Monitoring patient survival times in surgical systems using a risk-adjusted AFT regression chart

Negin Asadayyoobi · Seyed Taghi Akhavan Niaki

27 citations0/0 files storedQuality Technology & Quantitative Management

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Advanced Statistical Process Monitoring Healthcare Quality and Management Fault Detection and Control Systems

Abstract

Monitoring surgical processes has gained prominence by accounting for patients’ health condition prior to surgery in recent years. However, most of previous researchers have focused on Phase-II monitoring based on binary outcomes, while very little attention has been paid to Phase-I monitoring procedures, especially when the outcomes are continuous. In this paper, a general Phase-I accelerated failure time-based risk-adjusted control chart is proposed to monitor continuous surgical outcomes based on a likelihood-ratio test derived from a change-point model. Different from the existing models, this paper shows that continuous outcomes depend not only on the patient conditions described by the Parsonnet score, but also is dependent on other categorical operational covariates such as surgeons. The proposed risk-adjustment model is fitted by incorporating dummy variables to reflect different surgeon groups as a categorical covariate. We show that the categorical covariate can effectively reflect the inherent data heterogeneity, thus can improve the estimation of the parameters of the risk-adjustment model and hence can enhance the detection power of the proposed risk-adjusted control chart.

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References

2006The Use of Control Charts in Health-Care and Public-Health Surveillance 2000Monitoring surgical performance using risk-adjusted cumulative sum charts 2003Use of risk-adjusted CUSUM and RSPRTcharts for monitoring in medical contexts 2009Risk‐adjusted survival time monitoring with an updating exponentially weighted moving average (EWMA) control chart 2008A risk‐adjusted CUSUM in continuous time based on the Cox model 2009Risk‐adjusted monitoring of survival times 2007A Simple Risk-Adjusted Exponentially Weighted Moving Average 2012Phase I Risk-Adjusted Control Charts for Monitoring Surgical Performance by Considering Categorical Covariates

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2020A new risk‐adjusted EWMA control chart based on survival time for monitoring surgical outcome quality 2019Risk-adjusted Bernoulli chart in multi-stage healthcare processes based on state-space model with a latent risk variable and dynamic probability control limits 2020Risk-adjusted frailty-based CUSUM control chart for phase I monitoring of patients’ lifetime 2018Robust AFT-based monitoring procedures for reliability data

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