Phase I risk-adjusted Bernoulli chart in multistage healthcare processes based on the state-space model

Abstract

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.

KEYWORDS:

• Bernoulli state-space model
• Phase I
• multistage healthcare processes
• Kalman filter and smoother
• expectation-maximization algorithm

Disclosure statement

No potential conflict of interest was reported by the author(s).