Keywords: Digital Health Governance, Artificial Intelligence, Terminology Standards, Continuity of Care, Quantitative Forecasting, Health Policy

Background:

The digital transformation of healthcare, accelerating via AI and telemedicine, is outpacing regulatory capacity to ensure semantic interoperability. This widening gap threatens the "continuity of care" by fracturing the evidence base required for safe, longitudinal patient management across systems.

Research questions:

When will the rate of terminology complexity in digital health fatally outstrip regulatory harmonization capacity, creating a quantifiable "point of no return" for global health governance?

Method:

We employed a mixed-methods approach integrating quantitative cycle analytics and Bayesian scenario modeling. We analyzed 17 international regulatory cycles (e.g., HL7, SNOMED) and technology adoption rates (AI, Digital Twins) from 2020 to 2025 across five jurisdictions (US, EU, UK, Singapore). The model simulated the divergence between "technology cycle duration" (Ttech) and "regulatory development time" (Treg) to predict systemic failure probabilities.

Results:

Our model identifies a "point of no return" emerging in Q3 2027 (median cumulative probability: 76%). By this date, the technology innovation cycle (projected at 1.25 years) will be less than half the median regulatory response time (2.5 years). This "governance collapse" creates "Artificial Epidemiology," where unmappable data undermines the reliable transfer of clinical information essential for continuity of care.

Conclusions:

Without immediate intervention, specifically i.e. mandatory, real-time global terminology registries, the digital infrastructure intended to support continuity of care will instead degrade it. We provide a quantitative roadmap to avert this probable collapse before the 2027 threshold.

Points for discussion:

Methodology validation: Is the Bayesian forecasting model robust across different European healthcare systems?

Policy implications: How can GPs advocate for "registry-mapped" compliance to protect longitudinal data integrity?