Journal of medical Internet research
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J. Med. Internet Res. · May 2018
ReviewArtificial Intelligence for Diabetes Management and Decision Support: Literature Review.
Artificial intelligence methods in combination with the latest technologies, including medical devices, mobile computing, and sensor technologies, have the potential to enable the creation and delivery of better management services to deal with chronic diseases. One of the most lethal and prevalent chronic diseases is diabetes mellitus, which is characterized by dysfunction of glucose homeostasis. ⋯ We obtained evidence of an acceleration of research activity aimed at developing artificial intelligence-powered tools for prediction and prevention of complications associated with diabetes. Our results indicate that artificial intelligence methods are being progressively established as suitable for use in clinical daily practice, as well as for the self-management of diabetes. Consequently, these methods provide powerful tools for improving patients' quality of life.
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J. Med. Internet Res. · May 2018
Trigger Tool-Based Automated Adverse Event Detection in Electronic Health Records: Systematic Review.
Adverse events in health care entail substantial burdens to health care systems, institutions, and patients. Retrospective trigger tools are often manually applied to detect AEs, although automated approaches using electronic health records may offer real-time adverse event detection, allowing timely corrective interventions. ⋯ We observed broad interstudy variation in reported adverse event prevalence and positive predictive value. The lack of sufficiently described methods led to difficulties regarding interpretation. To improve quality, we see the need for a set of recommendations to endorse optimal use of research designs and adequate reporting of future adverse event detection studies.
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J. Med. Internet Res. · May 2018
"It's Totally Okay to Be Sad, but Never Lose Hope": Content Analysis of Infertility-Related Videos on YouTube in Relation to Viewer Preferences.
Infertility patients frequently use the internet to find fertility-related information and support from people in similar circumstances. YouTube is increasingly used as a source of health-related information and may influence health decision making. There have been no studies examining the content of infertility-related videos on YouTube. ⋯ YouTube is a source of both technical and personal experience-based information about infertility. However, videos that include personal experiences may elicit greater viewer engagement. Positive videos and stories of treatment success may provide hope to viewers but could also create and perpetuate unrealistic expectations about the success rates of fertility treatment.
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J. Med. Internet Res. · May 2018
An eHealth Capabilities Framework for Graduates and Health Professionals: Mixed-Methods Study.
The demand for an eHealth-ready and adaptable workforce is placing increasing pressure on universities to deliver eHealth education. At present, eHealth education is largely focused on components of eHealth rather than considering a curriculum-wide approach. ⋯ The results of this study inform a cross-faculty eHealth curriculum that aligns with workforce expectations. There is a need for educational curriculum to reinforce existing eHealth capabilities, adapt existing capabilities to make them transferable to novel eHealth contexts, and introduce new learning opportunities for interactions with technologies within education and practice encounters. As such, the capability framework developed may assist in the application of eHealth by emerging and existing health care professionals. Future research needs to explore the potential for integration of findings into workforce development programs.
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J. Med. Internet Res. · May 2018
Effects of Contributor Experience on the Quality of Health-Related Wikipedia Articles.
Consulting the Internet for health-related information is a common and widespread phenomenon, and Wikipedia is arguably one of the most important resources for health-related information. Therefore, it is relevant to identify factors that have an impact on the quality of health-related Wikipedia articles. ⋯ Our findings indicate an effect of contributor experience on the quality of health-related Wikipedia articles. However, only the number of previously edited articles was a predictor of the articles' quality but not the mere volume of edits. More research is needed to disentangle the different aspects of contributor experience. We have discussed the implications of our findings with respect to ensuring the quality of health-related information in collaborative knowledge-building platforms.