Li Zhengtu, Wang Xidong, Xu Mengke, Li Yongming, Wang Yinguang, Chen Yijun, Li Shaoqiang, Li Zhun, Yang Jinglu, Tang Chun, Xiong Fangshu, Jian Wenhua, He Peimei, Zhan Yangqing, Zheng Jinping, Ye Feng
State 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.
Guangzhou Tianpeng Technology Co., Ltd., Guangzhou, China.
J Thorac Dis. 2022 Sep;14(9):3398-3407. doi: 10.21037/jtd-22-532.
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.
在中国，专科医院与基层医院之间以及发达地区与欠发达地区之间，肺曲霉病（PA）的诊断和治疗存在相当大的差异。缺乏协助呼吸内科医生规范PA诊断和治疗的电子系统。
我们从与PA相关的最新指南中提取了26个质量控制点，并基于自然语言处理（NLP）技术开发了一个电子健康记录（EHR）的PA质量控制系统。我们获取了广州医科大学附属第一医院呼吸内科的PA患者记录，以与呼吸专家的人工评估相比较来验证该系统的有效性。
我们成功开发了PA质量控制系统；获取并由该系统评估了广州医科大学附属第一医院2015年1月至2020年3月EHR中的699份PA病历；发现162处缺陷，其中包括19份诊断缺陷病历、76份检查缺陷病历和80份治疗缺陷病历；抽取200份病历进行验证，发现肺曲霉病质量控制系统（QCSA）与专家评估相比，灵敏度和准确率分别为0.99和0.96，F1值为0.85，召回率为0.77。
我们的系统成功利用医学指南和NLP技术检测PA诊断和治疗中的缺陷，这有助于提高PA患者的管理质量。
