Multi-Modal FHIR Sensor Fusion for Real-Time Clinical Decision Support: From Hospital Bedside to Remote Patient Monitoring

Remote patient monitoring and telemedicine demand sensor fusion architectures integrating heterogeneous multi-modal streams, such as wearable sensors and continuous telemetry, into timely clinical decisions outside the hospital. We present MedROAD V3, a real-time, closed-loop FHIR R4 streaming platform treating sensor fusion as a first-class architectural framework, validated in a cardiac ICU. Five layers implement the pipeline: FHIR event sourcing from OpenEMR’s FHIR R4 server; Apache Kafka brokering; Faust stream processing assembling a 86-element vector over five-minute tumbling windows; an XGBoost- LSTM-Transformer ensemble combined by logistic regression meta-learning with SHAP attribution; and bidirectional FHIR write-back to the patient chart. On 1,161 held-out patients the calibrated ensemble reaches AUROC 0.774 with an Expected Calibration Error of 0.002. Restricting the feature set to what a three-device home kit measures costs 0.031 AUROC. Inferences exceeding the calibrated threshold trigger a structured three-sentence clinical narrative from the Claude API. The inference path measures 3.1 ms at the median, leaving end-to-end latency dominated by FHIR transport. Because the FHIR R4 Observation resource is device-agnostic, the identical pipeline ingests wearable streams without modification, positioning MedROAD V3 as a general sensor fusion framework for both hospital and telemedicine use.