Liu, C, Zowghi, D & Talaei-Khoei, A 2020, 'An empirical study of the antecedents of data completeness in electronic medical records', International Journal of Information Management, vol. 50, pp. 155-170.View/Download from: UTS OPUS or Publisher's site
© 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.
© 2019, Springer-Verlag GmbH Austria, part of Springer Nature. The Internet of Things (IoT) is driving technological change and the development of new products and services that rely heavily on the quality of the data collected by IoT devices. There is a large body of research on data quality management and improvement in IoT, however, to date a systematic review of data quality measurement in IoT is not available. This paper presents a systematic literature review (SLR) about data quality in IoT from the emergence of the term IoT in 1999 to 2018. We reviewed and analyzed 45 empirical studies to identify research themes on data quality in IoT. Based on this analysis we have established the links between data quality dimensions, manifestations of data quality problems, and methods utilized to measure data quality. The findings of this SLR suggest new research areas for further investigation and identify implications for practitioners in defining and measuring data quality in IoT.
Liu, C, Talaei-Khoei, A & Zowghi, D 2018, 'Theoretical Support for Enhancing Data Quality: Application in Electronic Medical Records', 2018 Americas Conference on Information Systems, Americas Conference on Information Systems, New Orleans.View/Download from: UTS OPUS
This paper aims at reviewing the existing theoretical support to enhance data quality and utilizing the findings of the review in the context of electronic medical records (EMRs). For this to happen, we first conducted a survey of publications that have a focus on an empirical investigation of factors influencing data quality in the conceptual models. By using a well-established taxonomy development method from the discipline of information systems, we then proposed 3 dimensions for studying factors influencing data quality and constructing the conceptual model for enhancing data quality: breadth, depth, and interaction, within 9 characteristics under different dimensions. Last, we compared related studies using the proposed dimensions and utilized the findings of the review in enhancing EMRs quality to disclose the limitations and possibilities of new areas for further study.
Liu, C, Zowghi, D, Talaei-Khoei, A & Daniel, J 2018, 'Achieving Data Completeness in Electronic Medical Records: A Conceptual Model and Hypotheses Development', Proceedings of the 51st Hawaii International Conference on System Sciences, Hawaii International Conference on System Sciences, -, Hilton Waikoloa Village, Hawaii, USA, pp. 2824-2833.View/Download from: UTS OPUS
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.