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Established kilovoltage cone‐beam computed tomography (kV‐CBCT) image quality assurance (QA) guidelines often rely on recommendations provided by the American Association of Physicists in Medicine (AAPM) task group (TG) reports with metrics that use visual analysis. This can result in measurement variations by different users, especially in visually subjective analyzes such as low contrast resolution. Consequently, there is a growing interest in more automated means of image QA analysis that can offer increased consistency, accuracy, and convenience. This work compares visual QA to semi‐automated software QA analysis to establish the performance and viability of a semi‐automated method. In this study, a commercial product (RIT Radia. Radiological Imaging Technology, Colorado Springs, CO) was used to evaluate 68 months of kV‐CBCT images of a Catphan® 504 phantom obtained from a Varian TrueBeam® linear accelerator. Six key metrics were examined: high contrast resolution, low contrast resolution, Hounsfield unit constancy, uniformity and noise, and spatial linearity. The results of this method were then compared to those recorded visually using Bland‐Altman, and/or paired sample t‐test. Comparison of all modules showed a non‐random, statistically significant difference between visual and semi‐automated methods except for LDPE and Teflon in the Hounsfield unit constancy analysis, which falls outside the paired sample t‐test's 5% significance level. A small high contrast resolution bias indicates the two analysis methods are largely equivalent, while a large low contrast resolution bias indicates greater semi‐automated target detection. Wide limits of agreement in the uniformity module suggests variability due to multiple visual observers. Spatial linearity results measured differences of less than 0.17%. Semi‐automated QA analysis offered greater stability over visual analysis. Additionally, semi‐automated QA results satisfied or exceeded visual QA passing criteria and allowed for fast and consistent image quality analysis. [ABSTRACT FROM AUTHOR]

Incident reporting and learning systems provide an opportunity to identify systemic vulnerabilities that contribute to incidents and potentially degrade quality. The narrative of an incident is intended to provide a clear, easy to understand description of an incident. Unclear, incomplete or poorly organized narratives compromise the ability to learn from them. This report provides guidance for drafting effective narratives, with particular attention to the use of narratives in incident reporting and learning systems (IRLS). Examples are given that compare effective and less than effective narratives. This report is mostly directed to organizations that maintain IRLS, but also may be helpful for individuals who desire to write a useful narrative for entry into such a system. Recommendations include the following: (1) Systems should allow a one‐ or two‐sentence, free‐text synopsis of an incident without guessing at causes; (2) Information included should form a sequence of events with chronology; and (3) Reporting and learning systems should consider using the headings suggested to guide the reporter through the narrative: (a) incident occurrences and actions by role; (b) prior circumstances and actions; (c) method by which the incident was identified; (d) equipment related details if relevant; (e) recovery actions by role; (f) relevant time span between responses; (g) and how individuals affected during or immediately after incident. When possible and appropriate, supplementary information including relevant data elements should be included using numerical scales or drop‐down choices outside of the narrative. Information that should not be included in the narrative includes: (a) patient health information (PHI); (b) conjecture or blame; (c) jargon abbreviations or details without specifying their significance; (d) causal analysis. [ABSTRACT FROM AUTHOR]