Cordier Quentin, Prieur Hugo, Duclos Antoine
Health Data Department, Hospices Civils de Lyon, Lyon, France
Research on Healthcare Performance (RESHAPE), INSERM U1290, Université Claude Bernard Lyon 1 - Domaine de Rockefeller, Lyon, France.
BMJ Qual Saf. 2025 Apr 17;34(5):330-338. doi: 10.1136/bmjqs-2024-017935.
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.
为提高患者安全，外科医生可持续监测其患者的手术结果。为此，他们可使用统计过程控制工具，这些工具主要起源于制造业，如今在医疗保健领域广泛应用。这些工具属于一个广泛的类别，因此难以确定监测手术结果的最合适方法。所选工具必须在统计严谨性与外科医生的易用性之间取得平衡，既要能对随时间的趋势进行统计解读，又要让作为主要使用者的外科医生能够理解。一方面，观察值减去预期值（O-E）图表是一种简单直观的工具，可让没有统计专业知识的外科医生查看和解读其手术活动；然而，它可能不具备准确识别手术表现重要变化所需的复杂算法。另一方面，像累积和（CUSUM）方法这样统计稳健的工具可能会有所帮助，但对于未经适当统计培训的外科医生来说，在实践中解读和应用可能过于复杂。为解决这一问题，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图表的统计解读能力相结合。借助RA O-E CUSUM图表，外科医生可以有效地监测患者的结果，并识别统计异常变化的序列，这表明手术结果是恶化还是改善。他们还可以量化这些序列中潜在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，以随着时间的推移潜在地提高其表现并增强患者安全。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展示其应用。
