Currently submitted to: JMIRx Med
Date Submitted: Feb 17, 2026
Open Peer Review Period: Feb 19, 2026 - Apr 16, 2026
(currently open for review)
Warning: This is an author submission that is not peer-reviewed or edited. Preprints - unless they show as "accepted" - should not be relied on to guide clinical practice or health-related behavior and should not be reported in news media as established information.
A National-Scale FHIR Health Record Platform for Longitudinal AI Analytics and Emergency Blood and Organ Donor Matching
ABSTRACT
Background:
Patient medical records remain frag-mented across hospitals, laboratories, and clinics, preventing clinicians from accessing complete longitudinal health information. Emergency blood and organ allocation further suffers from time delays that significantly increase mortality risk.
Conclusions:
Objective:
This study proposes and evaluates a national-scale centralized health record platform integrating standardized FHIR-based data aggregation, longitudinal artificial intelligence analytics, and emergency blood and organ donor discovery networks.
Methods:
All diagnostic laboratories mandatorily upload test results using HL7 FHIR standards to cre-ate unified patient records. Machine learning models including Random Forest classifiers, ARIMA time-series forecasting, geospatial matching, LSTM net-works, and explainable AI techniques were applied for donor eligibility, blood shortage prediction, and longitudinal disease tracking. Large-scale synthetic datasets were generated to simulate national deploy-ment scenarios.
Results:
The Random Forest model achieved 100% recall for donor eligibility detection. ARIMA forecasting predicted blood shortages with 89% accuracy, and geospatial matching identified compatible donors within a 5 km radius. Simulation of 2,000 emergency blood requests demonstrated a 76% re-duction in delivery time (58 to 14 minutes) and fulfillment improvement from 82% to 95%. Availability of rare blood types increased by 27–33%.
Conclusions:
Centralized FHIR-based health data combined with longitudinal AI analytics and real-time donor discovery networks can substantially improve emergency response, disease management, and healthcare equity at national scale. Keywords: FHIR; electronic health records; med-ical informatics; machine learning; longitudinal disease analysis; emergency blood donation; interoper-ability
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Copyright
© The authors. All rights reserved. This is a privileged document currently under peer-review/community review (or an accepted/rejected manuscript). Authors have provided JMIR Publications with an exclusive license to publish this preprint on it's website for review and ahead-of-print citation purposes only. While the final peer-reviewed paper may be licensed under a cc-by license on publication, at this stage authors and publisher expressively prohibit redistribution of this draft paper other than for review purposes.