Background: In China, a long waiting time for registration is a common occurrence in many tertiary hospitals. This study aimed to analyze the effects of a comprehensive reservation service for non-emergency registration on appointment registration rate, patient waiting time, patient satisfaction and outpatient volume at the Guangzhou Women and Children's Medical Center. Methods: This was a cross-sectional study. This study investigated the effects of a comprehensive reservation service for non-emergency registration in Guangzhou Women and Children's Medical Center in China starting in October 2015. In total, 2194 patients completed a satisfaction survey administered by the Guangdong Situation Research Center. The content of the questionnaire consisted of six aspects: general impression, service attitude, service quality, hospital environment, price perception and medical ethics. A Likert 5-point rating scale was used in the questionnaire; answers were classified as "very satisfied", "relatively satisfied", "neutral", "unsatisfied" and "very unsatisfied". The method of application was paper-based. T-tests were used to compare the sample means, and chisquare tests were used to compare the rates. A multiple-test procedure was performed to evaluate the differences in the reservation rates during a 12-month period. Results: After the implementation of the comprehensive reservation service for non-emergency registration in our hospital, which has an annual outpatient volume of approximately 4 million, the monthly appointment registration rate increased from (34.95 ± 2.91)% to(89.13 ± 3.12)%,P < 0.01. The patient waiting time was significantly reduced (P < 0.01), and the proportion of patients who believed that the waiting time required improvement was decreased significantly (P < 0.01). Moreover, the third-party evaluation result of outpatient satisfaction significantly improved (P < 0.01). The total hospital outpatient volume decreased(P < 0.01). The outpatient volume of the Department of General Pediatrics decreased.

Background A large-scale global outbreak of coronavirus disease-19 (COVID-19) out of Wuhan, from China, occurred in January 2020. To examine the clinical characteristics of COVID-19 in infected patients out of Wuhan, from China. Methods Thirteen patients were confirmed to be infected with novel coronavirus-2019 (2019-nCoV) between January 27 and February 8, 2020, in Baoji city, Shannxi, northwestern China. Epidemiological and clinical information, and computed to morphology imaging data from all COVID-19 patients were collected; cases were divided into two groups according to the severity of infection (mild or severe). Results Nine (9/13) COVID-19 patients exhibited mild disease severity, and defined as second-generation human-to-human transmission cases. Most patients (11/13) had a history of travel to or from Wuhan. There were no differences in sex and age between the mild and severe cases (all P > 0.05). A moderate degree of fever (11/13), cough (13/13), and fatigue (8/13) were common symptoms; however, there was no statistical difference between mild and severe cases in this regard (all P > 0.05). Oxyhemoglobin saturation and oxygenation index decreased, and C-reactive protein (CRP) and serum amyloid A (SAA) levels were elevated in all patients with COVID-19 infection, with statistically significant differences between those with severe disease and mild infection (all P < 0.05). Twelve of 13 COVID-19 patients exhibited changes in chest CT imaging features, and time course changes were different between mild and severe cases (all P < 0.05). Conclusion Most cases of COVID-19 infection were second-generation human-to-human transmissions from Wuhan and were mild in severity. The clinical characteristics of COVID-19 varied. Oxyhemoglobin saturation, oxygenation index, CRP and SAA levels, and CT features were reliable parameters to evaluate the severity of COVID-19 infection. However, a few patients with mild COVID-19 disease lacked typical characteristics such as fever and changes in CT imaging features.

Background: Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is a virus that causes severe respiratory illness in humans, which results in global outbreak of novel coronavirus disease currently. This study aimed to evaluate the characteristics of publications involving coronaviruses as well as COVID-19 by using topic modeling. Methods: We extracted all abstracts and retained the most informative words from the COVID-19 Open Research Dataset, which contains 35,092 pieces of coronavirus related literature published up to March 20, 2020. Using Latent Dirichlet Allocation modeling, we trained a topic model from the corpus, analyzed the semantic relationships between topics and compared the topic distribution between COVID-19 and other CoV infections. Results: Eight topics emerged overall: clinical characterization, pathogenesis research, therapeutics research, epidemiological study, virus transmission, vaccines research, virus diagnostics, and viral genomics. It was observed that current COVID-19 research puts more emphasis on clinical characterization, epidemiological study, and virus transmission. In contrast, topics about diagnostics, therapeutics, vaccines, genomics and pathogenesis only account for less than 10% or even 4% of all the COVID-19 publications, much lower than those of other CoV infections. Conclusions: These results identified knowledge gaps in the area of COVID-19 and offered directions for future research.

Children of severe hand, foot, and mouth disease (HFMD) often present with same clinical features as those of mild HFMD during the early stage, yet later deteriorate rapidly with a fulminant disease course. Our goal was to: (1) develop a machine learning system to automatically identify cases with high risk of severe HFMD at the time of admission; (2) compare the effectiveness of the new system with the existing risk scoring system. Data on 2,532 HFMD children admitted between March 2012 and July 2015, were collected retrospectively from a medical center in China. By applying a holdout strategy and a 10-fold cross validation method, we developed four models with the random forest algorithm using different variable sets. The prediction system HFMD-RF based on the model of 16 variables from both the structured and unstructured data, achieved 0.824 sensitivity, 0.931 specificity, 0.916 accuracy, and 0.916 area under the curve in the independent test set. Most remarkably, HFMD-RF offers significant gains with respect to the commonly used pediatric critical illness score in clinical practice. As all the selected risk factors can be easily obtained, HFMD-RF might prove to be useful for reductions in mortality and complications of severe HFMD.

Objectives: Sepsis-3 consensus suggests “the need to develop similar updated definitions for pediatric populations.” Sequential organ failure assessment (SOFA) and systemic inflammatory response syndrome (SIRS) criteria are two systems widely used to define the status of infection. However, it is still unclear whether SOFA is more accurate than SIRS in predicting children mortality in low- and middle-income countries. Thus, we validated the accuracy of age-adapted SOFA and SIRS in predicating the poor prognosis of infected children in China's pediatric intensive care unit (PICU). Methods: We performed a retrospective and observational cohort study of children admitted for infection to PICU in the hospital between January 1, 2009 and December 31, 2017. The indexes within 24 h after intensive care unit (ICU) admission were analyzed according to age-adapted SOFA and SIRS, and all data were sourced from the hospital's electronic health record database. The prognosis was illustrated with primary outcome and secondary outcome. Primary outcome referred to in-hospital mortality, and secondary outcome to in-hospital mortality or ICU length of stay ≥ 7 days. The predictive power of age-adapted SOFA and SIRS was compared using crude and adjusted area under the receiver operating characteristic curve (AUROC). Results: Of 1,831 PICU-admitted children due to infection, 164 (9.0%) experienced primary outcome, and 948 (51.8%) secondary outcome. Of 164 deaths, 65.9% were males (median age of 7.53 months, range of 2.67–41.00 months). Children who scored ≥ 2 in age-adapted SOFA or met two SIRS criteria accounted for 92.5% and 73.3%, respectively. In addition, age-adapted SOFA score of ≥2 predicted adverse outcome more accurately than pediatric SIRS (adjusted AUROC, 0.753; 0.713–0.796 vs. 0.674; 0.631–0.702; P < 0.001). Conclusion: Compared with SIRS criteria, age-adapted SOFA score of ≥ 2 enjoys a more accuracy in predicting in-hospital mortality of PICU-admitted children, and a higher sensitivity in identifying children with severe infection.