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

Montgomery, Logan; Fava, Paolo; Freeman, Carolyn R.; Hijal, Tarek; Maietta, C.; Parker, William; Kildea, J.

doi:10.1002/acm2.12218

Cited by 8 publications

(20 citation statements)

References 12 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…The survey had three components: Basic demographics: profession, role, state/country and years in career. SC perceptions using HSPC’s validated and widely used survey method and questions in 10 SC areas 14 Knowledge and understanding of incident reporting and ILSs, including barriers to use and investigation levels, aligning question topics with other reported studies to compare findings where possible 6,8,23,27,28 …”

Section: Methodsmentioning

confidence: 99%

Safety culture and incident learning systems in radiation oncology: Staff perceptions across Australia and New Zealand

et al. 2022

View full text Add to dashboard Cite

Introduction Radiation therapy has a highly complex pathway and uses detailed quality assurance protocols and incident learning systems (ILSs) to mitigate risk; however, errors can still occur. The safety culture (SC) in a department influences its commitment and effectiveness in maintaining patient safety. Methods Perceptions of SC and knowledge and understanding of ILSs and their use were evaluated for radiation oncology staff across Australia and New Zealand (ANZ). A validated healthcare survey tool (the Hospital Survey on Patient Safety Culture) was used, with additional specialty‐focussed supporting questions. A total of 220 radiation oncologists, radiation therapists and radiation oncology medical physicists participated. Results An overall positive SC was indicated, with strength in teamwork (83.7%), supervisor/manager/leader support (83.3%) and reporting events (77.1%). The weakest areas related to communication about error (63.9%), hospital‐level management support (60.5%) and handovers and information exchange (58.0%). Barriers to ILS use included ‘it takes too long’ and that many respondents must use multiple reporting systems, including mandatory hospital‐level systems. These are generally not optimal for specific radiation oncology needs. Varied understanding was indicated of what and when to report. Conclusion The findings report the ANZ perspective on ILS and SC, highlighting weaknesses, barriers and areas for further investigation. Differences observed in some areas suggest that a unified state, national or bi‐national ILS specific to radiation oncology might eliminate multiple reporting systems and reduce reporting time. It could also provide more consistent and robust approaches to incident reporting, information sharing and analysis.

show abstract

Section: Methodsmentioning

confidence: 99%

Safety culture and incident learning systems in radiation oncology: Staff perceptions across Australia and New Zealand

et al. 2022

View full text Add to dashboard Cite

show abstract

“…In 2016, our group incorporated the NSIR-RT taxonomy into an open-source ILS software called the Safety and Incident Learning System (SaILS) 13,14 and deployed it in our radiotherapy center as part of a quality and safety improvement project. 15 As shown in Figure 1a, the SaILS incident reporting interface includes only a small number of data elements, in order to facilitate rapid submission of incidents into the database. The most substantial component of the initial incident report is the incident description, which is a free-text description of the incident that should be written in a no-blame manner.…”

Section: Introductionmentioning

confidence: 99%

“…In 2016, our group incorporated the NSIR‐RT taxonomy into an open‐source ILS software called the Safety and Incident Learning System (SaILS) 13 , 14 and deployed it in our radiotherapy center as part of a quality and safety improvement project. 15 …”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

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

Mathew

Wang²,

Montgomery

et al. 2021

J Applied Clin Med Phys

Self Cite

View full text Add to dashboard Cite

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.

show abstract

“…Zur Aufzeichnung unbeabsichtigter Expositionen und Beinahe-Expositionen dienen Meldesysteme, sogenannte Critical Incident Reporting Systems (CIRS), die bislang überwiegend im Bereich allgemeiner klinischer Prozesse sowie der Strahlentherapie bekannt sind 2 3 4 5 6 7 8 9 . In der Röntgendiagnostik wurde bisher wenig zu diesem Thema veröffentlicht.…”

Section: Introductionunclassified

Handling of Incidents in the Clinical Application of Ionizing Radiation in Diagnostic and Interventional Radiology – a Multi-center Study

Müller

Singer

Stamm

et al. 2021

Rofo

View full text Add to dashboard Cite

Purpose According to the German legislation and regulation of radiation protection, i. e. Strahlenschutzgesetz und Strahlenschutzverordnung (StrlSchG and StrlSchV), which came into force on 31st December 2018, significant unintended or accidential exposures have to be reported to the competent authority. Furthermore, facilities have to implement measures to prevent and to recognize unintended or accidental exposures as well as to reduce their consequences. We developed a process to register incidents and tested its application in the framework of a multi-center-study. Materials and Methods Over a period of 12 months, 16 institutions for x-ray diagnostics and interventions, documented their incidents. Documentation of the incidents was conducted using the software CIRSrad, which was developed, released for testing purposes and implemented in the frame of the study. Reporting criteria of the project were selected to be more sensitive compared to the legal criteria specifying “significant incidents”. Reported incidents were evaluated after four, eight, and twelve months. Finally, all participating institutions were interviewed on their experience with the software and the correlated effort. Results The rate of reported incidents varied between institutions as well as between modalities. The majority of incidents were reported in conventional x-ray imaging, followed by computed tomography and therapeutic interventions. Incidents were attributed to several different causes, amongst others to the technical setup and patient positioning (19 %) and patient movement or insufficient cooperativeness of the patient (18 %). Most incidents were below corresponding thresholds stated in StrlSchV. The workload for documenting the incidents was rated as appropriate. Conclusion It is possible to monitor and handle incidents complient with legal requirements with an acceptable effort. The number of reported incidents can be increased by frequent trainings on the detection and the processing workflow, on the software and legal regulation as well as by a transparent error handling within the institution. Key Points: Citation Format

show abstract

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

Cited by 8 publications

References 12 publications

Safety culture and incident learning systems in radiation oncology: Staff perceptions across Australia and New Zealand

Safety culture and incident learning systems in radiation oncology: Staff perceptions across Australia and New Zealand

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

Handling of Incidents in the Clinical Application of Ionizing Radiation in Diagnostic and Interventional Radiology – a Multi-center Study

Contact Info

Product

Resources

About