中国卒中杂志 ›› 2024, Vol. 19 ›› Issue (8): 873-879.DOI: 10.3969/j.issn.1673-5765.2024.08.003

• 专题论坛 • 上一篇    下一篇

基于DMAIC模型的妊娠合并脑血管病急诊就诊流程优化研究

梁艳超,王晓岩,单凯   

  1. 北京 100070 首都医科大学附属北京天坛医院医务处
  • 收稿日期:2024-05-20 出版日期:2024-08-20 发布日期:2024-08-20
  • 通讯作者: 王晓岩wxyann18088@163.com 单凯shankaittyy@aliyun.com
  • 基金资助:

    北京市卫生健康委员会高层次公共卫生技术人才建设项目(学科骨干-03-01

Research on optimization of the emergency treatment process for pregnancy complicated by cerebrovascular diseases Based on the DMAIC model

LIANG Yanchao, WANG Xiaoyan, SHAN Kai   

  1. Department of Medical Affairs, Beijing Tiantan Hospital, Capital Medical University, Beijing 100070, China
  • Received:2024-05-20 Online:2024-08-20 Published:2024-08-20
  • Contact: WANG Xiaoyan, E-mail: wxyann18088@163.com SHAN Kai, E-mail: shankaittyy@aliyun.com

摘要:

目的       利用精益六西格玛(lean six sigmaLSS)管理中的界定、测量、分析、改进和控制(definemeasureanalyzeimprovecontrolDMAIC)模型,优化妊娠合并脑血管病患者就医流程,提高患者就医效率,保障母婴安全。

方法       202112月,首都医科大学附属北京天坛医院根据DMAIC模型,对危重孕产妇的就诊流程进行优化:梳理就诊流程,明确到院—医嘱开立、采血—送检、医嘱开立—影像学检查为院内延误的关键环节,对上述环节进行流程跟踪及分析,找出延误原因,并采取改进措施,优化流程。本研究回顾性纳入流程优化前(20191月—202112月)的妊娠合并脑血管病患者为优化前组,流程优化后(20221月—202312月)的患者为优化后组。比较两组患者就诊流程中的到院—医嘱开立、采血—送检、医嘱开立—影像学检查、到院—办理住院的时间。

结果       急诊就诊流程优化后,妊娠合并脑血管病患者的总体就诊效率提高。到院—医嘱开立
[24.0
13.538.5minvs.39.017.598.0minP=0.027]、医嘱开立—影像学检查[48.010.073.0min
vs.
65.522.790.7minP=0.025]以及到院—办理入院总时间[120.093.0149.0minvs.218.0123.0382.7minP0.001]均较优化前缩短,差异有统计学意义;采血—送检时间有缩短趋势,但优化前后差异无统计学意义。

结论       使用DMAIC模型能够明确流程优化的关键环节,优化妊娠合并脑血管病患者的急诊就诊流程。

文章导读: 妊娠合并脑血管病是严重危害孕产妇和胎儿健康的急重症,本研究基于DMAIC模型,通过科学的论证,对医院急诊接诊此类患者的流程进行了优化,结果显示,流程优化后可以显著缩短患者的院内延误时间,提高救治效率。

关键词: 精益六西格玛管理; 妊娠; 脑血管病; 急诊; 流程优化

Abstract:

Objective  To optimize the medical treatment process of pregnant women with cerebrovascular diseases using the define, measure, analyze, improve, control (DMAIC) model in the lean six sigma (LSS) management, improve the efficiency of medical treatment, and ensure the safety of both mothers and infants. 

Methods  In December 2021, Beijing Tiantan Hospital, Capital Medical University optimized the medical treatment process for critically ill pregnant women according to the DMAIC model with the measures listed below: sorting out the medical treatment process, identifying the key nodes of hospital delays, such as hospital arrival to medical order issuance, blood sampling to testing, and medical order issuance to imaging examination, tracking and analying the processes of the above nodes, finding out the reasons for the delay, and taking improvement measures to optimize the process. This study retrospectively included pregnant women with cerebrovascular diseases before process optimization (from January 2019 to December 2021) as the pre-optimization group, and patients after process optimization (from January 2022 to December 2023) as the post-optimization group. The time of hospital arrival to medical order issuance, blood sampling to testing, medical order issuance to imaging examination, and hospital arrival to hospitalization was compared between the two groups.

Results  After the optimization of the medical treatment process, the overall efficiency of emergency treatment for pregnant women with cerebrovascular diseases was improved. After optimization, the time of hospital arrival to medical order issuance [24.0 (13.5-38.5) minvs.
39.0 (17.5-98.0) min, P=0.027], medical order issuance to imaging examination
[48.0 (10.0-73.0) min
vs.65.5 (22.7-90.7) min, P=0.025], and hospital arrival to hospitalization [120.0 (93.0-149.0) minvs.218.0 (123.0-382.7) min, P<0.001] as all reduced compared with that before optimization, and the difference was statistically significant. The time of blood sampling to testing showed a trend of shortening, but there was no statistically significant difference before and after optimization.

Conclusions  Using the DMAIC model can identify the key links and points of process optimization and has substantial effects on optimizing the emergency treatment process for pregnant patients with cerebrovascular diseases.

Key words: Lean six sigma; Pregnancy; Cerebrovascular disease; Emergency; Process optimization

中图分类号: