AJR: Quality control cuts 3D post-processing errors, preserves productivity
Because it is highly operator dependent, 3D post-processing of volumetric patient data can have inconsistent quality depending on the experience of the technologist. Quality control (QC) programs, however, can help reduce errors without negatively impacting productivity, according to a study in the January issue of the American Journal of Roentgenology.
“To reduce risk of harm to patients, 3D imaging must be of the highest quality on a consistent basis,” wrote Laura Pierce, RT, of the Duke Multi-Dimensional Image Processing Laboratory at Duke University in Durham, N.C., and colleagues. While manufacturers perform QC tests on radiology workstations, the authors noted that QC programs to measure the quality of 3D images has been largely non-existent.
This absence of QC in 3D imaging led the researchers to develop a perpetual QC program which borrowed from the Breakthrough approach to Six Sigma. The program prevents errors through extensive training and structured management involvement. The system included steps to define problems, while measuring, analyzing, improving and controlling quality. It was incorporated into the routine workflow and included training as well as daily error discussions based on a QC reporting database that is searchable, tracks trends and provides immediate feedback to operators.
Six 3D technologists, with experience levels of six months to 11 years, were observed using standard clinical protocols for three months. Training sessions aimed at eliminating the observed errors were given over a three-month period and then error rates were re-measured over a nine-month post-training period.
Results showed that error rates, particularly among the least experienced technologists, were greatly reduced despite an increase in examination volume that occurred in the post-training period. Overall error rates after training were 7.2 percent, compared with 16.1 percent during the initial observation period. Technologists with less than four years of experience saw their error rates drop from 18.5 percent pre-training to 8.4 percent post-training.
“These results suggest that technologists new to 3D imaging need sufficient training and ongoing mentoring to ensure they continually produce high-quality 3D images,” wrote the authors. “Because it helped reduce inter-technologist variability by approximately one half, training may also have the effect of producing more consistent, higher-quality 3D output.”
Error reporting can be time-consuming, noted Pierce et al, but far from slowing down workflow, the QC program also produced better turnaround times. At baseline, about one-in-four exams were processed in less than four hours. After the training, one-in-three were completed within four hours.
“Therefore, although our QC program reduced error rates, it did not reduce efficiency,” wrote the authors. “In healthcare settings with restricted finances, this factor is important to consider in the introduction of quality improvement processes.”
“To reduce risk of harm to patients, 3D imaging must be of the highest quality on a consistent basis,” wrote Laura Pierce, RT, of the Duke Multi-Dimensional Image Processing Laboratory at Duke University in Durham, N.C., and colleagues. While manufacturers perform QC tests on radiology workstations, the authors noted that QC programs to measure the quality of 3D images has been largely non-existent.
This absence of QC in 3D imaging led the researchers to develop a perpetual QC program which borrowed from the Breakthrough approach to Six Sigma. The program prevents errors through extensive training and structured management involvement. The system included steps to define problems, while measuring, analyzing, improving and controlling quality. It was incorporated into the routine workflow and included training as well as daily error discussions based on a QC reporting database that is searchable, tracks trends and provides immediate feedback to operators.
Six 3D technologists, with experience levels of six months to 11 years, were observed using standard clinical protocols for three months. Training sessions aimed at eliminating the observed errors were given over a three-month period and then error rates were re-measured over a nine-month post-training period.
Results showed that error rates, particularly among the least experienced technologists, were greatly reduced despite an increase in examination volume that occurred in the post-training period. Overall error rates after training were 7.2 percent, compared with 16.1 percent during the initial observation period. Technologists with less than four years of experience saw their error rates drop from 18.5 percent pre-training to 8.4 percent post-training.
“These results suggest that technologists new to 3D imaging need sufficient training and ongoing mentoring to ensure they continually produce high-quality 3D images,” wrote the authors. “Because it helped reduce inter-technologist variability by approximately one half, training may also have the effect of producing more consistent, higher-quality 3D output.”
Error reporting can be time-consuming, noted Pierce et al, but far from slowing down workflow, the QC program also produced better turnaround times. At baseline, about one-in-four exams were processed in less than four hours. After the training, one-in-three were completed within four hours.
“Therefore, although our QC program reduced error rates, it did not reduce efficiency,” wrote the authors. “In healthcare settings with restricted finances, this factor is important to consider in the introduction of quality improvement processes.”
Evan Godt, Writer
Evan joined TriMed in 2011, writing primarily for Health Imaging. Prior to diving into medical journalism, Evan worked for the Nine Network of Public Media in St. Louis. He also has worked in public relations and education. Evan studied journalism at the University of Missouri, with an emphasis on broadcast media.