Evaluation of near-miss and adverse events in radiation oncology using a comprehensive causal factor taxonomy

Practical Implementation of Emergent After-Hours Radiation Treatment Process Using Remote Treatment Planning on Optimized Diagnostic CT Scans

The purpose of this report is to present the implementation of a process for after-hours radiation treatment (RT) utilizing remote treatment planning based on optimized diagnostic computed tomography (CT) scans for the urgent palliative treatment of inpatients. A standardized operating procedure was developed by an interprofessional panel to improve the quality of after-hours RT and minimize the risk of treatment errors. A new diagnostic CT protocol was created that could be performed after-hours on hospital scanners and would ensure a reproducible patient position and adequate field of view. An on-call structure for dosimetry staff was created utilizing remote treatment planning. The optimized CT protocol was developed in collaboration with the radiology department, and a novel order set was created in the electronic health system. The clinical workflow begins with the radiation oncologist notifying the on-call team (therapist, dosimetrist, and physicist) and obtaining an optimized diagnostic CT scan on a hospital-based scanner. The dosimetrist remotely creates a plan; the physicist checks the plan; and the patient is treated. Plans are intentionally simple (parallel opposed fields, symmetric jaws) to expedite care and reduce the risk of error. Education on the new process was provided for all relevant staff. Our process was successfully implemented with the use of an optimized CT protocol and remote treatment planning. This approach has the potential to improve the quality and safety of emergent after-hours RT by better approximating the normal process of care.

Quality and Safety With Technological Advancements in Radiotherapy: An Overview and Journey Narrative From a Low- and Middle-Income Country Institution

To present an overview of quality and safety in radiotherapy from the context of low- and middle-income countries on the basis of a recently conducted annual meeting of our institution and our experience of implementing an error management system at our center.

Quality and safety improvement with evolving technology in LMIC, a journey described.

Root Cause Analysis Using the Prevention and Recovery Information System for Monitoring and Analysis Method in Healthcare Facilities: A Systematic Literature Review

OBJECTIVES

Unintended events (UEs) are prevalent in healthcare facilities, and learning from them is key to improve patient safety. The Prevention and Recovery Information System for Monitoring and Analysis (PRISMA)-method is a root cause analysis method used in healthcare facilities. The aims of this systematic review are to map the use of the PRISMA-method in healthcare facilities worldwide, to assess the insights that the PRISMA-method offers, and to propose recommendations to increase its usability in healthcare facilities.

METHODS

PubMed, EMBASE.com, CINAHL, and The Cochrane Library were systematically searched from inception to February 26, 2020. Studies were included if the PRISMA-method for analyzing UEs was applied in healthcare facilities. A quality appraisal was performed, and relevant data based on an appraisal checklist were extracted.

RESULTS

The search provided 2773 references, of which 25 articles reporting 10,816 UEs met our inclusion criteria. The most frequently identified root causes were human-related, followed by organizational factors. Most studies took place in the Netherlands (n = 20), and the sample size ranged from 1 to 2028 UEs. The study setting and collected data used for PRISMA varied widely. The PRISMA-method performed by multiple persons resulted in more root causes per event.

CONCLUSIONS

To better understand UEs in healthcare facilities and formulate optimal countermeasures, our recommendations to further improve the PRISMA-method mainly focus on combining information from patient files and reports with interviews, including multiple PRISMA-trained researchers in an analysis, and modify the Eindhoven Classification Model if needed.

The role of surface-guided radiation therapy for improving patient safety

Emerging data indicates SGRT could improve safety and quality by preventing errors in its capacity as an independent system in the treatment room. The aim of this work is to investigate the utility of SGRT in the context of safety and quality. Three incident learning systems (ILS) were reviewed to categorize and quantify errors that could have been prevented with SGRT: SAFRON (International Atomic Energy Agency), UW-ILS (University of Washington) and AvIC (Skåne University Hospital). A total of 849/9737 events occurred during the pre-treatment review/verification and treatment stages. Of these, 179 (21%) events were predicted to have been preventable with SGRT. The most common preventable events were wrong isocentre (43%) and incorrect accessories (34%), which appeared at comparable rates among SAFRON and UW-ILS. The proportion of events due to wrong accessories was much smaller in the AvIC ILS, which may be attributable to the mandatory use of SGRT in Sweden. Several case scenarios are presented to demonstrate that SGRT operates as a valuable complement to other quality-improvement tools routinely used in radiotherapy. Cases are noted in which SGRT itself caused incidents. These were mostly related to workflow issues and were of low severity. Severity data indicated that events with the potential to be mitigated by SGRT were of higher severity for all categories except wrong accessories. Improved vendor integration of SGRT systems within the overall workflow could further enhance its clinical utility. SGRT is a valuable tool with the potential to increase patient safety and treatment quality in radiotherapy.

Initial Clinical Experience of Cherenkov Imaging in External Beam Radiation Therapy Identifies Opportunities to Improve Treatment Delivery

PURPOSE

The value of Cherenkov imaging as an on-patient, real-time, treatment delivery verification system was examined in a 64-patient cohort during routine radiation treatments in a single-center study.

METHODS AND MATERIALS

Cherenkov cameras were mounted in treatment rooms and used to image patients during their standard radiation therapy regimen for various sites, predominantly for whole breast and total skin electron therapy. For most patients, multiple fractions were imaged, with some involving bolus or scintillators on the skin. Measures of repeatability were calculated with a mean distance to conformity (MDC) for breast irradiation images.

RESULTS

In breast treatments, Cherenkov images identified fractions when treatment delivery resulted in dose on the contralateral breast, the arm, or the chin and found nonideal bolus positioning. In sarcoma treatments, safe positioning of the contralateral leg was monitored. For all 199 imaged breast treatment fields, the interfraction MDC was within 7 mm compared with the first day of treatment (with only 7.5% of treatments exceeding 3 mm), and all but 1 fell within 7 mm relative to the treatment plan. The value of imaging dose through clear bolus or quantifying surface dose with scintillator dots was examined. Cherenkov imaging also was able to assess field match lines in cerebral-spinal and breast irradiation with nodes. Treatment imaging of other anatomic sites confirmed the value of surface dose imaging more broadly.

CONCLUSIONS

Daily radiation therapy can be imaged routinely via Cherenkov emissions. Both the real-time images and the posttreatment, cumulative images provide surrogate maps of surface dose delivery that can be used for incident discovery and/or continuous improvement in many delivery techniques. In this initial 64-patient cohort, we discovered 6 minor incidents using Cherenkov imaging; these otherwise would have gone undetected. In addition, imaging provides automated, quantitative metrics useful for determining the quality of radiation therapy delivery.

Benchmarking failure mode and effects analysis of electronic brachytherapy with data from incident learning systems

PURPOSE

Failure modes and effects analysis (FMEA) is a prospective risk assessment tool for identifying failure modes in equipment or processes and informing the design of quality control systems. This work aims to benchmark the performance of FMEAs for electronic brachytherapy (eBT) of the skin and for breast by comparing predicted versus actual failure modes reported in multiple incident learning systems (ILS).

METHODS AND MATERIALS

Two public and our institution's internal ILS were queried for Xoft Axxent eBT-related events over 9 years. The failure modes and Risk Priority Numbers (RPNs) were taken from FMEAs previously performed for Xoft eBT of nonmelanoma skin cancer and breast intraoperative radiation therapy (IORT). For each event, the treatment site and primary failure mode was compared with the failure modes and RPNs from that site's FMEA.

RESULTS

49 events involving Xoft eBT were identified. Thirty-one (63.3%) involved breast IORT, and 18 (36.7%) involved the skin. Three events could not be linked to an FMEA failure mode. In 87.7% of events, the primary failure mode ranked in the FMEA top 10 by RPNs. In 83.3% of skin events, the failure modes ranked in the top 10 by RPN or severity. In 90.3% of IORT events, the failure modes ranked within the top 10 by RPN or severity.

CONCLUSIONS

Evaluating FMEA failure modes against ILS data demonstrates that FMEA is effective at predicting failure modes but can be dependent on user experience. ILS data can improve FMEA by identifying potential failure modes and suggesting realistic occurrence, detectability, and severity values.

The Emergence of Artificial Intelligence within Radiation Oncology Treatment Planning

BACKGROUND

The future of artificial intelligence (AI) heralds unprecedented change for the field of radiation oncology. Commercial vendors and academic institutions have created AI tools for radiation oncology, but such tools have not yet been widely adopted into clinical practice. In addition, numerous discussions have prompted careful thoughts about AI's impact upon the future landscape of radiation oncology: How can we preserve innovation, creativity, and patient safety? When will AI-based tools be widely adopted into the clinic? Will the need for clinical staff be reduced? How will these devices and tools be developed and regulated?

SUMMARY

In this work, we examine how deep learning, a rapidly emerging subset of AI, fits into the broader historical context of advancements made in radiation oncology and medical physics. In addition, we examine a representative set of deep learning-based tools that are being made available for use in external beam radiotherapy treatment planning and how these deep learning-based tools and other AI-based tools will impact members of the radiation treatment planning team. Key Messages: Compared to past transformative innovations explored in this article, such as the Monte Carlo method or intensity-modulated radiotherapy, the development and adoption of deep learning-based tools is occurring at faster rates and promises to transform practices of the radiation treatment planning team. However, accessibility to these tools will be determined by each clinic's access to the internet, web-based solutions, or high-performance computing hardware. As seen by the trends exhibited by many technologies, high dependence on new technology can result in harm should the product fail in an unexpected manner, be misused by the operator, or if the mitigation to an expected failure is not adequate. Thus, the need for developers and researchers to rigorously validate deep learning-based tools, for users to understand how to operate tools appropriately, and for professional bodies to develop guidelines for their use and maintenance is essential. Given that members of the radiation treatment planning team perform many tasks that are automatable, the use of deep learning-based tools, in combination with other automated treatment planning tools, may refocus tasks performed by the treatment planning team and may potentially reduce resource-related burdens for clinics with limited resources.

Prevention of Radiation Therapy Treatment Deviations by a Novel Combined Biometric, Radiofrequency Identification, and Surface Imaging System

PURPOSE

To evaluate the impact of Varian Identify, a novel combined radiofrequency identification, biometric and surface-matching technology, on its potential for patient safety and prevention of radiation therapy treatment deviations.

METHODS AND MATERIALS

One hundred eight radiation therapy treatment deviation reports at our facility over the past 8 years were analyzed. Three major categories were defined based on the time point of occurrence: physician order deviations (19.4%), treatment-planning deviations (24.1%), and machine treatment deviations (56.5%). The impact of Identify on potential prevention of machine treatment deviations was analyzed. A failure mode and effects analysis was performed on the 5 most frequently occurring errors preventable with Identify. Safety analysis of the Identify system was reported based on 3.5 years of clinical data post-Identify system installation on 3 treatment vaults.

RESULTS

Of the 61 machine treatment deviations, 47 (77%) were interpreted as being preventable by using Identify. Our failure mode and effects analysis showed reductions in all risk priority numbers post-Identify application. Safety analysis of the Identify system from our direct observation that for approximately 7 cumulative years of Identify use in 3 different treatment vaults, where 9 deviations would have been expected to occur over this combined period, zero machine treatment events occurred.

CONCLUSIONS

The combination of Identify biometric, radiofrequency identification, and surface-matching technologies was observed to enable an effective process for enhancing safety and efficiency of radiation therapy treatment. A significant reduction in machine-related deviations was observed.

The journey towards safer radiotherapy: are we on a road to nowhere?

Background:

Harnessing available knowledge and learning from our errors are prerequisites of delivering on the challenge of improving patient safety. Towards Safer Radiotherapy, published in 2008, was a response from the UK’s (UK) radiotherapy community to concerns arising from high profile errors. The report was a driver for the development of a national reporting and learning system for radiotherapy.

Materials and methods:

A literature review was conducted covering the years from 2009 to 2020. Search terms used were radiotherapy errors, patient safety, incident learning, human factors and trend analysis. A total of 10 papers reported recommendations or implementation of changes to service delivery models following systematic error analysis. None of these were from UK service providers.

Conclusions:

Twelve years on from the publication of Towards Safer Radiotherapy, there is little evidence of impact on safety culture within the UK radiotherapy community. Although the UK has a large radiotherapy error dataset, there remain unanswered questions about the impact on the safety culture in radiotherapy.

Pediatric Anesthesia Outside the Operating Room: Case Management

Anesthesiology teams care for children in diverse locations, including diagnostic and interventional radiology, gastroenterology and pulmonary endoscopy suites, radiation oncology units, and cardiac catheterization laboratories. To provide safe, high-quality care, anesthesiologists working in these environments must understand the unique environmental and perioperative considerations and risks involved with each remote location and patient population. Once these variables are addressed, anesthesia and procedural teams can coordinate to ensure that patients and families receive the same high-quality care that they have come to expect in the operating room. This article also describes some of the considerations for anesthetic care in outfield locations.

Strategies for effective physics plan and chart review in radiation therapy: Report of AAPM Task Group 275

BACKGROUND

While the review of radiotherapy treatment plans and charts by a medical physicist is a key component of safe, high-quality care, very few specific recommendations currently exist for this task.

AIMS

The goal of TG-275 is to provide practical, evidence-based recommendations on physics plan and chart review for radiation therapy. While this report is aimed mainly at medical physicists, others may benefit including dosimetrists, radiation therapists, physicians and other professionals interested in quality management.

METHODS

The scope of the report includes photon/electron external beam radiotherapy (EBRT), proton radiotherapy, as well as high-dose rate (HDR) brachytherapy for gynecological applications (currently the highest volume brachytherapy service in most practices). The following review time points are considered: initial review prior to treatment, weekly review, and end-of-treatment review. The Task Group takes a risk-informed approach to developing recommendations. A failure mode and effects analysis was performed to determine the highest-risk aspects of each process. In the case of photon/electron EBRT, a survey of all American Association of Physicists in Medicine (AAPM) members was also conducted to determine current practices. A draft of this report was provided to the full AAPM membership for comment through a 3-week open-comment period, and the report was revised in response to these comments.

RESULTS

The highest-risk failure modes included 112 failure modes in photon/electron EBRT initial review, 55 in weekly and end-of-treatment review, 24 for initial review specific to proton therapy, and 48 in HDR brachytherapy. A 103-question survey on current practices was released to all AAPM members who self-reported as working in the radiation oncology field. The response rate was 33%. The survey data and risk data were used to inform recommendations.

DISCUSSION

Tables of recommended checks are presented and recommendations for best practice are discussed. Suggestions to software vendors are also provided.

CONCLUSIONS

TG-275 provides specific recommendations for physics plan and chart review which should enhance the safety and quality of care for patients receiving radiation treatments.

Collision prediction for intracranial stereotactic radiosurgery planning: An easy-to-implement analytical solution

PURPOSE

Gantry collision is a concern in linac-based stereotactic radiosurgery (SRS). Without collision screening, the planner may compromise optimal planning, unnecessary re-planning delays can occur, and incomplete treatments may be delivered. To address these concerns, we developed a software for collision prediction based on simple machine measurements.

MATERIALS AND METHODS

Three types of collision were identified; gantry-couch mount, gantry-couch and gantry-patient. Trigonometric formulas to calculate the distance from each potential point of collision to the gantry rotation axis were generated. For each point, collision occurs when that distance is greater than the gantry head to gantry rotational axis distance. The colliding arc for each point is calculated. A computer code incorporating these formulas was generated. The inputs required are the couch coordinates relative to the isocenter, the patient dimensions, and the presence or absence of a circular SRS collimator. The software outputs the collision-free gantry angles, and for each point, the shortest distance to the gantry or the colliding sector when collision is identified. The software was tested for accuracy on a TrueBEAM® machine equipped with BrainLab® accessories for 80 virtual isocenter-couch angle configurations with and without a circular collimator and a parallelepiped phantom.

RESULTS

The software predicted the absence of collision for 19 configurations. The mean absolute error between the measured and predicted gantry angle of collision for the remaining 61 cases was 0.86 (0.01-2.49).

CONCLUSION

This tool accurately predicted collisions for linac-based intracranial SRS and is easy to implement in any radiotherapy facility.

The Fusion of Incident Learning and Failure Mode and Effects Analysis for Data-Driven Patient Safety Improvements

PURPOSE

Incident learning is a critical part of the quality improvement process for all radiation therapy clinics. Failure mode and effects analysis has also been adopted as a hazard analysis method within the field of radiation oncology based on the recommendations of American Association of Physicists in Medicine Task Group 100. In this work, we demonstrate a fusion of these techniques that is efficient and transferrable to all types of clinics and that allows data-driven targeting of the highest risk error types.

METHODS AND MATERIALS

Four clinical physicists recorded safety events detected during physics treatment plan quality assurance over a 27-month period. Events were sorted into the broad categories of either a documentation or plan construction error. Events were further stratified into subcategories until sufficiently discriminated against for analysis. Event risks were quantified using reduced-resolution TG-100 severity scores combined with observed occurrence rates. The highest risk categories were examined for intervention strategies.

RESULTS

A total of 871 events were identified over the study period. Of these, 652 (74.9%) were classified as low severity, 178 (20.4%) as medium severity, and 41 (4.7%) as high severity. Four of the top 5 ranked categories could be targeted by a preplanning chart rounds. Several of the categories could be targeted by additional automation in the planning and QA processes.

CONCLUSIONS

The retrospective classification and risk analysis of safety events allows clinics to design targeted workflow and quality assurance changes aimed at reducing the occurrence of high-risk events. The method presented here leverages incident learning efforts that many clinics are already performing, allows the severity of events to be efficiently assigned, and generates actionable results without requiring a complete failure mode and effects analysis.

Technical Report: Diagnostic Scan-Based Planning (DSBP), A Method to Improve the Speed and Safety of Radiation Therapy for the Treatment of Critically Ill Patients

PURPOSE

Treating critically ill patients in radiation oncology departments poses multiple safety risks. This study describes a method to improve the speed of radiation treatment for patients in the intensive care unit by eliminating the need for computed tomography (CT) simulation or on-table treatment planning using patients' previously acquired diagnostic CT scans.

METHODS AND MATERIALS

Initially, a retrospective planning study was performed to assess the applicability and safety of diagnostic scan-based planning (DSBP) for 3 typical indications for radiation therapy in patients in the intensive care unit: heterotopic ossification (10), spine metastases (cord compression; 10), and obstructive lung lesions (5). After identification of an appropriate diagnostic CT scan, treatment planning was performed using the diagnostic scan data set. These treatment plans were then transferred to the patients' simulation scans, and a dosimetric comparison was performed between the 2 sets of plans. Additionally, a time study of the first 10 patients treated with DSBP in our department was performed.

RESULTS

The retrospective analysis demonstrated that DSBP resulted in treatment plans that, when transferred to the CT simulation data sets, provided excellent target coverage, a median D_95% of 96% (range, 86%-100%) of the prescription dose with acceptable hot spots, and a median D_max108% (range, 102%-113%). Subsequently, DSBP has been used for 10 critically ill patients. The patients were treated without CT simulation, and the median time between patient check-in to the department and completion of radiation therapy was 28 minutes (range, 18-47 minutes.) CONCLUSIONS: This study demonstrates that it is possible to safely use DSBP for the treatment of critically ill patients. This method has the potential to simplify the treatment process and improve the speed and safety of treatment.

A Radiation Oncology-Specific Automated Trigger Indicator Tool for High-Risk, Near-Miss Safety Events

PURPOSE

Error detection in radiation oncology relies heavily on voluntary reporting, and many adverse events and near misses likely go undetected. Trigger tools use existing data in patient charts to identify otherwise-unaccounted-for events and have been successfully employed in other areas of medicine. We developed an automated radiation oncology-specific trigger tool and validated it against near-miss data from a high-volume incident learning system (ILS).

METHODS AND MATERIALS

Twenty triggers were derived from an electronic radiation oncology information system. Data from the systems over an approximately 3.5-year period were split randomly into training and test sets. The probability of a high-grade (grade 3-4) near miss for each treatment course in the training set was estimated using a regularized logistic regression model. The predictive model was applied to the test set. Records for 25 flagged treatment courses with an ILS entry were reviewed to explore the association between triggers and near misses, and 25 flagged courses without an ILS entry were reviewed to detect unreported near misses.

RESULTS

Of the 3159 treatment courses analyzed, 357 had a grade 3 to 4 ILS entry; 2210 courses composed the training set, and the test set had 949 courses. Areas under the curve on the training and test sets were 0.650 and 0.652, respectively. Of 20 triggers, 9 reached statistical significance on univariate analysis. Fifty percent of the 25 treatment courses in the test set with the highest predicted likelihood of a high-grade near miss with an ILS entry had a direct relationship between the triggers and the near miss. Review of the 25 treatment courses with the highest predicted likelihood of high-grade near miss without an ILS entry found 2 unreported near-miss events.

CONCLUSIONS

The radiation oncology-specific automated trigger tool performed modestly and identified additional treatment courses with near-miss events. Radiation oncology trigger tools deserve further exploration.

A Qualitative Analysis of Human Error During the DIBH Procedure

INTRODUCTION

This quality assurance study analyzed human errors that occurred during the radiation treatment delivery of the deep-inspiration breath hold (DIBH) technique at a tertiary cancer centre. The intention is to recommend solutions and system changes that have the potential to decrease the frequency of errors based on human factors principles.

METHODS

Eighty-two incident reports from January 2012 to July 2017 were retrieved and analysed to determine theme bins of performance-influencing factors contributing to the error. Performance-influencing factors were generated from the incident reports and from focus group discussions with volunteer radiation therapists in the department. Potential solutions to mitigate the error were sought from incident reports, focus groups, literature search, and an interview with a human factors specialist. The solutions were ranked based on the hierarchy of effectiveness, and recommendations were classified using a priority matrix.

RESULTS

Eighty-nine percent of the errors captured in the incident reports were defined as a slip or lapse error type, and 11% of the remaining errors were defined as a mistake error type. Treatment-related problem solving and distractions/interruptions were the highest frequency causative factors that contributed to the observed error. Potential solutions that were suggested across sources included implementing a forcing function, such as the real-time position management system, adding reminders, such as a console sign-off, and updating the current task checklist.

DISCUSSION

The potential solutions generated were summarized into four recommendations that have varying degrees of association with known causative factors. The four recommendations include investing in (1) a forcing function, (2) updating/reinforcing the procedure, (3) managing workload, and (4) updating the checklist. A priority matrix was used to assess both potential effectiveness and cost/effort of each recommendation. Ideally, recommendation 1 would be implemented; however, it is understood that there would be an associated cost. It is therefore suggested that recommendations 2, 3, and 4 are implemented together to increase the effectiveness of the intervention until recommendation 1 can be achieved.

CONCLUSION

This qualitative study introduced a method that analyzed human factors in a specialized procedure used in the treatment of a specific population of patients with cancer. Recommendations were formulated and proposed to the radiation therapy department in hopes of potentially decreasing the frequency of this specific error in the future.

Collision Risk Mitigation of Varian TrueBeam Linear Accelerator With Supplemental Live-View Cameras

BACKGROUND

Noncoplanar radiation therapy techniques such as 4π have potential dosimetric advantages but introduce complexities in treatment delivery that increase the risk for collision. Direct or remote visual confirmation of clearance is a safeguard against collisions of the gantry, couch, and patient. With our institution's Varian TrueBeam system, we identified configurations that cannot be visualized with the included closed-circuit television cameras. At our practice, electronic, portal imaging device (EPID) collision risk also exists because of the routine deployment to capture exit-dose images for treatment quality assurance. We propose a simple, cost-effective solution using network cameras to help eliminate blind spots that permits safe, noncoplanar arrangements with an EPID-acquired exit dose.

METHODS AND MATERIALS

Two Panasonic cameras were installed overhead while a third Panasonic camera was mounted onto the pedestal to monitor the couch undersurface. Live views from each camera were accessed with a web-based client. The EPID and gantry were visually assessed at 52 couch and gantry rotational angle configurations at 6 couch translational positions. Visibility was compared for the standard and supplemental camera setups at each configuration (χ² test).

RESULTS

Of the 294 assessable couch-gantry configurations, the standard camera setup had limited visibility of either gantry or EPID for 146 configurations compared with 72 configurations with additional cameras (51% blind-spot reduction; P < .01). An 87% blind-spot reduction was observed for our laterally centered, cranial-based, couch translational position (P < .01).

CONCLUSIONS

The supplemental cameras were simple, effective additions for collision detection, especially for noncoplanar radiation therapy with EPID-acquired, exit-dose imaging. Over half of the assessable noncoplanar configurations had blind spots using standard cameras, which was reduced to <25% with additional cameras. In practice, there were almost no blind spots for patients with brain tumors who were treated with our templated beam arrangements. Using live-view camera feeds, vault re-entry to visually confirm clearance was reduced approximately 10-fold, which increased the treatment efficiency. In the most recent 12 months, no collision or near-collision events have been reported.

Risk factors for near-miss events and safety incidents in pediatric radiation therapy

BACKGROUND AND PURPOSE

Factors contributing to safety- or quality-related incidents (e.g. variances) in children are unknown. We identified clinical and RT treatment variables associated with risk for variances in a pediatric cohort.

MATERIALS AND METHODS

Using our institution's incident learning system, 81 patients age ≤21 years old who experienced variances were compared to 191 pediatric patients without variances. Clinical and RT treatment variables were evaluated as potential predictors for variances using univariate and multivariate analyses.

RESULTS

Variances were primarily documentation errors (n = 46, 57%) and were most commonly detected during treatment planning (n = 14, 21%). Treatment planning errors constituted the majority (n = 16 out of 29, 55%) of near-misses and safety incidents (NMSI), which excludes workflow incidents. Therapists reported the majority of variances (n = 50, 62%). Physician cross-coverage (OR = 2.1, 95% CI = 1.04-4.38) and 3D conformal RT (OR = 2.3, 95% CI = 1.11-4.69) increased variance risk. Conversely, age >14 years (OR = 0.5, 95% CI = 0.28-0.88) and diagnosis of abdominal tumor (OR = 0.2, 95% CI = 0.04-0.59) decreased variance risk.

CONCLUSIONS

Variances in children occurred in early treatment phases, but were detected at later workflow stages. Quality measures should be implemented during early treatment phases with a focus on younger children and those cared for by cross-covering physicians.

Incident learning in radiation oncology: A review

Incident learning is a key component for maintaining safety and quality in healthcare. Its use is well established and supported by professional society recommendations, regulations and accreditation, and objective evidence. There is an active interest in incident learning systems (ILS) in radiation oncology, with over 40 publications since 2010. This article is intended as a comprehensive topic review of ILS in radiation oncology, including history and summary of existing literature, nomenclature and categorization schemas, operational aspects of ILS at the institutional level including event handling and root cause analysis, and national and international ILS for shared learning. Core principles of patient safety in the context of ILS are discussed, including the systems view of error, culture of safety, and contributing factors such as cognitive bias. Finally, the topics of medical error disclosure and second victim syndrome are discussed. In spite of the rapid progress and understanding of ILS, challenges remain in applying ILS to the radiation oncology context. This comprehensive review may serve as a springboard for further work.