Blockchain-enabled predictive digital twin approach for healthcare: Enhancing accuracy and performance with federated learning

Data management in healthcare faces significant challenges in balancing security and anonymity during information exchange. Recent advancements in decentralized distributed ledgers and digital twins offer promising solutions to enhance knowledge sharing. A predictive digital twin creates a virtual model using data from various stakeholders to forecast outcomes, support decision-making for efficient resource allocation, and benchmark operational effectiveness. Digital twins can leverage blockchain technology to enable secure and decentralized data exchange while ensuring privacy and regulatory compliance. This paper introduces a novel dynamic blockchain-integrated predictive digital twin model aimed at improving information sharing and fostering collaboration among healthcare providers and stakeholders. Using federated learning, each provider site independently trains its model and shares aggregated insights to benchmark performance against peers. This collaborative approach encourages knowledge sharing and helps identify areas for operational improvement. Our dynamic and iterative approach is validated through three distinct applications: digitized cancer images, voice recordings from Parkinson’s disease patients, and a more generalized application for collaborative supplier selection, evaluating factors such as price, quality, and customer feedback. We find that federated learning can offer a robust and stable level of performance, as a valuable compromise between fully local and centralized learning approaches. By allowing healthcare institutions to collaborate without compromising patient confidentiality, federated learning improves prediction accuracy and overall outcomes. Our solution enables secure, privacy-preserving collaboration among healthcare institutions, allowing them to compare models, identify best practices, and improve operational effectiveness. Thus, our study makes an original contribution to improving healthcare cost-effectiveness through digital twins, with implications for personalized medicine and promoting interoperability.