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某院门诊人次变化特征分析
引用本文:叶惠青.某院门诊人次变化特征分析[J].中国病案,2012(9):55+12-55,12.
作者姓名:叶惠青
作者单位:515031 汕头市 汕头市中心医院病案统计室
摘    要:目的探讨医院门诊人次变化特征,为医院提供科学有效的门诊统计数据,使门诊医疗服务得到进一步的提高。方法对某院2002年至2011年门诊人次进行直线回归分析,并计算季节指数。结果直线回归方程有显著的统计学意义(P〈0.001),回归平方和占总平方和的91.1%,回归系数为正值,显示门诊人次有直线上升趋势,每年大约增加21222人次。季节指数计算结果显示门诊人次有明显的季节波动趋势。二季度和三季度季节指数都大于100%,是门诊流量高峰期,一季度的季节指数最低,是门诊流量的低谷期。4月至9月以及11月的季节指数都大于100%,是门诊流量的高峰期,其它月份的季节指数都小于100%,是门诊流量的低谷期,其中2月份是门诊流量的最低谷。结论医院应根据门诊人次的变化特征优化配置人员和设备。

关 键 词:门诊人次  长期趋势  季节性

Characteristics Analysis of the Outpatients Number Changes in a Hospital
Yehuiqing.Characteristics Analysis of the Outpatients Number Changes in a Hospital[J].Chinese Medical Record,2012(9):55+12-55,12.
Authors:Yehuiqing
Institution:Yehuiqing,Department of Medical Records of Statistics,Shantou Central Hospital,Shantou 515031,China
Abstract:Objective To explore the characteristics of outpatients number changes in the past decade,and provide more scientific and effective outpatients statistics data to hospital,thus further improve outpatients medical services. Methods The number of outpatients from 2002 to 2011 were analyzed by linear regression and season index analysis.Result The linear regression equation was statistically significant(P<0.001),regression sum of squares is 91.1% of the total sum of squares,the regression coefficient is positive,these data showed outpatients number straight up,outpatients visits increased by about 21,222 people annually.The seasonal index calculations showed that outpatients number had significant seasonal fluctuation. Second quarter and third quarter were peak traffic of the outpatient flow with the seasonal index greater than 100%.The first quarter was trough of the outpatients flow with the lowest seasonal index.April to September and November were outpatients peak traffic of the outpatients flow with the seasonal index greater than 100%,while other months were less than 100%,February was the lowest point of the outpatients flow.Conclusions The hospital should optimally allocate the staff and equipment according to the characteristics of outpatients number changes.
Keywords:Outpatients number  Long-term trend  Seasonal characteristics
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