This article proposes a new class of control charts that may be used for monitoring and improving the quality of care. Unlike conventional control charts that rely on observed performance data, these charts use risk-adjusted data in addition to the observed data. The resulting time-ordered charts are capable of reducing time-to-time variation that may stem from uncontrollable changes in patient mix over time. Depending on how observed and risk-adjusted data are combined, proposed charts are categorized under the framework of either additive or multiplicative models. Risk-adjusted rates are obtained using multivariate logistic regression models. It was found that the risk-adjusted control charts could be effective in reducing biases that arise from variation in patient mix. These charts can potentially achieve higher sensitivity and specificity compared with ordinary control charts.
In a previous article (M. K. Hart, Qual Manag Health Care. 2003;12(1):5-19), the authors presented risk-adjusted control charts applicable for attributes data. The present article discusses a similar class of control charts applicable for variables data that are often skewed. The key feature of these charts is their application of risk-adjusted data in addition to actual performance data. The resulting charts should decrease the occurrence of both type I and type II errors as compared to the unadjusted control charts. This article presents several control charts that vary in the data transformation and combination approaches. Data depicting hospital length of stay following coronary artery bypass graft procedures were used to illustrate the use of transformed and risk-adjusted control charts.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.