中文摘要：

目的全面了解成都地区某三甲综合医院门诊患者挂号时间分布特征,为医院门诊管理决策提供依据。方法在医院信息系统中,提取成都军区总医院2010—2013年门诊患者数据,采用医院管理统计方法,分析门诊患者挂号时间（季度、月、周及日）分布情况。结果 2010—2013年,累计接诊303.6万门诊患者,第四季度门诊患者挂号量所占构成比最高（82.1万次,占27.0%）,第一季度所占构成比最低（61.9万次,占20.4%）。7、11、10月门诊患者挂号量居前三位,分别为28.7、28.3、27.4万人次;2、1、6月居后三位,分别为17.5、19.1、24.9万人次。一周内,星期一门诊患者挂号量最高（占19.5%）,星期日最低（占7.6%）。一日内,门诊患者挂号量呈现大小两峰分布,上午8~9时挂号人数最多（占22.3%）,下午14~15时挂号人数最多（占8.0%）。结论一年内该医院门诊患者挂号量第四季度最多,12个月中7月份挂号量最多,一周内星期一挂号量最多,一日内上午和下午各有一个高峰。采集医院信息系统门诊诊疗信息,全面调查门诊患者挂号时间分布特征,可为预测年度门诊量、引导患者错峰就诊等门诊精细化管理提供决策支持。

英文摘要：

Objective To fully understand the distribution characteristics of outpatients＇ registration time in a third-grade class-A general hospital,thus to provide a basis for hospital outpatient management decisions. Methods This study extracted the outpatients＇ data（ 2010—2013） from the hospital information system of Chengdu Military General Hospital to analyze the distribution of outpatients＇ registration time using hospital management statistical method. Results From 2010 to2013,the cumulative accepted 303 600 outpatients,the constituent ratio of outpatients＇ registration of the Fourth quarter was the highest（ 821 000 person-times,27. 0%）,that of the First quarter was the lowest（ 619 000 person-times,20. 4%）. The number of outpatients registration in July,November,October ranked among the top 3（ 287 000,283 000,274 000 person-times,respectively）,that of outpatients＇ registration in February,January,June ranked among the last 3（ 175 000,191 000,249 000 person-times,respectively）. Within a week,Monday outpatients＇ registration was the highest（ 19. 5%）,and the lowest（ 7. 6%） was in Sunday. There were 2 peaks in outpatients＇ registration in one day： the outpatients＇ registration were the most at 8-9 a. m（ accounting for 22. 3%） and at 14-15 p. m（ accounting for 8. 0%）. Conclusion The hospital outpatient registration number in Fourth quarter is highest,in July is maximum during 12 months,in Monday is maximum during one week,in the morning and afternoon have a peak during one day. Full investigation of outpatients＇ registration time distribution can provide decision support for clinic service delicacy management such as predicting the annual outpatients＇ visits and guiding patients to avoid the peak hour for their visits.