An empirical study of the antecedents of data completeness in electronic medical records

Liu, C; Zowghi, D; Talaei-Khoei, A

An empirical study of the antecedents of data completeness in electronic medical records

Liu, C

Zowghi, D

Talaei-Khoei, A

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Publication Type:: Journal Article
Citation:: International Journal of Information Management, 2020, 50 pp. 155 - 170
Issue Date:: 2020-02-01

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Field	Value	Language
dc.contributor.author	Liu, C https://orcid.org/0000-0002-9641-4712	en_US
dc.contributor.author	Zowghi, D https://orcid.org/0000-0002-6051-0155	en_US
dc.contributor.author	Talaei-Khoei, A	en_US
dc.date.issued	2020-02-01	en_US
dc.identifier.citation	International Journal of Information Management, 2020, 50 pp. 155 - 170	en_US
dc.identifier.issn	0268-4012	en_US
dc.identifier.uri	http://hdl.handle.net/10453/133791
dc.description.abstract	© 2019 Elsevier Ltd There is a body of research that highlights the role of data management to improve the quality of data, which in return improves organizational performance. The literature in data management has indicated the five theoretical constructs used to understand the factors influencing data quality, including top management support, capability on the regulation and process management, business-IT alignment, staff participation, and integration of information systems. However, it is unclear how these theoretical constructs can be utilized to understand the antecedents of data completeness as a dimension of data quality. Following that stream of research, the current paper examines the factors influencing data completeness in electronic medical records (EMR). The scope of this study is by only surveying medical professionals at healthcare settings in northern Nevada. The empirical results reveal that resources should be added as one of the antecedents of data completeness in EMR.	en_US
dc.relation.ispartof	International Journal of Information Management	en_US
dc.relation.isbasedon	10.1016/j.ijinfomgt.2019.05.001	en_US
dc.subject.classification	Information Systems	en_US
dc.title	An empirical study of the antecedents of data completeness in electronic medical records	en_US
dc.type	Journal Article
utslib.citation.volume	50	en_US
utslib.for	0805 Distributed Computing	en_US
utslib.for	0806 Information Systems	en_US
utslib.for	0807 Library and Information Studies	en_US
pubs.embargo.period	Not known	en_US
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/DVC (Research)
pubs.organisational-group	/University of Technology Sydney/DVC (Research)/Graduate Research School
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Computer Science
pubs.organisational-group	/University of Technology Sydney/Strength - HCTD - Human Centred Technology Design
pubs.organisational-group	/University of Technology Sydney/Strength - STEM Education Futures
utslib.copyright.status	closed_access
pubs.publication-status	Published	en_US
pubs.volume	50	en_US

Abstract:

© 2019 Elsevier Ltd There is a body of research that highlights the role of data management to improve the quality of data, which in return improves organizational performance. The literature in data management has indicated the five theoretical constructs used to understand the factors influencing data quality, including top management support, capability on the regulation and process management, business-IT alignment, staff participation, and integration of information systems. However, it is unclear how these theoretical constructs can be utilized to understand the antecedents of data completeness as a dimension of data quality. Following that stream of research, the current paper examines the factors influencing data completeness in electronic medical records (EMR). The scope of this study is by only surveying medical professionals at healthcare settings in northern Nevada. The empirical results reveal that resources should be added as one of the antecedents of data completeness in EMR.

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http://hdl.handle.net/10453/133791