The Dark Side of AI We see the same mistakes over and over again in Artificial Intelligence (AI) and Revenue Cycle Management (RCM) projects: data drift, misaligned KPIs, and weak governance. An example of issues that shrink projected revenue and create quality and compliance risks on implementation projects is provided below: Drift and Decay: The AI Model Gets Worse Silently The Pitfalls:   Teams launch new AI models and forget about them. The environments begin to change over time—policies,...

AI Audit Ready RCM AI is reshaping revenue cycle management (RCM)— but is it compliant? We provide a concise set of guardrails to make sure your AI-driven RCM is audit-ready. Start with immutable audit trails that record AI decisions, data sources, and model versions. Require regular bias and fairness assessments that cover demographic and payer-related impacts. Maintain documentation standards: data lineage, validation results, change logs, and approved use cases. Map AI outputs to medical...

This American Institute of Healthcare Compliance (AIHC) article, written by Corliss Collins and Dr. Tami M. Harris, takes a clear-eyed look at healthcare interoperability—what’s working, what’s not, and why it matters now more than ever. While interoperability promises better data exchange, improved care coordination, and operational efficiency, it also introduces real challenges around system fragmentation, artificial intelligence, data governance, security, and accountability. This piece...