The majority of errors affecting anatomic and clinical pathology laboratories occur during the pre-analytical phase of testing. While we often feel more in control of the variables that cause analytical and post-analytical errors, managing pre-analytical errors is critical to optimizing laboratory workflows. Rather than feeling helpless about errors that occur prior to specimen receipt in the laboratory, there are options that can make an impact on pre-analytical error rates and the quality of care provided to our patients.
As with many performance improvement initiatives, using data to drive changes in behavior tends to be effective. In order to best collect and compare data on specimen rejection and pre-analytical errors, the first step is to standardize test cancellation reasons. Since laboratories have different methods for documenting pre-analytical errors, standardizing as much of the error documentation workflow as possible is key to comparing your lab’s data to benchmarks. Performing an audit of test cancellation reasons in your lab information system (LIS) is a must. Depending on how your staff enter data into the LIS, this can be done using free text or a drop-down menu with pre-assigned cancelation reasons. The free text option lends itself to more variation in the categories documented for specimen rejection. Utilizing a drop-down menu with consensus-driven predetermined reasons is a very effective way to decrease the number of potential causes listed. However, even this drop-down option needs to be audited in order to determine whether there is overlap between categories or if relevant categories have been excluded, leading to erroneous reporting.
ASCP’s National Pathology Quality Registry (NPQR) offers a Pre-analytical Errors Module that provides national benchmarks to standardize and compare pre-analytical error rates. In order to create this module, expert pathologists and laboratory professionals created a list of best practice pre-analytical error categories. These categories are mapped to existing categories from participating NPQR sites, allowing for the standardization of reasons for pre-analytical errors and the tracking of these errors in real time. If a particular site has an error that is not included in the NPQR that they wish to track, customization options are possible. However, we find that “common things are common,” and the patterns of cancellation reasons are fairly standard across organizations.
Just the exercise in becoming aware of available pre-analytical error reasons and the way they are collected in your laboratory’s workflow is valuable in driving continuous improvement. Once this process is optimized, data can be used to educate and encourage clinical departments to pay attention to certain patterns and errors that arise. The laboratory can use these data to emphasize its value to other departments by offering to collaborate and share best practices on how to reduce these errors. The ability to benchmark across national standards as well as between submitting departments within one organization can help encourage a culture that comes together to tackle errors that ultimately impact patients.