摘要
目的应用大数据处理方法,分析2005—2014年福建省直单位参保人群中心脑血管疾病患者的门诊就诊流向,了解分级诊疗现状,为推进分级诊疗制度提供参考。方法 2015年6月,从福建省医疗保险管理中心就医数据库中筛选2005-01-01至2014-12-31符合高血压、糖尿病、血脂异常、心肌梗死、卒中诊断的患者共70 052例,提取这些患者所有门诊记录共15 468 958人次,导入Oracle数据库形成数据库文件,利用大数据处理工具对患者的就诊机构相关情况进行分析。结果 (1)2014年福建省直单位参保人群中心脑血管疾病患者的门诊就诊人次、门诊费用的分布情况:三级医院分别占52.87%、61.63%,二级医院分别占26.26%、23.40%,一级医院分别占20.87%、14.97%。(2)高血压、糖尿病、血脂异常(以下简称"三高")患者在一级医院的就诊人次自2008年开始上升,于2012年(血脂异常为2011年)达峰值,其后略有下调或持平;"三高"患者一级医院门诊费用也同步于2008年开始上升,至2012年(血脂异常为2011年)达顶峰,其后略有下调或持平。但一级医院在就诊人次及门诊费用上的增长并未影响三级医院的占比,反而是二级医院的份额被压缩。(3)2014年"三高"患者三级医院次均门诊费用是一级医院的3.3~5.3倍。2014年高血压患者各级医院合计门诊就诊418 635人次(其中三级医院、二级医院、一级医院分别占53.87%、25.54%、20.59%),门诊费用12 364 587元;如果按照2017年总门诊就诊人次与2014年一致、一级医院门诊就诊人次达到65%的目标推算,则仅高血压一项疾病的年节约费用就可达442余万元;假使一级医院的次均门诊费用在2014年的基础上增长50%,则年节约费用仍可达265余万元。结论现阶段患者就诊机构流向仍呈明显的倒金字塔结构,采取有效措施促进患者流向次均就医费用较低的一级医院,切实落实分级诊疗制度,是减少心脑血管疾病门诊费用的有效途径。
Objective By using large data processing method,to analyze the outpatient service flow of patients with cardio-cerebrovascular diseases( CVDs) from 2005 to 2014 who covered by Fujian Provincial Health Insurance and to understand the current status of grading diagnosis and treatment in order to provide references for promoting the system of tiered medical services. Methods In June 2015, a total of 70 052 patients with hypertension, diabetes mellitus, dyslipidemia,myocardial infarction and stroke from January 1,2005 to December 31,2014 were selected from Fujian Provincial Health Insurance. A total of 15 468 958 outpatient records were extracted in these patients and imported the Oracle database to form a database file. By using large data processing tools,the relevant situation of patients visiting institutions was analyzed. Results( 1) In 2014,the distribution of outpatient service organization and cost in patients with CVDs covered by Fujian Provincial Health Insurance: tertiary hospitals accounted for 52. 87% and 61. 63%,respectively; secondary-class hospitals accounted for 26. 26%,23. 40%,respectively; and first-class hospitals accounted for 20. 87%,14. 97%,respectively.( 2) The visit of patients with hypertension,diabetes and dyslipidemia( " three high") in the first-class hospitals began to rise since 2008,reached a peak in 2012( dylipidemial in 2011),and followed by a slight down or flat. The outpatient cost of " three high" patients in the first-class hospitals synchronously began to rise in 2008,peaked in 2012( dyslipidemia in 2011),followed by a slight down or flat. However,the growth of outpatient visit and cost in first-class hospitals did not affect the proportion of tertiary hospitals,but the share of secondary-class hospitals was reduced.( 3) In 2014,the average outpatient cost of " three high" patients in tertiary hospitals was 3. 3-5. 3 times of that in first-class hospitals. In 2014,the total outpatient visit of patients with hypertension at all levels of hospitals was 418 635 times( including tertiary hospitals,secondary-class hospitals,first-class hospitals accounted for 53. 87%,25. 54%,20. 59%,respectively),the outpatient cost was 12 364 587 Yuan. If calculated according to the goal that the total number of outpatient visit in 2017 was similar with that in 2014,and the proportion of visit in first-class hospitals reached 65%,the annual cost savings of the only disease of hypertension would be expected to save 4. 42 million Yuan per year. If the average outpatient cost of the first-class hospitals increased by 50% on the basis of 2014,the annual savings could still reach more than 2. 65 million Yuan. Conclusion At present,the flow of patients' visit institutions still presented as an obvious inverted pyramid structure. It is an effective way to reduce the cost of outpatient service for CVDs to take effective measures to promote the flow of patients to first-class hospitals with lower medical cost,and to conscientiously carry out the system of tiered medical services.
出处
《中国全科医学》
CAS
北大核心
2017年第24期3008-3014,共7页
Chinese General Practice
基金
基金项目:福建省直单位参保人群心血管疾病负担及控费策略研究 (闽医保协文[2015]26号)
关键词
心血管疾病
脑血管障碍
分级诊疗
医疗保险
大数据
福建
Cardiovascular diseases
Cerebrovascular disorders
Grading health care
Medical insurance
Big data
Fujian