Real-Time and Near-Real-Time Analytics in Healthcare Data Ecosystems

Abstract

The fact that connected medical gadgets, wearable sensors and digital health platforms are rapidly expansive has added to pressure on the requirement of real-time and near-real-time analytics in healthcare data ecosystems. With newer changes in the healthcare systems, where the aspect of retrospective reporting is being replaced with the concept of continuous monitoring and acting upon the available data in real-time, the capacity to process, analyse and respond to streaming data in a minimal balance of latencies has gained significant importance. Nevertheless, poor system architecture, lack of interoperability and true data governance and scale are still in the way of the smooth assimilation of real-time analytics into clinical and organizational functions. This paper analyzes the architectural principles, interoperability solutions and governance needs that makes real-time and near real-time healthcare analytics a reality. Based on the latest publications in the field of stream processing, IoT-supported healthcare, edge-cloud processing, AI-based analytics, and interoperability standards like FHIR, the paper carries synthesis of both technical and ecosystem views to create a cohesive framework of real-time healthcare data ecosystem. The analysis proposed four mutually supporting layers in data generation by means of multimodal sensaging infrastructure, stream processing or event-based analytics infrastructure, adaptive Artificial Intelligence (AI)-provided intelligence to aid clinical decision-making, and governance infrastructures to achieve privacy, compliance with regulations, and system reliability. The paper also identifies trade-offs that are very crucial between latency, accuracy, scalability and security, especially in deployed distributed environment as also in edge-enabled environment. Through the alignment of technical architectures and ecosystem governance issues, the present paper presents a comprehensive model of how to operationalize real-time healthcare analytics. The findings provide insights on how to guide system developers, healthcare providers and policymakers to be able to develop resilient play low-latency and ethically controlled healthcare data infrastructures.