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HCC coding AI requires more than extracting diagnosis mentions from clinical text. A valid coding recommendation must be supported by current documentation, MEAT evidence, ICD/HCC mapping logic, provenance, and human review. This session presents a technical architecture for audit-defensible clinical coding AI, covering OCR, evidence retrieval, MEAT validation, ICD/HCC mapping, reviewer workflows, deployment considerations, and evaluation metrics beyond F1, including substantiation precision, evidence faithfulness, and unsupported HCC false-positive rate.

Hasham Ul Haq
Hasham Ul Haq

Hasham Ul Haq is the Co-Founder of Martlet AI, where he focuses on Medical AI solutions for risk adjustment, quality measurement, and medical coding automation. He also serves as Principal ML Engineer at John Snow Labs, leading the development of products such as Total Patient Journey, Audit-Grade De-identification, and workflow automation solutions. With a background in deep tech and artificial intelligence, Hasham has contributed to research published in leading venues, including NeurIPS. His work spans applied machine learning, healthcare AI, and the design of scalable intelligent systems that bridge cutting-edge research with real-world impact.


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