A Geolocation-Integrated Emergency Blood Donation Response System (GI-BDERS)

A Geolocation-Integrated Emergency Blood Donation Response System (GI-BDERS) was developed to enhance the speed and accuracy of emergency blood donation processes in Nigeria. The system integrates geolocation tracking, automated donor matching, and SMS-based communication to improve emergency response coordination. GI-BDERS was designed using an agile methodology and implemented with Django, PostgreSQL, Google Maps API for location tracking, and Termii API for notifications. Real-world testing demonstrated significant improvements: average donor response times ranged between 7–19 minutes, rare blood types achieved up to 57% engagement, and matching accuracy reached 100% using a Python-based compatibility matrix. These results show the system’s capability to reduce delays, enhance donor mobilization, and improve operational efficiency in emergency healthcare delivery. Future work includes integrating machine learning to enable predictive analytics for optimized emergency response.

Institutions

Publication Details

Journal
Zenodo (CERN European Organization for Nuclear Research)
Published
2025-11-14
DOI
https://doi.org/10.5281/zenodo.17607940
Primary Topic
Blood donation and transfusion practices
Type
article
Field-Weighted Citation Impact
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article

A Geolocation-Integrated Emergency Blood Donation Response System (GI-BDERS)

Zenodo (CERN European Organization for Nuclear Research)
Blood donation and transfusion practices
article

A Geolocation-Integrated Emergency Blood Donation Response System (GI-BDERS)

article en

Abstract

A Geolocation-Integrated Emergency Blood Donation Response System (GI-BDERS) was developed to enhance the speed and accuracy of emergency blood donation processes in Nigeria. The system integrates geolocation tracking, automated donor matching, and SMS-based communication to improve emergency response coordination. GI-BDERS was designed using an agile methodology and implemented with Django, PostgreSQL, Google Maps API for location tracking, and Termii API for notifications. Real-world testing demonstrated significant improvements: average donor response times ranged between 7–19 minutes, rare blood types achieved up to 57% engagement, and matching accuracy reached 100% using a Python-based compatibility matrix. These results show the system’s capability to reduce delays, enhance donor mobilization, and improve operational efficiency in emergency healthcare delivery. Future work includes integrating machine learning to enable predictive analytics for optimized emergency response.

Zenodo (CERN European Organization for Nuclear Research)
Abiola Ajimobi Technical University Ibadan (NG)
Good health and well-being
Openalex Percentile: Top 33%
Blood donation and transfusion practices
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