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Purpose Incident learning is a critical tool to improve patient safety. The Patient Safety and Quality Improvement Act of 2005 established essential legal protections to allow for the collection and analysis of medical incidents nationwide. Methods and materials Working with a federally listed patient safety organization (PSO), the American Society for Radiation Oncology and the American Association of Physicists in Medicine established RO-ILS: Radiation Oncology Incident Learning System (RO-ILS). This paper provides an overview of the RO-ILS background, development, structure, and workflow, as well as examples of preliminary data and lessons learned. RO-ILS is actively collecting, analyzing, and reporting patient safety events. Results As of February 24, 2015, 46 institutions have signed contracts with Clarity PSO, with 33 contracts pending. Of these, 27 sites have entered 739 patient safety events into local database space, with 358 events (48%) pushed to the national database. Conclusions To establish an optimal safety culture, radiation oncology departments should establish formal systems for incident learning that include participation in a nationwide incident learning program such as RO-ILS.

Purpose The Radiation Oncology Incident Learning System (RO-ILS) receives event reports from facilities across the country. This effort extracted common error pathways seen in the data. These pathways, expressed as fault trees, demonstrate the need for, and opportunities for, preventing these errors and/or limiting their propagation to treatment. Methods and materials As of the third quarter of 2016, 2344 event reports had been submitted to RO-ILS and reviewed. A total of 396 of the reports judged highest priority were rereviewed and assigned up to 3 keywords to classify events. Based on patterns among the keyword assignments, the data were further aggregated into pathways leading to 3 general error types: "problematic plan approved for treatment," "wrong shift instructions given to therapists," and "wrong shift performed at treatment." Fault trees were created showing how different errors at different stages in the treatment process combine to flow into these general error types. Results A total of 173 of the 396 (44%) events were characterized as belonging to 1 of these 3 general error types. Ninety-nine events were defined as "problematic plan approved for treatment," 40 as "wrong shift instructions given to therapists," and 34 as "wrong shift performed at treatment." Seventy-six of these events (44%) resulted in incorrectly delivered treatment. Event discovery was by therapists (n = 76), physicists (n = 45), physicians (n = 23), dosimetrists (n = 15), or not identified (n = 9); 5 events were found as a result of the patient questioning the staff. For the event type "problematic plan approved for treatment," 64 of the 99 (65%) events were attributable to physician error: incorrect target or dosing pattern prescribed. Conclusions Data extracted from RO-ILS event reports demonstrate common error pathways in radiation oncology that propagate all the way to treatment. Additional study and coordination of efforts is needed to develop and share best practices to address the sources of these errors and curtail their propagation.

Background To compare plan robustness of volumetric modulated arc therapy (VMAT) with intensity modulated radiation therapy (IMRT) and to compare the effectiveness of 3 plan robustness quantification methods. Methods and materials The VMAT and IMRT plans were created for 9 head and neck cancer patients. For each plan, 6 new perturbed dose distributions were computed using ±3 mm setup deviations along each of the 3 orientations. Worst-case analysis (WCA), dose-volume histogram (DVH) band (DVHB), and root-mean-square dose-volume histogram (RVH) were used to quantify plan robustness. In WCA, a shaded area in the DVH plot bounded by the DVHs from the lowest and highest dose per voxel was displayed. In DVHB, we displayed the envelope of all DVHs in band graphs of all the 7 dose distributions. The RVH represents the relative volume on the vertical axis and the root-mean-square-dose on the horizontal axis. The width from the first 2 methods at different target DVH indices (such as D 95% and D 5% ) and the area under the RVH curve for the target were used to indicate plan robustness. Results were compared using Wilcoxon signed-rank test. Results The DVHB showed that the width at D 95% of IMRT was larger than that of VMAT (unit Gy) (1.59 vs 1.18) and the width at D 5% of IMRT was comparable to that of VMAT (0.59 vs 0.54). The WCA showed similar results between IMRT and VMAT plans (D 95% : 3.28 vs 3.00; D 5% : 1.68 vs 1.95). The RVH showed the area under the RVH curve of IMRT was comparable to that of VMAT (1.13 vs 1.15). No statistical significance was found in plan robustness between IMRT and VMAT. Conclusions The VMAT is comparable to IMRT in terms of plan robustness. For the 3 quantification methods, WCA and DVHB are DVH parameter–dependent, whereas RVH captures the overall effect of uncertainties.