The Canadian National System for Incident Reporting in Radiation Treatment (NSIR-RT) Taxonomy

Milosevic, Michael; Angers, Crystal; Liszewski, Brian; Drodge, C. Suzanne; Marchand, Eve-Lyne; Bissonnette, Jean Pierre; Brown, Erika; Dunscombe, Peter; Hunt, Jordan; Jiang, Haiyan; Louie, Krista; Mitera, Gunita; Moran, Kathryn; Panzarella, Tony; Parliament, Matthew; Ross, Spencer; Brundage, Michael

doi:10.1016/j.prro.2016.01.013

Cited by 18 publications

(26 citation statements)

References 6 publications

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“…These include ASTRO's ''Target Safely" campaign [9], The European society for radiotherapy and oncology's (ESTRO) Radiation Oncology Safety Education Information System (ROSEIS) [10], a voluntary web based reporting system designed for individual clinic use as well as facilitating information exchange between clinics. Safety in Radiation Oncology (SAFRON) [11] which is a voluntary reporting and learning system of radiotherapy incidents developed by the International Atomic Energy Agency, as well as the Canadian National System for Incident Reporting in radiation therapy [12,13]. Incident reporting and ILS have been proposed as key factors in the safety management, not just in RO, but also in a variety of other safety critical industries, such as aviation [14] as it facilitates improvements in practice that enhance safety [4].…”

Section: Introductionmentioning

confidence: 99%

Quality management in radiation therapy: A 15 year review of incident reporting in two integrated cancer centres

Smith

Wallis

King

et al. 2020

Technical Innovations & Patient Support in Radiation Oncolo

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a b s t r a c tFifteen years of reported incidents were reviewed to provide insight into the effectiveness of an Incident Learning System (ISL). The actual error rate over the 15 years was 1.3 reported errors per 1000 treatment attendances. Incidents were reviewed using a Mann-Whitney U Test. The average number of incidents per year and the number of incidents per thousand attendances declined over time. Two seven-year periods were considered for analysis and the average for the first period (2005-2011) was 6 reported incidents per 1000 attendances compared to 2 incidents for the later period (2012-2018), p < 0.05. SAC 1 and SAC 2 errors have reduced over time and the reduction could be attributed to the quality assurance aspect of IGRT where the incident is identified prior to treatment delivery rather than after, reducing the severity of any potential incidents. The reasoning behind overall reduction in incident reporting over time is unclear but may be associated to quality and technology initiatives, issues with the ISL itself or a change in the staff reporting culture.Crown

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Section: Introductionmentioning

confidence: 99%

Quality management in radiation therapy: A 15 year review of incident reporting in two integrated cancer centres

Smith

Wallis

King

et al. 2020

Technical Innovations & Patient Support in Radiation Oncolo

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“…Collaborative incident learning in radiation therapy, including use of a shared nomenclature, has the potential to improve and standardize quality of care across participating institutions . Use of a shared nomenclature and anonymized, aggregated incident data allowed us to objectively compare staff participation in our incident learning process with other institutions.…”

Section: Discussion Of the Role Of Nsir‐rtmentioning

confidence: 99%

“…In Canada, the National System for Incident Reporting – Radiation Treatment (NSIR‐RT) was developed in 2014 and 2015 by the Canadian Partnership for Quality Radiotherapy (CPQR) and the Canadian Institute for Health Information (CIHI) . NSIR‐RT comprises a national registry for radiation therapy incident data as well as an online interface for submitting incidents and visualizing data.…”

Section: Introductionmentioning

confidence: 99%

“…The NSIR‐RT taxonomy, which is detailed in the NSIR‐RT Minimum Data Set, was developed with pan‐Canadian participation including input from all core professions involved in radiation therapy. A pilot deployment of NSIR‐RT began in September 2015 and concluded in December 2016 . The results of the pilot project are currently being compiled for publication.…”

Section: Introductionmentioning

confidence: 99%

“…These broadly reaching collaborative ILSes provide channels for dissemination, discussion, and analysis of incident data among participants, and aim to improve and standardize quality of care across participating institutions . However, the software solutions that accompany these national or international systems are not optimized for integration into the local clinical workflows of individual radiation therapy centers.…”

Section: Introductionmentioning

confidence: 99%

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Development and implementation of a radiation therapy incident learning system compatible with local workflow and a national taxonomy

Montgomery

Fava

Freeman

et al. 2017

J Applied Clin Med Phys

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PurposeCollaborative incident learning initiatives in radiation therapy promise to improve and standardize the quality of care provided by participating institutions. However, the software interfaces provided with such initiatives must accommodate all participants and thus are not optimized for the workflows of individual radiation therapy centers. This article describes the development and implementation of a radiation therapy incident learning system that is optimized for a clinical workflow and uses the taxonomy of the Canadian National System for Incident Reporting – Radiation Treatment (NSIR‐RT).MethodsThe described incident learning system is a novel version of an open‐source software called the Safety and Incident Learning System (SaILS). A needs assessment was conducted prior to development to ensure SaILS (a) was intuitive and efficient (b) met changing staff needs and (c) accommodated revisions to NSIR‐RT. The core functionality of SaILS includes incident reporting, investigations, tracking, and data visualization. Postlaunch modifications of SaILS were informed by discussion and a survey of radiation therapy staff.ResultsThere were 240 incidents detected and reported using SaILS in 2016 and the number of incidents per month tended to increase throughout the year. An increase in incident reporting occurred after switching to fully online incident reporting from an initial hybrid paper‐electronic system. Incident templating functionality and a connection with our center's oncology information system were incorporated into the investigation interface to minimize repetitive data entry. A taskable actions feature was also incorporated to document outcomes of incident reports and has since been utilized for 36% of reported incidents.ConclusionsUse of SaILS and the NSIR‐RT taxonomy has improved the structure of, and staff engagement with, incident learning in our center. Software and workflow modifications informed by staff feedback improved the utility of SaILS and yielded an efficient and transparent solution to categorize incidents with the NSIR‐RT taxonomy.

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Natural language processing and machine learning to assist radiation oncology incident learning

Mathew

Wang²,

Montgomery

et al. 2021

J Applied Clin Med Phys

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To develop a Natural Language Processing (NLP) and Machine Learning (ML) pipeline that can be integrated into an Incident Learning System (ILS) to assist radiation oncology incident learning by semi-automating incident classification. Our goal was to develop ML models that can generate label recommendations, arranged according to their likelihoods, for three data elements in Canadian NSIR-RT taxonomy. Methods: Over 6000 incident reports were gathered from the Canadian national ILS as well as our local ILS database. Incident descriptions from these reports were processed using various NLP techniques. The processed data with the expert-generated labels were used to train and evaluate over 500 multi-output ML algorithms. The top three models were identified and tuned for each of three different taxonomy data elements, namely: (1) process step where the incident occurred, (2) problem type of the incident and (3) the contributing factors of the incident. The best-performing model after tuning was identified for each data element and tested on unseen data. Results: The MultiOutputRegressor extended Linear SVR models performed best on the three data elements. On testing, our models ranked the most appropriate label 1.48 ± 0.03, 1.73 ± 0.05 and 2.66 ± 0.08 for process-step, problemtype and contributing factors respectively. Conclusions: We developed NLP-ML models that can perform incident classification. These models will be integrated into our ILS to generate a drop-down menu. This semi-automated feature has the potential to improve the usability, accuracy and efficiency of our radiation oncology ILS.

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The Canadian National System for Incident Reporting in Radiation Treatment (NSIR-RT) Taxonomy

Cited by 18 publications

References 6 publications

Quality management in radiation therapy: A 15 year review of incident reporting in two integrated cancer centres

Quality management in radiation therapy: A 15 year review of incident reporting in two integrated cancer centres

Development and implementation of a radiation therapy incident learning system compatible with local workflow and a national taxonomy

Natural language processing and machine learning to assist radiation oncology incident learning

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