Open Access
Real-Time Patient Monitoring and Alerting in Hospitals Using AWS Lake House Architecture
4
Sr Manager, Analytics Specialist SA, Amazon Web Services, USA
Abstract
A modern data architecture that integrates the capabilities of a data lake and a data warehouse into a single, cohesive platform is an Amazon Web Services (AWS) Lake House. It provides a robust platform for real-time Patient Monitoring (PM). Nevertheless, there is a lack of understanding of Real-Time (RT) PM and alerting using AWS, particularly in its clinical outcomes. Thus, the present research aims to inspect the role of AWS Lake House in RT Monitoring (RTM) of patients and alerting in hospitals. From various secondary sources, the study collects data. As per the study, the AWS-based approach overcomes the challenges faced by the traditional PM system. AWS Health Lake (HL) enables semantic indexing and querying of FHIR-compliant patient records. RT ingestion and alerting require complementary services, such as Amazon Kinesis Data Streams (KDS) for streaming data, AWS Lambda for event processing, Amazon Event Bridge for routing alerts, and Amazon SNS for notifications. Amazon Redshift and Amazon Quick Sight provide low-latency access to patient data for analysis. AWS solutions can permit Healthcare (HC) professionals to have RT analysis, secure storage, along with scalable solutions for heightened HC management and Decision-Making (DM). Moreover, leveraging AWS HL for RTM leads to improved care coordination, personalized treatment plans, and more efficient resource allocation. By leveraging AWS’s pay- as-you-go model, HL reduces infrastructure costs for HC organizations, enabling them to allocate resources more effectively. Further, the study finds that AWS allows for early intervention and improved outcomes. Data interoperability is the major challenge of AWS Lake House.
How to Cite
Shruti Worlikar. (2025). Real-Time Patient Monitoring and Alerting in Hospitals Using AWS Lake House Architecture. Frontiers in Emerging Computer Science and Information Technology, 2(08), 07–14. https://doi.org/10.37547/fecsit/Volume02Issue08-02
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T. Adewale, “Cloud computing for real-time health monitoring and wearable devices,” tech. rep., Research Gate, 2024.
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