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Background The Ottawa Hospital's Radiation Oncology program maintains the Incident Learning System (ILS)—a quality assurance program that consists of report submissions of errors and near misses arising from all major domains of radiation. In March 2020, the department adopted workflow changes to optimize patient and provider safety during the COVID‐19 pandemic. Purpose In this study, we analyzed the number and type of ILS submissions pre‐ and postpandemic precautions to assess the impact of COVID‐19‐related workflow changes. Methods ILS data was collected over six one‐year time periods between March 2016 and March 2021. For all time periods, the number of ILS submissions were counted. Each ILS submission was analyzed for the specific treatment domain from which it arose and its root cause, explaining the impetus for the error or near miss. Results Since the onset of COVID‐19‐related workflow changes, the total number of ILS submissions have reduced by approximately 25%. Similarly, there were 30% fewer ILS submissions per number of treatment courses compared to prepandemic data. There was also an increase in the proportion of “treatment planning” ILS submissions and a 50% reduction in the proportion of “decision to treat” ILS submissions compared to previous years. Root cause analysis revealed there were more incidents attributable to “poor, incomplete, or unclear documentation” during the pandemic year. Conclusions COVID‐19 workflow changes were associated with fewer ILS submissions, but a relative increase in submissions stemming from poor documentation and communication. It is imperative to analyze ILS submission data, particularly in a changing work environment, as it highlights the potential and realized mistakes that impact patient and staff safety.

Non-invasive cardiac radioablation is an emerging therapy for the treatment of ventricular tachycardia (VT). Electrophysiologic, anatomic and molecular imaging studies are used to localize the breakout region of the VT, but current therapy planning is tedious and prone to error due to a lack of data integration. In this work we present the design and development of a software platform and workflow to facilitate precision-targeted therapy planning, including affine non-rigid multimodality image registration and 2D-3D-4D visualization across modalities. Registration accuracy was measured using Dice Similarity and Hausdorff Distance of total left ventricle tissue volumes, which were 0.914 ± 0.013 and 2.65mm ± 0.34mm, respectively (average ± standard deviation). Electrocardiographic maps of VT parameters were registered temporally to surface electrode data to recreate familiar ECG tracings. 2D polar maps, 3D slice-views, and 4D cine-renderings were used for hybrid fusion displays of molecular and electroanatomic images. Segmentations of the cardiac-gated contrast CT blood-pool and molecular images of perfusion and glucose metabolism were used to identify regions of fibrotic scar tissue and hibernating myocardium in the 3D scene. Ablation targets were painted onto the 2D polar map, 3D slice or 4D-cine views, and exported as DICOM for import to radiotherapy planning software. We anticipate that the combination of accurate multimodality image registration and visualizations will enable more reliable therapy planning, expedite treatment and may improve understanding of the underlying pathophysiology of these lethal arrhythmias.

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