Background: Recently, the problem of traditional Chinese medicine (TCM) safety has attracted attention worldwide. To prevent the spread of counterfeit drugs, it is necessary to establish a drug traceability system. A traditional drug traceability system can record the whole circulation process of drugs, from planting, production, processing, and warehousing to use by hospitals and patients. Once counterfeit drugs are found, they can be traced back to the source. However, traditional drug traceability systems have some drawbacks, such as failure to prevent tampering and facilitation of sensitive disclosure. Blockchain (including Bitcoin and Ethernet Square) is an effective technology to address the problems of traditional drug traceability systems. However, some risks impact the reliability of blockchain, such as information explosion, sensitive information leakage, and poor scalability.
Objective: To avoid the risks associated with the application of blockchain, we propose a lightweight block chain framework.
Methods: In this framework, both horizontal and vertical segmentations are performed when designing the blocks, and effective strategies are provided for both segmentations. For horizontal segmentation operations, the header and body of the blockchain are separated and stored in the blockchain, and the body is stored in the InterPlanetary File System. For vertical segmentation operations, the blockchain is cut off according to time or size. For the addition of new blocks, miners only need to copy the latest part of the blockchain and append the tail and vertical segmentation of the block through the consensus mechanism.
Results: Our framework could greatly reduce the size of the blockchain and improve the verification efficiency.
Conclusions: Experimental results have shown that the efficiency improves compared with ethernet when a new block is added to the blockchain and a search is conducted.
Keywords: IPFS; TCM; blockchain; fake drugs; fraud; traceability; traceability system; traditional Chinese medicine.
©Zhengfei Wang, Lai Wang, Fu'an Xiao, Qingsong Chen, Liming Lu, Jiaming Hong. Originally published in the Journal of Medical Internet Research (https://www.jmir.org), 21.06.2021.