Assessment and Prioritization of AI-Enhanced Blockchain Factors in Healthcare Supply Chains: A Hybrid Multi-Criteria Decision-Making Approach

Abstract

The convergence of artificial intelligence (AI) and blockchain technologies holds the potential to revolutionize healthcare supply chains by enhancing data security, operational transparency, and service quality. Blockchain offers a decentralized and secure infrastructure, while AI excels at analyzing complex datasets to uncover insights and predict treatment outcomes. This study aims to evaluate and prioritize key factors influencing the integration of AI and blockchain within healthcare supply chains by employing a hybrid Fuzzy Analytic Hierarchy Process (F-AHP) and Fuzzy Decision-Making Trial and Evaluation Laboratory (F-DEMATEL) methodology. Through an extensive literature review, four main criteria and twenty-three sub-criteria were identified. Initially, F-AHP was utilized to rank these factors based on expert evaluations. Subsequently, F-DEMATEL was applied to examine the interrelationships among the sub-criteria, distinguishing between causal and effect factors. Results indicated that \"integration of treatment processes,\" \"provision of fair services,\" \"health monitoring,\" \"medical data security,\" and \"clinical decision support\" emerged as the top priorities. Furthermore, \"stakeholder participation\" and \"technology acceptance\" were identified as key causal factors, while \"monitoring the treatment process\" and \"patient-centered treatment strategies\" were found to be critical effect factors. The findings highlight the transformative potential of AI-blockchain integration in optimizing healthcare supply chain management.

Introduction

Healthcare is a vital and complex system involving services like telemedicine, medication distribution, health monitoring, and sanitation. Efficient management of healthcare supply chains (HSC) — which connect producers, intermediaries, and providers to deliver medical products and services — is crucial for operational success but faces challenges due to complexity and risks.

Advanced technologies such as Artificial Intelligence (AI), Blockchain, Cloud Computing, Internet of Things (IoT), and Machine Learning (ML) play essential roles in optimizing healthcare operations, improving transparency, security, and patient outcomes.

• AI in healthcare supply chains enhances design, risk assessment, and demand forecasting by analyzing big data. It supports chronic disease management, precise diagnostics, personalized treatment, and improves areas like clinical documentation and epidemic prediction.

• Blockchain technology provides decentralized, tamper-proof, and transparent data management that secures healthcare information, reduces fraud, and increases efficiency in drug development, clinical trials, supply chains, and insurance.

• The integration of AI and blockchain is promising for healthcare, combining blockchain’s secure data handling with AI’s real-time analytics and decision-making. This synergy helps address privacy, security, system inefficiencies, and improves pharmaceutical supply chains, clinical trials, and patient empowerment.

Despite these benefits, research has yet to fully prioritize the critical factors for successful AI-blockchain integration in healthcare supply chains. This study aims to identify and rank those factors using hybrid decision-making methods to guide strategic improvements.

AI overview:
AI, evolving since 1956, refers to intelligent systems capable of perceiving environments, making data-driven decisions, and handling uncertainty. AI ranges from narrow (task-specific) to general and hypothetical superintelligent forms.

Blockchain overview:
Blockchain records transactions in linked, immutable blocks across decentralized networks, offering decentralization, traceability, transparency, anonymity, and immutability. It exists in public, private, and consortium forms, each with different accessibility.

AI-blockchain integration:

• AI can optimize blockchain performance (e.g., scalability, security).

• Blockchain can ensure AI data authenticity and trustworthiness, especially in sensitive fields like healthcare.

The literature highlights AI’s transformative potential and blockchain’s security benefits in healthcare supply chains but underscores the need for frameworks to successfully merge these technologies.

Conclusion

This study aimed to evaluate and prioritize critical factors involved in the integration of artificial intelligence (AI) and blockchain technologies within the healthcare supply chain, employing a hybrid decision-making approach that combined Fuzzy Analytic Hierarchy Process (F-AHP) and Fuzzy Decision-Making Trial and Evaluation Laboratory (F-DEMATEL). The hybrid framework facilitated both the hierarchical prioritization and causal analysis of criteria and sub-criteria identified from the literature and expert assessments. The results from the F-AHP analysis revealed that the most significant sub-criteria in the context of AI-blockchain integration in healthcare supply chains were integration of treatment processes (C32), provide fair service (C31), health monitoring (C12), security of medical data (C34), and clinical decision support (C21). These findings highlight the importance of operational cohesion, equitable access, continuous patient monitoring, and robust data security infrastructure as foundational to effective implementation of these emerging technologies. The F-DEMATEL analysis further contributed to understanding the dynamic interrelationships among the sub-criteria by classifying them into causal and effect groups. Notably, stakeholder participation (C42), technology acceptance (C44), and integration of treatment processes (C32) were identified as primary causal factors. These criteria exert significant influence on other sub-criteria and are thus critical levers for successful integration. Conversely, sub-criteria such as monitoring the treatment process (C15) and patient-centered treatment strategies (C22) emerged as dependent (effect) variables, suggesting they are outcomes shaped by upstream decisions and systemic configurations. The joint application of F-AHP and F-DEMATEL provided a comprehensive perspective by combining priority rankings with structural influence analysis. The results underscore the need for a holistic, system-level approach to integrating AI and blockchain in healthcare. In particular, the promotion of stakeholder engagement, investment in scalable infrastructure, and fostering of technological acceptance are essential for driving downstream improvements in clinical decision-making, patient engagement, and treatment monitoring. From a practical standpoint, these findings offer guidance to healthcare policymakers, system designers, and technology developers by identifying high-impact areas where resource allocation and strategic focus can accelerate the adoption and effectiveness of AI-blockchain solutions. Future studies could expand on this work by incorporating real-world case data or exploring longitudinal impacts of implementation across diverse healthcare contexts.

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Copyright

Copyright © 2025 Dr. .S. Gomathi Meena, Mohd Jamshed Ali, Mandalapu Sivaparvathi, B. Rajalingam, Mr. Vishal Agarwal, Prasanjit Singh, Anitha E, S. Rethinavelan. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.