中国血液净化

中国血液净化 ›› 2017, Vol. 16 ›› Issue (06): 383-386.doi: 10.3969/j.issn.1671-4091.2017.06.007

• 临床研究 • 上一篇    下一篇

应用大数据进行基层透析单位费用和质量评估的初步探讨

曹雯1,冯海欢2,曾筱茜1,2,石敏1,孙麟2,周莉1,2,张伟2,付平1,2   

  1. 1. 四川大学华西医院肾脏内科
    2. 四川大学生物医学大数据中心
  • 收稿日期:2016-08-30 修回日期:2017-04-01 出版日期:2017-06-12 发布日期:2017-06-14
  • 通讯作者: 周莉，付平为共同通讯作者 zhouli126@hotmail.com E-mail:fupinghx@163.com
  • 基金资助:

    成都市医保局课题：成都市医保门诊透析病人支付方式改革费用及评价体系研究(项目编号:CDSRSJ-3)；“十二五”国家科技支撑计划项目(No.2011BAI10B08)

Preliminary evaluation of cost and hemodialysis quality in a local area via big data analysis

  • Received:2016-08-30 Revised:2017-04-01 Online:2017-06-12 Published:2017-06-14

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摘要： 目的应用大数据分析某地区2013年维持性血液透析患者费用及部分透析质量相关指标，对透析单位进行质量评估及管控。方法从某地区医疗费用数据库筛选出2013年维持性血液透析患者。对目标人群按照城镇职工组和城乡居民组进行分类，对各透析中心的费用及部分血液透析质量相关指标进行分析，筛选出离群数据。结果目标患者中，城镇职工组和城乡居民组患者之间的总费用存在差异[(12.39±2.08）万元及（10.01±2.13）万元，t=23.107，P＜0.001]；某些医院在住院率、再住院率、血管通路手术、血源性传染病、血液灌流器和药占比等方面存在数据离群，提示可能存在潜在血液透析质量控制风险。结论以费用为主的数据库可以在大数据分析方法下发现血液透析中心可能存在的费用离群和医疗质量缺陷，有望为该地区有针对性地进行血液透析质量的管控提供数据支持。

关键词: 血液透析, 费用, 质量评估, 大数据

Abstract: Objective To analyze the related markers for cost and hemodialysis quality in hemodialysis patients in a local area in 2013 via big data analyses. Method Maintenance hemodialysis patients in 2013 were screened out from medical cost database in this area. Target population was divided into urban worker group
and urban and rural resident group according to their medical insurance type. Results There was significant difference in total cost between hemodialysis patients in urban worker group and urban and rural resident group (t=23.107, P<0.001). Some biased data existed in hospitalization and rehospitalization, vascular access operation, blood transmissible disease, cost proportion for use of hemoperfusion apparatus and drug in certain hospitals, suggesting the potential risks for quality control in some hemodialysis centers. Conclusion Big data analytical method for cost-centered database can detect possible cost bias and medical quality defects, hopefully providing the data useful for the continuous hemodialysis quality improvement.

Key words: Hemodialysis, Cost, Quality evaluation, Big data