## EP18P3 – More comments on risk management

One of the tasks that is confusing is assigning probability and severity to (error) events. This is important since it is the basis for Pareto charts, where events are ranked for the purpose of deciding which problems to fix.

Consider the following sequence of events, which is a laboratory example of patient sample mix-up.

There could be more events within this series, but to keep things simple, there are just these three events.

Issue 1: the severity of event 1 – The original error is event 1, which by itself does not cause patient harm. Actually nothing in the laboratory by itself causes patient harm. It is the downstream effects of event 1 that cause patient harm. Thus, the severity of event 1 is given by the severity of event 3. That is, if event 3 can cause harm, then the severity of that harm is assigned the severity of event 1.

Issue 2: multiple outcomes – Assume the assay is glucose. Providing the wrong glucose result can have a variety of consequences. If a 94 mg/dL is given instead of a 95 mg/dL, no harm is likely, not so if a 35 mg/dL is given instead of a 420 mg/dL. Typically, one assigns the severity corresponding to the worst possible outcome.

Issue 3: probability – The issue is should one assign the probability of occurrence to event 1 or event 3 and if event 3 which outcome.

Since the severity has been assigned to the worst outcome, if one were to assign a probability to event 3, it would be for the worst outcome. Typically, the probability for event 3 will be much lower than that for event 1. Consider two examples:

Central lab Glucose – one could get the distribution of glucose results for the laboratory and randomly sample two results from that distribution to get a probability for a “bad” patient mismatch to occur. One then has to speculate the percentage of times that a clinician would act on the result leading to patient harm.

Newborn screening – Here, a bad result would be a positive that is called a negative (usually worse than a false positive). This could be estimated by the prevalence of the disorder. Since most newborn screening disorders have low prevalence, the most common result of the patient sample mix-up would be mixing up a negative with a negative – which causes no harm.

In either the glucose or newborn screening case, one has to multiply the probability of event 3 by the probability of event 1, which gives a very low overall probability.

Why probability should be assigned to event 1 – Although it is possible to estimate a probability for event 3, the problem with using that probability is that it contains chance events, which are beyond the control of the laboratory. One cannot do anything about those chance events, one can only lower the probability of occurrence of event 1.

One might argue that this affects the ranking in a Pareto. This is true. For the glucose and newborn screening example, using the probability for event 1 might give a different ranking than using the probability for event 3 (the two event 1s are likely to have different probabilities since newborn screening is a filter paper assay and central lab glucose is a serum assay. The problem is this level of ranking is not important. Within a certain class, all problems need to be addressed since any serious harm due to laboratory error is unacceptable. The ranking is important when one moves from one severity class to another as occurs in a larger Pareto with hundreds of events, especially when events cover other areas, such as complaints, accreditation, finances, and so on.