High-Throughput Data Ingestion: Architecting Spring Batch and RabbitMQ Pipelines for Real-Time HL7 and EMR Record Processing
Author(s): Anupam Ojha
Publication #: 2605016
Date of Publication: 10.07.2022
Country: United States
Pages: 1-5
Published In: Volume 8 Issue 4 July-2022
DOI: https://doi.org/10.62970/IJIRCT.v8.i4.2605016
Abstract
The digitization of modern healthcare has led to a data explosion, where clinical sys-tems must ingest millions of high-fidelity HL7 and EMR records with zero margin for error. Traditional monolithic batch architectures fail to provide the sub-second latency required for real-time Clinical Decision Support (CDS). This paper presents a high-throughput, dis-tributed ingestion architecture that leverages the stateful processing of Spring Batch and the asynchronous orchestration of RabbitMQ. I propose a ”Partitioned Ingestion Pattern” that enables horizontal scaling while maintaining strict message ordering for patient safety. My research contributes a formal mathematical model for throughput optimization in med-ical messaging and introduces a novel multi-stage reconciliation algorithm to ensure 100% data durability. Experimental results demonstrate a peak throughput of 18,500 messages per second, representing a 4.4x improvement over legacy sequential pipelines.
Keywords: Spring Batch, RabbitMQ, HL7 v2, EMR, Distributed Systems, Message-Oriented Middleware, Real-time Informatics, Data Resilience.
Download/View Count: 6
Share this Article