Empirical Investigation of the Factors Influencing Data Completeness in Electronic Medical Records

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Recent advances in electronic medical records (EMR) in healthcare highlight the role of high-quality data, because poor data quality can destabilise clinical decision-making that in turn may impact on the quality of care. Data completeness as an essential dimension of data quality has been the subject of much research in the last three decades, while addressing data completeness in EMR is still a significant challenge in healthcare. Exploring the factors that influence data completeness could be a starting point to address data quality issues in EMR. Constructing a conceptual model can be an effective exercise for studying complex phenomena such as data completeness in EMR. We do not have a conceptual model of factors influencing data completeness in EMR and specific relationships between these factors remain unclear. This study presents a novel conceptual model of the factors influencing data completeness in EMR and investigates the relationships between them. This thesis begins with presenting the first literature review of data completeness in healthcare and provides a novel synthesis of the identified challenges that has led to a categorisation of the factors influencing data completeness in electronic records from human, managerial, technical, and external perspectives. The thesis also presents a taxonomy of factors influencing data quality using breadth, depth, and interaction dimensions, and suggests guidelines for developing and comparing the factors influencing data quality. The empirical evaluation of the proposed conceptual model is carried out by surveying clinical practitioners in Australia, China, and USA. The results of data analysis reveal that the priority of the included factors is different among the three countries. Moreover, cultural differences are identified and highlighted in the relationships between these factors. The findings of this study draw specific attention to the important factors and help clinical practitioners identify which of these areas need to be addressed in order to improve EMR data completeness. In addition, the conceptual model proposed in this thesis can serve as the basis for the development of tools, methods and techniques for addressing data completeness in EMR.
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