Evaluation and Classification Risks of Implementing Blockchain in the Drug Supply Chain with a New Hybrid Sorting Method

Parisa Sabbagh,Mohammadali Beheshti,Amin Salih Mohammed,Marischa Elveny,Ahmed Sayed M Metwally,Rana Pourmohamad,Shafaqat Ali,Afshin Davarpanah

doi:10.3390/su132011466

Abstract

In blockchain technology, all registered information, from the place of production of the product to its point of sale, is recorded as permanent and unchangeable, and no intermediary has the ability to change the data of other members and even the data registered by them without public consensus. In this way, users can trust the accuracy of the data. Blockchain systems have a wide range of applications in the medical and health sectors, from creating an integrated system for recording and tracking patients’ medical records to creating transparency in the drug supply chain and medical supplies. However, implementing blockchain technology in the supply chain has limitations and sometimes has risks. In this study, BWM methods and VIKORSort have been used to classify the risks of implementing blockchain in the drug supply chain. The results show that cyberattacks, double spending, and immutability are very dangerous risks for implementation of blockchain technology in the drug supply chain. Therefore, the risks of blockchain technology implementation in the drug supply chain have been classified based on a literature review and opinions of the experts. The risks of blockchain technology implementation in the supply chain were determined from the literature review.

Highlights

In recent years, the emergence of new technologies including blockchain, artificial intelligence, and machine learning and their applications has grown in the field of healthcare [1,2,3,4,5,6,7,8]
The risks of blockchain technology implementation in the drug supply chain have been classified based on the literature review and opinions of the experts
Examining the supply chain environment of the company to identify the risks identified by the company: After long meetings with the officials of different departments, all the known risks in the company documents and previous projects were provided to the researcher

Summary

Introduction

The emergence of new technologies including blockchain, artificial intelligence, and machine learning and their applications has grown in the field of healthcare [1,2,3,4,5,6,7,8]. In 2020, Wang et al constructed a new efficient hybrid learning framework, namely the CMWOAFS-SVM [9,10,11,12,13,14,15], for support vector machine (SVM), which was successfully applied to diagnose different diseases, including breast cancer, diabetes, and Sustainability 2021, 13, 11466. In 2018, Li et al developed a new artificial-intelligence-based diagnostic model that was accurate, fast, non-invasive, and cost effective to diagnose tuberculosis (TPE), which employed a moth-flame-optimization-based support vector machine with feature selection (FS-MFO-SVM) based on simple clinical signs, blood samples, and pleural effusion samples [31,32,33,34,35,36,37,38,39,40]. In 2016, Chen et al explored the potential of an extreme learning machine (ELM) and kernel ELM (KELM) for early diagnosis of Parkinson’s disease (PD) [41,42,43,44,45,46,47,48,49,50]

Methods

Results

Conclusion