| Detection Systems – Fault Isolation, Automation, and Diagnostic Accuracy – 6/2006
First, a quick review
A clinical laboratory’s product is the report provided to clinicians, whose main element is the assay result. The result needs to be as error free as possible to prevent harm to patients. Assay performance goals can be expressed in terms of error grids such as are available for glucose. It is helpful to conceptualize clinical laboratory errors in terms of a fault tree or FMEA. The top level error one wants to prevent is providing an incorrect result to a clinician.
Another possible top level error is delay in the reporting of a result – to keep things simple that is not considered here, but could also lead to patient harm.
This top level error is the “effect” of many possible lower level errors (e.g., causes). In order to prevent the top level error, the clinical laboratory’s quality program tries to address lower level errors either by
Note that detection without recovery is not useful and that these are two (separate) steps.
The use of quality control
Quality control is a means of detecting errors. The recovery part of quality control is simple – after a failed quality control result is observed, no patient results are reported since the last successful quality control . This raises an immediate concern about the CMS proposal to allow quality control to be run once a month, as this makes recovery rather useless – all of these potentially incorrect patient results will have been reported to clinicians. To summarize, quality control detects lower level errors and prevents the effect of these errors. In this way, it blocks the error cascade expressed by a fault tree or FMEA.
There is a another task that clinical laboratories must do after a failed quality control and that is to determine why the quality control failed, so as to correct the problem. This is where fault isolation plays a role.
Fault Isolation – Why its important
Fault isolation, when it is present, refers to a detection system, which points to a single root cause for the failure. To see why this is important, consider the following case, where incorrect results are generated by an assay system because of regent degradation caused by the reagent being stored above its maximum allowable storage temperature. To prevent this error, training would be used and perhaps the use of redundant refrigeration systems. In addition, consider two different detection systems to deal with this failure.
Fault isolation absent
Quality Control – The bad reagent can lead to a failed QC. Since failed QC can be caused by many factors, there is no fault isolation. So one must follow a troubleshooting protocol to determine the root cause of failed QC. This troubleshooting ensures that the next set of results will not fail QC – at least not for that root cause!
Fault isolation present
Temperature Sensor on Reagent – A sensor of the reagent box that indicates storage at a too high temperature by a color change does has fault isolation. Of course this relies on another detection step, where one looks at the temperature sensor.
Ideally, one would like all detection systems to have fault isolation since no troubleshooting is required which returns the system quicker to an error free state. But to design in detection systems with fault isolation for all errors, one must have a complete knowledge of all the ways a system can fail.
For the reasons this knowledge is often not the case, see the AACC expert session.
The value of quality control is that in many cases it detects errors, even though no one (the clinical laboratory or the manufacturer) has knowledge that such an error may occur. The disadvantage of quality control is that there is no fault isolation and a corrective action could involve a substantial amount of work. When this corrective action occurs before product release, it is simply part of product development, but when it occurs after product release in a clinical laboratory, it is also product development but conducted in part by the clinical laboratory.
Automated detection recovery systems:
Automated detection recovery systems are desirable and are prevalent on instrument systems. As an example, a sample’s response curve is evaluated by an algorithm. The algorithm can detect whether the response is too noisy, and if so signal the analyzer to suppress reporting that result (e.g., the recovery). Note that either the previous temperature sensor detection system or quality control are manual detection recovery systems.
There is no guarantee that an automated detection recovery system has fault isolation. In the noisy response example, there is no indication of what is causing the noise. For example, it could be a lipemic specimen or alternatively a dirty reaction chamber.
The final dimension in this essay is the diagnostic accuracy of the detection system. This was also covered in the AACC expert session and relates the to number of false positives and false negatives that occur with the detection process.
With sufficient knowledge, one would either design a system without errors or employ detection systems for all possible failures. However, one does not have this knowledge. Good detection systems have high diagnostic accuracy, are automated, and have fault isolation. The value of quality control is that in spite of not having fault isolation or being automated, it can catch errors that are missed by detection systems.
Detection Systems – Fault Isolation, Automation, and Diagnostic Accuracy – 6/2006