The value of risk management is becoming more widely recognized in laboratory medicine since risk management can deal with all clinical laboratory errors including:
- and post-analytical errors
Unfortunately, this has led to some misconceptions about risk management. The purpose of this essay is to further explain the use of risk management (see previous essay).
A brief review
Error events are classified in risk management using two quantities:
- the severity of the possible adverse consequence and
- the probability of occurrence of each event
- probability is used for potential error events
- frequency of occurrence is used for observed error events.
Two types of risk management: quantitative vs. qualitative
Probability (or frequency) of occurrence can be assessed qualitatively (e.g., 1-5) or quantitatively. Expert judgment is often used for qualitative assessment while modeling and counting are used for quantitative assessment. Unfortunately, one can usually infer that most people that advocate use of risk management in laboratory medicine, mean qualitative risk management.
The problem with qualitative risk management
To understand the problem with qualitative risk assessment, consider assay precision, which has a long tradition of quantitative assessment. In risk management terms:
quantitative assessment means a traditional precision experiment to quantify the SD and CV of an assay so that one can quantify the probability of assay values that exceed a desired limit.
qualitative assessment means that by using judgment (typically no precision experiment would be carried out) one would classify that an assay value that exceeds a desired limit is very likely, somewhat likely, or not likely (or other qualitative categories).
No one would tolerate a qualitative assessment of precision but this is how risk management is often positioned for many other assay attributes, particularly those that are difficult to quantify such as large infrequent errors (e.g., outliers). Moreover the distinction between qualitative and quantitative risk management is not made, one simply hears: “that problem can be handled by risk management.” This works most likely because of the unfamiliarly with risk management in the clinical laboratory. For example, quantitative risk management terms such as “minimal cut sets” never appear in risk management proposals – this term does not appear in the ISO standard (14971) on risk management. Yet quantitative risk management does exist and quantification often involves time consuming, expensive experiments often with huge sample sizes. The qualitative alternatives are quick to perform but lack the confidence available in actual data.
So be on the look out for people that advocate “risk management” as an alternative to quantifying things.