(Wednesday, 8/1/2018) 4:30 PM - 6:00 PM
Room: Karl Dean Ballroom A1
Purpose: RIRAS is a web-based incident learning system implemented across radiation oncology services in the Veterans Health Administration. While most incident learning systems are designed to aggregate user reported incident data, RIRAS features secondary causal analysis and feedback provided by subject matter experts from the National Office. This generates greater contextual depth to accurately extract detailed information specific to each incident.
Methods: One hundred and ninety-six reports were individually analyzed to obtain contextual information on the categories, causes, and checkpoints related to specific incidents. Broad categories were further divided into subcategories for the most commonly encountered failure modes. Causal analysis was performed to determine appropriate checkpoints depending on the process step where the incident occurred. Special attention was placed on actual events that were reported despite existing checkpoints.
Results: The most frequently reported incidents belonged to dosimetry (30%), patient setup (13%), and contouring (9%) categories while the highest number of subcategory events resulted from geometric misses due to isocenter placement and shifts (11), treatments delivered with incorrect plans (5), and setups requiring bolus (5). The majority of incidents were caused by human factors, with inadequate attention and training combining for 59% of events and 38% of total incidents. Also, 63% of events occurred despite existing checkpoints already in place, with 41% attributed to deficiencies during time-out. It was discovered during analysis that several events were misidentified as good catches at the user-end.
Conclusion: Secondary analysis of RIRAS data presents opportunities to classify radiotherapy incidents with greater contextual detail. This allows corrections to misattributed incident types, accurate determination of cause in ambiguous cases, and highlights to areas with systematic failures. The result is an improvement in the understanding of failure points, which are specific to each reported incident thus facilitating real learning and process improvements at each reporting service.
Funding Support, Disclosures, and Conflict of Interest: Jatinder Palta is the Vice President of Center for Assessment of Radiological Sciences (CARS), a not-for-profit organization
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