Internet of Things (IoT) technology plays an important role in smart healthcare. This paper discusses IoT solution for emergency medical devices in hospitals. Based on the cloud-edge-device architecture, different medical devices were connected; Streaming data were parsed, distributed, and computed at the edge nodes; Data were stored, analyzed and visualized in the cloud nodes. The IoT system has been working steadily for nearly 20 months since it run in the emergency department in January 2021. Through preliminary analysis with collected data, IoT performance testing and development of early warning model, the feasibility and reliability of the in-hospital emergency medical devices IoT was verified, which can collect data for a long time on a large scale and support the development and deployment of machine learning models. The paper ends with an outlook on medical device data exchange and wireless transmission in the IoT of emergency medical devices, the connection of emergency equipment inside and outside the hospital, and the next step of analyzing IoT data to develop emergency intelligent IoT applications.
Objective To explore the influencing factors of internet game addiction among middle school students. Methods Students from a certain district in Sichuan between September 2022 and March 2023 were included as participants. Basic information such as gender, age, whether the subjects were only children, place of residence, parental education, and subjective economic status were investigated. The nine-item Internet Gaming Disorder Scale-short form was used to investigate whether participants had internet game addiction, and the Berkman-Syme Social Network Index was used to evaluate the participants’ social level. Multiple linear regression analysis was used to conduct multivariate analysis to explore the influencing factors of internet game addiction. Results A total of 594 questionnaires were distributed, and 592 valid questionnaires were ultimately obtained. The detection rate of internet game addiction was 12.0%. Multiple linear regression analysis showed that gender (t=?8.281, P<0.001), age (t=3.211, P=0.001), subjective economic status in the region (t=2.025, P=0.043), and social level (t=?4.239, P<0.001) were the influencing factors of online game addiction. Due to the P value was close to the set test level (0.05), subjective economic status in the region was not considered an influencing factor of internet game addiction. Conclusion Teenagers with male gender, older age, and lower social skills are more likely to develop addiction to internet games.
Basing on the internet education implemented during China’s fighting against the outbreak of coronavirus disease 2019, and combining with literature review and personal experience in internet teaching, the paper puts forward 10 suggestions from the perspective of problem-analysis, including policy guidance, enhancement of hardware and software capacities, teacher talent teams, diversity of education and teaching, standardization and normalization, integration of quality resources, examination system, supporting industry, exchange and cooperation, and network information security education, to further strengthen the construction and development of internet education in China.
At present, the rapid integration and development of internet technology and medical services have made internet diagnosis and treatment an important part of medical services, and it is also an inevitable development trend of future diagnosis and treatment services. In order to meet the needs of patients for more timely, accurate and convenient medical treatment, West China Hospital of Sichuan University has innovated the internet diagnosis and treatment mode, adopted the innovative mode of diversified online services, pre-treatment mode, expert team mode, specialized medical consortium platform and whole process management, optimized medical resources and structure, promoted regional medical association linkage, and improved patients’ medical experience. The West China Internet Hospital of Sichuan University takes the whole process ecological closed-loop of “medical+health” as the goal, has greatly improved the efficiency and quality of diagnosis and treatment, which is of great significance for the positioning and development of internet hospital. This article will share the construction experience of West China Internet Hospital of Sichuan University.
ObjectiveTo explore the development and application of a novel ventilator alarm management model in critically ill patients receiving invasive mechanical ventilation (MV) in the intensive care unit (ICU) using machine learning (ML) and Internet of Medical Things (IoMT). The study aims to identify alarms’ intervention requirements. MethodsA retrospective cohort study and ML analysis were conducted, including adult patients receiving invasive MV in the ICU at West China Hospital from February 10, 2024, to July 22, 2024. A total of 76 ventilator alarm-related parameters were collected through the IoMT system. Feature selection was performed using a stratified approach, and six ML algorithms were applied: Gaussian Naive Bayes, K-Nearest Neighbors, Linear Discriminant Analysis, Support Vector Machine, Categorical Boosting (CatBoost), and Logistic Regression. Model performance was evaluated using the area under the receiver operating characteristic curve (AUC-ROC). ResultsA total of 107 patients and their associated ventilator alarm records were included. Thirteen highly relevant features were selected from the 76 parameters for model training through stratified feature selection. The CatBoost model demonstrated the best predictive performance, with an AUC-ROC of 0.984 7 and an accuracy of 0.912 3 in the training set. External validation of the CatBoost model yielded an AUC-ROC of 0.805 4. ConclusionThe CatBoost-based ML model successfully constructed in this study has high accuracy and reliability in predicting the ventilator alarms in ICU patients, providing an effective tool for ventilator alarm management. The CatBoost-based ML method exhibited remarkable efficacy in predicting the necessity of ventilator intervention in critically ill ICU patients. Further large-scale multicenter studies are recommended to validate its clinical application value and promote model optimization and implementation.
ObjectiveTo summarize the experience of earthquake rescue with the help of wise information technology.MethodsThe Jiuzhaigou earthquake of magnitude 7.0 occurred at 21:19 on August 8th 2017. Three hours and 38 minutes after the earthquake, a triad model of remote consultation, mobile video consultation and mobile text consultation was established to assist the earthquake rescue based on the mobile on-line medical consultation application and telemedicine center in West China Hospital. Patients classification, primary diagnosis, psychological counseling, victims searching were done by this novel rescue model.ResultWithin 72 hours after the earthquake, there were 114 doctors taking part in the earthquake rescue, including 4 remote consultations (hospital to hospital), 7 video consultations (doctor to victim), 487 mobile text consultations (doctor to victim), and 32 cases of which were highly relative to earthquake rescue, including one case of positioning for victim-searching.ConclusionThe triad model of earhquake rescue which was first initiated by West China Hospital played an important role in assisting earthquake rescue and achieved good results.
The intensive care unit (ICU) is a highly equipment-intensive area with a wide variety of medical devices, and the accuracy and timeliness of medical equipment data collection are highly demanded. The integration of the Internet of Things (IoT) into ICU medical devices is of great significance for enhancing the quality of medical care and nursing, as well as for the advancement of digital and intelligent ICUs. This study focuses on the construction of the IOT for ICU medical devices and proposes innovative solutions, including the overall architecture design, devices connection, data collection, data standardization, platform construction and application implementation. The overall architecture was designed according to the perception layer, network layer, platform layer and application layer; three modes of device connection and data acquisition were proposed; data standardization based on Integrating the Healthcare Enterprise-Patient Care Device (IHE-PCD) was proposed. This study was practically verified in the Chinese People’s Liberation Army General Hospital, a total of 122 devices in four ICU wards were connected to the IoT, storing 21.76 billion data items, with a data volume of 12.5 TB, which solved the problem of difficult systematic medical equipment data collection and data integration in ICUs. The remarkable results achieved proved the feasibility and reliability of this study. The research results of this paper provide a solution reference for the construction of hospital ICU IoT, offer more abundant data for medical big data analysis research, which can support the improvement of ICU medical services and promote the development of ICU to digitalization and intelligence.
Cohort study is the observational study with the highest strength of causality demonstration, which is often used to test the etiological hypothesis and determine the risk factors of diseases. However, it often takes a lot of manpower, material and financial resources to carry out a cohort study, and it is time-consuming. At the same time, due to the long follow-up time, it is difficult to maintain the compliance of the study population, which is prone to loss to follow-up bias. In recent years, driven by network technology, a new type of cohort study design, e-cohort study, has gradually emerged, which is based on the network to recruit participants, follow up and collect data. Taking advantage of the wide coverage and high flexibility of the network, it provides a new strategy for improving the recruitment speed, participant participation and compliance of cohort study. This article summarizes the development history, current status, key points of design and implementation, advantages and challenges of e-cohort study, so as to help researchers to fully understand and apply this study design to solve practical clinical problems.
With the increasing demand for medical and health services in China, Internet hospitals have emerged, which can provide the public with diversified health services from multiple levels and dimensions. Based on the Internet hospital policies issued, this article sorts out and summarizes the information of Internet hospitals publicly reported in China, compares and analyzes the classification characteristics and similarities and differences of the two major service models of Internet hospitals, which are the second-named entity medical institutions, and Internet hospitals independently established by medical institutions, puts forward suggestions on how to improve the service model of Internet hospitals, and reveals the challenges faced by Internet hospitals. It aims to provide a reference for the promotion and development of Internet hospitals in China in the future.
Chronic kidney disease (CKD) has become a global public health problem because of its high prevalence, low awareness, poor prognosis, and high medical costs. Effective follow-up management can facilitate timely adjustment of the treatment of the CKD patients and delay the disease progression. The application of internet of things (IoT) technology in dynamic monitoring and telemedicine is helpful for the self-management of patients with chronic diseases, and can provide convenient, intelligent, and humanized medical and health services. In the future, with the rapid growth of demands of CKD management and innovations in information technology, new medical IoT industry will accelerate the intelligent development of CKD management. Multi-disciplinary and multi-industrial collaboration should be promoted to solve current challenges, such as evaluation of actual effectiveness, the system design and construction, and the accessibility of intelligent healthcare services, to ensure that IoT products can improve clinical outcomes, reduce medical expenditure, and lower disease burden.
