A critical necessity towards provision of universal healthcare by the Kenyan government is to ensure constant blood supply in the countries blood donor unit. Rare blood group donors always play a critical role in healthcare sector by provision of life saving support to patients with specific medical needs to live longer and with higher quality of life. However, the scarcity of these rare blood types namely; AB negative, B negative, A negative and O negative leads to a great challenge especially in emergency or high demand situations. Despite the crucial need for blood donations, donors may be less motivated to donate blood regularly due to lack of effective incentives. Traditional blood donation management systems are often not transparent, inadequate donor identification and delayed incentives to rare blood group donors. This demoralizes them from donating blood again particularly those individuals with rare blood types. To address these challenges, this study aimed to develop a blockchain based model prototype for provision of incentives to rare blood group donors that offered trust, transparency and security tailored to increase blood donation. Study involved creation of a solidity smart contract that was deployed and implemented through the use of React js and is available at https://grandmullah.github.io/donor. For the purpose of regulatory hurdles, data protection measures are used to safeguard donor information which includes encryption, access controls and also ensuring compliance with data privacy regulations. In conclusion, a blockchain based model for provision of incentives to rare blood group donors presents an approach to address the challenges encountered by the blood donation systems, particularly when it comes to rare blood types.
@artical{l14102025ijcatr14101004,
Title = "An Implementation of a Blockchain-Based Model for Provision of Incentives to Rare Group Blood Donors ",
Journal ="International Journal of Computer Applications Technology and Research (IJCATR)",
Volume = "14",
Issue ="10",
Pages ="13 - 20",
Year = "2025",
Authors ="Leah Chebet Bunei, Prof.Simon Maina Karume, Dr. Ruth Oginga"}