Repeat/reject analysis makes case for digital training

It’s been argued that repeat/reject analysis, a quality-assurance tool to observe trends and make improvements that help reduce the need for repeat images, is not necessary when using digital equipment. While it is true that digital imaging reduces some error rates, the results on the Mayo Clinic digital poster exhibit at the RSNA meeting showed that mistakes still occur in the digital environment.

Jill Lucas, a radiology technologist at the Mayo Clinic in Rochester, MN, presented a poster showing repeat/reject analysis findings collected by a digital radiology information system (RIS) with an automated entry method that allowed technologists to enter the reason for a repeat image by scanning a barcode. The barcode makes it easy for the technician to select a reason for the repeat and provides a consistent method of tracking the reasons.

One challenge in the study was that the using the barcode entry method was not mandatory. The technologist was neither rewarded for selecting nor penalized for failing to select a barcode reason for repeat/reject images.

However, Mayo Clinic has the ability to check each device and see if the reasons for repeat/reject entered match the data collected in the RIS. Also, visual reminders are available for the technologist.

The results of the study show that error rates due to operator error are about the same after introducing digital imaging as before its introduction at the institution. While digital imaging has reduced patient exposure to x-ray dose and lowered the number of positioning errors, the findings still showed that some errors occur at the same rate as with film imaging.

“The majority of the repeat images are due to clipped errors, where part of the anatomy is missing, and rotation errors, where the part of the anatomy needed is not lined up,” Lucas said.

The Mayo Clinic has guidelines for what percentage of repeat/reject studies is permissible. In areas where the guidelines are not met, attempts are made to correct errors. Quality control in areas with higher rates of repeat/reject digital imaging errors could lead to training sessions to show technologists the proper positioning of, for example, a knee.