Dataset Information

Application of an incident taxonomy for radiation therapy: Analysis of five years of data from three integrated cancer centres.

ABSTRACT: Aim:To develop and apply a clinical incident taxonomy for radiation therapy. Background:Capturing clinical incident information that focuses on near-miss events is critical for achieving higher levels of safety and reliability. Methods and materials:A clinical incident taxonomy for radiation therapy was established; coding categories were prescription, consent, simulation, voluming, dosimetry, treatment, bolus, shielding, imaging, quality assurance and coordination of care. The taxonomy was applied to all clinical incidents occurring at three integrated cancer centres for the years 2011-2015. Incidents were managed locally, audited and feedback disseminated to all centres. Results:Across the five years the total incident rate (per 100 courses) was 8.54; the radiotherapy-specific coded rate was 6.71. The rate of true adverse events (unintended treatment and potential patient harm) was 1.06. Adverse events, where no harm was identified, occurred at a rate of 2.76 per 100 courses. Despite workload increases, overall and actual rates both exhibited downward trends over the 5-year period. The taxonomy captured previously unidentified quality assurance failures; centre-specific issues that contributed to variations in incident trends were also identified. Conclusions:The application of a taxonomy developed for radiation therapy enhances incident investigation and facilitates strategic interventions. The practice appears to be effective in our institution and contributes to the safety culture. The ratio of near miss to actual incidents could serve as a possible measure of incident reporting culture and could be incorporated into large scale incident reporting systems.

SUBMITTER: Greenham S

PROVIDER: S-EPMC5948319 | biostudies-literature | 2018 May-Jun

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Application of an incident taxonomy for radiation therapy: Analysis of five years of data from three integrated cancer centres.

Greenham Stuart S Manley Stephen S Turnbull Kirsty K Hoffmann Matthew M Fonseca Amara A Westhuyzen Justin J Last Andrew A Aherne Noel J NJ Shakespeare Thomas P TP

Reports of practical oncology and radiotherapy : journal of Greatpoland Cancer Center in Poznan and Polish Society of Radiation Oncology 20180510 3

<h4>Aim</h4>To develop and apply a clinical incident taxonomy for radiation therapy.<h4>Background</h4>Capturing clinical incident information that focuses on near-miss events is critical for achieving higher levels of safety and reliability.<h4>Methods and materials</h4>A clinical incident taxonomy for radiation therapy was established; coding categories were prescription, consent, simulation, voluming, dosimetry, treatment, bolus, shielding, imaging, quality assurance and coordination of care. ...[more]

PMID: 29760597

Similar Datasets

Project description:Collaborative 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).The 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.There 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.Use 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.

Project description:Background and Purpose: During the months and years post-stroke, treatment benefits from endovascular therapy (EVT) may be magnified by disability-related differences in morbidity/mortality or may be eroded by recurrent strokes and non-stroke-related disability/mortality. Understanding the extent to which EVT benefits may be sustained at 5 years, and the factors influencing this outcome, may help us better promote the sustenance of EVT benefits until 5 years post-stroke and beyond. Methods: In this review, undertaken 5 years after EVT became the standard of care, we searched PubMed and EMBASE to examine the current state of the literature on 5-year post-stroke outcomes, with particular attention to modifiable factors that influence outcomes between 3 months and 5 years post-EVT. Results: Prospective cohorts and follow-up data from EVT trials indicate that 3-month EVT benefits will likely translate into lower 5-year disability, mortality, institutionalization, and care costs and higher quality of life. However, these group-level data by no means guarantee maintenance of 3-month benefits for individual patients. We identify factors and associated "action items" for stroke teams/systems at three specific levels (medical care, individual psychosocioeconomic, and larger societal/environmental levels) that influence the long-term EVT outcome of a patient. Medical action items include optimizing stroke rehabilitation, clinical follow-up, secondary stroke prevention, infection prevention/control, and post-stroke depression care. Psychosocioeconomic aspects include addressing access to primary care, specialist clinics, and rehabilitation; affordability of healthy lifestyle choices and preventative therapies; and optimization of family/social support and return-to-work options. High-level societal efforts include improving accessibility of public/private spaces and transportation, empowering/engaging persons with disability in society, and investing in treatments/technologies to mitigate consequences of post-stroke disability. Conclusions: In the longtime horizon from 3 months to 5 years, several factors in the medical and societal spheres could negate EVT benefits. However, many factors can be leveraged to preserve or magnify treatment benefits, with opportunities to share responsibility with widening circles of care around the patient.

Project description:We examined the adequacy of existing shielding guidelines using five-year clinical data from a busy CyberKnife center. From June 2006 through July 2011, 1,370 patients were treated with a total of 4,900 fractions and 680,691 radiation beams using a G4 CyberKnife. Prescription dose and total monitor units (MU) were analyzed to estimate the shielding workload and modulation factor. In addition, based on the beam's radiation source position, targeting position, MU, and beam collimator size, the MATLAB program was used to project each beam toward the shielding barrier. The summation of the projections evaluates the distribution of the shielding load. On average, each patient received 3.6 fractions, with an average 9.1 Gy per fraction prescribed at the 71.1% isodose line, using 133.7 beams and 6,200 MU. Intracranial patients received an average of 2.7 fractions, with 8.6 Gy per fraction prescribed at the 71.4% isodose line, using 133 beams and 5,083 MU. Extracranial patients received an average of 3.94 fractions, with 9.2 Gy per frac- tion prescribed at the 71% isodose line, using 134 beams and 6,514 MU. Most- used collimator sizes for intracranial patients were smaller (7.5 to 20 mm) than for extracranial patients (20 to 40 mm). Eighty-five percent of the beams exited through the floor, and about 40% of the surrounding wall area received no direct beam. For the rest of the wall, we found "hot" areas that received above-average MU. The locations of these areas were correlated with the projection of the nodes for extracranial treatments. In comparison, the beam projections on the wall were more spread for intracranial treatments. The maximum MU any area received from intracranial treatment was less than 0.25% of total MU used for intracranial treatments, and was less than 1.2% of total MU used for extracranial treatments. The combination of workload, modulation factor, and use factor in our practice are about tenfold less than recommendations in the existing CyberKnife shielding guidelines. The current guidelines were found to be adequate for shielding, even in a busy center. There may be a potential to reduce shielding in areas with no or few direct beams in the current G4 model. Since a newer model CyberKnife (M6) has recently been introduced, the patterns of usage reported here may be changed in the future. The uneven distribution of use factor we have found may, however, be considered in the vault design.

Dataset Information

Application of an incident taxonomy for radiation therapy: Analysis of five years of data from three integrated cancer centres.

Publications

Application of an incident taxonomy for radiation therapy: Analysis of five years of data from three integrated cancer centres.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure