Journal of medical Internet research
-
J. Med. Internet Res. · May 2021
Characterizing Health Care Delays and Interruptions in the United States During the COVID-19 Pandemic: Internet-Based, Cross-sectional Survey Study.
The COVID-19 pandemic has broader geographic spread and potentially longer lasting effects than those of previous disasters. Necessary preventive precautions for the transmission of COVID-19 has resulted in delays for in-person health care services, especially at the outset of the pandemic. ⋯ The lessons learned from the initial surge of COVID-19 cases can inform systemic mitigation strategies for potential future disruptions. This study addresses the demand side of health care delays by exploring the determinants of such delays. More research on health care delays during the pandemic is needed, including research on their short- and long-term impacts on patient-level outcomes such as mortality, morbidity, mental health, people's quality of life, and the experience of pain.
-
J. Med. Internet Res. · May 2021
Experiences and Attitudes of Elementary School Students and Their Parents Toward Online Learning in China During the COVID-19 Pandemic: Questionnaire Study.
Due to widespread SARS-CoV-2 infection, an emergency homeschooling plan was rigorously implemented throughout China. ⋯ Online learning can prevent the spread of infectious diseases while still allowing elementary school students to attain knowledge. However, in our study, children's completion of the courses and homework were not satisfactory. Furthermore, their parents often experienced stress and had many concerns and complaints. Measures such as increasing the interactivity of the courses and prohibiting teachers from assigning tasks to parents could improve the effectiveness of these courses and the mental health of parents and students.
-
J. Med. Internet Res. · May 2021
Review Meta AnalysisMachine Learning and Natural Language Processing in Mental Health: Systematic Review.
Machine learning systems are part of the field of artificial intelligence that automatically learn models from data to make better decisions. Natural language processing (NLP), by using corpora and learning approaches, provides good performance in statistical tasks, such as text classification or sentiment mining. ⋯ Machine learning and NLP models have been highly topical issues in medicine in recent years and may be considered a new paradigm in medical research. However, these processes tend to confirm clinical hypotheses rather than developing entirely new information, and only one major category of the population (ie, social media users) is an imprecise cohort. Moreover, some language-specific features can improve the performance of NLP methods, and their extension to other languages should be more closely investigated. However, machine learning and NLP techniques provide useful information from unexplored data (ie, patients' daily habits that are usually inaccessible to care providers). Before considering It as an additional tool of mental health care, ethical issues remain and should be discussed in a timely manner. Machine learning and NLP methods may offer multiple perspectives in mental health research but should also be considered as tools to support clinical practice.
-
J. Med. Internet Res. · May 2021
Multicenter StudyEffects of an Animated Blood Clot Technology (Visual Clot) on the Decision-Making of Users Inexperienced in Viscoelastic Testing: Multicenter Trial.
Viscoelastic test-guided coagulation management has become increasingly important in assessing hemostasis. We developed Visual Clot, an animated, 3D blood clot that illustrates raw rotational thromboelastometry (ROTEM) parameters in a user-centered and situation awareness-oriented method. ⋯ This study emphasizes the practical benefit of presenting viscoelastic test results in a user-centered way. Visual Clot may allow inexperienced users to be involved in the decision-making process to treat bleeding-associated coagulopathy. The increased diagnostic confidence, diagnostic certainty, reduced workload, and positive user feedback associated with this visualization may promote the further adoption of viscoelastic methods in diverse health care settings.