Big Data and Blockchain Synergies in Healthcare: Opportunities for Enhanced Decision-Making and Operational Management
Keywords:
Big Data, Blockchain, Healthcare Analytics, AI-driven Decision-Making, Predictive ModelingAbstract
Healthcare can only benefit from the integration of Big Data and Blockchains that improve decision making, operational management and data security. This research examines how Big Data analytics and AI driven algorithms can improve patient care, resource allocation, and predictive modeling as well as Blockchain technology to assures data lapses, integrity, privacy, and supply chain security. Four healthcare data analysis focused approaches, i.e., Federated Learning, Time-Series Prediction, Deep Learning and Smart Management Information Systems were implemented. By using the proposed model, the accuracy on disease prediction, fraud detection and medical supply chain management are achieved 95.4, 87.6 and 92.3 respectively, and are better than existing centralized systems. Related work comparative analysis reveals that solutions leveraging Blockchain into Big Data have better security, scalability, and real time processing when compared to standard Big Data. Finally, the study shows that privacy preserving AI models integrated with decentralized blockchain ledgers can decrease data breach by 74.2 percent and optimize the decision making of medical problems by 89.5 percent. While faced by regulatory impediments and interoperability difficulties, the findings emphasize the big data and Blockchain synergy as a potent means to revolutionize the healthcare managements. The focus in future research should be on scalability improvements, regulations for cross border data sharing and optimization of the Blockchain for real time processing in order to take full advantage of this technology.
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AJAYI, O.O., KURIEN, A.M., DJOUANI, K. and DIENG, L., 2024. 4IR Applications in the Transport Industry: Systematic Review of the State of the Art with Respect to Data Collection and Processing Mechanisms. Sustainability, 16(17), pp. 7514.
ARDITO, L., CERCHIONE, R., MAZZOLA, E. and RAGUSEO, E., 2022. Industry 4.0 transition: a systematic literature review combining the absorptive capacity theory and the data–information–knowledge hierarchy. Journal of Knowledge Management, 26(9), pp. 2222-2254.
ASFAHANI, A.M., 2024. Fusing talent horizons: the transformative role of data integration in modern talent management. Discover Sustainability, 5(1), pp. 25.
ASTUTI, R. and HIDAYATI, L., 2023. How might blockchain technology be used in the food supply chain? A systematic literature review. Cogent Business & Management, 10(2),.
BACHMANN, N., TRIPATHI, S., BRUNNER, M. and JODLBAUER, H., 2022. The Contribution of Data-Driven Technologies in Achieving the Sustainable Development Goals. Sustainability, 14(5), pp. 2497.
BADSHAH, A., DAUD, A., ALHARBEY, R., BANJAR, A., BUKHARI, A. and ALSHEMAIMRI, B., 2024. Big data applications: overview, challenges and future. The Artificial Intelligence Review, 57(11), pp. 290.
BEHZAD, M.V. and GIOVANNI, P.D., 2024. The Impact of Business Continuity on Supply Chain Practices and Resilience Due to COVID-19. Logistics, 8(2), pp. 41.
BENDARY, M.G. and RAJADURAI, J., 2024. Emerging Technologies and Public Innovation in the Saudi Public Sector: An Analysis of Adoption and Challenges Amidst Vision 2030. The Innovation Journal, 29(1), pp. 1-42.
BILLANES, J.D., GRACE, Z., MA and JØRGENSEN, B.N., 2025. Data-Driven Technologies for Energy Optimization in Smart Buildings: A Scoping Review. Energies, 18(2), pp. 290.
CHOI, N. and KIM, H., 2025. Technological Convergence of Blockchain and Artificial Intelligence: A Review and Challenges. Electronics, 14(1), pp. 84.
DANIL, L., JAHROH, S., SYARIEF, R. and TARYANA, A., 2025. Technological Innovation in Start-Ups on a Pathway to Achieving Sustainable Development Goal (SDG) 8: A Systematic Review. Sustainability, 17(3), pp. 1220.
DE FELICE, F., TRAVAGLIONI, M. and PETRILLO, A., 2021. Innovation Trajectories for a Society 5.0. Data, 6(11), pp. 115.
DELIU, D. and OLARIU, A., 2024. The Role of Artificial Intelligence and Big Data Analytics in Shaping the Future of Professions in Industry 6.0: Perspectives from an Emerging Market. Electronics, 13(24), pp. 4983.
DIANA-ANDREEA CĂUNIAC, ANDREEA-ALEXANDRA CÎRNARU, OPREA, S. and BÂRA, A., 2025. Intersections of Big Data and IoT in Academic Publications: A Topic Modeling Approach. Sensors, 25(3), pp. 906.
DING, H., TIAN, J., YU, W., WILSON, D.I., YOUNG, B.R., CUI, X., XIN, X., WANG, Z. and LI, W., 2023. The Application of Artificial Intelligence and Big Data in the Food Industry. Foods, 12(24), pp. 4511.
GUO, H. and POLAK, P., 2024. Finance centralization—research on enterprise intelligence. Humanities & Social Sciences Communications, 11(1), pp. 1536.
HASSANI, H., KOMENDANTOVA, N., KROOS, D., UNGER, S. and YEGANEGI, M.R., 2022. Big Data and Energy Security: Impacts on Private Companies, National Economies and Societies. IoT, 3(1), pp. 29.
JAVED, A., BASIT, A., EJAZ, F., HAMEED, A., FODOR, Z.J. and HOSSAIN, M.B., 2024. The role of advanced technologies and supply chain collaboration: during COVID-19 on sustainable supply chain performance. Discover Sustainability, 5(1), pp. 46.
JUNG, D.H., 2022. Enhancing Competitive Capabilities of Healthcare SCM through the Blockchain: Big Data Business Model’s Viewpoint. Sustainability, 14(8), pp. 4815.
KARRAS, A., GIANNAROS, A., THEODORAKOPOULOS, L., KRIMPAS, G.A., KALOGERATOS, G., KARRAS, C. and SIOUTAS, S., 2023. FLIBD: A Federated Learning-Based IoT Big Data Management Approach for Privacy-Preserving over Apache Spark with FATE. Electronics, 12(22), pp. 4633.
KASHPRUK, N., PISKOR-IGNATOWICZ, C. and BARANOWSKI, J., 2023. Time Series Prediction in Industry 4.0: A Comprehensive Review and Prospects for Future Advancements. Applied Sciences, 13(22), pp. 12374.
LĂZĂROIU, G., ANDRONIE, M., IATAGAN, M., GEAMĂNU, M., ȘTEFĂNESCU, R. and DIJMĂRESCU, I., 2022. Deep Learning-Assisted Smart Process Planning, Robotic Wireless Sensor Networks, and Geospatial Big Data Management Algorithms in the Internet of Manufacturing Things. ISPRS International Journal of Geo-Information, 11(5), pp. 277.
LI, X., YUE, J., WANG, S., LUO, Y., CHENG, S., ZHOU, J., XU, D. and LU, H., 2024. Development of Geographic Information System Architecture Feature Analysis and Evolution Trend Research. Sustainability, 16(1), pp. 137.
LIANG, C., WANG, X., GU, D., LI, P., CHEN, H. and XU, Z., 2023. Smart Management Information Systems (Smis): Concept, Evolution, Research Hotspots and Applications. Data Intelligence, 5(4), pp. 857-884.
MADANCHIAN, M., 2024. The Role of Complex Systems in Predictive Analytics for E-Commerce Innovations in Business Management. Systems, 12(10), pp. 415.
MISHRA, P. and SINGH, G., 2023. Internet of Medical Things Healthcare for Sustainable Smart Cities: Current Status and Future Prospects. Applied Sciences, 13(15), pp. 8869.
NEGI, P., PATHANI, A., BHATT, B.C., SWAMI, S., SINGH, R., GEHLOT, A., THAKUR, A.K., GUPTA, L.R., PRIYADARSHI, N., TWALA, B. and VINEET, S.S., 2024. Integration of Industry 4.0 Technologies in Fire and Safety Management. Fire, 7(10), pp. 335.
NOWROZY, R., 2025. GPT, ontology, and CAABAC: A tripartite personalized access control model anchored by compliance, context and attribute. PLoS One, 20(1),.
ÖHMAN, P., RAHMAN, M., RANA, T. and XU, Y., 2024. Bridging BI and AI Enhancing Operational Efficiency in the Chinese Financial Sector. Journal of Global Information Management, 32(1), pp. 1-27.
ORDÓÑEZ-MARTÍNEZ, D., SEGUÍ-PONS, J.,M. and RUIZ-PÉREZ, M., 2024. Conceptual Framework and Prospective Analysis of EU Tourism Data Spaces. Sustainability, 16(1), pp. 371.
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