- By:
- Kumar, Mohit ; Lee, Sangkeun M; Park, Byung H; Blum, James; Ward, Merry; Nebeker, Jonathan R.
- Page Number:
- 1-4
- Issue Number:
- 2020 IEEE
- Book Title:
- 2020 IEEE International Conference on Big Data (Big Data)
- Publication Date:
- December 27, 2023
- Conference Name:
- Big Data Tools, Methods, and Use Cases for Innovative Scientific Discovery (BTSD) 2020
- Conference Location:
- Atlanta, Georgia, United States of America
- Conference Sponsor:
- IEEE
- View DOI Listing:
- https://doi.org/10.1109/BigData50022.2020.9442793
Abstract
Health Information Technology (HIT) aims to improve healthcare outcomes by organizing and analyzing various health-related data. With data accumulating at a staggering rate, the importance of real-time analytics has been increasing dramatically, shifting the focus of informatics from batch processing to streaming analytics. HIT is also facing unprecedented challenges in adapting to this new requirement and leveraging advanced IT technologies. This paper introduces a HIT data and compute platform that supports multi-granularity real-time analytics from heterogeneous data sources. The paper first identifies functional requirements and proposes a framework that satisfies the requirements using state-of-the-art big data technologies including Apache Kafka, Spark Structured Streaming Engine, and Delta Lake. To demonstrate its capability to support data analytics in multiple time granularities analytics, a statistical process control-based hazard detection algorithm has been implemented on top of the framework to detect unexpected hazards from order cancellation data of the Department of US Veterans Affairs (VA) in near real-time.
