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Much of the variation in breast pathology quality assurance data in the UK can be explained by the random order in which cases arrive at individual centres, but some true outliers do exist
Author(s) -
Cross Simon S,
Stephenson Timothy J,
Harrison Robert F
Publication year - 2011
Publication title -
histopathology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.626
H-Index - 124
eISSN - 1365-2559
pISSN - 0309-0167
DOI - 10.1111/j.1365-2559.2011.03919.x
Subject(s) - quality assurance , outlier , variation (astronomy) , quality (philosophy) , medicine , pathology , statistics , medical physics , computer science , mathematics , external quality assessment , philosophy , physics , epistemology , astrophysics
Cross S S, Stephenson T J & Harrison R F
(2011) Histopathology 59 , 594–599 Much of the variation in breast pathology quality assurance data in the UK can be explained by the random order in which cases arrive at individual centres, but some true outliers do exist Aims: To investigate the role of random temporal order of patient arrival at screening centres in the variability seen in rates of node positivity and breast cancer grade between centres in the NHS Breast Screening Programme. Methods and results: Computer simulations were performed of the variation in node positivity and breast cancer grade with the random temporal arrival of patients at screening centres based on national UK audit data. Cumulative mean graphs of these data were plotted. Confidence intervals for the parameters were generated, using the binomial distribution. UK audit data were plotted on these control limit graphs. The results showed that much of the variability in the audit data could be accounted for by the effects of random order of arrival of cases at the screening centres. Confidence intervals of 99.7% identified true outliers in the data. Conclusions: Much of the variation in breast pathology quality assurance data in the UK can be explained by the random order in which cases arrive at individual centres. Control charts with confidence intervals of 99.7% plotted against the number of reported cases are useful tools for identification of true outliers.