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.
The health care industry is slowly embracing the use of statistical process control (SPC) to monitor and study causes of variation in health care processes. While the statistics and principles underlying the use of SPC are relatively straightforward, there is a need to be cognizant of the perils that await the user who is not well versed in the key concepts of SPC. This article introduces the theory behind SPC methodology, describes successful tactics for educating users, and discusses the challenges associated with encouraging adoption of SPC among health care professionals. To illustrate these benefits and challenges, this article references the National Hospital Quality Measures, presents critical elements of SPC curricula, and draws examples from hospitals that have successfully embedded SPC into their overall approach to performance assessment and improvement.
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.
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