Real-Time Healthcare Event Processing: Stream Analytics for Clinical Decision Support
DOI:
https://doi.org/10.63282/3050-922X.IJERET-V1I4P106Keywords:
Real-Time Healthcare Analytics, Clinical Decision Support (CDS), Event-Driven Architecture, Healthcare Event Streaming, Stream Analytics, Apache Kafka, Real-Time Alerting, EHR Integration, Healthcare Interoperability, Clinical Response OptimizationAbstract
The growing complexity of modern healthcare, along with the abundance of real-time data in the form of electronic health records (EHRs), wearables, bedside monitors, and the like, poses both a challenge and an opportunity for driving clinical decision support (CDS) to the next level. Traditional batch systems are also often insufficient for controlling the high velocity, volume, and variety of healthcare data, which require timely analysis and response. To address these constraints, this paper presents a new event-driven architecture for real-time healthcare event processing, enabling continuous monitoring and rapid response for informed clinical decision-making. Our proposed approach is based on the use of stream analytics frameworks (e.g., Apache Kafka, Apache Flink) to ingest, transform, and analyze data from various clinical environments in real-time, providing a scalable and low-latency alternative to traditional healthcare information systems. The primary research challenge in this paper is to design and implement real-time clinical decision support mechanisms that respond promptly to streaming health events. We introduce healthcare event streaming patterns that enable systems to identify abnormalities, such as a sudden rise in blood pressure, irregular heartbeats, and early signs of sepsis. These patterns underlie real-time alerting systems that automatically launch clinical interventions, direct information to the appropriate care teams, and record data in the EHR for traceability and compliance. This architecture is completely real-time – clinical decisions are not hampered by processing backlogs or outdated data; all available data is made up-to-date, thereby minimizing errors that can lead to complications.
One of the key technical contributions of this work is a 40% reduction in clinical response times for multiple use cases. These include emergency triage, intensive care unit (ICU) monitoring, and early warnings for chronic diseases. We experimentally verify the superior performance in early detection and response to clinical critical conditions by conducting extensive simulations with synthetic clinical time-series data, as well as real-world healthcare datasets. We further present how stream analytics is integrated with the current instance of the hospital information system (HIS), ensuring backward compatibility and ease of adoption, without disrupting hospital availability. Our system is designed to be HIPAA-compliant, featuring data encryption, audit logging, and access control. In addition, our approach is modular and can be easily deployed in the cloud, at the edge, and in hybrid settings, providing flexibility for a variety of health care environments, from tertiary care hospitals to rural health clinics. We demonstrate, through our implementation results, a significant reduction in time-to-decision, an enhancement in the accuracy of event detection, and a high level of clinician satisfaction with the CDS system
This paper presents a comprehensive solution for enhancing clinical decision support through Real-time Stream Analytics Handling healthcare events in real-time not only speeds up clinical response but also enhances the reliability and scalability of decision-making in urgent care events. This investigation has revealed that real-time event processing is not just a theoretical goal, but a strategic necessity within our increasingly digital healthcare environments, one that drives the emergence of responsive, intelligent, and patient-centric care
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