The association between event learning and continuous quality improvement programs and culture of patient safety

Mazur, Łukasz; Chera, Bhishamjit S.; Mosaly, Prithima; Taylor, Kinley; Tracton, Gregg; Johnson, Kendra; Comitz, Elizabeth; Adams, Robert D.; Pooya, Pegah; Ivy, Julie S.; Rockwell, John; Marks, Lawrence B.

doi:10.1016/j.prro.2015.04.010

Cited by 32 publications

(33 citation statements)

References 27 publications

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“…In Figs 1-3, the only QC steps that are shown are at the top of the tree and represent the final checks that are universally employed: checks done by physicians, physicists, and therapists before the first treatment (Figs 1 and 2) and the final decision by a therapist to initiate treatment after performing a shift (Fig 3). Other QC steps can and often are used, 9,17 but not in a standardized fashion throughout our field; thus, the final plan and chart check by a physicist and therapist are responsible for identifying the wide variety of potential errors that may happen earlier in the process (ie, the roots of the trees in Figs 1-3). It may not be surprising, therefore, that these checks are far less than 100% effective in practice.…”

Section: Physician Errors In Defining Targets or Prescribingmentioning

confidence: 99%

Common error pathways seen in the RO-ILS data that demonstrate opportunities for improving treatment safety

Ezzell

Chera

Dicker

et al. 2018

Practical Radiation Oncology

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Section: Physician Errors In Defining Targets or Prescribingmentioning

confidence: 99%

Common error pathways seen in the RO-ILS data that demonstrate opportunities for improving treatment safety

Ezzell

Chera

Dicker

et al. 2018

Practical Radiation Oncology

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“…3,4 The most typical types of QC/QA processes include a combination of physics plan check, physician plan review, peer-review chart rounds, pretreatment QA for intensity-modulated radiation therapy (IMRT), therapist timeouts, and physics weekly chart check. 5 As the majority of errors in radiotherapy originate in treatment planning, 6 the physics plan check was found to be the most effective individual QC step in the radiotherapy workflow. 7 However, its sensitivity to identify a defect is still low: according to Gopan et al,only 38% of errors that could have been detected at the time of physics plan check were actually detected, the remainder 62% went undetected.…”

Section: Introductionmentioning

confidence: 99%

Optimizing efficiency and safety in external beam radiotherapy using automated plan check (APC) tool and six sigma methodology

Shi

Bush

Bertini

et al. 2019

J Applied Clin Med Phys

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To develop and implement an automated plan check (APC) tool using a Six Sigma methodology with the aim of improving safety and efficiency in external beam radiotherapy. Methods: The Six Sigma define-measure-analyze-improve-control (DMAIC) framework was used by measuring defects stemming from treatment planning that were reported to the departmental incidence learning system (ILS). The common error pathways observed in the reported data were combined with our departmental physics plan check list, and AAPM TG-275 identified items. Prioritized by risk priority number (RPN) and severity values, the check items were added to the APC tool developed using Varian Eclipse Scripting Application Programming Interface (ESAPI). At 9 months post-APC implementation, the tool encompassed 89 check items, and its effectiveness was evaluated by comparing RPN values and rates of reported errors. To test the efficiency gains, physics plan check time and reported error rate were prospectively compared for 20 treatment plans. Results: The APC tool was successfully implemented for external beam plan checking. FMEA RPN ranking re-evaluation at 9 months post-APC demonstrated a statistically significant average decrease in RPN values from 129.2 to 83.7 (P < .05). After the introduction of APC, the average frequency of reported treatment-planning errors was reduced from 16.1% to 4.1%. For high-severity errors, the reduction was 82.7% for prescription/plan mismatches and 84.4% for incorrect shift note. The process shifted from 4σ to 5σ quality for isocenter-shift errors. The efficiency study showed a statistically significant decrease in plan check time (10.1 ± 7.3 min, P = .005) and decrease in errors propagating to physics plan check (80%). Conclusions: Incorporation of APC tool has significantly reduced the error rate. The DMAIC framework can provide an iterative and robust workflow to improve the efficiency and quality of treatment planning procedure enabling a safer radiotherapy process.

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“…First, this study quantifies the actual sensitivity of a simulated pretreatment physics plan review while previous studies (with the exception of one study) only quantified its potential sensitivity, that is, the “best case scenario” for possible detection rates in an idealized scenario. These previous studies found the potential sensitivity of the pretreatment physics plan review to be 62%, 53%, and 51–80% . However, in considering the “best‐case scenario”, these studies do not measure actual performance of error checking and assume that all the errors that could have been detected are detected.…”

Section: Discussionmentioning

confidence: 99%

Utilizing simulated errors in radiotherapy plans to quantify the effectiveness of the physics plan review

et al. 2018

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Purpose The review of a radiation therapy plan by a physicist prior to treatment is a standard tool for ensuring the quality of treatments. However, little is known about how well this task is performed in practice. The goal of this study is to present a novel method to measure the effectiveness of physics plan review by introducing simulated errors into computerized “mock” treatment charts and measuring the performance of plan review by physicists. Methods We generated six simulated treatment charts containing multiple errors. To select errors, we compiled a list based on events from a departmental incident learning system and an international incident learning system (SAFRON). Seventeen errors with the highest scores for frequency and severity were included in the simulations included six mock treatment charts. Eight physicists reviewed the simulated charts as they would a normal pretreatment plan review, with each chart being reviewed by at least six physicists. There were 113 data points for evaluation. Observer bias was minimized using a simple error vs hidden error approach, using detectability scores for stratification. The confidence interval for the proportion of errors detected was computed using the Wilson score interval. Results Simulated errors were detected in 67% of reviews [58–75%] (95% confidence interval [CI] in brackets). Of the errors included in the simulated plans, the following error scenarios had the highest detection rates: an incorrect isocenter in DRR (93% [70–99%]), a planned dose different from the prescribed dose (92% [67–99%]) and invalid QA (85% [58–96%]). Errors with low detection rates included incorrect CT dataset (0%, [0–39%]) and incorrect isocenter localization in planning system (38% [18–64%]). Detection rates of errors from simulated charts were compared against observed detection rates of errors from a departmental incident learning system. Conclusions It has been notoriously difficult to quantify error and safety performance in oncology. This study uses a novel technique of simulated errors to quantify performance and suggests that the pretreatment physics plan review identifies some errors with high fidelity while other errors are more challenging to detect. These data will guide future work on standardization and automation. The example process studied here was physics plan review, but this approach of simulated errors may be applied in other contexts as well and may also be useful for training and education purposes.

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The association between event learning and continuous quality improvement programs and culture of patient safety

Cited by 32 publications

References 27 publications

Common error pathways seen in the RO-ILS data that demonstrate opportunities for improving treatment safety

Common error pathways seen in the RO-ILS data that demonstrate opportunities for improving treatment safety

Optimizing efficiency and safety in external beam radiotherapy using automated plan check (APC) tool and six sigma methodology

Utilizing simulated errors in radiotherapy plans to quantify the effectiveness of the physics plan review

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