摘要目的:基于广州医科大学附属第二医院急诊科大数据探究临床规律，建立急诊临床科研一体化平台最终应用于临床实践。方法:基于广州医科大学附属第二医院的医院信息系统（HIS）、实验室信息系统（LIS）、急诊专科系统、影像归档和通信系统（PACS）及电子病历系统，将2019年3月至2022年4月急诊科就诊患者的结构及非结构化信息通过抽取融合、标化归一、脱敏质控等手段建立数据库。此外，从数据库中提取2019年3月至2022年4月急诊就诊的预检分诊Ⅲ级及以下成人患者的数据，如人口学特征、预检分诊时生命体征、诊疗特征、诊断和分级、时间指标和结局指标等，分析患者不良预后的独立危险因素。结果:①广州医科大学附属第二医院急诊数据库记录2019年3月至2022年4月急诊科就诊患者共计338 681人次，就诊信息包括人口学信息、分诊信息、就诊信息、绿通和抢救、诊断信息、病历信息、检验检查概况、检验信息、检查信息、微生物信息、用药信息、治疗信息及住院信息、胸痛管理及卒中管理等15个模块数据，数据库保证了数据的可视化和可操作性。②从急诊数据库中筛选出预检分诊Ⅲ级及以下患者140 868例，纳入患者的性别、年龄、来院方式、脉搏、血压、格拉斯哥昏迷评分（GCS）等指标，以急诊入手术室、急诊入介入室、急诊入重症监护病房（ICU）或急诊死亡为不良预后，构建预检分诊Ⅲ级及以下患者预后不良预测模型。通过受试者工作特征曲线和森林图结果表明，该模型具有较好的预测效能，用于临床有利于降低急诊预检分诊不足的风险。结论:基于急诊科大数据建立高质量的临床数据库，有利于挖掘大数据中的临床价值，并辅助临床决策，提高临床诊治质量。

abstractsObjective:To explore clinical rules based on the big data of the emergency department of the Second Affiliated Hospital of Guangzhou Medical University, and to establish an integrated platform for clinical research in emergency, which was finally applied to clinical practice.Methods:Based on the hospital information system (HIS), laboratory information system (LIS), emergency specialty system, picture archiving and communication systems (PACS) and electronic medical record system of the Second Affiliated Hospital of Guangzhou Medical University, the structural and unstructured information of patients in the emergency department from March 2019 to April 2022 was extracted. By means of extraction and fusion, normalization and desensitization quality control, the database was established. In addition, data were extracted from the database for adult patients with pre screening triage level Ⅲ and below who underwent emergency visits from March 2019 to April 2022, such as demographic characteristics, vital signs during pre screening triage, diagnosis and treatment characteristics, diagnosis and grading, time indicators, and outcome indicators, independent risk factors for poor prognosis in patients were analyzed.Results:① The data of 338 681 patients in the emergency department of the Second Affiliated Hospital of Guangzhou Medical University from March 2019 to April 2022 were extracted, including 15 modules, such as demographic information, triage information, visit information, green pass and rescue information, diagnosis information, medical record information, laboratory examination overview, laboratory information, examination information, microbiological information, medication information, treatment information, hospitalization information, chest pain management and stroke management. The database ensured data visualization and operability. ② Total 140 868 patients with pre-examination and triage level Ⅲ and below were recruited from the emergency department database. The gender, age, type of admission to the hospital, pulse, blood pressure, Glasgow coma scale (GCS) and other indicators of the patients were included. Taking emergency admission to operating room, emergency admission to intervention room, emergency admission to intensive care unit (ICU) or emergency death as poor prognosis, the poor prognosis prediction model for patients with pre-examination and triage level Ⅲ and below was constructed. The receiver operator characteristic curve and forest map results showed that the model had good predictive efficiency and could be used in clinical practice to reduce the risk of insufficient emergency pre-examination and triage.Conclusions:The establishment of high-quality clinical database based on big data in emergency department is conducive to mining the clinical value of big data, assisting clinical decision-making, and improving the quality of clinical diagnosis and treatment.