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There are considerable differences in the diagnosis and treatment of pulmonary aspergillosis (PA) between specialized hospitals and primary hospitals or developed areas and underdeveloped areas in China. There is a lack of electronic systems that assist respiratory physicians in standardizing the diagnosis and treatment of PA.We extracted 26 quality control points from the latest guidelines related to PA, and developed a PA quality control system of electronic health record (EHR) based on natural language processing (NLP) techniques. We obtained PA patient records in the Department of Respiratory Medicine of the First Affiliated Hospital of Guangzhou Medical University to verify the effectiveness of the system comparing with manually evaluation of respiratory experts.We successfully developed quality control system of PA; 699 PA medical records from EHR of the First Affiliated Hospital of Guangzhou Medical University between January 2015 and March 2020 were obtained and assessed by the system; 162 defects were found, which included 19 medical records with diagnostic defects, 76 medical records with examination defects, and 80 medical records with treatment defects; 200 medical records were sampled for validation, and found that the sensitivity and accuracy of quality control system for pulmonary aspergillosis (QCSA) were 0.99 and 0.96, F1 value was 0.85, and the recall rate was 0.77 compared with experts' evaluation.Our system successfully uses medical guidelines and NLP technology to detect defects in the diagnosis and treatment of PA, which helps to improve the management quality of PA patients.

Background Nosocomial outbreaks of pandemic influenza A (H1N1) 2009 virus [A(H1N1)pdm09] easily develop due to its high transmissibility. This study aimed to investigate the clinical impacts of a nosocomial outbreak of A(H1N1)pdm09 between 21 January and 17 February 2016. Methods Patients who developed influenza-like illness (ILI) more than 48 hours after hospitalization in the index ward were enrolled as suspected patients, defined as group A and quarantined. Patients in other wards were defined as group B. A phylogenetic tree was constructed to determine the origins of the hemagglutinin and neuraminidase genes. Results After the implementation of an infection control measure bundle, the outbreak was limited to eight patients with ILIs in group A. Nasal swabs from seven patients were positive for A(H1N1)pdm09. All the patients recovered after treatment. Prolonged viral shedding was observed in a patient with bronchiectasis and Penicillium marneffei infection. Compared to the expected duration of hospitalization in patients without fever, those with fever had a median 7-day delay in discharge and a mean excess cost of 3,358 RMB. The four influenza strains identified were genetically identical to the A/California/115/2015 strain. Six of the 54 patients in group B who underwent bronchoscopy developed transient fever. These patients were hospitalized in various wards of the hospital and recovered after a short-term course of empirical antibiotics. Conclusions After the implementation of infection control measures, the nosocomial A(H1N1)pdm09 outbreak was rapidly contained; infected patients had a delay in discharge and excess costs, but no deaths occurred.

Joint hospital grand rounds have been held in Guangzhou Institute of Respiratory Disease (GIRD) routinely every week for more than 10 years. Recently, more and more hospitals are taking part in our joint rounds through real-time video communication. Such events are broadcast live to healthcare providers from up to 500 hospitals in China.

BACKGROUND: Since there are reports of cases of 2019-coronavirus disease (COVID-19) asymptomatic carriers in China recently and fever is one of the main symptoms, we aimed to distinguish COVID-19 cases from other febrile patients with clinical examinations in this study. METHODS: A total of 134 suspected COVID-19 patients in the isolation ward of the First Affiliated Hospital of Guangzhou Medical University were recruited from January 23 to May 23, 2020. We analyze the pathogenic form and clinical characteristics. RESULTS: Among them, pathogens were identified in only 84 patients (62.7%), including 23 (17.1%) with severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2), 30 (22.3%) with other viruses, 31 (25.0%) with other pathogens and 3 (3.5%) with mixed infections. The commonly observed symptoms of COVID-19 patients were cough, fever, fatigue, and muscle aches, which were significantly different than the symptoms of nonviral infections (P0.05). Furthermore, lactate dehydrogenase and the neutrophil/lymphocyte were found significantly high in COVID-19 patients compared to non-COVID-19 patients (P