The Impact of New Technologies on Radiation Oncology Events and Trends in the Past Decade: An Institutional Experience

Errors in manual radiotherapy treatment procedures and their evolution in a low resource setting: Uganda's experience

PURPOSE

In Uganda, two-dimensional (2D) radiotherapy treatments have been in use since the establishment of radiotherapy in 1995. Preliminary investigations of treatment records in November 2019 showed evidence of gaps requiring urgent attention. The purpose of this study was to improve the safety of the treatments.

METHODS

Records of 1164 patients treated in 1387 courses (1412 sites) on Cobalt-60 units were reviewed todetermine the frequency and dosimetric implications of events that occurred at different stepsof the radiotherapy process. The results were presented and discussed with the differentprofessionals for learning purposes.

RESULTS

Most common dosimetric eventswere omission of block tray, bolus and couch transmission factors in time calculations, incorrect field sizes and depths, wrong beam weighting, independent calculations and prescription doses contributing 28.6 %, 10.1 %, 6.0 %,11.9 %, 10.1 %, 5.4 %, 4.8 % and 8.9 % to the 168 observed errors. Comparison of the calculated treatment doses with the prescribed doses showed that 88 % of the 1412 sites were treated with radiation doses within an accuracy of ± 5 %. However, an analysis of the evolution along the years demonstrated an improvement from 82.8 % in 2018 to 86.1 % in 2019, and 93.2 % in 2020. Most common procedural events were incomplete setup instructions and missing patient data in the record and verify system of the Co-60 units for 57 % and 60.1 % of the 1164 patients.

CONCLUSIONS

Opportunities for improvement of safety in the delivery of radiotherapy treatments were identified. Learning from these past errors should raise awareness in the team leading to a safer treatments.

The rise of negative portrayals of radiation oncology: A textual analysis of media news

BACKGROUND AND PURPOSE

There has been growing concern about the media's negative portrayal of radiation oncology in recent years. Our study shows changes in media sentiment toward radiotherapy over the years, identifies prevalent themes, and analyzes their shifts over time.

MATERIALS AND METHODS

We analyzed articles about radiation oncology published in The New York Times since the journal's inception in 1851. Initially, we collected 30 427 articles containing the keywords "radiation" or "radiotherapy" up to July 2023. In the next step, we selected 342 articles on radiation oncology using keyword searches, prompting the Chat GPT language model and manual assessment. Ultimately, we created a codebook summarizing the media topics related to radiotherapy and categorized the articles into these categories.

RESULTS

Our analysis identified ten distinct categories representing media themes related to radiation oncology: five negative, three positive, and two neutral. Our findings indicate a rising negative sentiment toward radiotherapy. In the 21st century, over 50% of articles negatively described radiation oncology. The media coverage has shifted its focus away from describing scientific breakthroughs and the implementation of new techniques and toward treatment errors, toxicity, and ineffectiveness.

CONCLUSION

The increasing negative media sentiment surrounding radiation oncology may influence public perceptions and impact patients' decisions. Radiation oncologists should remain vigilant about this situation, ensuring the dissemination of accurate information and addressing negative portrayals.

Using a daily monitoring system to reduce treatment position override rates in external beam radiation therapy

PURPOSE/OBJECTIVES

To report our 7-year experience with a daily monitoring system to significantly reduce couch position overrides and errors in patient treatment positioning.

MATERIALS AND METHODS

Treatment couch position override data were extracted from a radiation oncology-specific electronic medical record system from 2012 to 2018. During this period, we took several actions to reduce couch position overrides, including reducing the number of tolerance tables from 18 to 6, tightening tolerance limits, enforcing time outs, documenting reasons for overrides, and timely reviewing of overrides made from previous treatment day. The tolerance tables included treatment categories for head and neck (HN) (with/without cone beam CT [CBCT]), body (with/without CBCT), stereotactic body radiotherapy (SBRT), and clinical setup for electron beams. For the same time period, we also reported treatment positioning-related incidents that were recorded in our departmental incident report system. To verify our tolerance limits, we further examined couch shifts after daily kilovoltage CBCT (kV-CBCT) for the patients treated from 2018 to 2021.

RESULTS

From 2012 to 2018, the override rate decreased from 11.2% to 1.6%/year, whereas the number of fractions treated in the department increased by 23%. The annual patient positioning error rate was also reduced from 0.019% in 2012, to 0.004% in 2017 and 0% in 2018. For patients treated under daily kV-CBCT guidance from 2018 to 2021, the applied couch shifts after imaging registration that exceeded the tolerance limits were low, <1% for HN, <1.2% for body, and <2.6% for SBRT.

CONCLUSIONS

The daily monitoring system, which enables a timely review of overrides, significantly reduced the number of treatment couch position overrides and ultimately resulted in a decrease in treatment positioning errors. For patients treated with daily kV-CBCT guidance, couch position shifts after CBCT image guidance demonstrated a low rate of exceeding the set tolerance.

The Impact of Artificial Intelligence and Machine Learning in Radiation Therapy: Considerations for Future Curriculum Enhancement

Artificial intelligence (AI) and machine learning (ML) approaches have caught the attention of many in health care. Current literature suggests there are many potential benefits that could transform future clinical workflows and decision making. Embedding AI and ML concepts in radiation therapy education could be a fundamental step in equipping radiation therapists (RTs) to engage in competent and safe practice as they utilise clinical technologies. In this discussion paper, the authors provide a brief review of some applications of AI and ML in radiation therapy and discuss pertinent considerations for radiation therapy curriculum enhancement. As the current literature suggests, AI and ML approaches will impose changes to routine clinical radiation therapy tasks. The emphasis in RT education could be on critical evaluation of AI and ML application in routine clinical workflows and gaining an understanding of the impact on quality assurance, provision of quality of care and safety in radiation therapy as well as research. It is also imperative RTs have a broader understanding of AI/ML impact on health care, including ethical and legal considerations. The paper concludes with recommendations and suggestions to deliberately embed AI and ML aspects in RT education to empower future RT practitioners.

Chasing Zero Harm in Radiation Oncology: Using Pre-treatment Peer Review

Purpose: The Joint Commission has encouraged the healthcare industry to become “High Reliability Organizations” by “Chasing Zero Harm” in patient care. In radiation oncology, the time point of quality checks determines whether errors are prevented or only mitigated. Thus, to “chase zero” in radiation oncology, peer review has to be implemented prior to treatment initiation. A multidisciplinary group consensus peer review (GCPR) model is used pre-treatment at our institution and has been successful in our efforts to “chase zero harm” in patient care.

Methods: With the GCPR model, policy-defined complex cases go through a treatment planning conference, which includes physicians, residents, physicists, and dosimetrists. Three major plan aspects are reviewed: target volumes, target and normal tissue dose coverage, and dose distributions. During the review, any team member can ask questions and afterwards a group consensus is taken regarding plan approval.

Results: The GCPR model has been implemented through a commitment to peer review and creative conference scheduling. Automated analysis software is used to depict color-coded results for department approved target coverage and dose constraints. About 8% of plans required re-planning while about 23% required minor changes. The mean time for review of each plan was 8 min.

Conclusions: Catching errors prior to treatment is the only way to “chase zero” in radiation oncology. Various types of errors may exist in treatment plans and our GCPR model succeeds in preventing many errors of all shapes and sizes in target definition, dose prescriptions, and treatment plans from ever reaching the patients.

An Audit for Radiotherapy Planning and Treatment Errors From a Low-Middle-Income Country Centre

AIMS

To report the findings of an audit for radiotherapy errors from a low-middle-income country (LMICs) centre. This would serve as baseline data for radiotherapy error rates, their severity and causes, in such centres where modern error reporting and learning processes still do not exist.

MATERIALS AND METHODS

A planned cross-sectional weekly audit of electronic radiotherapy charts at the radiotherapy planning and delivery step for all patients treated with curative intent was conducted. Detailed analysis was carried out to determine the step of origin of error, time and contributing factors. They were graded as per indigenous institutional (TMC) radiotherapy error grading (TREG) system and the contributing factors identified were prioritised using the product of frequency, severity and ease of detection.

RESULTS

In total, 1005 consecutive radically treated patients' charts were audited, 67 radiotherapy errors affecting 60 patients, including 42 incidents and 25 near-misses were identified. Transcriptional errors (29%) were the most common type. Most errors occurred at the time of treatment planning (59.7%), with "plan information transfer to the radiation oncology information system" being the most frequently affected sub-step of the radiotherapy process (47.8%). More errors were noted at cobalt units (52/67; 77.6%) than at linear accelerators. Trend analysis showed an increased number of radiotherapy incidents on Fridays and near-misses on Mondays. Trend for increased radiotherapy errors noted in the evening over other shifts. On severity grading, most of the errors (54/60; 90%) were clinically insignificant (grade I/II). Inadequacies and non-adherence towards standard operating procedures, poor documentation and lack of continuing education were the three most prominent causes.

CONCLUSION

Preliminary data suggest a vulnerability of LMIC set-up to radiotherapy errors and emphasises the need for the development of longitudinal prospective processes, such as voluntary reporting and a continued education system, to ensure robust and comprehensive safe practises on par with centres in developed countries.

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.

Risk factors for radiotherapy incidents: a single institutional experience

We aimed to analyze risk factors for incidents occurring during the practice of external beam radiotherapy (EBRT) at a single Japanese center. Treatment data for EBRT from June 2014 to March 2017 were collected. Data from incident reports submitted during this period were reviewed. Near-miss cases were not included. Risk factors for incidents, including patient characteristics and treatment-related factors, were explored using uni- and multivariate analyses. Factors contributing to each incident were also retrospectively categorized according to the recommendations of the American Association of Physicists in Medicine (AAPM). A total of 2887 patients were treated during the study period, and 26 incidents occurred (0.90% per patient). Previous history of radiotherapy and large fraction size were identified as risk factors for incidents by univariate analysis. Only previous history of radiotherapy was detected as a risk factor in multivariate analysis. Identified categories of contributing factors were human behavior (50.0%), communication (40.6%), and technical (9.4%). The incident rate of EBRT was 0.90% per patient in our institution. Previous history of radiotherapy and large fraction size were detected as risk factors for incidents. Human behavior and communication errors were identified as contributing factors for most incidents.

Are we making an impact with incident learning systems? Analysis of quality improvement interventions using total body irradiation as a model system

PURPOSE

Despite increasing interest in incident learning systems (ILS) to improve safety and quality in radiation oncology, little is known about interventions developed in response to safety data. We used total body irradiation (TBI) as a model system to study the effectiveness of interventions from our institutional ILS.

METHODS AND MATERIALS

Near-miss event reports specific to TBI were identified from a departmental ILS from March 2012 to December 2015. The near-miss risk index was rated at multidisciplinary review from 0 (no potential harm) to 4 (critical potential harm). Interventions were analyzed for effectiveness with a schema adapted from The Joint Commission and other agencies: "most reliable" (eg, forcing functions, automation), "somewhat reliable" (eg, checklists, standardization), and "least reliable" (eg, training, rules, procedures). Causal factors of each event were drawn from the casual factor schema used in radiation oncology ILS.

RESULTS

Of 4007 safety-related reports, 266 reports pertained to TBI. TBI reports had a somewhat higher proportion of high-risk events (near-miss risk index 3-4) compared with non-TBI reports (25% vs 17%, P = .0045). A total of 117 interventions were implemented. The reliability indicators for the interventions were: most reliable (11% of interventions), somewhat reliable (17%), and least reliable (72%). Interventions were more likely to be applied to high-risk events (54% vs 41%, P = .03). There was a pattern of high-reliability interventions with increased risk score of events. Events involving human error (eg, slips) and equipment/information technology lent themselves more often to high-reliability interventions. Events related to communication, standardization, and training were associated with low-reliability interventions.

CONCLUSIONS

Over a 3.5-year period, 117 quality improvement strategies were developed for TBI based on ILS. Interventions were more likely to be applied to high-risk events and high-risk events were more likely to be associated with high-quality interventions. These results may be useful to institutions seeking to develop interventions based on ILS data.

Predictive time-series modeling using artificial neural networks for Linac beam symmetry: an empirical study

Over half of cancer patients receive radiotherapy (RT) as partial or full cancer treatment. Daily quality assurance (QA) of RT in cancer treatment closely monitors the performance of the medical linear accelerator (Linac) and is critical for continuous improvement of patient safety and quality of care. Cumulative longitudinal QA measurements are valuable for understanding the behavior of the Linac and allow physicists to identify trends in the output and take preventive actions. In this study, artificial neural networks (ANNs) and autoregressive moving average (ARMA) time-series prediction modeling techniques were both applied to 5-year daily Linac QA data. Verification tests and other evaluations were then performed for all models. Preliminary results showed that ANN time-series predictive modeling has more advantages over ARMA techniques for accurate and effective applicability in the dosimetry and QA field.

Bayesian network models for error detection in radiotherapy plans

The purpose of this study is to design and develop a probabilistic network for detecting errors in radiotherapy plans for use at the time of initial plan verification. Our group has initiated a multi-pronged approach to reduce these errors. We report on our development of Bayesian models of radiotherapy plans. Bayesian networks consist of joint probability distributions that define the probability of one event, given some set of other known information. Using the networks, we find the probability of obtaining certain radiotherapy parameters, given a set of initial clinical information. A low probability in a propagated network then corresponds to potential errors to be flagged for investigation. To build our networks we first interviewed medical physicists and other domain experts to identify the relevant radiotherapy concepts and their associated interdependencies and to construct a network topology. Next, to populate the network's conditional probability tables, we used the Hugin Expert software to learn parameter distributions from a subset of de-identified data derived from a radiation oncology based clinical information database system. These data represent 4990 unique prescription cases over a 5 year period. Under test case scenarios with approximately 1.5% introduced error rates, network performance produced areas under the ROC curve of 0.88, 0.98, and 0.89 for the lung, brain and female breast cancer error detection networks, respectively. Comparison of the brain network to human experts performance (AUC of 0.90 ± 0.01) shows the Bayes network model performs better than domain experts under the same test conditions. Our results demonstrate the feasibility and effectiveness of comprehensive probabilistic models as part of decision support systems for improved detection of errors in initial radiotherapy plan verification procedures.

A comprehensive quality assurance program for personnel and procedures in radiation oncology: value of voluntary error reporting and checklists

PURPOSE

This report describes the value of a voluntary error reporting system and the impact of a series of quality assurance (QA) measures including checklists and timeouts on reported error rates in patients receiving radiation therapy.

METHODS AND MATERIALS

A voluntary error reporting system was instituted with the goal of recording errors, analyzing their clinical impact, and guiding the implementation of targeted QA measures. In response to errors committed in relation to treatment of the wrong patient, wrong treatment site, and wrong dose, a novel initiative involving the use of checklists and timeouts for all staff was implemented. The impact of these and other QA initiatives was analyzed.

RESULTS

From 2001 to 2011, a total of 256 errors in 139 patients after 284,810 external radiation treatments (0.09% per treatment) were recorded in our voluntary error database. The incidence of errors related to patient/tumor site, treatment planning/data transfer, and patient setup/treatment delivery was 9%, 40.2%, and 50.8%, respectively. The compliance rate for the checklists and timeouts initiative was 97% (P<.001). These and other QA measures resulted in a significant reduction in many categories of errors. The introduction of checklists and timeouts has been successful in eliminating errors related to wrong patient, wrong site, and wrong dose.

CONCLUSIONS

A comprehensive QA program that regularly monitors staff compliance together with a robust voluntary error reporting system can reduce or eliminate errors that could result in serious patient injury. We recommend the adoption of these relatively simple QA initiatives including the use of checklists and timeouts for all staff to improve the safety of patients undergoing radiation therapy in the modern era.

New perspectives in radiation oncology: Young radiation oncologist point of view and challenges

AIM

To assess the role of the young radiation oncologist in the context of important recent advancements in the field of radiation oncology, and to explore new perspectives and competencies of the young radiation oncologist.

BACKGROUND

Radiation oncology is a field that has rapidly advanced over the last century. It holds a rich tradition of clinical care and evidence-based practice, and more recently has advanced with revolutionary innovations in technology and computer science, as well as pharmacology and molecular biology.

MATERIALS AND METHODS

Several young radiation oncologists from different countries evaluated the current status and future directions of radiation oncology.

RESULTS

For young radiation oncologists, it is important to reflect on the current practice and future directions of the specialty as it relates to the role of the radiation oncologist in the comprehensive management of cancer patients. Radiation oncologists are responsible for the radiation treatment provided to patients and its subsequent impact on patients' quality of life. Young radiation oncologists must proactively master new clinical, biological and technical information, as well as lead radiation oncology teams consisting of physicists, dosimetrists, nurses and technicians.

CONCLUSIONS

The role of the young radiation oncologist in the field of oncology should be proactive in developing new competencies. Above all, it is important to remember that we are dealing with the family members and loved ones of many individuals during the most difficult part of their lives.