Healthcare processes comprise multiple stages in practice. Also, few researchers have addressed Phase I monitoring of healthcare outcomes. Hence, the purpose of the proposed method is Phase I monitoring by two risk adjusted control charts in multistage healthcare processes. The proposed control charts are based on the Bernoulli state space model and consider other categorical covariates in addition to patient’s risk. To estimate the model parameters, an expectation-maximization algorithm is applied in a Kalman filter and smoother framework. The performance of the proposed monitoring schemes is compared in two and three stages. The simulation results show that the standardized likelihood ratio test method has competitive performance relative to Hotelling’s chart under different step shifts and drift. Also, Hotelling’s chart is superior to the standardized likelihood ratio test method in for outlier patients. Finally, a real case is utilized to show the applicability of the proposed risk adjusted charts.

医疗过程在实践中包括多个阶段。同样，很少有研究人员谈到第一阶段医疗结果监测。因此，提出的方法的目的是在多阶段医疗保健过程中通过两个风险调整后的控制图进行第一阶段监控。拟议的控制图基于伯努利状态空间模型，除考虑患者风险外，还应考虑其他分类协变量。为了估计模型参数，在卡尔曼滤波器和更平滑的框架中应用了期望最大化算法。在两个和三个阶段中比较了所提出的监视方案的性能。仿真结果表明，在不同的步移和漂移条件下，标准化似然比检验方法相对于Hotelling图具有竞争优势。也，对于离群患者，Hotelling的图表优于标准似然比测试方法。最后，利用一个实际案例来显示所建议的风险调整图表的适用性。