The Cost of Ontological Drift: A Health-Economic Analysis of How Coding Distortion Propagates Through Clinical AI Systems

Clinical AI systems trained on administrative health data inherit coding distortions introduced by billing incentives, documentation workflows, and terminology fragmentation. Prior work has characterised the mechanisms of this distortion, the feedback loops through which AI amplifies it, and the architectural responses available to mitigate it. None has quantified what ontological drift costs. This paper presents a structured health-economic argument estimating the annual burden of coding distortion and its AI amplification in European healthcare systems. Drawing on published cost estimates from interoperability research, coding quality audits, ICD transition analyses, and regulatory impact assessments, we identify four cost categories: (1) direct clinical costs from misclassification-driven diagnostic and therapeutic errors, (2) AI system costs from model degradation, retraining, and audit obligations, (3) interoperability costs from terminology fragmentation and failed data exchange, and (4) regulatory costs from compliance overhead amplified by ontological uncertainty. We synthesise these into an order-of-magnitude estimate suggesting that ontological drift may represent EUR 8-26 billion annually across EU-27 member states, corresponding to approximately 0.5-1.5% of total European healthcare expenditure. Three limitations constrain the analysis: (1) most anchoring cost data originates from the US healthcare system and requires cautious extrapolation to European contexts, (2) the AI amplification multiplier is a theoretical construct rather than an empirically calibrated parameter, and (3) the four cost categories overlap in ways that resist clean summation. These estimates require empirical validation through prospective health-economic studies; we offer them as a structured hypothesis about the economic burden of ontological drift, not as a definitive measurement.