Empirical study of Data Completeness in Electronic Health Records in China

Liu, C; Zowghi, D; Jin, Z; Talaei Khoei, A

Empirical study of Data Completeness in Electronic Health Records in China

Liu, C Zowghi, D

Jin, Z Talaei Khoei, A

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Publisher:: Association for Information Systems
Publication Type:: Journal Article
Citation:: Pacific Asia Journal of the Association for Information Systems, 2020, 12, (2), pp. 103-128
Issue Date:: 2020-07-08

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Field	Value	Language
dc.contributor.author	Liu, C
dc.contributor.author	Zowghi, D https://orcid.org/0000-0002-6051-0155
dc.contributor.author	Jin, Z
dc.contributor.author	Talaei Khoei, A
dc.date.accessioned	2021-01-06T23:58:45Z
dc.date.available	2021-01-06T23:58:45Z
dc.date.issued	2020-07-08
dc.identifier.citation	Pacific Asia Journal of the Association for Information Systems, 2020, 12, (2), pp. 103-128
dc.identifier.issn	1943-7536
dc.identifier.uri	http://hdl.handle.net/10453/145135
dc.description.abstract	Background: As a dimension of data quality in electronic health records (EHR), data completeness plays an important role in improving quality of care. Although many studies of data management focus on constructing the factors that influence data quality for the purpose of quality improvement, the constructs that are developed for interpreting factors influencing data completeness in the EHR context have received limited attention. Methods: Based on related studies, we constructed the factors influencing EHR data completeness in a conceptual model. We then examined the proposed model by surveying clinical practitioners in China. Results: Our results show that the data quality management literature can serve as a starting point to derive a conceptual model of factors influencing data completeness in the EHR context. This study also demonstrates that “resources” should be added as a factor that influences data completeness in EHR. Conclusion: Our resulting conceptual model shows a substantial explanation of data completeness in EHR assessed in this study. Although the proposed relationships between the included factors were previously supported in the literature, our work provides the beginning empirical evidence that some relationships may not be always significantly supported. The possible explanation of these differences has been discussed in the present research. This study thus benefits decision makers and EHR program managers in implementing EHR as well as EHR vendors in the EHR integration by addressing data completeness issues.
dc.language	en
dc.publisher	Association for Information Systems
dc.relation.ispartof	Pacific Asia Journal of the Association for Information Systems
dc.relation.isbasedon	10.17705/1pais.12204
dc.rights	info:eu-repo/semantics/openAccess
dc.subject	0806 Information Systems, 0807 Library and Information Studies
dc.title	Empirical study of Data Completeness in Electronic Health Records in China
dc.type	Journal Article
utslib.citation.volume	12
utslib.for	0806 Information Systems
utslib.for	0806 Information Systems
utslib.for	0807 Library and Information Studies
pubs.organisational-group	/University of Technology Sydney/DVC (Research)
pubs.organisational-group	/University of Technology Sydney/Strength - HCTD - Human Centred Technology Design
pubs.organisational-group	/University of Technology Sydney/Strength - STEM Education Futures
pubs.organisational-group	/University of Technology Sydney/DVC (Research)/Graduate Research School
pubs.organisational-group	/University of Technology Sydney
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
utslib.copyright.status	open_access	*
pubs.consider-herdc	false
dc.date.updated	2021-01-06T23:58:39Z
pubs.issue	2
pubs.publication-status	Published
pubs.volume	12
utslib.citation.issue	2

Abstract:

Background: As a dimension of data quality in electronic health records (EHR), data completeness plays an important role in improving quality of care. Although many studies of data management focus on constructing the factors that influence data quality for the purpose of quality improvement, the constructs that are developed for interpreting factors influencing data completeness in the EHR context have received limited attention. Methods: Based on related studies, we constructed the factors influencing EHR data completeness in a conceptual model. We then examined the proposed model by surveying clinical practitioners in China. Results: Our results show that the data quality management literature can serve as a starting point to derive a conceptual model of factors influencing data completeness in the EHR context. This study also demonstrates that “resources” should be added as a factor that influences data completeness in EHR. Conclusion: Our resulting conceptual model shows a substantial explanation of data completeness in EHR assessed in this study. Although the proposed relationships between the included factors were previously supported in the literature, our work provides the beginning empirical evidence that some relationships may not be always significantly supported. The possible explanation of these differences has been discussed in the present research. This study thus benefits decision makers and EHR program managers in implementing EHR as well as EHR vendors in the EHR integration by addressing data completeness issues.

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http://hdl.handle.net/10453/145135