| Issue |
E3S Web Conf.
Volume 692, 2026
3rd International Conference on Intelligent and Sustainable Power and Energy Systems (ISPES 2025)
|
|
|---|---|---|
| Article Number | 03007 | |
| Number of page(s) | 7 | |
| Section | Artificial Intelligence and Human-Computer Interaction | |
| DOI | https://doi.org/10.1051/e3sconf/202669203007 | |
| Published online | 04 February 2026 | |
Decentralized Blockchain-Based Fund Management System for Transparent and Secure Medical Transactions
Department of CS&E, BGS College of Engineering and Technology, Bengaluru, India
* Corresponding author: This email address is being protected from spambots. You need JavaScript enabled to view it.
Abstract
The healthcare industry continues to have issues regarding the transparency and trust of the financial transactions, and especially in case of handling of insurance claims and the funding of the patient. Intermediaries and centralization is generally accompanied by inefficiencies, delay and lack of accountability. To eliminate these problems, in this paper, Medicare Chain is proposed as a decentralized blockchain-based fund management system in order to ensure the secure and transparent medical transaction. The system utilizes smart contracts of the Ethereum network to automate the process of transfer of funding between the patients, doctors and donors without the need of centralized authority in the process. Data and transaction logs of nurses is set into the InterPlanetary File System (IPFS) to ensure integrity and prevent any kind of tampering. Django-based web interface allows users authentication, access control and access to the blockchain network. By introducing a framework for auditable, secure and efficient management of medical funds using the concepts of decentralization, the proposed framework shows the possibilities of decentralized systems to create more reliability and trust amongst the healthcare ecosystems.
© The Authors, published by EDP Sciences, 2026
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.
Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.
Initial download of the metrics may take a while.

