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This proof-of-concept application demonstrates real-world usage of a predictive model using FHIR (Fast Healthcare Interoperability Resources). The FHIR server contains generated/synthetic data for sample patients and includes demographics, vital signs, and laboratory values using interoperable LOINC coding, as would be done in a real FHIR deployment. The application polls this Electronic Health Record database in real-time to extract values needed for the model (physiologic parameters from the first 4 hours of admission). The pre-trained Random Forest models for mortality and morbidity/mortality are run, and the explanatory model demonstrates which features in this patient's physiology are most predictive of outcome.