GM(1,1)和Verhulst模型在三级医院门急诊量预测中的应用比较
|
摘要
目的比较分析GM(1,1)和Verhulst模型在三级医院门急诊量中的预测效果,并通过模型预测三级医院门诊量未来发展趋势。方法根据上海市8所三级医院2004—2015年的年均门急诊量,通过Matlab8.3软件建立GM(1,1)和Verhulst模型对上海市三级医院年均门急诊量进行拟合,比较分析两种模型的预测效果。结果建立的GM(1,1)数学模型为Y(t)=2 058.556321e~(0.080452(t-1))-1 905.468821,模型后验差比值C=0.169,小误差概率P=1.000;建立的Verhulst数学模型为■。利用Verhulst模型预测2016—2020年的预测值(万人次)分别是386.24、401.35、415.14、427.62、438.82。结论 Verhulst模型预测效果优于GM(1,1)模型,自2009年以来三级综合医院门急诊人次增长率总体呈下降趋势,表明上海市医疗服务体系改革有一定效果显现;在无外界因素影响的情况下,未来几年上海市三级医院的门急诊量仍将会持续上升。
Objective To compare and analyze the prediction effect of GM(1,1) and model Verhulst in tertiary hospitals' outpatient and emergency department's quantity through the annual average outpatient and emergency department's quantity records of 8 tertiary hospitals in Shanghai for 12 years, and predict the development trend of the tertiary hospital's outpatient and emergency department's quantity by using the model to provide decision-making basis for the implementation and optimization of the Shanghai's medical reform. Methods According to the average annual outpatient and emergency department's quantity of tertiary hospitals in Shanghai from 2004 to 2015, GM(1,1) and Verhulst models were established with Matlab 8.3 to fit the tertiary hospitals of Shanghai annual outpatient and emergency department's quantity. Results The established GM(1, 1) mathematical model was Y(t)=2 058.556321 e~(0.080452(t-1))-1 905.468821, C=0.169, P=1.000; the established Verhulst mathematics model was ■. The forecast values(million) of 2016-2020 years using the Verhulst model were 386.24, 401.35, 415.14, 427.62, and 438.82. Conclusion The Verhulst model is better than GM(1,1) model in prediction effect; tertiary general hospital's outpatient and emergency department's quantity growth rate shows a decreasing trend since 2009, suggesting the effect of Shanghai's medical reform. And the outpatient and emergency department's quantity of the tertiary hospitals in Shanghai will rise continuously without the influence of external factors.
Objective To compare and analyze the prediction effect of GM(1,1) and model Verhulst in tertiary hospitals' outpatient and emergency department's quantity through the annual average outpatient and emergency department's quantity records of 8 tertiary hospitals in Shanghai for 12 years, and predict the development trend of the tertiary hospital's outpatient and emergency department's quantity by using the model to provide decision-making basis for the implementation and optimization of the Shanghai's medical reform. Methods According to the average annual outpatient and emergency department's quantity of tertiary hospitals in Shanghai from 2004 to 2015, GM(1,1) and Verhulst models were established with Matlab 8.3 to fit the tertiary hospitals of Shanghai annual outpatient and emergency department's quantity. Results The established GM(1, 1) mathematical model was Y(t)=2 058.556321 e~(0.080452(t-1))-1 905.468821, C=0.169, P=1.000; the established Verhulst mathematics model was ■. The forecast values(million) of 2016-2020 years using the Verhulst model were 386.24, 401.35, 415.14, 427.62, and 438.82. Conclusion The Verhulst model is better than GM(1,1) model in prediction effect; tertiary general hospital's outpatient and emergency department's quantity growth rate shows a decreasing trend since 2009, suggesting the effect of Shanghai's medical reform. And the outpatient and emergency department's quantity of the tertiary hospitals in Shanghai will rise continuously without the influence of external factors.
引文
[1] 刘志勤.灰色数列GM(1,1)模型在门诊人次预测中的应用[J].医学信息(上旬刊),2011,24(9):5619.
[2] 陈茹,段光锋,金春林,等.上海市8所三级综合性医院经济运行状况分析[J].中国卫生资源,2012,15(6):436-438.
[3] 张嵬,段光锋,金春林,等.上海市八所三级甲等综合性医院2009—2013年经济运行状况分析[J].中国卫生经济,2015,34(6):78-80.
[4] 袁磊,朱勃睿,段光锋,等.上海市八所三级综合性医院2012—2015年经济运行分析[J].解放军医院管理杂志,2018,25(3):259-262.
[5] 毛瑛,井朋朋,吴静娴,等.我国卫生人力资源的组合预测模型构建及应用[J].中国卫生经济,2015,34(5):21-24.
[6] 刘文,王振环,刘勇.GM(1,1)模型和Verhulst模型在人口预测中的应用[J].孝感学院学报,2008(3):27-30.
[7] 马春柳,刘海霞,李小升,等.灰色预测模型GM(1,1)在医院门诊量预测中的应用[J].中国病案,2012,13(12):23-25.
[8] 张闯,彭振斌,彭文祥.优化的灰色离散Verhulst模型在基坑沉降预测中的应用[J].中南大学学报(自然科学版),2017,48(11):3030-3036.
[9] 张嘉琦.道路交通事故死亡人数预测模型对比研究[J].中国安全科学学报,2016,26(9):45-49.
[10] 戴福青,郑哲.灰色Verhulst神经网络组合预测区域空域流量[J].数学的实践与认识,2016,46(6):47-53.
[11] 王桐远.基于灰色连续区间Verhulst模型的大规模地震灾害救援药品需求预测研究[D].重庆:重庆工商大学,2016:1-18.
[12] 魏芬芬.灰色预测模型在血糖预测中的研究[D].郑州:郑州大学,2016:5-25.
[13] 程舒媛,何智敏,曾讯,等.2005—2013年广州某大型综合医院门急诊人次变动趋势分析[J].中国循证医学杂志,2015,15(4):389-392.
[14] 安凤梅,姚峥.门诊开展“三好一满意”活动实践探讨[J].临床和实验医学杂志,2012,11(5):363-365.
[15] 白鸽,崔欣,戴瑞明,等.大型医院可下沉门诊病人的筛选方法与实证研究[J].中国医院管理,2017,37(11):1-2.
[16] 罗力,白鸽,张天天,等.分级诊疗制度的上海模式及推进建议[J].中国医院管理,2017,37(12):1-3.
[17] 王凡,程丹,王志成,等.三级医院优质医疗资源下沉基层的实践与思考[J].中国医院管理,2017,37(1):73-74.
[2] 陈茹,段光锋,金春林,等.上海市8所三级综合性医院经济运行状况分析[J].中国卫生资源,2012,15(6):436-438.
[3] 张嵬,段光锋,金春林,等.上海市八所三级甲等综合性医院2009—2013年经济运行状况分析[J].中国卫生经济,2015,34(6):78-80.
[4] 袁磊,朱勃睿,段光锋,等.上海市八所三级综合性医院2012—2015年经济运行分析[J].解放军医院管理杂志,2018,25(3):259-262.
[5] 毛瑛,井朋朋,吴静娴,等.我国卫生人力资源的组合预测模型构建及应用[J].中国卫生经济,2015,34(5):21-24.
[6] 刘文,王振环,刘勇.GM(1,1)模型和Verhulst模型在人口预测中的应用[J].孝感学院学报,2008(3):27-30.
[7] 马春柳,刘海霞,李小升,等.灰色预测模型GM(1,1)在医院门诊量预测中的应用[J].中国病案,2012,13(12):23-25.
[8] 张闯,彭振斌,彭文祥.优化的灰色离散Verhulst模型在基坑沉降预测中的应用[J].中南大学学报(自然科学版),2017,48(11):3030-3036.
[9] 张嘉琦.道路交通事故死亡人数预测模型对比研究[J].中国安全科学学报,2016,26(9):45-49.
[10] 戴福青,郑哲.灰色Verhulst神经网络组合预测区域空域流量[J].数学的实践与认识,2016,46(6):47-53.
[11] 王桐远.基于灰色连续区间Verhulst模型的大规模地震灾害救援药品需求预测研究[D].重庆:重庆工商大学,2016:1-18.
[12] 魏芬芬.灰色预测模型在血糖预测中的研究[D].郑州:郑州大学,2016:5-25.
[13] 程舒媛,何智敏,曾讯,等.2005—2013年广州某大型综合医院门急诊人次变动趋势分析[J].中国循证医学杂志,2015,15(4):389-392.
[14] 安凤梅,姚峥.门诊开展“三好一满意”活动实践探讨[J].临床和实验医学杂志,2012,11(5):363-365.
[15] 白鸽,崔欣,戴瑞明,等.大型医院可下沉门诊病人的筛选方法与实证研究[J].中国医院管理,2017,37(11):1-2.
[16] 罗力,白鸽,张天天,等.分级诊疗制度的上海模式及推进建议[J].中国医院管理,2017,37(12):1-3.
[17] 王凡,程丹,王志成,等.三级医院优质医疗资源下沉基层的实践与思考[J].中国医院管理,2017,37(1):73-74.
© 2004-2018 中国地质图书馆版权所有 京ICP备05064691号 京公网安备11010802017129号 地址：北京市海淀区学院路29号 邮编：100083 电话：办公室：(+86 10)66554848；文献借阅、咨询服务、科技查新：66554700 |