Real-Time Clinical Data Integration Systems

Abstract

Clinical decision-making increasingly depends on integrating heterogeneous data streams in real time: vital signs from bedside monitors, laboratory results from analysers, imaging from PACS, genomic reports from sequencing pipelines, medication orders from pharmacy systems, and clinical notes from EHR documentation. Yet these data streams arrive through separate interfaces, in different formats, at varying latencies -- from seconds (vital signs) to days (genomic results) -- creating a fragmented information landscape that clinicians must mentally integrate under time pressure. Real-time clinical data integration (RTCDI) systems aim to unify these streams into a coherent, continuously updated patient representation that supports automated clinical decision support (CDS). We present the Real-Time Clinical Integration Assessment Framework (RTCIAF), evaluating five integration architectures -- stream processing pipelines, event-driven microservices, FHIR-native integration platforms, digital twin patient models, and LLM-powered clinical synthesisers -- across four clinical scenarios (ICU patient monitoring, perioperative care coordination, emergency department triage, and chronic disease remote monitoring). Our Real-Time Integration Performance Score (RTIPS) measures data latency, integration completeness, CDS accuracy, system reliability, and clinical workflow impact. FHIR-native integration platforms achieve the highest RTIPS (0.924) through standards-based data ingestion that aligns with existing hospital IT infrastructure, while LLM-powered synthesisers achieve the highest CDS accuracy (0.960) through contextual reasoning over multimodal patient data.