Risk-adjusted observed minus expected cumulative sum (RA O-E CUSUM) chart for visualisation and monitoring of surgical outcomes.

IF 5.6 1区 医学 Q1 HEALTH CARE SCIENCES & SERVICES
Quentin Cordier, Hugo Prieur, Antoine Duclos
{"title":"Risk-adjusted observed minus expected cumulative sum (RA O-E CUSUM) chart for visualisation and monitoring of surgical outcomes.","authors":"Quentin Cordier, Hugo Prieur, Antoine Duclos","doi":"10.1136/bmjqs-2024-017935","DOIUrl":null,"url":null,"abstract":"<p><p>To improve patient safety, surgeons can continually monitor the surgical outcomes of their patients. To this end, they can use statistical process control tools, which primarily originated in the manufacturing industry and are now widely used in healthcare. These tools belong to a broad family, making it challenging to identify the most suitable methodology to monitor surgical outcomes. The selected tools must balance statistical rigour with surgeon usability, enabling both statistical interpretation of trends over time and comprehensibility for the surgeons, their primary users. On one hand, the observed minus expected (O-E) chart is a simple and intuitive tool that allows surgeons without statistical expertise to view and interpret their activity; however, it may not possess the sophisticated algorithms required to accurately identify important changes in surgical performance. On the other hand, a statistically robust tool like the cumulative sum (CUSUM) method can be helpful but may be too complex for surgeons to interpret and apply in practice without proper statistical training. To address this issue, we developed a new risk-adjusted (RA) O-E CUSUM chart that aims to provide a balanced solution, integrating the visualisation strengths of a user-friendly O-E chart with the statistical interpretation capabilities of a CUSUM chart. With the RA O-E CUSUM chart, surgeons can effectively monitor patients' outcomes and identify sequences of statistically abnormal changes, indicating either deterioration or improvement in surgical outcomes. They can also quantify potentially preventable or avoidable adverse events during these sequences. Subsequently, surgical teams can try implementing changes to potentially improve their performance and enhance patient safety over time. This paper outlines the methodology for building the tool and provides a concrete example using real surgical data to demonstrate its application.</p>","PeriodicalId":9077,"journal":{"name":"BMJ Quality & Safety","volume":" ","pages":""},"PeriodicalIF":5.6000,"publicationDate":"2024-12-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"BMJ Quality & Safety","FirstCategoryId":"3","ListUrlMain":"https://doi.org/10.1136/bmjqs-2024-017935","RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"HEALTH CARE SCIENCES & SERVICES","Score":null,"Total":0}
引用次数: 0

Abstract

To improve patient safety, surgeons can continually monitor the surgical outcomes of their patients. To this end, they can use statistical process control tools, which primarily originated in the manufacturing industry and are now widely used in healthcare. These tools belong to a broad family, making it challenging to identify the most suitable methodology to monitor surgical outcomes. The selected tools must balance statistical rigour with surgeon usability, enabling both statistical interpretation of trends over time and comprehensibility for the surgeons, their primary users. On one hand, the observed minus expected (O-E) chart is a simple and intuitive tool that allows surgeons without statistical expertise to view and interpret their activity; however, it may not possess the sophisticated algorithms required to accurately identify important changes in surgical performance. On the other hand, a statistically robust tool like the cumulative sum (CUSUM) method can be helpful but may be too complex for surgeons to interpret and apply in practice without proper statistical training. To address this issue, we developed a new risk-adjusted (RA) O-E CUSUM chart that aims to provide a balanced solution, integrating the visualisation strengths of a user-friendly O-E chart with the statistical interpretation capabilities of a CUSUM chart. With the RA O-E CUSUM chart, surgeons can effectively monitor patients' outcomes and identify sequences of statistically abnormal changes, indicating either deterioration or improvement in surgical outcomes. They can also quantify potentially preventable or avoidable adverse events during these sequences. Subsequently, surgical teams can try implementing changes to potentially improve their performance and enhance patient safety over time. This paper outlines the methodology for building the tool and provides a concrete example using real surgical data to demonstrate its application.

用于可视化和监控手术结果的风险调整观察值减去预期值累积和(RA O-E CUSUM)图。
为了提高患者的安全性,外科医生可以持续监控患者的手术效果。为此,他们可以使用统计过程控制工具,这些工具主要起源于制造业,现在已广泛应用于医疗保健领域。这些工具种类繁多,因此确定最适合监控手术效果的方法具有挑战性。所选工具必须兼顾统计的严谨性和外科医生的可用性,既要能对随时间变化的趋势进行统计分析,又要能让其主要使用者--外科医生理解。一方面,观察值减去预期值(O-E)图是一种简单直观的工具,允许没有统计专业知识的外科医生查看和解释他们的活动;但是,它可能不具备准确识别手术效果重要变化所需的复杂算法。另一方面,像累积总和(CUSUM)法这样的统计稳健工具可能会有所帮助,但对于没有接受过适当统计培训的外科医生来说,其解释和实际应用可能过于复杂。为了解决这个问题,我们开发了一种新的风险调整(RA)O-E CUSUM 图表,旨在提供一种平衡的解决方案,将用户友好的 O-E 图表的可视化优势与 CUSUM 图表的统计解释能力相结合。通过 RA O-E CUSUM 图表,外科医生可以有效地监控患者的治疗效果,并识别统计上的异常变化序列,这表明手术效果在恶化或改善。他们还可以量化这些序列中潜在的可预防或可避免的不良事件。随后,手术团队可以尝试实施变革,以逐步改善其表现并提高患者安全。本文概述了构建该工具的方法,并提供了一个使用真实手术数据的具体示例来演示其应用。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 求助全文
来源期刊
BMJ Quality & Safety
BMJ Quality & Safety HEALTH CARE SCIENCES & SERVICES-
CiteScore
9.80
自引率
7.40%
发文量
104
审稿时长
4-8 weeks
期刊介绍: BMJ Quality & Safety (previously Quality & Safety in Health Care) is an international peer review publication providing research, opinions, debates and reviews for academics, clinicians and healthcare managers focused on the quality and safety of health care and the science of improvement. The journal receives approximately 1000 manuscripts a year and has an acceptance rate for original research of 12%. Time from submission to first decision averages 22 days and accepted articles are typically published online within 20 days. Its current impact factor is 3.281.
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信