The coronavirus disease (COVID-19) pandemic has spread rapidly throughout the world and has had a long-term impact. The pandemic has caused great harm to society and caused serious psychological trauma to many people. Children are a vulnerable group in this global public health emergency, as their nervous systems, endocrine systems, and hypothalamic-pituitary-adrenal axes are not well developed. Psychological crises often cause children to produce feelings of abandonment, despair, incapacity, and exhaustion, and even raise the risk of suicide. Children with mental illnesses are especially vulnerable during the quarantine and social distancing period. The inclusion of psychosocial support for children and their families are part of the health responses to disaster and disaster recovery. Based on the biopsychosocial model, some children may have catastrophic thoughts and be prone to experience despair, numbness, flashbacks, and other serious emotional and behavioral reactions. In severe cases, there may be symptoms of psychosis or posttraumatic stress disorder. Timely and appropriate protections are needed to prevent the occurrence of psychological and behavioral problems. The emerging digital applications and health services such as telehealth, social media, mobile health, and remote interactive online education are able to bridge the social distance and support mental and behavioral health for children. Based on the psychological development characteristics of children, this study also illustrates interventions on the psychological impact from the COVID-19 pandemic. Even though the world has been struggling to curb the influences of the pandemic, the quarantine and social distancing policies will have long-term impacts on children. Innovative digital solutions and informatics tools are needed more than ever to mitigate the negative consequences on children. Health care delivery and services should envision and implement innovative paradigms to meet broad well-being needs and child health as the quarantine and social distancing over a longer term becomes a new reality. Future research on children's mental and behavioral health should pay more attention to novel solutions that incorporate cutting edge interactive technologies and digital approaches, leveraging considerable advances in pervasive and ubiquitous computing, human-computer interaction, and health informatics among many others. Digital approaches, health technologies, and informatics are supposed to be designed and implemented to support public health surveillance and critical responses to children’s growth and development. For instance, human-computer interactions, augmented reality, and virtual reality could be incorporated to remote psychological supporting service for children’s health; mobile technologies could be used to monitor children’s mental and behavioral health while protecting their individual privacy; big data and artificial intelligence could be used to support decision making on whether children should go out for physical activities and whether schools should be reopened. Implications to clinical practices, psychological therapeutic practices, and future research directions to address current effort gaps are highlighted in this study.

At present, the coronavirus disease (COVID-19) is spreading around the world. It is a critical and important task to take thorough efforts to prevent and control the pandemic. Compared with severe acute respiratory syndrome and Middle East Respiratory Syndrome, COVID-19 spreads more rapidly owing to increased globalization, a longer incubation period, and unobvious symptoms. As the coronavirus has the characteristics of strong transmission and weak lethality, and since the large-scale increase of infected people may overwhelm health care systems, efforts are needed to treat critical patients, track and manage the health status of residents, and isolate suspected patients. The application of emerging health technologies and digital practices in health care, such as artificial intelligence, telemedicine or telehealth, mobile health, big data, 5G, and the Internet of Things, have become powerful “weapons” to fight against the pandemic and provide strong support in pandemic prevention and control. Applications and evaluations of all of these technologies, practices, and health delivery services are highlighted in this study.

Background The COVID-19 pandemic is a global public health crisis that has not only endangered the lives of patients but also resulted in increased psychological issues among medical professionals, especially frontline health care workers. As the crisis caused by the pandemic shifts from acute to protracted, attention should be paid to the devastating impacts on health care workers’ mental health and social well-being. Digital technologies are being harnessed to support the responses to the pandemic, which provide opportunities to advance mental health and psychological support for health care workers. Objective The aim of this study is to develop a framework to describe and organize the psychological and mental health issues that health care workers are facing during the COVID-19 pandemic. Based on the framework, this study also proposes interventions from digital health perspectives that health care workers can leverage during and after the pandemic. Methods The psychological problems and mental health issues that health care workers have encountered during the COVID-19 pandemic were reviewed and analyzed based on the proposed MEET (Mental Health, Environment, Event, and Technology) framework, which also demonstrated the interactions among mental health, digital interventions, and social support. Results Health care workers are facing increased risk of experiencing mental health issues due to the COVID-19 pandemic, including burnout, fear, worry, distress, pressure, anxiety, and depression. These negative emotional stressors may cause psychological problems for health care workers and affect their physical and mental health. Digital technologies and platforms are playing pivotal roles in mitigating psychological issues and providing effective support. The proposed framework enabled a better understanding of how to mitigate the psychological effects during the pandemic, recover from associated experiences, and provide comprehensive institutional and societal infrastructures for the well-being of health care workers. Conclusions The COVID-19 pandemic presents unprecedented challenges due to its prolonged uncertainty, immediate threat to patient safety, and evolving professional demands. It is urgent to protect the mental health and strengthen the psychological resilience of health care workers. Given that the pandemic is expected to exist for a long time, caring for mental health has become a “new normal” that needs a strengthened multisector collaboration to facilitate support and reduce health disparities. The proposed MEET framework could provide structured guidelines for further studies on how technology interacts with mental and psychological health for different populations.

Practice facilitation is an effective approach to implementing quality improvement (QI) interventions in practice-based research networks (PBRNs). Regular facilitator-practice interactions are necessary for successful facilitation, and missed engagements may hinder the process of practice improvement. This study employs a mixed-methods approach to characterize the dynamics of practice facilitation and examine facilitation delays and barriers, as well as their association with the achievement of QI program goals in a PBRN initiative. Methods: This study presents a secondary analysis of data from 226 primary care practices that participated in the Healthy Hearts in the Heartland (H3) initiative. We performed a time series analysis to identify delays in facilitation activities, and then qualitatively analyzed practice facilitators' notes (n = 4358) to uncover facilitation barriers. Finally, we assessed the relationship between delays, barriers, and QI intervention completion. Results: While most facilitation activities occurred at regular, practice-specific tempos, nearly all practices experienced at least 1 delay. Practices with more delays had lower QI intervention completion rates. Practices with more delays were more likely to have encountered barriers such as lack of time and staff, lack of staff engagement, technical issues, and staff turnover. Discussion and Conclusion: This study is the first to quantify irregular intervals between facilitation activities and demonstrate their negative association with project completion. The analytic method can be applied to identify at-risk practices and to accelerate timely interventions in future studies. Our delay detection algorithm could inform the design of a decision support system that notifies facilitators which practices may benefit from timely attention and resources.

Background Diabetes mellitus is a prevalent metabolic disease characterized by chronic hyperglycemia. The avalanche of healthcare data is accelerating precision and personalized medicine. Artificial intelligence and algorithm-based approaches are becoming more and more vital to support clinical decision-making. These methods are able to augment health care providers by taking away some of their routine work and enabling them to focus on critical issues. However, few studies have used predictive modeling to uncover associations between comorbidities in ICU patients and diabetes. This study aimed to use Unified Medical Language System (UMLS) resources, involving machine learning and natural language processing (NLP) approaches to predict the risk of mortality. Methods We conducted a secondary analysis of Medical Information Mart for Intensive Care III (MIMIC-III) data. Different machine learning modeling and NLP approaches were applied. Domain knowledge in health care is built on the dictionaries created by experts who defined the clinical terminologies such as medications or clinical symptoms. This knowledge is valuable to identify information from text notes that assert a certain disease. Knowledge-guided models can automatically extract knowledge from clinical notes or biomedical literature that contains conceptual entities and relationships among these various concepts. Mortality classification was based on the combination of knowledge-guided features and rules. UMLS entity embedding and convolutional neural network (CNN) with word embeddings were applied. Concept Unique Identifiers (CUIs) with entity embeddings were utilized to build clinical text representations. Results The best configuration of the employed machine learning models yielded a competitive AUC of 0.97. Machine learning models along with NLP of clinical notes are promising to assist health care providers to predict the risk of mortality of critically ill patients. Conclusion UMLS resources and clinical notes are powerful and important tools to predict mortality in diabetic patients in the critical care setting. The knowledge-guided CNN model is effective (AUC = 0.97) for learning hidden features.