基于医院信息数据挖掘的信息化临床路径在临床医疗费用监控中的应用
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摘要
目的:基于信息数据挖掘技术,探讨临床路径在医疗费用监控管理中的应用价值。方法:采用3σ准则和多元线性回归对2014-2015年500例患者的原始诊疗数据进行分析,建立脑梗死、肺癌、慢性肾衰竭和急性心肌梗死4类临床常见疾病的临床路径。将2016-2017年医院接治的608例患者按照疾病诊断相关分类(DRGs)随机分为观察组和对照组,每组304例,观察组在临床路径指导下开展诊疗活动,对照组采用常规诊疗方案。统计分析两组药品费用、耗材费用和医疗费用总额等3项数据的差异性。结果:观察组4类疾病的药品费用均低于对照组,其差异有统计学意义(t=2.197,t=2.971,t=2.711,t=2.224;P<0.05);观察组4类疾病的耗材费用指标均低于对照组,其差异有统计学意义(t=2.120,t=2.477,t=2.323,t=2.654;P<0.05);观察组4类疾病的医疗费用总额指标均低于对照组,其差异有统计学意义(t=3.395,t=3.630,t=2.398,t=4.037;P<0.05)。结论:医院信息数据挖掘可有效指导医疗费用监控,降低患者诊疗相关费用,对减轻患者经济负担和促进医院医疗费用监控管理具有重要意义。
Objective: To explore the application value of clinical pathway in monitoring medical expenses based on data mining technique. Methods: 3-σ criterion and multiple-linear regression were adopted to analyze original data of 500 patients from 2014 to 2015, and to establish the clinical pathway of four kinds of common clinical diseases included cerebral infarction, lung cancer, chronic renal failure and acute myocardial infarction. A total of 608 patients who were treated by the hospital from 2016 to 2017 were randomly divided into the observation group(304 cases) and the control group(304 cases) as diagnosis related groups(DRGs). The observation group received treatment under the guidance of clinical pathway, while the control group received routine treatment. The differences of three indicators,included drug cost, consumables cost and total amount of medical cost, of two groups were analyzed as statistical methods. Results: The drug costs of four diseases of observation group were significantly lower than those of control group(t=2.197, t=2.971, t=2.711, t=2.224, P<0.05). And the consumables costs of four diseases of observation group were significantly lower than those of control group(t=2.120, t=2.477, t=2.323, t=2.654, P<0.05). Besides, the total amount of medical costs of four diseases of observation group were significantly lower than those of control group(t=3.395, t=3.630, t=2.398, t=4.037, P<0.05). Conclusions: The mining of information data of hospital can effectively guide the monitoring of medical expenses, and can decrease the related expenses of patients during the diagnosis and treatment, and it has important significance in reducing the financial burden of patients and promoting the monitoring management of medical cost of hospital.
Objective: To explore the application value of clinical pathway in monitoring medical expenses based on data mining technique. Methods: 3-σ criterion and multiple-linear regression were adopted to analyze original data of 500 patients from 2014 to 2015, and to establish the clinical pathway of four kinds of common clinical diseases included cerebral infarction, lung cancer, chronic renal failure and acute myocardial infarction. A total of 608 patients who were treated by the hospital from 2016 to 2017 were randomly divided into the observation group(304 cases) and the control group(304 cases) as diagnosis related groups(DRGs). The observation group received treatment under the guidance of clinical pathway, while the control group received routine treatment. The differences of three indicators,included drug cost, consumables cost and total amount of medical cost, of two groups were analyzed as statistical methods. Results: The drug costs of four diseases of observation group were significantly lower than those of control group(t=2.197, t=2.971, t=2.711, t=2.224, P<0.05). And the consumables costs of four diseases of observation group were significantly lower than those of control group(t=2.120, t=2.477, t=2.323, t=2.654, P<0.05). Besides, the total amount of medical costs of four diseases of observation group were significantly lower than those of control group(t=3.395, t=3.630, t=2.398, t=4.037, P<0.05). Conclusions: The mining of information data of hospital can effectively guide the monitoring of medical expenses, and can decrease the related expenses of patients during the diagnosis and treatment, and it has important significance in reducing the financial burden of patients and promoting the monitoring management of medical cost of hospital.
引文
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[2]李静,马效恩,华永新,等.基于结构变动度的不同付费方式住院医疗费用控制研究[J].中国医院管理,2015,35(12):65-67.
[3]Buczak-Stec E,Goryński P,Nitsch-Osuch A,et al.The impact of introducing a new hospital financing system(DRGs)in Poland on hospitalisations for atherosclerosis:An interrupted time series analysis(2004-2012)[J].Health policy,2017,121(11):1186-1193.
[4]Bizopoulou A,Mangita A,Chardalias C,et al.Health Professionals Information for Diagnostics Related Groups(DRGs)with the Use of a Website[J].Acta inform Med,2017,25(3):195-197.
[5]梁小玲.探讨医疗大数据环境背景下健康信息的分析方法[J].信息技术与信息化,2018(06):87-88,91.
[6]戴召洋.浅谈临床医疗信息数据挖掘[J].中国农村卫生事业管理,2009,29(6):470-471.
[7]冷建飞,高旭,朱嘉平.多元线性回归统计预测模型的应用[J].统计与决策,2016(7):82-85.
[8]顾刘金.应用SPSS软件实现多重线性回归分析[J].预防医学,2018,30(3):323-324.
[9]黄冠,龚丽洁,于润吉.控制公立医院医疗费用不合理增长是医改的重要任务[J].中国卫生经济,2016,35(7):64-65.
[10]李文,孙媛,张少伟.新医改背景下如何控制医疗费用不合理增长[J].中国卫生产业,2017,14(7):82-84.
[11]李中凯,潘雯,廖祖达,等.某院次均住院费用结构变动度分析[J].中国医疗保险,2018(4):55-58.
[12]杨晓胜,刘海兰,郭建.医疗服务质量在卫生总费用增长中的作用探讨[J].中国卫生经济,2014,33(3):52-54.