A Framework for Big Data Governance to Advance RHINs: A Case Study of China

The emergence of big data presents a serious challenge to the fast growth of regional health information networks (RHINs) globally. In China, many constructors of RHINs have spontaneously and independently created governance measures, which may be valuable as a point of reference for other countries...

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Main Authors: Quan Li, Lan Lan, Nianyin Zeng, Lei You, Jin Yin, Xiaobo Zhou, Qun Meng
Format: Article
Language:English
Published: IEEE 2019-01-01
Series:IEEE Access
Subjects:
Online Access:https://ieeexplore.ieee.org/document/8689011/
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spelling doaj-c478ed696c20400e91c7700e39474e832021-03-29T22:20:36ZengIEEEIEEE Access2169-35362019-01-017503305033810.1109/ACCESS.2019.29108388689011A Framework for Big Data Governance to Advance RHINs: A Case Study of ChinaQuan Li0https://orcid.org/0000-0003-1866-3622Lan Lan1Nianyin Zeng2https://orcid.org/0000-0002-6957-2942Lei You3Jin Yin4Xiaobo Zhou5Qun Meng6School of Public Health, Sun Yat-sen University, Guangzhou, ChinaWest China Biomedical Big Data Center, West China Hospital, Sichuan University, Chengdu, ChinaDepartment of Instrumental and Electrical Engineering, Xiamen University, Xiamen, ChinaSchool of Biomedical Informatics, The University of Texas Health Science Center at Houston, Houston, TX, USAWest China Biomedical Big Data Center, West China Hospital, Sichuan University, Chengdu, ChinaWest China Biomedical Big Data Center, West China Hospital, Sichuan University, Chengdu, ChinaSchool of Public Health, Sun Yat-sen University, Guangzhou, ChinaThe emergence of big data presents a serious challenge to the fast growth of regional health information networks (RHINs) globally. In China, many constructors of RHINs have spontaneously and independently created governance measures, which may be valuable as a point of reference for other countries. This paper aimed to propose a big data governance framework for healthcare data based on the governance activities associated with the processing of RHINs in China. Typical methodology for RHIN case studies in China, including rich personal experience in nationwide consulting, literature review, expert consultation, and interpretative structural modeling methods, was adopted. Based on the analysis of ten typical RHIN case studies, healthcare big data governance practices in China were summarized. A framework with 3 domains and 12 elements was proposed, which include a drive domain (big data strategy planning, laws and regulations, open transaction, and industry support), capability domain (healthcare big data organization, collection, storage, process and analysis, and usage), and support domain (healthcare big data resource planning, standards system, and privacy and security protection). We obtained 12 guidelines for healthcare big data governance. A big data governance framework with 3 domains and 12 elements was presented based on Chinese practice, which might serve as valuable references for the cross-dimensional development of RHINs, provide overall guidance for the sustainable development of regional health informatization, and contribute to realizing the business value of healthcare big data.https://ieeexplore.ieee.org/document/8689011/Big data governanceframeworkregional health information networks (RHINs)
collection DOAJ
language English
format Article
sources DOAJ
author Quan Li
Lan Lan
Nianyin Zeng
Lei You
Jin Yin
Xiaobo Zhou
Qun Meng
spellingShingle Quan Li
Lan Lan
Nianyin Zeng
Lei You
Jin Yin
Xiaobo Zhou
Qun Meng
A Framework for Big Data Governance to Advance RHINs: A Case Study of China
IEEE Access
Big data governance
framework
regional health information networks (RHINs)
author_facet Quan Li
Lan Lan
Nianyin Zeng
Lei You
Jin Yin
Xiaobo Zhou
Qun Meng
author_sort Quan Li
title A Framework for Big Data Governance to Advance RHINs: A Case Study of China
title_short A Framework for Big Data Governance to Advance RHINs: A Case Study of China
title_full A Framework for Big Data Governance to Advance RHINs: A Case Study of China
title_fullStr A Framework for Big Data Governance to Advance RHINs: A Case Study of China
title_full_unstemmed A Framework for Big Data Governance to Advance RHINs: A Case Study of China
title_sort framework for big data governance to advance rhins: a case study of china
publisher IEEE
series IEEE Access
issn 2169-3536
publishDate 2019-01-01
description The emergence of big data presents a serious challenge to the fast growth of regional health information networks (RHINs) globally. In China, many constructors of RHINs have spontaneously and independently created governance measures, which may be valuable as a point of reference for other countries. This paper aimed to propose a big data governance framework for healthcare data based on the governance activities associated with the processing of RHINs in China. Typical methodology for RHIN case studies in China, including rich personal experience in nationwide consulting, literature review, expert consultation, and interpretative structural modeling methods, was adopted. Based on the analysis of ten typical RHIN case studies, healthcare big data governance practices in China were summarized. A framework with 3 domains and 12 elements was proposed, which include a drive domain (big data strategy planning, laws and regulations, open transaction, and industry support), capability domain (healthcare big data organization, collection, storage, process and analysis, and usage), and support domain (healthcare big data resource planning, standards system, and privacy and security protection). We obtained 12 guidelines for healthcare big data governance. A big data governance framework with 3 domains and 12 elements was presented based on Chinese practice, which might serve as valuable references for the cross-dimensional development of RHINs, provide overall guidance for the sustainable development of regional health informatization, and contribute to realizing the business value of healthcare big data.
topic Big data governance
framework
regional health information networks (RHINs)
url https://ieeexplore.ieee.org/document/8689011/
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