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.
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JMIR medical informatics · Aug 2020
ReviewUtilization Barriers and Medical Outcomes Commensurate With the Use of Telehealth Among Older Adults: Systematic Review.
Rising telehealth capabilities and improving access to older adults can aid in improving health outcomes and quality of life indicators. Telehealth is not being used ubiquitously at present. ⋯ The literature suggests that the elimination of barriers could increase the prevalence of telehealth use by older adults. By increasing use of telehealth, proximity to care is no longer an issue for access, and thereby care can reach populations with chronic conditions and mobility restrictions. Future research should be conducted on methods for personalizing telehealth in older adults before implementation.
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JMIR medical informatics · Aug 2020
Prediction of Cardiac Arrest in the Emergency Department Based on Machine Learning and Sequential Characteristics: Model Development and Retrospective Clinical Validation Study.
The development and application of clinical prediction models using machine learning in clinical decision support systems is attracting increasing attention. ⋯ We developed a prediction model of cardiac arrest in the ED using machine learning and sequential characteristics. The model was validated for clinical usefulness by chronological visualization focused on clinical usability.
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JMIR medical informatics · Jul 2020
Reliability of Supervised Machine Learning Using Synthetic Data in Health Care: Model to Preserve Privacy for Data Sharing.
The exploitation of synthetic data in health care is at an early stage. Synthetic data could unlock the potential within health care datasets that are too sensitive for release. Several synthetic data generators have been developed to date; however, studies evaluating their efficacy and generalizability are scarce. ⋯ The results of this study are promising with small decreases in accuracy observed in models trained with synthetic data compared with models trained with real data, where both are tested on real data. Such deviations are expected and manageable. Tree-based classifiers have some sensitivity to synthetic data, and the underlying cause requires further investigation. This study highlights the potential of synthetic data and the need for further evaluation of their robustness. Synthetic data must ensure individual privacy and data utility are preserved in order to instill confidence in health care departments when using such data to inform policy decision-making.