Biological variation data are necessary prerequisites for objective autoverification of clinical laboratory data

Abstract

The wealth of quantitative data on random biological variation has been used for setting quality specifications, assessing the utility of conventional reference values, and deciding of the significance of changes in serial laboratory results. Most analytes have marked individuality and this makes conventional population-based reference values of low utility. In consequence, reference limits are not ideal for autoverification strategies. Clinical decision limits may be better criteria for holding results for verification by laboratory professionals. Changes in serial results are significant only when the reference change value is exceeded. Such values can be generated by all laboratories and can be implemented, not only to flag reports, but also in delta checking and autoverification since these are objective rather than empirical. We have put these considerations into operation into our laboratory. Apart from special cases, our general approach is that results flagged as having changed 0.05<P<0.01, flagged as just outside the reference limits, or not flagged in any way, are autoverified and reported to the user without intervention. Only results outside pre-set clinical limits and those that have changed highly significantly P<0.01 are held for verification by clinical scientists and medical staff. This strategy allows autoverification of ca. 60% of reports.