Monitoring surgical outcome quality by risk‐adjusted control charts has attracted wide attention. The hidden medical errors may cause increasing of adverse events such as infection, rehospitalization, and even death. Quickly and timely detecting abnormal changes of surgical performance helps reduce the probability of adverse events and improve health care quality. Most existing monitoring schemes focus on the binary surgical outcomes. However, continuous survival times of patients should be considered for more accurate monitoring. In this paper, a new exponentially weighted moving average (EWMA) control chart is proposed for monitoring continuous surgical outcomes. To describe surgical performance, a patient's actual survival time and predicted mortality are combined in an illustrative and interpretable way. Performance of the proposed chart is evaluated with different chart parameters under different shifts by a simulation study. We compare our chart with the risk‐adjusted survival time cumulative sum chart, and the simulation results demonstrate that the proposed monitoring scheme has better efficiency. The implementation of the proposed chart is illustrated by a real example. Besides an analysis of the entire dataset, the surgical performance of each surgeon is monitored, because each of them has patients with different risk levels.

中文翻译：

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基于生存时间的新的风险调整后EWMA控制图，用于监控手术结果质量

通过风险调整后的控制图监测手术结果质量已引起广泛关注。隐藏的医疗错误可能导致不良事件的增加，例如感染，再次住院，甚至死亡。快速及时地发现手术性能的异常变化有助于减少不良事件的发生率并提高医疗质量。现有的大多数监测方案都将重点放在二元手术结局上。但是，应考虑患者的连续生存时间以进行更准确的监测。本文提出了一种新的指数加权移动平均（EWMA）控制图，用于监测连续手术的效果。为了描述外科手术的性能，将患者的实际生存时间和预测的死亡率以说明性和可解释性的方式结合在一起。通过仿真研究，使用不同图表参数在不同班次下对建议图表的性能进行评估。我们将我们的图表与经过风险调整的生存时间累积总和图表进行了比较，仿真结果表明所提出的监控方案具有更高的效率。实际示例说明了建议的图表的实现。除了分析整个数据集外，还监视每个外科医生的手术表现，因为他们每个人都有风险级别不同的患者。实际示例说明了建议的图表的实现。除了分析整个数据集外，还监视每个外科医生的手术表现，因为他们每个人都有风险级别不同的患者。实际示例说明了建议的图表的实现。除了分析整个数据集外，还监视每个外科医生的手术表现，因为他们每个人都有风险级别不同的患者。