A parametric control chart adjustment for handling serial dependence in health care quality data

Quality Management in Health Care
Alok MadanMichael R Nash

Abstract

Numerous tools are available to health care quality managers geared toward helping them make data-based inferences about quality processes. Recently, in this journal, Tukey's control chart technique was promoted as a good option for handling short streams of time series data when the assumption of data normality cannot be confirmed. Although this technique does not appear to perform well with serially dependent (or autocorrelated) data, an autocorrelation-corrected version of the technique is now available. However, when managers wish to capitalize on the superior power of parametric control charts (ie, when data sets are large and normality can be confirmed), there are currently few options available in the way of statistical process control techniques that appropriately handle autocorrelated data. In this article, the authors report the empirical false-positive rates and power performance of the mean-sigma (X-S) control chart technique under various levels of autocorrelation. Results indicate that this popular technique offers poor false-positive control with autocorrelated data. Next, the authors describe a method for autocorrelation correction and finally compare the autocorrelation-corrected X-S chart with the original X-S...Continue Reading

References

Nov 10, 2004·Quality Management in Health Care·Farrokh Alemi
May 24, 2005·Quality Management in Health Care·Jeffrey J BorckardtChris Pelic
Jul 20, 2006·Quality Management in Health Care·Jeffrey J BorckardtSue Hardesty
Sep 2, 2006·Journal for Healthcare Quality : Official Publication of the National Association for Healthcare Quality·Jeffrey J BorckardtChristopher Pelic

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Citations

Jul 22, 2008·Quality Management in Health Care·Marco D HueschJeffrey J Borckardt
Nov 19, 2011·Research in Nursing & Health·Denise F Polit, Wendy Chaboyer

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