Journal of medical Internet research
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J. Med. Internet Res. · Aug 2020
Telemedicine in Germany During the COVID-19 Pandemic: Multi-Professional National Survey.
In an effort to contain the effects of the coronavirus disease (COVID-19) pandemic, health care systems worldwide implemented telemedical solutions to overcome staffing, technical, and infrastructural limitations. In Germany, a multitude of telemedical systems are already being used, while new approaches are rapidly being developed in response to the crisis. However, the extent of the current implementation within different health care settings, the user's acceptance and perception, as well as the hindering technical and regulatory obstacles remain unclear. ⋯ Telemedicine has a broad acceptance among German medical professionals. However, to establish telemedical structures within routine care, technical and regulatory burdens must be overcome.
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J. Med. Internet Res. · Aug 2020
Effects of Internet Hospital Consultations on Psychological Burdens and Disease Knowledge During the Early Outbreak of COVID-19 in China: Cross-Sectional Survey Study.
Coronavirus disease (COVID-19) has become a global threat to human health. Internet hospitals have emerged as a critical technology to bring epidemic-related web-based services and medical support to the public. However, only a few very recent scientific literature reports have explored the effects of internet hospitals on psychological burden and disease knowledge in major public health emergencies such as the COVID-19 pandemic. ⋯ During the early outbreak of COVID-19, internet hospitals could help relieve psychological burdens and increase disease awareness through timely and rapid spread of knowledge regarding COVID-19 prevention and control. Internet hospitals should be an important aspect of a new medical model in public health emergency systems.
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J. Med. Internet Res. · Aug 2020
Machine Learning Model for Risk Prediction of Community-Acquired Acute Kidney Injury Hospitalization From Electronic Health Records: Development and Validation Study.
Community-acquired acute kidney injury (CA-AKI)-associated hospitalizations impose significant health care needs and contribute to in-hospital mortality. However, most risk prediction models developed to date have focused on AKI in a specific group of patients during hospitalization, and there is limited knowledge on the baseline risk in the general population for preventing CA-AKI-associated hospitalization. ⋯ A machine learning-generated risk score model can identify patients at risk of developing CA-AKI-related hospitalization through a routine care data-driven approach. The validated multivariate risk assessment tool could help clinicians to stratify patients in primary care, and to provide monitoring and early intervention for preventing AKI while improving the quality of AKI care in the general population.