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Updated: Aug 25, 2025
Detection of Invasive Pulmonary Aspergillosis in Haematological Malignancy Patients by using Lateral-flow Technology
Published on: March 22, 2012
Development and clinical application of an electronic health record quality control system for pulmonary
Zhengtu Li1, Xidong Wang1, Mengke Xu2
1State Key Laboratory of Respiratory Disease, National Clinical Research Center for Respiratory Disease, Guangzhou Institute of Respiratory Health, the First Affiliated Hospital of Guangzhou Medical University, Guangzhou, China.
Background:
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.
Methods:
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.
Results:
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.
Conclusions:
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.
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