JMIR medical informatics
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JMIR medical informatics · Sep 2020
Nomogram for Predicting COVID-19 Disease Progression Based on Single-Center Data: Observational Study and Model Development.
In late December 2019, a pneumonia caused by SARS-CoV-2 was first reported in Wuhan and spread worldwide rapidly. Currently, no specific medicine is available to treat infection with COVID-19. ⋯ This study presents a predictive nomogram of critical patients with COVID-19 based on LASSO and Cox regression analysis. Clinical use of the nomogram may enable timely detection of potential critical patients with COVID-19 and instruct clinicians to administer early intervention to these patients to prevent the disease from worsening.
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JMIR medical informatics · Sep 2020
Medical Insurance Information Systems in China: Mixed Methods Study.
Since the People's Republic of China (PRC), or China, established the basic medical insurance system (MIS) in 1998, the medical insurance information systems (MIISs) in China have effectively supported the operation of the MIS through several phases of development; the phases included a stand-alone version, the internet, and big data. In 2018, China's national medical security systems were integrated, while MIISs were facing reconstruction. We summarized China's experience in medical insurance informatization over the past 20 years, aiming to provide a reference for the building of a new basic MIS for China and for developing countries. ⋯ In the future, the building of China's basic MIISs should be deployed at the national, provincial, prefectural, and municipal levels on a unified basis. Efforts should be made to strengthen the development of standard codes, data exchange, and data utilization. Work should be done to formulate the rules and regulations for security and privacy protection and to balance the right to be informed with the mining and utilization of big data. Efforts should be made to intensify the interconnectivity between MISs and other health systems and to strengthen the application of medical insurance information in public health monitoring and early warning systems; this would ultimately improve the degree of trust from stakeholders, including individuals, medical service providers, and public health institutions, in the basic MIISs.