Real-Time Integration of IoMT and Patient Digital Twins with Blockchain-Anchored Provenance for Interoperable and Privacy-Preserving Electronic Health Records

Authors

• Dinesh Goyal, Department of Creative Technologies and Product Design, School of Innovative Design and Management, National Taipei University of Business, Taiwan AND Department of CSE, Poornima Institute of Engineering & Technology, Jaipur, IndiaFollow
• Sheng-Lung Peng, Department of Creative Technologies and Product Design, School of Innovative Design and Management, National Taipei University of Business, TaiwanFollow

Abstract

Electronic health records (EHRs) are not designed for continuous physiological streams, dynamic patient-state representations, and verifiable cross-provider exchange. This study presents an integrated healthcare architecture combining Internet of Medical Things (IoMT) telemetry, patient-specific Digital Twins, GRU-based risk analytics, blockchain-anchored provenance, and HL7 FHIR R4 interoperability. Its central contribution is the Synchronized State Update with Risk-Aware Blockchain Anchoring (SSU-RABA) algorithm, which couples Digital Twin synchronization with predicted clinical risk to prioritize asynchronous provenance anchoring without blocking the real-time update path. In controlled experiments with 18,000 synthetic IoMT packets, the Digital Twin achieved a mean update latency of 178 ms and synchronization accuracy above 97%, while blockchain verification overhead remained below 50 ms and all tested FHIR resources passed validation. External five-fold subject-independent validation on the BIDMC PPG and Respiration Dataset (53 patients; 24,273 windows) yielded a GRU AUC-ROC of 96.6% and recall of 87.1%; XGBoost achieved an AUC-ROC of 97.6% with higher precision but lower recall. These results show that the proposed architecture can combine low-latency state synchronization, predictive analytics, tamper-evident provenance, and standards-based interoperability under the evaluated conditions. Clinical deployment and prospective validation remain future work.

Recommended Citation

Goyal, Dinesh and Peng, Sheng-Lung (2026) "Real-Time Integration of IoMT and Patient Digital Twins with Blockchain-Anchored Provenance for Interoperable and Privacy-Preserving Electronic Health Records," Mesopotamian Journal of Artificial Intelligence in Healthcare: Vol. 4: Iss. 1, Article 7.