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RedHope: An AI-Integrated Smart Blood Donor Matching and Coordination System
Subject area: Biological & Medical Sciences · Area of research: Artificial Intelligence and Healthcare Informatics
Abstract
The critical shortage of timely blood availability remains a persistent challenge in healthcare systems, often resulting in preventable fatalities due to delays in locating compatible and willing donors. Traditional blood bank systems rely heavily on manual coordination, which lacks the speed and intelligence required during medical emergencies. This paper presents RedHope, an AI-integrated web-based blood donation coordination system designed to automate and optimise the donor-patient matching process. Built using Python Flask and SQLite, RedHope implements a multi-criteria smart matching algorithm that filters donors based on blood group compatibility, geographic proximity, time availability, and urgency level. Geographic proximity is computed using the Haversine formula, enabling real-world distance calculation between the patient's hospital and registered donors. The system further integrates the Groq AI API with the llama-3.3-70b-versatile large language model to deliver personalised, AI-generated eligibility feedback to donors who do not meet the health screening criteria, improving donor engagement and retention. RedHope features role-based authentication for Donor and Patient users, an eight-point medical eligibility screening mechanism, urgency-tiered blood request management, automated donation history recording, and a printable certificate of appreciation for donors. The system addresses key operational gaps in conventional blood coordination platforms including duplicate request prevention, automatic request expiry, and donor availability management. Experimental deployment across four cities — Chennai, Bangalore, Coimbatore, and Hyderabad — demonstrates the system's capability to identify and rank the top five most suitable donors within seconds, significantly reducing the time and effort required to fulfil critical blood requests.
Keywords
Blood Donor Matching, Artificial Intelligence, Flask Web Framework, Haversine Distance Formula, Role-Based Authentication, Healthcare Information System, Geospatial Proximity, Groq API, Natural Language Generation, Urgency Classification, Web-Based Application, SQLite.
References
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How to cite this paper
@article{1715671,
author = {Thejesh M, D. Revathy},
title = {RedHope: An AI-Integrated Smart Blood Donor Matching and Coordination System},
journal = {Iconic Research And Engineering Journals},
year = {2026},
volume = {9},
number = {9},
pages = {2652-2658},
issn = {2456-8880},
url = {https://www.irejournals.com/formatedpaper/1715671.pdf},
abstract = {The critical shortage of timely blood availability remains a persistent challenge in healthcare systems, often resulting in preventable fatalities due to delays in locating compatible and willing donors. Traditional blood bank systems rely heavily on manual coordination, which lacks the speed and intelligence required during medical emergencies. This paper presents RedHope, an AI-integrated web-based blood donation coordination system designed to automate and optimise the donor-patient matching process. Built using Python Flask and SQLite, RedHope implements a multi-criteria smart matching algorithm that filters donors based on blood group compatibility, geographic proximity, time availability, and urgency level. Geographic proximity is computed using the Haversine formula, enabling real-world distance calculation between the patient's hospital and registered donors. The system further integrates the Groq AI API with the llama-3.3-70b-versatile large language model to deliver personalised, AI-generated eligibility feedback to donors who do not meet the health screening criteria, improving donor engagement and retention. RedHope features role-based authentication for Donor and Patient users, an eight-point medical eligibility screening mechanism, urgency-tiered blood request management, automated donation history recording, and a printable certificate of appreciation for donors. The system addresses key operational gaps in conventional blood coordination platforms including duplicate request prevention, automatic request expiry, and donor availability management. Experimental deployment across four cities — Chennai, Bangalore, Coimbatore, and Hyderabad — demonstrates the system's capability to identify and rank the top five most suitable donors within seconds, significantly reducing the time and effort required to fulfil critical blood requests.},
keywords = {Blood Donor Matching, Artificial Intelligence, Flask Web Framework, Haversine Distance Formula, Role-Based Authentication, Healthcare Information System, Geospatial Proximity, Groq API, Natural Language Generation, Urgency Classification, Web-Based Application, SQLite.},
month = {March},
doi = {https://doi.org/10.64388/IREV9I9-1715671}
}