When firms move to adopt and implement a popular IT innovation, what knowledge must they have or gain, in order to be successful? Here we offer a model that explains a firm's success in terms of its adoption know-why and know-when and its implementation know-how. We examine this model in an exploratory survey of some 118 firms’ adoption and implementation of packaged business software in the 1990s. Using multivariate methods, we identify business coordination as know-why and management understanding and vendor support as know-how factors important to success, explaining nearly 60% of the variance. There is limited evidence that the right adoption know-why may help in acquiring or fostering the right implementation know-how. The findings serve to remind practitioners that they should attend carefully to adoption rationales, grounded in business benefits, when they innovate with new IT.

Background With the accumulation of electronic health records and the development of artificial intelligence, patients with cancer urgently need new evidence of more personalized clinical and demographic characteristics and more sophisticated treatment and prevention strategies. However, no research has systematically analyzed the application and significance of artificial intelligence based on electronic health records in cancer care. Objective The aim of this study was to conduct a review to introduce the current state and limitations of artificial intelligence based on electronic health records of patients with cancer and to summarize the performance of artificial intelligence in mining electronic health records and its impact on cancer care. Methods Three databases were systematically searched to retrieve potentially relevant papers published from January 2009 to October 2020. Four principal reviewers assessed the quality of the papers and reviewed them for eligibility based on the inclusion criteria in the extracted data. The summary measures used in this analysis were the number and frequency of occurrence of the themes. Results Of the 1034 papers considered, 148 papers met the inclusion criteria. Cancer care, especially cancers of female organs and digestive organs, could benefit from artificial intelligence based on electronic health records through cancer emergencies and prognostic estimates, cancer diagnosis and prediction, tumor stage detection, cancer case detection, and treatment pattern recognition. The models can always achieve an area under the curve of 0.7. Ensemble methods and deep learning are on the rise. In addition, electronic medical records in the existing studies are mainly in English and from private institutional databases. Conclusions Artificial intelligence based on electronic health records performed well and could be useful for cancer care. Improving the performance of artificial intelligence can help patients receive more scientific-based and accurate treatments. There is a need for the development of new methods and electronic health record data sharing and for increased passion and support from cancer specialists.

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According to previous studies of theory of mind (ToM), social environment and cultural background affect individuals’ cognitive ability to understand other people’s minds. There are cross-group differences in ToM. The present study aimed to examine whether social environment and culture affect the ToM in Uygur and Han groups and whether the individual’s cognitive ToM and affective ToM show in-group advantages. Han and Uygur college students were recruited as participants. The “self/other differentiation task” was used to measure cognitive ToM (Study 1), and the “Yoni task” was used to measure both cognitive and affective ToM (Study 2). We found that Han participants processed the cognitive and affective states of others faster and more accurately than Uygur ones. Uygur and Han participants processed in-group members’ cognitive and affective states faster and more accurately. Furthermore, Uygur participants were more accurate in the cognitive ToM processing of in-group members, while Han participants were faster in the affective ToM processing of in-group members. The findings indicated that ethnic culture and group identify might influence ToM processing. Strengthening exchanges between ethnic groups may enable individuals to better process out-group members’ psychological states.

BACKGROUND Social media play a critical role in health communications especially during global health emergencies such as the current COVID-19 pandemic. However, there is a lack of universal analytical framework to extract, quantify and compare content features in public discourse of emerging health issues on different social media platforms across a broad socio-cultural spectrum. OBJECTIVE We aim to develop a novel and universal content feature extraction and analytical framework, and contrast how content features differ with socio-cultural backgrounds in discussions of emerging health crisis on major social media platforms. METHODS We sampled 1,000 most shared viral Twitter and Sina Weibo posts regarding COVID-19, developed a comprehensive coding scheme to identify 77 potential features in six major categories (e.g., clinical and epidemiological, countermeasures, political and policy, responses), quantified feature values (0 or 1, indicating whether or not the content feature is mentioned in the post) in each viral post across social media platforms, and performed subsequent comparative analyses. Machine learning dimension reduction and clustering analysis were then applied to harness the power of social media data and provide more unbiased characterization of online health communications. RESULTS There were substantially different distributions, prevalence, and associations of content features in public discourse about the same COVID-19 pandemic on the two social media platforms. Weibo users were more likely to focus on the disease and health aspects while Twitter users engaged more about policy, political, and other societal issues. CONCLUSIONS We are able to extract a rich set of content features from social media data to accurately characterize public discourse of emerging health issues in different social-cultural backgrounds. In addition, this universal framework can be adopted to analyze social media discussions of other health issues beyond COVID-19.