Jul 2026· WSEAS Transactions on Computers· Vol 25, pp. 91· 0 citations· 9 references
TL;DR
This chapter introduces a prototype called the 'Smarter, Safe Healthcare Supply Chain,' a system that brings together fast telemetry, edge computing, blockchain smart contracts, and explainable anomaly detection to help stop compromised medications from reaching patients.
Abstract
The global healthcare supply chain is experiencing increasing challenges with respect to maintaining the security of medications, adequate storage of medications, and identifying potential issues with medications that may pose a risk to patient safety. This chapter introduces a prototype called the 'Smarter, Safe Healthcare Supply Chain.' The system brings together fast telemetry (simulating 4G and 5G), edge computing, blockchain smart contracts, and explainable anomaly detection to help stop compromised medications from reaching patients. Included in the prototype's design are an IoT temperature and location data sensing simulator; an edge service that checks the signature of a device and executes "explainable" checks; and two smart contracts associated with device tracking and alerting. Tools like latency simulation, on-chain device tracking, device allowance (i.e., allowing only those devices that have been verified via smart contracts to access the network), and audit logs allow for the prototype to demonstrate how new technologies can enhance, accelerate, and streamline operations and build trust throughout the supply chain. Some of the key results of the prototype include faster-than-anticipated response times under 5G simulated conditions, successful verification of devices (both at the edge and enterprise-level) via smart contract(s), and accurate alerting developed based on a predefined set of rules. The framework upon which the prototype is built follows the principles of Zero Trust (e.g., NIST SP 800-207A, GSMA 5G IoT Guidelines, and use case-specific Healthcare Compliance Controls). Limitations exist within the prototype (such as being a single-node blockchain and the use of rule-based alerting versus leveraging full machine learning capabilities); however, it presents a viable operational model for practical application within a regulated supply chain (e.g., pharmaceuticals). Future work will include multi-party blockchain networks, evolving AI algorithms, and demonstrating full integration of the prototype into existing regulatory workflow(s).
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