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

Ezzell, Gary A.; Chera, B.S.; Dicker, Adam P.; Ford, Eric; Potters, Louis; Santanam, Lakshmi; Weintraub, Sheri

doi:10.1016/j.prro.2017.10.007

Cited by 53 publications

(55 citation statements)

References 14 publications

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“…Similar effects were seen in the RTOG 9704 pancreatic cancer trial and in other trials as well . The data from Radiation Oncology Incident Learning System (RO‐ILS) also suggests that erroneous problematic treatment plans, such as dose per fraction in treatment and plan differ, is one of the major error pathways in radiation oncology …”

Section: Introductionmentioning

confidence: 58%

Characterization of a Bayesian network‐based radiotherapy plan verification model

et al. 2019

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Purpose The current process for radiotherapy treatment plan quality assurance relies on human inspection of treatment plans, which is time‐consuming, error prone and oft reliant on inconsistently applied professional judgments. A previous proof‐of‐principle paper describes the use of a Bayesian network (BN) to aid in this process. This work studied how such a BN could be expanded and trained to better represent clinical practice. Methods We obtained 51 540 unique radiotherapy cases including diagnostic, prescription, plan/beam, and therapy setup factors from a de‐identified Elekta oncology information system from the years 2010–2017 from a single institution. Using a knowledge base derived from clinical experience, factors were coordinated into a 29‐node, 40‐edge BN representing dependencies among the variables. Conditional probabilities were machine learned using expectation maximization module using all data except a subset of 500 patient cases withheld for testing. Different classes of errors that were obtained from incident learning systems were introduced to the testing set of cases which were withheld from the dataset used for building the BN. Different sizes of datasets were used to train the network. In addition, the BN was trained using data from different length epochs as well as different eras. Its performance under these different conditions was evaluated by means of Areas Under the receiver operating characteristic Curve (AUC). Results Our performance analysis found AUCs of 0.82, 0.85, 0.89, and 0.88 in networks trained with 2‐yr, 3‐yr 4‐yr and 5‐yr windows. With a 4‐yr sliding window, we found AUC reduction of 3% per year when moving the window back in time in 1‐yr steps. Compared to the 4‐yr window moved back by 4 yrs (2010–2013 vs 2014–2017), the largest component of overall reduction in AUC over time was from the loss of detection performance in plan/beam error types. Conclusions The expanded BN method demonstrates the ability to detect classes of errors commonly encountered in radiotherapy planning. The results suggest that a 4‐yr training dataset optimizes the performance of the network in this institutional dataset, and that yearly updates are sufficient to capture the evolution of clinical practice and maintain fidelity.

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Section: Introductionmentioning

confidence: 58%

Characterization of a Bayesian network‐based radiotherapy plan verification model

et al. 2019

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“…As discussed in numerous publications, processes that require manual calculation or transcription are prone to error . The fault tree for providing wrong shifts to therapists by Ezzell et al shows three major pathways including “error in manually calculating or transcribing” shifts.…”

Section: Discussionmentioning

confidence: 99%

“…Incident learning systems are a key component of safety culture in the medical field . The Radiation Oncology Incident Learning System (RO‐ILS) is an incident learning system sponsored by both ASTRO and AAPM which enables users to submit radiation oncology‐related incidents to a national database . With this database, RO‐ILS is able to review large numbers of events and provide education to clinics on the types of incidents reported via quarterly reports and case studies.…”

Section: Introductionmentioning

confidence: 99%

“…A recent paper by Ezzell et al looked at the highest priority reports from RO‐ILS . Out of over two thousand reports, 396 were deemed the highest priority and coded with up to three keywords.…”

Section: Introductionmentioning

confidence: 99%

“…[1][2][3][4][5] The Radiation Oncology Incident Learning System (RO-ILS) is an incident learning system sponsored by both ASTRO and AAPM which enables users to submit radiation oncology-related incidents to a national database. 6,7 With this database, RO-ILS is able to review large numbers of events and provide education to clinics on the types of incidents reported via quarterly reports and case studies. RO-ILS can also serve as an institution's primary ILS for review and management of incidents.…”

Section: Introductionmentioning

confidence: 99%

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Technical Note: Use of automation to eliminate shift errors

Covington

Popple

Cardan

2020

J Applied Clin Med Phys

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Purpose To create automated tools within the treatment planning system (TPS) that eliminate the common error pathway of providing incorrect shift instructions to therapists. Materials/Methods Two scripts were created within the TPS using the Eclipse API (Varian Medical Systems, Palo Alto, CA). One script detects whether or not the user origin has been placed correctly at the intersection of the simulation markers while the other calculates a shift instruction sheet that can be printed for treatment. Results Analysis of our RO‐ILS database identified eight errors caused by improper setting of the user origin in the treatment planning system. The user origin script flagged all of the treatment plans for markers inconsistent with user origin. Automated calculation of shifts eliminated the error pathway of miscalculating or transcribing shift values. Conclusion Automation can eliminate the common error pathway of providing the wrong shifts to therapists. The scripts have been made available as open‐source software for implementation at other radiotherapy clinics.

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A method to predict patient‐specific table coordinates for quality assurance in external beam radiation therapy

Saenz¹,

Astorga²,

Kirby³

et al. 2018

J Applied Clin Med Phys

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PurposeWhile external beam radiotherapy treatment planning determines nearly every mechanical and dosimetric parameter of the linear accelerator (LINAC), the table coordinates in all three dimensions are generally unknown until initial patient setup at the LINAC. Knowing these parameters in advance could help verify the direction of patient shifts and prevent wrong‐site errors. This study aims to determine the feasibility and accuracy of table coordinate prediction for indexed immobilization devices.MethodsA total of 303 table coordinates were predicted for patients on Varian and Elekta linear accelerators with immobilization devices including Orfit mask with baseplate, wingboard, breastboard and BodyFix. Predictions were made for all three spatial dimensions except for Body Fix setups due to the lack of a radiographically apparent indexing‐related landmark. Coordinates were predicted by measuring baseline table coordinates in all dimensions at specified landmark positions.ResultsPredictions were accurate within 2 cm for 86% of coordinates (71% within 1 cm). Table coordinates were predicted most accurately for head and neck patients with a base plate and the most difficult prediction was in the lateral direction for breastboard patients.ConclusionsWith proper indexing, table coordinates can be predicted with reasonable accuracy. The data suggest an action of level of 2 cm with certain exceptions for specific immobilization devices and directions.

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

Cited by 53 publications

References 14 publications

Characterization of a Bayesian network‐based radiotherapy plan verification model

Characterization of a Bayesian network‐based radiotherapy plan verification model

Technical Note: Use of automation to eliminate shift errors

A method to predict patient‐specific table coordinates for quality assurance in external beam radiation therapy

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