ZERO INFLATED POISSON EWMA CONTROL CHART FOR MONITORING RARE HEALTH-RELATED EVENTS

Abstract

Recently, rare health events issue has motivated many researches in the field of control charting. Various methods such as g-type control chart, g-type CUSUM control chart, sets method, CUSCORE method, SHDA method and the Bernoulli CUSUM have been developed in this regard, in which each of them has a specific approach to the problem. As a relatively new approach, zero inflation in Poisson distribution, named ZIP distribution can be applied. In this paper, an exponentially weighted moving average (EWMA) control chart is developed for the ZIP random variable to monitor rare health-related events with a predefined performance measure value. Since the ZIP-EWMA plotted data are dependent, Markov chain approach is applied to calculate average run lengths (ARLs) as the control chart performance criteria. Based on the ARL measure, the ZIP-EWMA chart performs better in comparison with the methods available in the literature. As the main contribution of this paper is the development a control chart which performs better than the previously proposed charts. Also, a motivating real case study related to monitoring needle-stick rare occurrences in a hospital is investigated to show the applicability of the developed chart.