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

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 char...

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Vydáno v:Journal of statistical computation and simulation Ročník 91; číslo 3; s. 522 - 542
Hlavní autoři: Sogandi, Fatemeh, Aminnayeri, Majid, Mohammadpour, Adel, Amiri, Amirhossein
Médium: Journal Article
Jazyk:angličtina
Vydáno: Abingdon Taylor & Francis 11.02.2021
Taylor & Francis Ltd
Témata:
Algorithms
Bernoulli state-space model
Control charts
expectation-maximization algorithm
Health care
Kalman filter and smoother
Kalman filters
Likelihood ratio
Monitoring
multistage healthcare processes
Parameter estimation
Phase I
Risk management
State space models
Telemedicine
Test methods
ISSN:0094-9655, 1563-5163
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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.
AbstractList 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.
Author Mohammadpour, Adel
Sogandi, Fatemeh
Aminnayeri, Majid
Amiri, Amirhossein
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SubjectTerms Algorithms
Bernoulli state-space model
Control charts
expectation-maximization algorithm
Health care
Kalman filter and smoother
Kalman filters
Likelihood ratio
Monitoring
multistage healthcare processes
Parameter estimation
Phase I
Risk management
State space models
Telemedicine
Test methods
Title Phase I risk-adjusted Bernoulli chart in multistage healthcare processes based on the state-space model
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