Empirical study of Data Completeness in Electronic Health Records in China

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. Available at: https://aisel.aisnet.org/pajais/vol12/iss2/4/ Recommended Citation Liu, Caihua; Zowghi, Didar; Talaei-Khoei, Amir; and Jin, Zhi (2020) "Empirical study of Data Completeness in Electronic Health Records in China," Pacific Asia Journal of the Association for Information Systems: Vol. 12: Iss. 2, Article 4. DOI: 10.17705/1pais.12204 Available at: https://aisel.aisnet.org/pajais/vol12/iss2/4

Digital Health Data Quality Issues: Systematic Review

“…For instance, as Abiy et al [ 67 ] noted “documentation and contents of data within an electronic medical record (EMR) must be accurate, complete, concise, consistent and universally understood by users of the data, and must support the legal business record of the organization by maintaining the required parameters such as consistency, completeness and accuracy.” By contrast, the context-aware perspective evaluates the dimensions of DQ with recognition of the context within which the data are used. For instance, as the International Organization for Standardization and Liu et al [ 78 ] noted, DQ is “the degree to which data satisfy the requirements defined by the product-owner organization” and can be reflected through its dimensions such as completeness and accuracy.…”

“…As illustrated in Figure 4 and Multimedia Appendix 7 [ 16 , 34 , 40 , 42 , 78 , 80 , 90 , 91 , 109 ], interrelationships were found among the digital health DQ dimensions.…”

“…Furthermore, completeness influences the accuracy of data; as Makeleni and Cilliers [ 31 ] noted, “data should be complete to ensure it is accurate.” The presence of inaccurate data was regularly linked to information fragmentation [ 88 ], incomplete data entry [ 109 ], and omissions [ 35 ]. Completeness also influenced contextual validity, as it is necessary to have all the data available to complete specific tasks [ 78 ]. When it comes to the secondary use of EHR data, evaluation of “completeness becomes extrinsic, and is dependent upon whether or not there are sufficient types and quantities of data to perform a research task of interest” [ 42 ].…”

“…By contrast, the context-aware perspective evaluates the dimensions of DQ with recognition of the context within which the data are used. For instance, as the International Organization for Standardization and Liu et al [78] noted, DQ is "the degree to which data satisfy the requirements defined by the product-owner organization"…”

Digital Health Data Quality Issues: Systematic Review (Preprint)

“…The presence of inaccurate data was regularly linked to information fragmentation [88], incomplete data entry [109], and omissions [35]. Completeness also influenced contextual validity, as it is necessary to have all the data available to complete specific tasks [78]. When it comes to the secondary use of EHR data, evaluation of "completeness becomes extrinsic, and is dependent upon whether or not there are sufficient types and quantities of data to perform a research task of interest" [42].…”