An autocorrelation-corrected nonparametric control chart technique for health care quality applications

Recently, Alemi proposed a nonparametric control chart technique (Tukey's control chart) for quality management applications when few data points are available and when data do not conform to the assumptions of traditional control chart techniques. Borckardt et al then published an empirical evaluation of the technique and concluded that the presence of autocorrelation in control-chart data negatively impacted the technique's ability to help managers make accurate decisions about the presence of special-cause variation in their data. Thus, there is still a need for control chart techniques that appropriately handle short data streams that do not necessarily conform to the assumptions of traditional control chart techniques but are not negatively impacted by autocorrelation in the data. In this article, the authors empirically evaluate a modified version of the technique presented by Alemi that is designed to account for autocorrelation. Empirical analyses indicate that the modified technique demonstrates superior false-positive performance with very little degradation of power compared with the original technique proposed by Alemi.