A unified statistical process control (SPC) scheme is developed to monitor work‐process duration of repetitive processes (constant work content), semi‐repetitive (work content somewhat varies between cycles) and memoryless (no typical work content). As a basic paradigm to develop the new scheme, we monitor surgery duration (SD), known to represent durations of all three types of work processes. Recently, a new bi‐variate model for SD was developed and empirically validated. Based on the new model, we derive an approximation to the distribution of SD sample average and use it to construct a two‐stage SPC scheme (for a medically specified subcategory of surgeries). In the first stage, SD distributional shape, as reflected in third and fourth moments, is monitored to determine that no shape‐changing assignable causes are active (e.g., work‐content variation is stable); in the second stage, location and scale parameters are separately monitored via a traditional Shewhart scheme. Using an existent database of surgery times, application of the new scheme is demonstrated, and average‐run‐length properties explored. Extensions (like incorporating co‐variates) and generalizations are addressed.

中文翻译：

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SPC方案可监控手术时间

开发了统一的统计过程控制（SPC）方案，以监视重复过程（恒定的工作内容），半重复（工作内容在周期之间有所不同）和无记忆（无典型工作内容）的工作过程持续时间。作为开发新计划的基本范例，我们监视手术持续时间（SD），已知该手术持续时间代表所有三种类型的工作流程的持续时间。最近，开发了一种新的SD双变量模型并进行了经验验证。基于新模型，我们得出SD样本平均值分布的近似值，并使用它来构建两阶段SPC方案（针对医学上指定的手术子类别）。在第一阶段，监测在第三和第四时刻反映的SD分布形状，以确定没有可改变形状的可分配原因（例如，工作内容变化稳定）; 在第二阶段，通过传统的Shewhart方案分别监视位置和比例参数。使用现有的手术时间数据库，证明了该新方案的应用，并探索了平均游程长度属性。解决了扩展（例如合并协变量）和泛化问题。