SIMMEON-Prep study: SIMulation of Medication Errors in ONcology: prevention of antineoplastic preparation errors

Sarfati, L.; Ranchon, Florence; Vantard, Nicolas; Schwiertz, Vérane; Gauthier, Noémie; He, Sophie; Kiouris, Eléna; Gourc-Berthod, Chloé; Guédat, Marie-Gabrielle; Alloux, Céline; Gustin, Marie-Paule; You, Benoît; Trillet‐Lenoir, Véronique; Rioufol, Catherine

doi:10.1111/jcpt.12225

Cited by 14 publications

(21 citation statements)

References 35 publications

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“…21 After training, the percentage of errors not identified significantly decreased to 20% (P-value = 0.04). 21 The current study extends these concepts into the field of radiation oncology.…”

Section: Discussionmentioning

confidence: 91%

“…20 Introducing medical errors into the clinical workflow has also been used to quantify the impact of training interventions. 21 In one study, medical errors were introduced into pharmacy technicians' instruction sheets, and the percentage of medication errors that were not identified was measured to be 48%. 21 After training, the percentage of errors not identified significantly decreased to 20% (P-value = 0.04).…”

Section: Discussionmentioning

confidence: 99%

“…21 In one study, medical errors were introduced into pharmacy technicians' instruction sheets, and the percentage of medication errors that were not identified was measured to be 48%. 21 After training, the percentage of errors not identified significantly decreased to 20% (P-value = 0.04). 21 The current study extends these concepts into the field of radiation oncology.…”

Section: Discussionmentioning

confidence: 99%

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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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Section: Discussionmentioning

confidence: 91%

Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

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

et al. 2018

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“…Such programs included from 11 to 96 healthcare providers, depending on the communication campaign and on whether participation was voluntary or obligatory (Table ). Studies were conducted in the context of daily activity, with deliberate insertion of patient identification errors, in the preparation of antineoplastic drugs by technicians, and in crisis scenarios such as management of acute anaphylaxis or septic shock . These 8 studies assessed drug preparation or administration error rates in nurses, pharmacy technicians, or anaesthesia trainees.…”

Section: Resultsmentioning

confidence: 99%

“…Two studies demonstrated the efficacy of prevention tools such as awareness campaigns on iatrogenic risk associated with ME and the use of a bar code to detect patient identity error . Controlled trials used simulation programs to evaluate the superiority of certain professional practices compared to others: using prefilled syringes instead of preparation at the bedside in emergency situations, using checklists during the administration process to improve double‐checking, or mitigating error rates by reducing interruptions, such as request from a colleague or a question from a patient, while administering chemotherapy .…”

Section: Resultsmentioning

confidence: 99%

Human‐simulation‐based learning to prevent medication error: A systematic review

Sarfati

Ranchon

Vantard

et al. 2018

Evaluation Clinical Practice

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Exploring the extent to which simulation-based education addresses contemporary patient safety priorities: A scoping review

et al. 2019

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SIMMEON-Prep study: SIMulation of Medication Errors in ONcology: prevention of antineoplastic preparation errors

Cited by 14 publications

References 35 publications

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

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

Human‐simulation‐based learning to prevent medication error: A systematic review

Exploring the extent to which simulation-based education addresses contemporary patient safety priorities: A scoping review

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