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AIM AND BACKGROUND: We describe a successful implementation of a departmental incident learning system (ILS) across a regionally expanding academic radiation oncology department, dovetailing with a structured integration of the safety and quality program across clinical sites. MATERIALS AND METHODS M: Over 6 years between 2011 and 2017, a long-standing departmental ILS was deployed to 4 clinical locations beyond the primary clinical location where it had been established. We queried all events reported to the ILS during this period and analyzed trends in reporting by clinical site. The chi-square test was used to determine whether differences over time in the rate of reporting were statistically significant. We describe a synchronous development of a common safety and quality program over the same period. RESULTS: There was an overall increase in the number of event reports from each location over the time period from 2011 to 2017. The percentage increase in reported events from the first year of implementation to 2017 was 457% in site 1, 166.7% in site 2, 194.3% in site 3, 1025% in site 4, and 633.3% in site 5, with an overall increase of 677.7%. A statistically significant increase in the rate of reporting was seen from the first year of implementation to 2017 (p < 0.001 for all sites). CONCLUSIONS: We observed significant increases in event reporting over a 6-year period across 5 regional sites within a large academic radiation oncology department, during which time we expanded and enhanced our safety and quality program, including regional integration. Implementing an ILS and structuring a safety and quality program together result in the successful integration of the ILS into existing departmental infrastructure.
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PURPOSE: The optimal approach to managing incident learning system (ILS) reports remains unclear. Here, we describe our experience with prospective coding of events reported to the ILS with comparisons of risk scores on the basis of event type and process map location. METHODS AND MATERIALS: Reported events were coded by type, origin, and method of discovery. Events were given a risk priority number (RPN) and near-miss risk index (NMRI) score. We compared workflow versus near-miss events with respect to origin and detection in the process map and by risk scores. A χ2 test was used to compare the differences between workflow and near-miss events. A comparison of RPN scores was done by independent t test. RESULTS: During 2016, 1351 events were reported. Of these events, 1300 (96.2%) were workflow and 51 (3.8%) near-miss events. Workflow events were more likely to both originate (1041 of 1300 events; 81.2%) compared with near-miss events (31 of 51 events; 62.7%; P = .005) and be detected in pre-treatment (997 of 1300 events; 76.7%) compared with near-miss events (24 of 51 events; 47%; P < .001). Average occurrence (scale: 1-10) was 6.14 for workflow versus 3.33 for near-miss events (P < .001), average severity was 2.94 versus 7.35 (P < .001), and average detectability was 1.33 versus 4.67 (P < .001). Mean overall RPN was 22.4 for workflow versus 108.4 for near-miss events (P = .07) and mean NMRI was 1.16 versus 3.19, respectively. Events that originated and were detected in treatment delivery had the greatest mean overall RPN (38.2 and 32.1, respectively) and NMRI scores (1.62 and 1.6, respectively). CONCLUSIONS: Our experience demonstrates that workflow event reports are far more common than near-misses and that near-miss events are more likely to both originate and be discovered in later treatment phases. The frequency of workflow reports highlights the imperative need for safety and operational teams to work collaboratively to maximize the benefit of ILS. We suggest a potential utility of the RPN system to guide mitigation strategies for future near-miss events.