Healthcare-CT: Solid PoD and Blockchain-Enabled Cyber Twin Approach for Healthcare 5.0 Ecosystems

The healthcare personals often use stored healthcare data to make crucial decisions, assess risk, and care for patients. The extraction of the required information from the saved healthcare data needs a healthcare ecosystem that can guarantee reliable data delivery. The reliability of cyber–physical data needs to be cross-examined using several sources of data of overlapping nature. The cross-examined data can be saved on blockchain and Solid PoD (SP) to preserve its reliability and privacy. Once the reliable healthcare data is stored on the blockchain and SP, the patients’ medical history can be delivered to data-operated systems to monitor, diagnose, and detect augmented healthcare anomalies. Cyber twins (CTs) combine the specific cyber–physical objects with digital tools portraying their actual settings. The creation of a live model for the delivery of healthcare services presents a novel opportunity in patient care comprising better evaluation of risk and assessment without hampering the activities of daily living. The introduction of blockchain technology can improve the notion of CTs by certifying transparency, decentralized data storage, data irreversibility, and person-to-person industrial communication. The storage and exchange of CT data in the healthcare ecosystem depend on disseminated ledgers and decentralized databases for storing and processing data to avoid single-point reliance. The present study develops an owner-centric decentralized sharing technique to fulfill the decentralized distribution of CT data.

I. Background

The generation of a digital fingerprint (or simulated model) encourages developments to diagnose healthcare problems, processes, and/or services. This type of model can investigate, forecast, and augment all operations before implementing it in actual-world conditions [1], [2]. The simulation of data using a closed loop is transferred back to the physical object to regulate the operations and improve the device’s performance [3]. This bidirectional recording between cyber–physical space (CPS) and simulated space is known as the cyber twin (CT). CT is the digital depiction of the objects or humans in the cybernetic world and works as the simulated network [4], [5]. To start the operation of physical resources, CTs assemble and assimilate data from numerous sources comprising wearable healthcare sensors, historical data of patients attained from the activities of daily living, and domain information to produce inclusive data in the form of prototypes, simulations, duplications, or behavioral analytics [6], [7]. During the clinical diagnosis, the CTs function synchronously with their corresponding physical objects with the basic objective to locate and find out the data discrepancies between the physical and simulated objects. The data discrepancies between the physical and simulated objects require improved standardization and testing policies that develop CT models corresponding to the physical objects to support precise measurement, forecast, and optimization of the diagnosis procedures for human in the loop [8].