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Session: Therapy General ePoster Viewing [Return to Session]

Event Severity, Self-Report Rate, and Textual Analysis From a Radiation Oncology Incident Learning System

R McGurk*, D Gu, J Dooley, A Amos, B Chera^, K Adapa, L Marks, L Mazur, University of North Carolina at Chapel Hill, Chapel Hill, NC. ^Author now at Medical University of South Carolina, Charleston, SC


PO-GePV-T-194 (Sunday, 7/10/2022)   [Eastern Time (GMT-4)]

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Purpose: To quantify the severity of patient safety events, the self-report rate, and keywords associated with events submitted to a large academic institution’s established incident learning system (ILS).

Methods: A multidisciplinary committee reviewed 275 events submitted to an in-house ILS during 2021. Four events were excluded due to being unassigned, leaving 271 evaluable. Each event was assigned a severity score categorized on a scale of 1-6, comparable to that of the Agency of Healthcare Research and Quality (AHRQ) harm scale. Further classification determined whether each event was a near-miss, incident (i.e. reached the patient), or an unsafe condition. It was also noted if it was self-reported. Frequency analysis on event descriptions was performed separately for near-misses (n=144) and incidents (n=91), to find keywords that elucidate differences in these events.

Results: The three highest categories of events were severity 1 near-misses (50%), severity 2 incidents (38%) and severity 2 near-misses (20%). The most severe reported incidents (severity 4, 4%) involved care coordination causing delays starting radiation or chemotherapy. The most severe reported unsafe conditions (severity 4, 1%) involved IT updates to clinical machines without prior notice. 36/271 (13%) of events were identified as self-reported. Frequency analysis on event descriptions showed that variations of the phrase “pre-tx check” (a physics quality assurance (QA) step) and “chart write up” (a therapy QA step) were the most common in near-misses, with the phrase “weekly chart check” and “was on vacation” more common with incidents.

Conclusion: The majority of events reported were near-misses and 13% of events were self-reports, which offer evidence of the effectiveness of established QA steps and a robust patient safety culture at our institution. Analysis of event descriptions further highlight established QA steps, but also weaknesses in the treatment pathway, such as cross-coverage, which can help guide future quality improvement efforts.

Funding Support, Disclosures, and Conflict of Interest: Drs Chera and Mazur have a financial relationship (e.g., royalties and equity) with CommunifyHealth, which provides software for incident reporting and analysis.


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