takes the multi-label coding model built in Part 2 and transforms it into a production-grade clinical AI system. Students survey the landscape of medical large language models (Med-PaLM 2, GPT-4, Meditron-70B), design systematic prompting strategies for reliable LLM-assisted coding, build a retrieval-augmented generation (RAG) pipeline grounded in institutional coded case history, generate SHAP and Integrated Gradients explanations for coder-facing and auditor-facing evidence highlighting, design HIPAA-compliant audit trails satisfying CMS and False Claims Act requirements, measure and mitigate demographic bias, implement three-tier human-review triage with active learning, build an MLOps monitoring and retraining pipeline, and produce an executive ROI model with Monte Carlo simulation. By the end, students have a deployed coding system with a human review workflow, compliance audit log, fairness monitoring report, and quantified financial impact analysis.
- Level: advanced
- Duration: 9 weeks
- 10 sections
- Price: $49.99
Topics: large language models, GPT-4, Med-PaLM, retrieval-augmented generation, RAG, FAISS, prompt engineering, chain-of-thought, SHAP, integrated gradients, explainable AI, HIPAA compliance, CMS audit, False Claims Act, fairness, demographic bias, human-in-the-loop, MLOps, drift detection, ROI, Monte Carlo simulation, production deployment
What's inside
- Medical Coding AI — Part 3: Generative AI, Compliance & Production Deployment
- Chapter 1: Clinical Foundation Models and LLMs
- Chapter 2: Prompt Engineering for Medical Coding
- Chapter 3: Retrieval-Augmented Coding
- Chapter 4: Explainable AI for Medical Coding
- Chapter 5: Compliance, Auditability, and Regulatory Requirements
- Chapter 6: Fairness, Bias, and Healthcare AI Risk
- Chapter 7: Human-in-the-Loop Coding Systems
- Chapter 8: MLOps for Medical Coding Systems
- Chapter 9: Economic Impact and ROI of AI Coding Systems