Zero-Knowledge Process Verification: A Comprehensive Framework for a Distributed Healthcare System.

Objective: This article introduces the ZK-PRET Business Process Prover framework that integrates Object Management Group (OMG) business process standards with zero-knowledge cryptographic verification to enable privacy-preserving healthcare process compliance across distributed systems. Methods: We developed a multilayer architecture combining formal business process modeling, zero-knowledge proof generation, and regulatory compliance verification. The framework extends established OMG standards with cryptographic verification capabilities to achieve verifiable compliance, privacy preservation, and regulatory accountability. Implementation testing were conducted in synthetic data environments designed to represent real-world healthcare scenarios. Results: The ZK-PRET Business Process Prover framework demonstrates practical applicability across many healthcare domains, including treatment planning, telemedicine coordination, healthcare administration, consumer health services, multientity clinical trials, and supply chain management. Implementation results demonstrate cryptographic verification capabilities that enable mathematical prevention of regulatory violations rather than post hoc detection. The results demonstrate configurable privacy preservation through zero-knowledge verification and consistent proof sizes suitable for modeling complex orchestrations, while leveraging already widely used Web 2 process models suitable for multiple runtime deployment topologies. Conclusions: Zero-knowledge healthcare process verification represents a foundational technology for regulatory compliance in distributed healthcare systems. While agentic AI systems present important opportunities for automation, the underlying requirement for verifiable process compliance through cryptographic means brings broader challenges. ZK-PRET Business Process Prover addresses these challenges in healthcare transformative flows, enabling safer deployment of autonomous systems while maintaining regulatory standards.