With the rapid development of AI techniques, Computer-aided Diagnosis has attracted much attention and has been successfully deployed in many applications of health care and medical diagnosis. For some specific tasks, the learning-based system can compare with or even outperform human experts’ performance. The impressive performance owes to the excellent expressiveness and scalability of the neural networks, although the models’ intuition usually cannot be represented explicitly. Interpretability is, however, very important, even the same as the diagnosis precision, for computer-aided diagnosis. To fill this gap, our approach is intuitive to detect pneumonia interpretably. We first build a large dataset of community-acquired pneumonia consisting of 35389 cases (distinguished from nosocomial pneumonia) based on actual medical records. Second, we train a prediction model with the chest X-ray images in our dataset, capable of precisely detecting pneumonia. Third, we propose an intuitive approach to combine neural networks with an explainable model such as the Bayesian Network. The experiment result shows that our proposal further improves the performance by using multi-source data and provides intuitive explanations for the diagnosis results.

Background: The outbreak of COVID-19 has caused a continuing global pandemic. Hospitals are integral to the control and prevention of COVID-19; however, they are facing numerous challenges during the epidemic. Objective: Our study aimed to introduce the practical experience of the design and implementation of a web-based COVID-19 service platform at a tertiary hospital in China as well as the preliminary results of the implementation. Methods: The web-based COVID-19 service platform was deployed within the health care system of the Guangdong Second Provincial General Hospital and Internet Hospital; the function of the platform was to provide web-based medical services for both members of the public and lay health care workers. The focal functions of this system included automated COVID-19 screening, related symptom monitoring, web-based consultation, and psychological support; it also served as a COVID-19 knowledge hub. The design and process of each function are introduced. The usage data for the platform service were collected and are represented by three periods: the pre-epidemic period (December 22, 2019, to January 22, 2020, 32 days), the controlled period (January 23 to March 31, 2020, 69 days), and the postepidemic period (April 1 to June 30, 2020, 91 days). Results: By the end of June 2020, 96,642 people had used the automated COVID-19 screening and symptom monitoring systems 161,884 and 7,795,194 times, respectively. The number of general web-based consultation services per day increased from 30 visits in the pre-epidemic period to 122 visits during the controlled period, then dropped to 73 visits in the postepidemic period. The psychological counseling program served 636 clients during the epidemic period. For people who used the automated COVID-19 screening service, 160,916 (99.40%) of the total users were classified in the no risk category. 464 (0.29%) of the people were categorized as medium to high risk, and 12 people (0.01%) were recommended for further COVID-19 testing and treatment. Among the 96,642 individuals who used the COVID-19 related symptoms monitoring service, 6696 (6.93%) were symptomatic at some point during the monitoring period. Fever was the most frequently reported symptom, with 2684/6696 symptomatic people (40.1%) having had this symptom. Cough and sore throat were also relatively frequently reported by the 6696 symptomatic users (1657 people, 24.7%, and 1622 people, 24.2%, respectively). Conclusions: The web-based COVID-19 service platform implemented at a tertiary hospital in China is exhibited to be a role model for using digital health technologies to respond to the COVID-19 pandemic. The digital solutions of automated COVID-19 screening, daily symptom monitoring, web-based care, and knowledge propagation have plausible acceptability and feasibility for complementing offline hospital services and facilitating disease control and prevention.

Hospital-at-home has been gaining increased attention as a potential remedy for the current shortcomings of our health care system, allowing for essential health services to be provided to patients in the comfort of their own homes. The advent of digital technology has revolutionized the way we provide medical and health care, leading to the emergence of a “hospital without walls.” The rapid adoption of novel digital health care technologies is revolutionizing remote health care provision, effectively dismantling the conventional boundary separating hospitals from the comfort of patients’ homes. The Guangdong Second Provincial General Hospital has developed a 5G-powered Smart Home Ward (SHW) that extends medical care services to the home setting and is tailored to meet the needs and settings of each patient’s household. The SHW was initially tested for its suitability for treating 4 specialized diseases, including cardiovascular disease, stroke, Parkinson disease, and Alzheimer disease. Understanding and addressing the potential challenges and risks associated with SHWs is essential for the successful implementation and maintenance of safe and effective home hospitalization.

This study unveils a cutting-edge camera-based system for the automated monitoring of hand hygiene practices within healthcare settings. Utilizing advanced computer vision and machine learning technologies, our system employs three strategically placed synchronized cameras around a wash basin. These cameras capture the handwashing process from multiple perspectives, allowing for detailed analysis of hand movements including finger and wrist dynamics. The extracted skeletal coordinate data are processed by a Gesture Category Model (GCM), which automatically identifies handwashing gestures. The model is rigorously trained on a dataset comprising video recordings from 55 healthcare professionals, focusing on the World Health Organizations seven-step hand-washing protocol. Furthermore, we introduce a Counting Algorithm to quantify the frequency and duration of each gesture, coupled with a Quality Assessment Model (QAM) that evaluates compliance with hand hygiene standards. The systems precision and its strong correlation with expert annotations highlight its potential to significantly enhance hand hygiene compliance and reduce healthcare-associated infections.

The COVID-19 pandemic has resulted in nosocomial transmission of COVID-19 within hospitals and other healthcare settings such as residential homes and primary care settings. Here, we discuss how a 5G network can be used to reduce such infections. Wen et al. discuss how implementing a 5G network in hospitals can be used to reduce nosocomial infections. Such systems can reduce the spread of COVID-19.