Abstract

PurposeThis study reviews our institutional error data and assesses the effectiveness of a policy implemented January 1, 2011, as a "no rushed treatment" initiative to avoid universal, large-scale replanning for all patients in the event that a treatment unit is down for ≤1 day. Methods and materialsRadiation error data between January 1, 2004, and December 31, 2014, were reviewed to determine absolute delivery error rates. Variables were compared (using a χ2 or Fisher exact test) before and after the policy change, including planning versus delivery error status and differences in error type. We also evaluated time of day in relation to therapist shift change, deviation from scheduled time, and weekend treatment as predictors of error using a test of proportions or χ2 test. ResultsTreatment delivery error rate over the entire period was 0.18% per fraction; the rate before intervention was 0.24% and after was 0.08%, P < .001. For the 5 years for which detailed records were available (2010-2014), 109 delivery errors were reported. Delivery error rate was 0.09%; before intervention 0.15% versus after, 0.08% (P = .005) and 94% were level 1 errors. Fifty-six percent were primary planning errors and 44% were primary delivery errors. Before intervention, large-scale replanning occurred 18 times/year versus 4.5/year after, with 21% versus 12% of errors directly attributable to large-scale replanning. Fourteen error reports specifically implicated a rushed environment as causal. There was no significant difference in error rate based on time of day (P = .631). Error rates were higher for weekend simulation and treatments, 1.3% versus 0.09% per fraction (P < .001). ConclusionsDelivery error rates at our institution were similar compared with published series from other academic institutions. A significant improvement in delivery error rate was appreciated after implementation of a "no rushed treatment" initiative. A significantly higher error rate for weekend treatments was noted, warranting consideration of additional quality assurance measures.

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