Advanced health information technology analytic framework and application to hazard detection

Mohit Kumar; Sangkeun Lee; Byung Hoon Park; James Blum; Merry Ward; Jonathan R. Nebeker

doi:10.1109/BigData50022.2020.9442793

Advanced health information technology analytic framework and application to hazard detection

Mohit Kumar, Sangkeun Lee, Byung Hoon Park, James Blum, Merry Ward, Jonathan R. Nebeker

Discrete Algorithms

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

1 Scopus citations

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.

Original language	English
Title of host publication	Proceedings - 2020 IEEE International Conference on Big Data, Big Data 2020
Editors	Xintao Wu, Chris Jermaine, Li Xiong, Xiaohua Tony Hu, Olivera Kotevska, Siyuan Lu, Weijia Xu, Srinivas Aluru, Chengxiang Zhai, Eyhab Al-Masri, Zhiyuan Chen, Jeff Saltz
Publisher	Institute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)	9781728162515
DOIs	https://doi.org/10.1109/BigData50022.2020.9442793
State	Published - Dec 10 2020
Event	8th IEEE International Conference on Big Data, Big Data 2020 - Virtual, Atlanta, United States Duration: Dec 10 2020 → Dec 13 2020

Publication series

Name	Proceedings - 2020 IEEE International Conference on Big Data, Big Data 2020
Volume	2020-January

Conference

Conference	8th IEEE International Conference on Big Data, Big Data 2020
Country/Territory	United States
City	Virtual, Atlanta
Period	12/10/20 → 12/13/20

Funding

ACKNOWLEDGMENT This work is sponsored by the US Department of Veterans Affairs under Inter-Agency Agreement number VA118-17-M-2015. Notice: This manuscript has been authored by UT-Battelle, LLC, under contract DE-AC05-00OR22725 with the US Department of Energy (DOE). The US government retains and the publisher, by accepting the article for publication, acknowledges that the US government retains a nonexclusive, paid-up, irrevocable, worldwide license to publish or reproduce the published form of this manuscript, or allow others to do so, for US government purposes. DOE will provide public access to these results of federally sponsored research in accordance with the DOE Public Access Plan (http://energy.gov/downloads/doe-public-access-plan).

Keywords

Big Data
HIT
Streaming Architecture

Access to Document

10.1109/BigData50022.2020.9442793

Cite this

Kumar, M., Lee, S., Hoon Park, B., Blum, J., Ward, M., & Nebeker, J. R. (2020). Advanced health information technology analytic framework and application to hazard detection. In X. Wu, C. Jermaine, L. Xiong, X. T. Hu, O. Kotevska, S. Lu, W. Xu, S. Aluru, C. Zhai, E. Al-Masri, Z. Chen, & J. Saltz (Eds.), Proceedings - 2020 IEEE International Conference on Big Data, Big Data 2020 Article 9442793 (Proceedings - 2020 IEEE International Conference on Big Data, Big Data 2020; Vol. 2020-January). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/BigData50022.2020.9442793

Kumar, Mohit ; Lee, Sangkeun ; Hoon Park, Byung et al. / Advanced health information technology analytic framework and application to hazard detection. Proceedings - 2020 IEEE International Conference on Big Data, Big Data 2020. editor / Xintao Wu ; Chris Jermaine ; Li Xiong ; Xiaohua Tony Hu ; Olivera Kotevska ; Siyuan Lu ; Weijia Xu ; Srinivas Aluru ; Chengxiang Zhai ; Eyhab Al-Masri ; Zhiyuan Chen ; Jeff Saltz. Institute of Electrical and Electronics Engineers Inc., 2020. (Proceedings - 2020 IEEE International Conference on Big Data, Big Data 2020).

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title = "Advanced health information technology analytic framework and application to hazard detection",

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.",

keywords = "Big Data, HIT, Streaming Architecture",

author = "Mohit Kumar and Sangkeun Lee and {Hoon Park}, Byung and James Blum and Merry Ward and Nebeker, {Jonathan R.}",

note = "Publisher Copyright: {\textcopyright} 2020 IEEE.; 8th IEEE International Conference on Big Data, Big Data 2020 ; Conference date: 10-12-2020 Through 13-12-2020",

year = "2020",

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day = "10",

doi = "10.1109/BigData50022.2020.9442793",

language = "English",

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publisher = "Institute of Electrical and Electronics Engineers Inc.",

editor = "Xintao Wu and Chris Jermaine and Li Xiong and Hu, {Xiaohua Tony} and Olivera Kotevska and Siyuan Lu and Weijia Xu and Srinivas Aluru and Chengxiang Zhai and Eyhab Al-Masri and Zhiyuan Chen and Jeff Saltz",

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Kumar, M, Lee, S, Hoon Park, B, Blum, J, Ward, M & Nebeker, JR 2020, Advanced health information technology analytic framework and application to hazard detection. in X Wu, C Jermaine, L Xiong, XT Hu, O Kotevska, S Lu, W Xu, S Aluru, C Zhai, E Al-Masri, Z Chen & J Saltz (eds), Proceedings - 2020 IEEE International Conference on Big Data, Big Data 2020., 9442793, Proceedings - 2020 IEEE International Conference on Big Data, Big Data 2020, vol. 2020-January, Institute of Electrical and Electronics Engineers Inc., 8th IEEE International Conference on Big Data, Big Data 2020, Virtual, Atlanta, United States, 12/10/20. https://doi.org/10.1109/BigData50022.2020.9442793

Advanced health information technology analytic framework and application to hazard detection. / Kumar, Mohit; Lee, Sangkeun; Hoon Park, Byung et al.
Proceedings - 2020 IEEE International Conference on Big Data, Big Data 2020. ed. / Xintao Wu; Chris Jermaine; Li Xiong; Xiaohua Tony Hu; Olivera Kotevska; Siyuan Lu; Weijia Xu; Srinivas Aluru; Chengxiang Zhai; Eyhab Al-Masri; Zhiyuan Chen; Jeff Saltz. Institute of Electrical and Electronics Engineers Inc., 2020. 9442793 (Proceedings - 2020 IEEE International Conference on Big Data, Big Data 2020; Vol. 2020-January).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - Advanced health information technology analytic framework and application to hazard detection

AU - Kumar, Mohit

AU - Lee, Sangkeun

AU - Hoon Park, Byung

AU - Blum, James

AU - Ward, Merry

AU - Nebeker, Jonathan R.

PY - 2020/12/10

Y1 - 2020/12/10

N2 - 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.

AB - 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.

KW - Big Data

KW - HIT

KW - Streaming Architecture

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U2 - 10.1109/BigData50022.2020.9442793

DO - 10.1109/BigData50022.2020.9442793

M3 - Conference contribution

AN - SCOPUS:85107037191

T3 - Proceedings - 2020 IEEE International Conference on Big Data, Big Data 2020

BT - Proceedings - 2020 IEEE International Conference on Big Data, Big Data 2020

A2 - Wu, Xintao

A2 - Jermaine, Chris

A2 - Xiong, Li

A2 - Hu, Xiaohua Tony

A2 - Kotevska, Olivera

A2 - Lu, Siyuan

A2 - Xu, Weijia

A2 - Aluru, Srinivas

A2 - Zhai, Chengxiang

A2 - Al-Masri, Eyhab

A2 - Chen, Zhiyuan

A2 - Saltz, Jeff

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 8th IEEE International Conference on Big Data, Big Data 2020

Y2 - 10 December 2020 through 13 December 2020

ER -

Kumar M, Lee S, Hoon Park B, Blum J, Ward M, Nebeker JR. Advanced health information technology analytic framework and application to hazard detection. In Wu X, Jermaine C, Xiong L, Hu XT, Kotevska O, Lu S, Xu W, Aluru S, Zhai C, Al-Masri E, Chen Z, Saltz J, editors, Proceedings - 2020 IEEE International Conference on Big Data, Big Data 2020. Institute of Electrical and Electronics Engineers Inc. 2020. 9442793. (Proceedings - 2020 IEEE International Conference on Big Data, Big Data 2020). doi: 10.1109/BigData50022.2020.9442793

Advanced health information technology analytic framework and application to hazard detection

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