ABSTRACT
Due to the large amount of private patient records, it is crucial to keep this data highly confidential. Anonymized patient data reduces privacy threats and ensures compliance with strict regulatory frameworks such as the General Data Protection Regulation (GDPR) and the Health Insurance Portability and Accountability Act (HIPAA), making it essential for medical research. This chapter explores the need for a transparent, efficient, and secure way to exchange anonymized patient data using blockchain and digital twin technologies. The significance of anonymized data in providing researchers with valuable information without jeopardizing patient privacy is emphasized. Blockchain technology is essential for maintaining patient data consistency and accuracy throughout its entire lifecycle. Moreover, blockchain enhances security and privacy by allowing patients to control who accesses their data through cryptographic keys. In healthcare, digital twins serve as virtual representations of patients’ real-time health data, facilitating personalized treatment, prognostication, and outcome simulation. By combining digital twins with blockchain, the system ensures patient data is both protected and dynamically employed for advanced medical research and clinical applications. A data anonymization module, a blockchain layer for secure data transactions, and an interface for digital twins for research and analysis are the key components of the proposed framework. The architecture ensures that patient data is anonymized before being shared or stored, with blockchain managing data transactions and access control, and digital twins providing a platform for simulations and analyses. The framework includes steps such as data collection from multiple sources, encryption of patient information, integration of blockchain for secure storage, and creation of digital twins for research purposes. Real-world case studies, such as Siemens Healthineers’ use of blockchain and digital twins to reduce unauthorized access attempts by 85%, further illustrate the practical benefits of the framework, highlighting improved data security, enhanced collaboration among researchers, and accelerated medical breakthroughs, such as the application of personalized radiation therapy for cancer patients.
