Quality control quantification (QCQ): a tool to measure the value of quality control checks in radiation oncology

To quantify the error-detection effectiveness of commonly used quality control (QC) measures.

We analyzed incidents from 2007-2010 logged into a voluntary in-house, electronic incident learning systems at 2 academic radiation oncology clinics. None of the incidents resulted in patient harm. Each incident was graded for potential severity using the French Nuclear Safety Authority scoring scale; high potential severity incidents (score >3) were considered, along with a subset of 30 randomly chosen low severity incidents. Each report was evaluated to identify which of 15 common QC checks could have detected it. The effectiveness was calculated, defined as the percentage of incidents that each QC measure could detect, both for individual QC checks and for combinations of checks.

In total, 4407 incidents were reported, 292 of which had high-potential severity. High- and low-severity incidents were detectable by 4.0 ± 2.3 (mean ± SD) and 2.6 ± 1.4 QC checks, respectively (P<.001). All individual checks were less than 50% sensitive with the exception of pretreatment plan review by a physicist (63%). An effectiveness of 97% was achieved with 7 checks used in combination and was not further improved with more checks. The combination of checks with the highest effectiveness includes physics plan review, physician plan review, Electronic Portal Imaging Device-based in vivo portal dosimetry, radiation therapist timeout, weekly physics chart check, the use of checklists, port films, and source-to-skin distance checks. Some commonly used QC checks such as pretreatment intensity modulated radiation therapy QA do not substantially add to the ability to detect errors in these data.

The effectiveness of QC measures in radiation oncology depends sensitively on which checks are used and in which combinations. A small percentage of errors cannot be detected by any of the standard formal QC checks currently in broad use, suggesting that further improvements are needed. These data require confirmation with a broader incident-reporting database.