Achieving Data Completeness in Electronic Medical Records: A Conceptual Model and Hypotheses Development

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

Achieving Data Completeness in Electronic Medical Records: A Conceptual Model and Hypotheses Development

Liu, C

Zowghi, D

Talaei-Khoei, A Daniel, J

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Publisher:: -
Publication Type:: Conference Proceeding
Citation:: Proceedings of the 51st Hawaii International Conference on System Sciences, 2018, pp. 2824 - 2833
Issue Date:: 2018-01-03

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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.contributor.author	Daniel, J https://orcid.org/0000-0002-8668-2502	en_US
dc.date	2018-01-03	en_US
dc.date.issued	2018-01-03	en_US
dc.identifier.citation	Proceedings of the 51st Hawaii International Conference on System Sciences, 2018, pp. 2824 - 2833	en_US
dc.identifier.isbn	978-0-9981331-1-9	en_US
dc.identifier.uri	http://hdl.handle.net/10453/121793
dc.description.abstract	This paper aims at proposing a conceptual model of achieving data completeness in electronic medical records (EMR). For this to happen, firstly, we draw on the model of factors influencing data quality management to construct our conceptual model. Secondly, we develop hypotheses of relationships between influencing factors for data completeness and mediators for achieving data completeness in EMR based on the literature. Our conceptual model extends the prior model for factors influencing data quality management by adding a new factor and exploring the relationships between the influencing factors within the context of data completeness in EMR. The proposed conceptual model and the presented hypotheses once empirically validated will be the basis for the development of tools and techniques for achieving data completeness in EMR.	en_US
dc.publisher	-	en_US
dc.relation.ispartof	Proceedings of the 51st Hawaii International Conference on System Sciences	en_US
dc.relation.ispartof	Hawaii International Conference on System Sciences	en_US
dc.title	Achieving Data Completeness in Electronic Medical Records: A Conceptual Model and Hypotheses Development	en_US
dc.type	Conference Proceeding
utslib.location	-	en_US
utslib.location.activity	Hilton Waikoloa Village, Hawaii, USA	en_US
utslib.for	0803 Computer Software	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/Faculty of Engineering and Information Technology/School of Systems, Management and Leadership
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	open_access
pubs.finish-date	2018-01-06	en_US
pubs.place-of-publication	-	en_US
pubs.start-date	2018-01-03	en_US

Abstract:

This paper aims at proposing a conceptual model of achieving data completeness in electronic medical records (EMR). For this to happen, firstly, we draw on the model of factors influencing data quality management to construct our conceptual model. Secondly, we develop hypotheses of relationships between influencing factors for data completeness and mediators for achieving data completeness in EMR based on the literature. Our conceptual model extends the prior model for factors influencing data quality management by adding a new factor and exploring the relationships between the influencing factors within the context of data completeness in EMR. The proposed conceptual model and the presented hypotheses once empirically validated will be the basis for the development of tools and techniques for achieving data completeness in EMR.

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http://hdl.handle.net/10453/121793