Automating medical coding presents significant challenges due to the intricate nature of health information management, particularly medical records, which form the bedrock of healthcare coding and billing. Protected Health Information (PHI) exists in various formats, including handwritten notes, patient intake forms, and dictated lab reports. This lack of standardization poses a considerable hurdle for AI systems.Revenue Cycle Management (RCM) professionals—such as coders, billers, and auditors—possess the contextual understanding necessary to interpret and extract crucial information from diverse and sometimes illegible records. The complexity of abstracting documentation is further compounded by ambiguous acronyms, localized jargon, and specialized medical language. Additionally, a substantial portion of the information provided to coders is subjective, demanding a level of interpretation that current AI technology may not yet possess.For AI to acquire these essential skills, medical coders will need to train the systems, and auditors will be responsible for verifying their accuracy. This technological shift is inevitable, so RCM professionals should consider developing these skills to remain competitive in the evolving IT landscape.Join us as host Terry Fletcher welcomes Sirisha Bommireddipalli to the show for an insightful discussion on this important topic.