A National-Scale FHIR Health Record Platform for Longitudinal AI Analytics and Emergency Blood and Organ Donor Matching (Preprint)

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