Effective implementation of strategic data-driven health analysis initiatives is heavily dependent on the quality of the electronic medical records that serve as the foundation from which to improve clinical decisions and, in turn, the quality of care. Although there is a large body of research on the quality of healthcare data, a systematical understanding of the methods used to address the issues of data quality is missing. This study analyzes research articles in health information systems/healthcare informatics on data quality to derive a set of dimensions for understanding data quality. Issues related to each dimension are identified and methods used to address them summarized. The issues and methods can inform healthcare professionals of how to improve data practices.

Online reviews have a continuing impact across all industries. Even industries with highly skilled workers are affected by online reviews, despite large gaps in experience and skill between reviewers and reviewees. The authors conducted a study amongst physicians in Nevada and China to measure the perception of online patient reviews from the perspective of healthcare providers to explore whether this skill gap affected the perception of online reviews. The authors distributed and collected survey responses from over 200 physicians and used structural equation modeling techniques to evaluate the relationships. These findings show that physician perception of online patient reviews is partially mediated by power distance, direct effects exist between the relationships identified in our model, and that cross-cultural effects are present between physician responses across Nevada and China. This study expands the existing work in the field of review evaluations by operationalizing social-psychological distance into the construct of power distance within the context of healthcare.

In recent years, deep learning has achieved remarkable results in the text-image retrieval task. However, only global image features are considered, and the vital local information is ignored. This results in a failure to match the text well. Considering that object-level image features can help the matching between text and image, this article proposes a text-image retrieval method that fuses salient image feature representation. Fusion of salient features at the object level can improve the understanding of image semantics and thus improve the performance of text-image retrieval. The experimental results show that the method proposed in the paper is comparable to the latest methods, and the recall rate of some retrieval results is better than the current work.

The visions of what constitutes Industry 4.0 is an industry based on gains in efficiency and productivity enhancements supported by integrated, smart information systems. This has caused information systems strategic misalignment that present a severe barrier to national and organizational aspirations. This paper studies the readiness of manufacturing companies for Industry 4.0 by using a case study of Chinese multinational enterprise in the aluminum production sector. The research design follows a rigorous grounded theory approach, which consisted of 41 semi-structured interviews in 7 different company branches. Based on this case study, the paper proposes an IS strategic misalignment model that identifies three levels of misalignment that need to be resolved before the vision of the smart industry can be realized. Six main categories of causes and five main categories of consequences of IS strategic misalignment are presented. This study contributes to the IS alignment literature and provides important implications for the achievement of Industry 4.0 in practice.