For decades, price transparency at the point of care has been a policy aspiration more than an operational reality. But new regulatory changes finalized by the Office of the National Coordinator for Health Information Technology (ONC) and the Centers for Medicare & Medicaid Services (CMS) mark a shift from aspiration to enforceable expectation.

The recent sentencing of Paul Njoku, former CEO of Opnet Health Care Services (operating as P & P Health Care Services), to more than six years in federal prison for orchestrating a multi-year Medicare fraud scheme, is less a story of exceptional criminality than it is a reflection of chronic system-level vulnerability. The mechanisms exploited, fabricated documentation, fraudulent credentialing, and the submission of falsified claims, are not outliers. They are foreseeable consequences of policy architecture that remains persistently ill-equipped to verify eligibility, documentation, or intent at the point of submission.

The Centers for Medicare & Medicaid Services (CMS) has unveiled the “Crushing Fraud Chili Cook-Off Competition,” a market-driven research initiative designed to leverage explainable artificial intelligence (AI) to identify fraud indicators and propose scalable solutions within the Medicare Fee-for-Service (FFS) program. The challenge invites innovative proposals that integrate machine learning (ML) with transparency and accountability, aiming to modernize fraud detection while preserving human oversight.

Cleveland Clinic has launched a system-wide rollout of Dyania Health’s Synapsis AI platform following a series of successful pilot programs in cardiology, oncology, and neurology. The deployment signals a growing commitment to integrating medically trained large language models (LLMs) into clinical research workflows to accelerate trial recruitment and improve patient access to experimental therapies.