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Krouwer Consulting
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Equivalent QC - Prevention vs. Detection of Errors A recent article in Clinical Laboratory News summarized the issues with the EQC (Equivalent Quality Control) proposal (1). I previously wrote an essay on this topic and also presented an expert online session at AACC. This essay deals with a statement in the Clinical Laboratory News article made by Fred Lasky, “You’re much better off working to prevent errors rather than trying to detect them. You will never produce a high quality product if your only way to assure quality is to sort out the bad cherries. That will never be 100% successful.” This statement is based on a common thread in quality circles which favors prevention over inspection (detection) as in “do it right the first time”. On the face of it, it’s hard to argue with this logic and would seem crazy to build systems with problems that can only be uncovered by inspection / detection. Why wouldn’t one design systems right the first time to prevent errors? The answer was discussed previously (2) and has the do with the state of knowledge that is available to the designers. This was also mentioned in the first essay about EQC. The quality method used depends on the state of knowledge The states of knowledge can be thought of as high – where knowledge can be expressed as mathematical equations based on physical properties such as Michaelis-Menton kinetics for an enzyme reaction. medium – where knowledge can be expressed with empirical equations based on physical properties such as those derived from factorial experiments low – where knowledge can be expressed semi-empirically, or by hit or miss methods such as trying virtually all surfactants to find one that works. Designers of diagnostic assays often are faced with all three states of knowledge as most assays today are a complex set integrated technologies. When the state of knowledge is high, engineers and scientists design out errors. However, when the state of knowledge is medium or low, detection strategies become important. These may be built into the system (e.g., internal monitoring systems) or are external (e.g., traditional QC). As illustrated in the AACC presentation (slide 15), internal monitoring systems can detect lower level system errors which implies significant knowledge of the system. On the other hand, external quality control can detect errors without knowledge of the system. Thus, external QC provides a net to catch errors which designers missed either because their knowledge was inadequate to design out all errors or because their model of the way the system might fail – their internal monitoring systems (e.g., risk analysis) was incorrect. As shown in slide 7 from the AACC presentation, laboratory errors that cause harm to patients are often caused by a cascade of errors. The cascade may be broken by either preventing or detecting an error – hence detection plays an important role in quality and should not be relegated to a lesser status. One could only devalue QC if it could be shown to have no benefit. This could in principle be shown by the so called option 4. The above discussion suggests that unless the state of knowledge is high enough, external QC will have benefit. Finally, the use of reliability growth management at Ciba Corning, a detection method of reliability improvement based on learning curve theory, was a key success factor for several instrument systems. It complemented design strategies and became part of them (3). The acronym used in mil-stds is TAAF (Test Analyze And Fix) which is politically incorrect in today’s quality world but nevertheless highly successful. References
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