Towards a Blockchain-based Medical Test Results Management System

The role of test results in the diagnosis and treatment of patients’ diseases at medical facilities cannot be ignored. Patients must have a series of tests that are related to their symptoms. This can be repeated as many times as possible, depending on the type of disease and treatment. Seriously, in the cases where the patients lose their medical test record (i.e., patient’s medical history), the diagnosis is difficult due to the lack of information about the medical history as well as the symptoms/complications in the previous treatments. Storing this treatment information in medical centers can address risks related to user failure (e.g., loss of medical test records and wet/fire documents). However, users face a bit of difficulty when they change to other medical centers for medical examination and treatment since the data is stored locally, and difficult to share this with others. Current solutions focus on empowering users (i.e., patients) to share medical information related to disease treatment. However, the main barrier to these approaches is the knowledge of the users. They must embrace some background in terms of the technologies, risks, and rights they may share with treatment facilities. To solve this problem, we propose a Blockchain-based medical test result management system where all information is stored and verified by the stakeholder. The data will be stored decentralized and updated throughout the treatment process. We implement a proof-of-concept based on the Hyperledger Fabric platform. To demonstrate the effectiveness of the proposed system, we conduct evaluation methods based on three main tasks of the system: initializing, accessing, and updating data on six different scenarios (i.e., increasing in size of processing requests). The evaluation based on Hyperledger Caliper helped us to have a deeper analysis of the proposed model.

@article{Trong2022,
  title     = {Towards a Blockchain-based Medical Test Results Management System},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {13},
  number    = {11},
  year      = {2022},
  publisher = {The Science and Information Organization},
  author    = {Phuc Nguyen Trong and Hong Khanh Vo and Luong Hoang Huong and Khiem Huynh Gia and Khoa Tran Dang and Hieu Le Van and Nghia Huynh Huu and Tran Nguyen Huyen and Loc Van Cao Phu and Duy Nguyen Truong Quoc and Bang Le Khanh and Kiet Le Tuan},
  doi       = {10.14569/IJACSA.2022.0131182},
  url       = {https://doi.org/10.14569/IJACSA.2022.0131182}
}