Evaluation of safety in a radiation oncology setting using failure mode and effects analysis

Process mapping with failure mode and effects analysis to identify determinants of implementation in healthcare settings: a guide

BACKGROUND

Generating and analyzing process maps can help identify and prioritize barriers to the implementation of evidence-based practices in healthcare settings. Guidance on how to systematically apply and report these methods in implementation research is scant. We describe a method combining a qualitative approach to developing process maps with a quantitative evaluation of maps drawn from the quality improvement literature called failure mode and effects analysis (FMEA).

METHODS

We provide an outline and guidance for how investigators can use process mapping with FMEA to identify and prioritize barriers when implementing evidence-based clinical interventions. Suggestions for methods and reporting were generated based on established procedures for process mapping with FMEA and through review of original research papers which apply both methods in healthcare settings. We provide case examples to illustrate how this approach can be operationalized in implementation research.

RESULTS

The methodology of process mapping with FMEA can be divided into four broad phases: 1) formulating a plan, 2) generating process maps to identify and organize barriers over time, 3) prioritizing barriers through FMEA, and 4) devising an implementation strategy to address priority barriers. We identified 14 steps across the four phases. Two illustrative examples are provided. Case 1 describes the implementation of referrals to chiropractic care for adults with low back pain in primary care clinics. Case 2 describes the implementation of a family navigation intervention for children with autism spectrum disorder seeking care in pediatric clinics. For provisional guidance for reporting, we propose the REporting Process mapping and Analysis for Implementation Research (REPAIR) checklist.

CONCLUSIONS

Process mapping with FMEA can elucidate barriers and facilitators to successful implementation of evidence-based clinical interventions. This paper provides initial guidance for more systematic applications of this methodology in implementation research. Future research should use a consensus-building approach, such as a multidisciplinary Delphi panel, to further delineate the reporting standards for studies that use process mapping with FMEA.

Analysis of human errors in the operation of various treatment planning systems over a 10-year period

The present study aimed to summarize and report data on errors related to treatment planning, which were collected by medical physicists. The following analyses were performed based on the 10-year error report data: (1) listing of high-risk errors that occurred and (2) the relationship between the number of treatments and error rates, (3) usefulness of the Automated Plan Checking System (APCS) with the Eclipse Scripting Application Programming Interface and (4) the relationship between human factors and error rates. Differences in error rates were observed before and after the use of APCS. APCS reduced the error rate by ~1% for high-risk errors and 3% for low-risk errors. The number of treatments was negatively correlated with error rates. Therefore, we examined the relationship between the workload of medical physicists and error occurrence and revealed that a very large workload may contribute to overlooking errors. Meanwhile, an increase in the number of medical physicists may lead to the detection of more errors. The number of errors was correlated with the number of physicians with less clinical experience; the error rates were higher when there were more physicians with less experience. This is likely due to the lack of training among clinically inexperienced physicians. An environment to provide adequate training is important, as inexperience in clinical practice can easily and directly lead to the occurrence of errors. In any environment, the need for additional plan checkers is an essential factor for eliminating errors.

Enhancing patient safety in radiotherapy: Implementation of a customized electronic checklist for radiation therapists

Introduction

The radiotherapy workflow involves the collaboration of multiple professionals and the execution of several steps to results in an effective treatment. In this study, we described the clinical implementation of an electronic checklist, developed to standardize the process of the chart review prior to the first treatment fraction by the radiation therapists (RTTs).

Materials and Methods

A customized electronic checklist was developed based on the recommendations of American Association of Physicists in Medicine (AAPM) Task Groups 275 and 315 and integrated into the Record and Verify System (RVS). The checklist consisted of 16 items requiring binary (yes/no) responses, with mandatory completion and review by RTTs prior to treatment. The utility of the checklist and its impact on workflow were assessed by analysing checklist reports, and by soliciting feedback to RTTs through an anonymized survey.

Results

During the first trial phase, from June to November 2023, 285 checklists were completed with a 98% compilation rate and 94.4% review rate. Forty errors were detected, mainly due to missing signed treatment plans and absence of Beam's Eye View documentation. Ninety percent of detected errors were fixed before the treatment start. In 4 cases, the problem could not be fixed before the first fraction, resulting in a suboptimal first treatment. The feedback survey showed that RTTs described the checklist as useful, with minimal impact on workload, and supported its implementation.

Discussion

The introduction of a customized electronic checklist improved the detection and correction of errors, thereby enhancing patient safety. The positive response from RTTs and the minimal impact on workflow underscore the value of the checklist as standard practice in radiotherapy departments.

Development and clinical implementation of a digital system for risk assessments for radiation therapy

Before introducing new treatment techniques, an investigation of hazards due to unintentional radiation exposures is a reasonable activity for proactively increasing patient safety. As dedicated software is scarce, we developed a tool for risk assessment to design a quality management program based on best practice methods, i.e., process mapping, failure modes and effects analysis and fault tree analysis. Implemented as a web database application, a single dataset was used to describe the treatment process and its failure modes. The design of the system and dataset allowed failure modes to be represented both visually as fault trees and in a tabular form. Following the commissioning of the software for our department, previously conducted risk assessments were migrated to the new system after being fully re-assessed which revealed a shift in risk priorities. Furthermore, a weighting factor was investigated to bring risk levels of the migrated assessments into perspective. The compensation did not affect high priorities but did re-prioritize in the midrange of the ranking. We conclude that the tool is suitable to conduct multiple risk assessments and concomitantly keep track of the overall quality management activities.

Process failure mode and effects analysis for external beam radiotherapy: Introducing a literature-based template and a novel action priority

PURPOSE

The first aim of the study was to create a general template for analyzing potential failures in external beam radiotherapy, EBRT, using the process failure mode and effects analysis (PFMEA). The second aim was to modify the action priority (AP), a novel prioritization method originally introduced by the Automotive Industry Action Group (AIAG), to work with different severity, occurrence, and detection rating systems used in radiation oncology.

METHODS AND MATERIALS

The AIAG PFMEA approach was employed in combination with an extensive literature survey to develop the EBRT-PFMEA template. Subsets of high-risk failure modes found through the literature survey were added to the template where applicable. Our modified AP for radiation oncology (RO AP) was defined using a weighted sum of severity, occurrence, and detectability. Then, Monte Carlo simulations were conducted to compare the original AIAG AP, the RO AP, and the risk priority number (RPN). The results of the simulations were used to determine the number of additional corrective actions per failure mode and to parametrize the RO AP to our department's rating system.

RESULTS

An EBRT-PFMEA template comprising 75 high-risk failure modes could be compiled. The AIAG AP required 1.7 additional corrective actions per failure mode, while the RO AP ranged from 1.3 to 3.5, and the RPN required 3.6. The RO AP could be parametrized so that it suited our rating system and evaluated severity, occurrence, and detection ratings equally to the AIAG AP.

CONCLUSIONS

An adjustable EBRT-PFMEA template is provided which can be used as a practical starting point for creating institution-specific templates. Moreover, the RO AP introduces transparent action levels that can be adapted to any rating system.

Technical note: A universal worksheet for failure mode and effects analysis-A project of the Japanese College of Medical Physics

BACKGROUND

Failure mode and effects analysis (FMEA), which is an effective tool for error prevention, has garnered considerable attention in radiotherapy. FMEA can be performed individually, by a group or committee, and online.

PURPOSE

To meet the needs of FMEA for various purposes and improve its accessibility, we developed a simple, self-contained, and versatile web-based FMEA risk analysis worksheet.

METHODS

We developed an FMEA worksheet using Google products, such as Google Sheets, Google Forms, and Google Apps Script. The main sheet was created in Google Sheets and contained elements necessary for performing FMEA by a single person. Automated tasks were implemented using Apps Script to facilitate multiperson FMEA; these functions were built into buttons located on the main sheet.

RESULTS

The usability of the FMEA worksheet was tested in several situations. The worksheet was feasible for individual, multiperson, seminar, meeting, and online purposes. Simultaneous online editing, automated survey form creation, automatic analysis, and the ability to respond to the form from multiple devices, including mobile phones, were particularly useful for online and multiperson FMEA. Automation enabled through Google Apps Script reduced the FMEA workload.

CONCLUSIONS

The FMEA worksheet is versatile and has a seamless workflow that promotes collaborative work for safety.

Improving Quality Assurance in a Radiation Oncology Using ARIA Visual Care Path

PURPOSE

Errors and incidents may occur at any point within radiotherapy (RT). The aim of the present retrospective analysis is to evaluate the impact of a customized ARIA Visual Care Path (VCP) on quality assurance (QA) for the RT process.

MATERIALS AND METHODS

The ARIA VCP was implemented in June 2019. The following tasks were customized and independently verified (by independent checks from radiation oncologists, medical physics, and radiation therapists): simulation, treatment planning, treatment start verification, and treatment completion. A retrospective analysis of 105 random and unselected patients was performed, and 945 tasks were reviewed. Patients' reports were categorized based on treatment years period: 2019-2020 (A); 2021 (B); and 2022-2023 (C). The QA metrics included data for timeliness of task completion and data for minor and major incidents. The major incidents were defined as incorrect prescriptions of RT dose, the use of different immobilization systems during RT compared to the simulation, the absence of surface-guided RT data for patients' positioning, incorrect dosimetric QA for treatment plans, and failure to complete RT as originally planned. A sample size of approximately 100 was able to obtain an upper limit of 95% confidence interval below 5-10% in the case of zero or one major incident.

RESULTS

From June 2019 to December 2023, 5300 patients were treated in our RT department, an average of 1300 patients per year. For the purpose of this analysis, one hundred and five patients were chosen for the study and were subsequently evaluated. All RT staff achieved a 100% compliance rate in the ARIA VCP timely completion. A total of 36 patients were treated in Period A, 34 in Period B, and 35 in Period C. No major incidents were identified, demonstrating a major incident rate of 0.0% (95% CI 0.0-3.5%). A total of 26 out of 945 analyzed tasks (3.8%) were reported as minor incidents: absence of positioning photo in 32 cases, lack of patients' photo, and absence of plan documents in 4 cases. When comparing periods, incidents were statistically less frequent in Period C.

CONCLUSIONS

Although the present analysis has some limitations, its outcomes demonstrated that software for the RT workflow, which is fully integrated with both the record-and-verify and treatment planning systems, can effectively manage the patient's care path. Implementing the ARIA VCP improved the efficiency of the RT care path workflow, reducing the risk of major and minor incidents.

Clinical implementation of failure modes and effects analysis for gynecological high-dose-rate brachytherapy

Purpose

To use failure modes and effects analysis (FMEA) to identify failure modes for gynecological high-dose-rate (HDR) brachytherapy pathway and score with severity, occurrence, and detectability.

Material and methods

A research team was organized to observe gynecological HDR brachytherapy pathway, and draw detailed process map to identify all potential failure modes (FMs). The whole team scored FMs based on three parameters, including occurrence (O), detectability (D), and severity (S), and then multiplied three scores to obtain risk priority number (RPN). All FMs were ranked according to RPNs and/or severity scores, and FMs with the highest RPN scores (> 100) and severity scores (> 8) were selected for in-depth analysis. Fault tree analysis (FTA) was applied to find progenitor causes of high-risk FMs and their propagation path, and determine which steps in the process need to be changed and optimized. Efficiency of each existing preventive methods to detect and stop FMs was analyzed, and proposals to improve quality management (QM) and ensure patient safety were suggested.

Results

The whole gynecological HDR brachytherapy pathway consisted of 5 sub-processes and 30 specific steps, in which 57 FMs were identified. Twelve high-risk FMs were found, including 7 FMs with RPNs > 100 and 5 FMs with severity scores > 8. For these FMs, 2 were in the insertion stage, 1 in the imaging stage, 4 in the treatment planning stage, and 5 in the final stage of treatment delivery. The most serious of these FMs was the change in organ at risk (OAR) during treatment delivery (RPN = 245.7). The FM that occurred most frequently was the applicator shift during patient transfer.

Conclusions

Failure modes and effects analysis is a prospective risk-based tool that can identity high-risk steps before failures occur, provide preventive measures to stop their occurrence, and improve quality management system.

AAPM Medical Physics Practice Guideline 8.b: Linear accelerator performance tests

The purpose of this guideline is to provide a list of critical performance tests to assist the Qualified Medical Physicist (QMP) in establishing and maintaining a safe and effective quality assurance (QA) program. The performance tests on a linear accelerator (linac) should be selected to fit the clinical patterns of use of the accelerator and care should be given to perform tests which are relevant to detecting errors related to the specific use of the accelerator. Current recommendations for linac QA were reviewed to determine any changes required to those tests highlighted by the original report as well as considering new components of the treatment process that have become common since its publication. Recommendations are made on the acquisition of reference data, routine establishment of machine isocenter, basing performance tests on clinical use of the linac, working with vendors to establish QA tests and performing tests after maintenance and upgrades. The recommended tests proposed in this guideline were chosen based on consensus of the guideline's committee after assessing necessary changes from the previous report. The tests are grouped together by class of test (e.g., dosimetry, mechanical, etc.) and clinical parameter tested. Implementation notes are included for each test so that the QMP can understand the overall goal of each test. This guideline will assist the QMP in developing a comprehensive QA program for linacs in the external beam radiation therapy setting. The committee sought to prioritize tests by their implication on quality and patient safety. The QMP is ultimately responsible for implementing appropriate tests. In the spirit of the report from American Association of Physicists in Medicine Task Group 100, individual institutions are encouraged to analyze the risks involved in their own clinical practice and determine which performance tests are relevant in their own radiotherapy clinics.

Unified risk analysis in radiation therapy

PURPOSE

The increasing complexity of new treatment methods as well as the Information Technology (IT) infrastructure within radiotherapy require new methods for risk analysis. This work presents a methodology on how to model the treatment process of radiotherapy in different levels. This subdivision makes it possible to perform workflow-specific risk analysis and to assess the impact of IT risks on the overall treatment workflow.

METHODS

A Unified Modeling Language (UML) activity diagram is used to model the workflows. The subdivision of the workflows into different levels is done with the help of swim lanes. The model created in this way is exported in an xml-compatible format and stored in a database with the help of a Python program.

RESULTS

Based on an existing risk analysis, the workflows CT Appointment, Glioblastoma Multiforme, and Deep Inspiration Breath Hold (DIBH) were modeled in detail. Part of the analysis are automatically generated workflow-specific risk matrices including risks of medical devices incorporated into a specific workflow. In addition, SQL queries allow to quickly retrieve e.g., the details of the medical device network installed in a department.

CONCLUSION

Activity diagrams of UML can be used to model workflows in radiotherapy. Through this, a connection between the different levels of the entire workflow can be established and workflow-specific risk analysis is possible.

Evaluación de riesgos y mejora de la seguridad biológica y radiológica en la toma de radiografía torácica a pacientes con COVID-19

INTRODUCTION

Health workers are at high risk of becoming infected with COVID-19. The objective of the study was to evaluate the risks and improve the biological and radiological safety measures for taking chest X-rays in patients with COVID-19 in a Social Security hospital in Utcubamba (Peru).

MATERIAL AND METHODS

Quasi-experimental intervention study type before and after without a control group, carried out between May and September 2020. A process map and an analysis of failure modes and effects (FMEA) of radiological care were prepared. The gravity (G), occurrence (O), and detectability (D) values ??were found and the risk priority number (RPN) was calculated for each failure mode (FM). FM with RPN ≥ 100 and G ≥ 7 were prioritized. Improvement actions were implemented based on the recommendations of recognized institutions and the O and D values ??were re-evaluated.

RESULTS

The process map consisted of 6 threads and 30 steps. 54 FM were identified, 37 of whom had RPN ≥ 100 and 48 had G ≥ 7. Most of the errors occurred during the examination 50% (27). After entering the recommendations, 23 FM had RPN ≥ 100.

CONCLUSIONS

Although none of the measures applied through the FMEA made the failure mode impossible, they made it more detectable and less frequent and reduced the RPN for each failure mode; however, a periodic update of the process is necessary.

Determining the quality control frequency of an MR-linac using risk matrix (RM) analysis

PURPOSE

Quality control (QC) is performed routinely through professional guidelines. However, the recommended QC frequency may not be optimal among different institutional settings. Here we propose a novel method for determining the optimal QC frequency using risk matrix (RM) analysis.

METHODS AND MATERIALS

A newly installed Magnetic Resonance linac (MR-linac) was chosen as the testing platform and six routine QC items were investigated. Failures of these QC items can adversely affect treatment outcome for the patient. Accordingly, each QC item with its assigned frequency forms a unique failure mode (FM). Using FM-effect analysis (FMEA), the severity (S), occurrence (O), and detection (D) of each FM was obtained. Next, S and D based on RM was used to determine the appropriate QC frequency. Finally, the performance of new frequency for each QC item was evaluated using the metric E = O/D.

RESULTS

One new QC frequency was the same as the old frequency, two new QC frequencies were less than the old ones, and three new QC frequencies were higher than the old ones. For six QC items, E values at the new frequencies were not less than their values at the old frequencies. This indicates that the risk of machine failure is reduced at the new QC frequencies.

CONCLUSIONS

The application of RM analysis provides a useful tool for determining the optimal frequencies for routine linac QC. This study demonstrated that linac QC can be performed in a way that maintains high performance of the treatment machine in a radiotherapy clinic.

Risk management patterns in radiation oncology-results of a national survey within the framework of the Patient Safety in German Radiation Oncology (PaSaGeRO) project

PURPOSE

Risk management (RM) is a key component of patient safety in radiation oncology (RO). We investigated current approaches on RM in German RO within the framework of the Patient Safety in German Radiation Oncology (PaSaGeRO) project. Aim was not only to evaluate a status quo of RM purposes but furthermore to discover challenges for sustainable RM that should be addressed in future research and recommendations.

METHODS

An online survey was conducted from June to August 2021, consisting of 18 items on prospective and reactive RM, protagonists of RM, and self-assessment concerning RM. The survey was designed using LimeSurvey and invitations were sent by e‑mail. Answers were requested once per institution.

RESULTS

In all, 48 completed questionnaires from university hospitals, general and non-academic hospitals, and private practices were received and considered for evaluation. Prospective and reactive RM was commonly conducted within interprofessional teams; 88% of all institutions performed prospective risk analyses. Most institutions (71%) reported incidents or near-events using multiple reporting systems. Results were presented to the team in 71% for prospective analyses and 85% for analyses of incidents. Risk conferences take place in 46% of institutions. 42% nominated a manager/committee for RM. Knowledge concerning RM was mostly rated "satisfying" (44%). However, 65% of all institutions require more information about RM by professional societies.

CONCLUSION

Our results revealed heterogeneous patterns of RM in RO departments, although most departments adhered to common recommendations. Identified mismatches between recommendations and implementation of RM provide baseline data for future research and support definition of teaching content.

Failure Mode and Effects Analysis for Experimental Use of FLASH on a Clinical Accelerator

PURPOSE

The use of a linear accelerator (LINAC) in ultrahigh-dose-rate (UHDR) mode can provide a conduit for wider access to UHDR FLASH effects, sparing normal tissue, but care needs to be taken in the use of such systems to ensure errors are minimized. The failure mode and effects analysis was carried out in a team that has been involved in converting a LINAC between clinical use and UHDR experimental mode for more than 1 year after the proposed methods of TG100.

METHODS AND MATERIALS

A team of 9 professionals with extensive experience were polled to outline the process map and workflow for analysis, and developed fault trees for potential errors, as well as failure modes that would result. The team scored the categories of severity magnitude, occurrence likelihood, and detectability potential in a scale of 1 to 10, so that a risk priority number (RPN = severity×occurrence×detectability) could be assessed for each.

RESULTS

A total of 46 potential failure modes were identified, including 5 with an RPN >100. These failure modes involved (1) patient set up, (2) gating mechanisms in delivery, and (3) detector in the beam stop mechanism. The identified methods to mitigate errors included the (1) use of a checklist post conversion, (2) use of robust radiation detectors, (3) automation of quality assurance and beam consistency checks, and (4) implementation of surface guidance during beam delivery.

CONCLUSIONS

The failure mode and effects analysis process was considered critically important in this setting of a new use of a LINAC, and the expert team developed a higher level of confidence in the ability to safely move UHDR LINAC use toward expanded research access.

Multi-Institutional Stereotactic Body Radiation Therapy Incident Learning: Evaluation of Safety Barriers Using a Human Factors Analysis and Classification System

OBJECTIVES

Stereotactic body radiation therapy (SBRT) can improve therapeutic ratios and patient convenience, but delivering higher doses per fraction increases the potential for patient harm. Incident learning systems (ILSs) are being increasingly adopted in radiation oncology to analyze reported events. This study used an ILS coupled with a Human Factor Analysis and Classification System (HFACS) and barriers management to investigate the origin and detection of SBRT events and to elucidate how safeguards can fail allowing errors to propagate through the treatment process.

METHODS

Reported SBRT events were reviewed using an in-house ILS at 4 institutions over 2014-2019. Each institution used a customized care path describing their SBRT processes, including designated safeguards to prevent error propagation. Incidents were assigned a severity score based on the American Association of Physicists in Medicine Task Group Report 275. An HFACS system analyzed failing safeguards.

RESULTS

One hundred sixty events were analyzed with 106 near misses (66.2%) and 54 incidents (33.8%). Fifty incidents were designated as low severity, with 4 considered medium severity. Incidents most often originated in the treatment planning stage (38.1%) and were caught during the pretreatment review and verification stage (37.5%) and treatment delivery stage (31.2%). An HFACS revealed that safeguard failures were attributed to human error (95.2%), routine violation (4.2%), and exceptional violation (0.5%) and driven by personnel factors 32.1% of the time, and operator condition also 32.1% of the time.

CONCLUSIONS

Improving communication and documentation, reducing time pressures, distractions, and high workload should guide proposed improvements to safeguards in radiation oncology.

Failure modes in stereotactic radiosurgery. A narrative review

OBJECTIVES

Stereotactic radiosurgery (SRS) refers to an advanced radiotherapy technique that requires a high level of precision and accuracy and a flawless workflow. Failures within the SRS process can lead to serious consequences due to high doses delivered per treatment. This narrative review aimed to identify the riskiest failure modes (FMs) and the stages at which they occur in the SRS process, as well as the strategies applied to mitigate the risks. It was based on the analysis of published failure mode and effects analysis (FMEA) data.

KEY FINDINGS

From the literature search in PubMed and Scopus, 7 articles met the eligibility criteria for inclusion in the qualitative synthesis. In total, 9 radiotherapy departments conducted FMEA in the SRS process. 4 of them were community hospitals and 5 were academic centers. Overall, 54 high-risk FMs were identified with treatment planning (FMs: 18), treatment delivery (FMs: 12), consultation and patient registration (FMs: 10) being the riskiest stages. 10 FMs were stereotactic specific, while the remaining 44 could be met in any radiotherapy technique. Failures associated with contouring, medical records review, target reirradiation, and patient positioning were mostly outlined. Risk mitigation strategies included timeouts, double-checks, checklists, training and changes in the working practice.

CONCLUSION

Our review demonstrated that crucial FMs can occur in all SRS stages. Although generalisations were challenging, the FMs analysis provided a significant source of information about potential high risks and continuous improvement strategies that can be applied both in the SRS and other radiotherapy processes.

IMPLICATIONS FOR PRACTICE

The results of this research will assist radiotherapy facilities in proactive risk management studies and will allow radiotherapy professionals to reflect on their practice and learn from others' experiences.

AAPM Task Group Report 303 endorsed by the ABS: MRI Implementation in HDR Brachytherapy-Considerations from Simulation to Treatment

The Task Group (TG) on Magnetic Resonance Imaging (MRI) Implementation in High Dose Rate (HDR) Brachytherapy - Considerations from Simulation to Treatment, TG 303, was constituted by the American Association of Physicists in Medicine's (AAPM's) Science Council under the direction of the Therapy Physics Committee, the Brachytherapy Subcommittee, and the Working Group on Brachytherapy Clinical Applications. The TG was charged with developing recommendations for commissioning, clinical implementation, and on-going quality assurance (QA). Additionally, the TG was charged with describing HDR brachytherapy (BT) workflows and evaluating practical consideration that arise when implementing MR imaging. For brevity, the report is focused on the treatment of gynecologic and prostate cancer. The TG report provides an introduction and rationale for MRI implementation in BT, a review of previous publications on topics including available applicators, clinical trials, previously published BT related TG reports, and new image guided recommendations beyond CT based practices. The report describes MRI protocols and methodologies, including recommendations for the clinical implementation and logical considerations for MR imaging for HDR BT. Given the evolution from prescriptive to risk-based QA,¹ an example of a risk-based analysis using MRI-based, prostate HDR BT is presented. In summary, the TG report is intended to provide clear and comprehensive guidelines and recommendations for commissioning, clinical implementation, and QA for MRI-based HDR BT that may be utilized by the medical physics community to streamline this process. This report is endorsed by the American Brachytherapy Society (ABS). This article is protected by copyright. All rights reserved.

Identifying risk characteristics using failure mode and effect analysis for risk management in online magnetic resonance-guided adaptive radiation therapy

•

Failure mode and effect analysis with process map revealed risks.

•

High-risk failure modes and their corrective measures were identified.

•

Hazardous processes and characteristics of the treatment were identified.

•

All failure modes including those identified in previous papers were summarized and compared.

Background and purpose

Materials and methods

Results

Conclusion

FMECA Application in Tomotherapy: Comparison between Classic and Fuzzy Methodologies

Accident analysis in radiotherapy highlighted the need to increase quality assurance (QA) programs by the identification of failures/errors with very low probability (rare event) but very severe consequences. In this field, a Failure Mode, Effects and Criticality Analysis (FMECA) technique, used in various industrial processes to rank critical events, has been met with much interest. The literature describes different FMECA methods; however, it is necessary to understand if these tools are incisive and effective in the healthcare sector. In this work, comparisons of FMECA methodologies in the risk assessment of patients undergoing treatments performed with helical tomotherapy are reported. Failure modes identified for the phases “treatment planning” and “treatment execution” are classified using the Risk Priority Number (RPN) index. Differences and similarities in the classification of failures/errors of the examined FMECA approaches are highlighted.

Quality assurance for on-table adaptive magnetic resonance guided radiation therapy: A software tool to complement secondary dose calculation and failure modes discovered in clinical routine

Online adaption of treatment plans on a magnetic resonance (MR)‐Linac enables the daily creation of new (adapted) treatment plans using current anatomical information of the patient as seen on MR images. Plan quality assurance (QA) relies on a secondary dose calculation (SDC) that is required because a pretreatment measurement is impossible during the adaptive workflow. However, failure mode and effect analysis of the adaptive planning process shows a large number of error sources, and not all of them are covered by SDC. As the complex multidisciplinary adaption process takes place under time pressure, additional software solutions for pretreatment per‐fraction QA need to be used. It is essential to double‐check SDC input to ensure a safe treatment delivery. Here, we present an automated treatment plan check tool for adaptive radiotherapy (APART) at a 0.35 T MR‐Linac. It is designed to complement the manufacturer‐provided adaptive QA tool comprising SDC. Checks performed by APART include contour analysis, electron density map examinations, and fluence modulation complexity controls. For nine of 362 adapted fractions (2.5%), irregularities regarding missing slices in target volumes and organs at risks as well as in margin expansion of target volumes have been found. This demonstrates that mistakes occur and can be detected by additional QA measures, especially contour analysis. Therefore, it is recommended to implement further QA tools additional to what the manufacturer provides to facilitate an informed decision about the quality of the treatment plan.

Risk analysis for radiotherapy at the Universitätsklinikum Erlangen

PURPOSE

Risk analysis is required by various laws and regulations in Germany and has an impact on each department of a large clinic. We provide an overview of the relevant laws and regulations in Germany and present the technical and organizational experience of introducing risk analysis in the Department of Radiation Oncology at the Universitätsklinikum Erlangen.

METHODS

Risk analysis was performed with an in-house developed extension of our intranet platform and ticketing system. Risks were classified according to occurrence and severity, each on a 5-level scale resulting into a risk matrix. An interdisciplinary team of six experienced members formed the core meeting weekly.

RESULTS

A total of 38 risks and 50 measures have been identified in 41 1h-meetings corresponding to approx. 260 working hours. Risk was distributed 8/20/13 to the categories critical (n=8), monitoring (n=20), and conditionally acceptable (n=13). Risk analysis has been evaluated before and after introducing measures.

CONCLUSION

The risk analysis method introduced has been successfully used in routine operations for over a year. Risk analysis takes time and effort. However, because experts from different disciplines meet each other every week, the overall workflow of the radiation oncology department can be improved efficiently and continuously.

Improving the Quality of Care in Radiation Oncology using Artificial Intelligence

Radiation therapy is a complex process involving multiple professionals and steps from simulation to treatment planning to delivery, and these procedures are prone to error. Additionally, the imaging and treatment delivery equipment in radiotherapy is highly complex and interconnected and represents another risk point in the quality of care. Numerous quality assurance tasks are carried out to ensure quality and to detect and prevent potential errors in the process of care. Recent developments in artificial intelligence provide potential tools to the radiation oncology community to improve the efficiency and performance of quality assurance efforts. Targets for artificial intelligence enhancement include the quality assurance of treatment plans, target and tissue structure delineation used in the plans, delivery of the plans and the radiotherapy delivery equipment itself. Here we review recent developments of artificial intelligence applications that aim to improve quality assurance processes in radiation therapy and discuss some of the challenges and limitations that require further development work to realise the potential of artificial intelligence for quality assurance.

Safety culture to improve accidental event reporting in radiotherapy

BACKGROUND AND PURPOSE

The potential for unintended and adverse radiation exposure in radiotherapy (RT) is real and should be studied because RT is a highly complex, multistep process, which requires input from numerous individuals from different areas and steps of the RT workflow. The 'Incident' (I) is an event the consequence of which is not negligible from the point of view of protection or safety. A 'near miss' (NM) is defined as an event that is highly likely to happen but did not occur. The purpose of this work is to show that through systematic reporting and analysis of these adverse events, their occurrence can be reduced.

MATERIALS AND METHODS

Staff were trained to report every type of unintended and adverse radiation exposure and to provide a full description of it.

RESULTS

By 2018, 110 worksheets had been collected, with an average of 6.1 adverse events per year (with 780 patients treated per year, meaning an average incident rate of 0.78%). In 2001-2009, 37 events were registered (13 I and 24 NM), the majority of them were in the decision phase (12/37), while in 2010-2013, there were 42 (1 I and 41 NM) in both the dose-calculation and transfer phase (19/42). In 2014-2018, 31 events (1 I and 30 NM) were equally distributed across the phases of the RT process. In 9/15 cases of I, some checkpoint was introduced.

CONCLUSION

The complexity of the RT workflow is prone to errors, and this must be taken into account by encouraging a safety culture. The aim of this paper is to present the collected incidents and near misses and to show how organization and practice were modified by the acquired knowledge.

A bi-institutional multi-disciplinary failure mode and effects analysis (FMEA) for a Co-60 based total body irradiation technique

BACKGROUND

We aim to assess the risks associated with total body irradiation (TBI) delivered using a commercial dedicated Co-60 irradiator, and to evaluate inter-institutional and inter-professional variations in the estimation of these risks.

METHODS

A failure mode and effects analysis (FMEA) was generated using guidance from the AAPM TG-100 report for quantitative estimation of prospective risk metrics. Thirteen radiation oncology professionals from two institutions rated possible failure modes (FMs) for occurrence (O), severity (S), and detectability (D) indices to generate a risk priority number (RPN). The FMs were ranked by descending RPN value. Absolute gross differences (AGD) in resulting RPN values and Jaccard Index (JI; for the top 20 FMs) were calculated. The results were compared between professions and institutions.

RESULTS

A total of 87 potential FMs (57, 15, 10, 3, and 2 for treatment, quality assurance, planning, simulation, and logistics respectively) were identified and ranked, with individual RPN ranging between 1-420 and mean RPN values ranging between 6 and 74. The two institutions shared 6 of their respective top 20 FMs. For various institutional and professional comparison pairs, the number of common FMs in the top 20 FMs ranged from 6 to 13, with JI values of 18-48%. For the top 20 FMs, the trend in inter-professional variability was institution-specific. The mean AGD values ranged between 12.5 and 74.5 for various comparison pairs. AGD values differed the most for medical physicists (MPs) in comparison to other specialties i.e. radiation oncologists (ROs) and radiation therapists (RTs) [MPs-vs-ROs: 36.3 (standard deviation SD = 34.1); MPs-vs-RTs: 41.2 (SD = 37.9); ROs-vs-RTs: 12.5 (SD = 10.8)]. Trends in inter-professional AGD values were similar for both institutions.

CONCLUSION

This inter-institutional comparison provides prospective risk analysis for a new treatment delivery unit and illustrates the institution-specific nature of FM prioritization, primarily due to operational differences. Despite being subjective in nature, the FMEA is a valuable tool to ensure the identification of the most significant risks, particularly when implementing a novel treatment modality. The creation of a bi-institutional, multidisciplinary FMEA for this unique TBI technique has not only helped identify potential risks but also served as an opportunity to evaluate clinical and safety practices from the perspective of both multiple professional roles and different institutions.

Failure mode and effect analysis for linear accelerator-based paraspinal stereotactic body radiotherapy

Introduction

Paraspinal stereotactic body radiotherapy (SBRT) involves risks of severe complications. We evaluated the safety of the paraspinal SBRT program in a large academic hospital by applying failure modes and effects analysis.

Methods

The analysis was conducted by a multidisciplinary committee (two therapists, one dosimetrist, four physicists, and two radiation oncologists). The paraspinal SBRT workflow was segmented into four phases (simulation, treatment planning, delivery, and machine quality assurance (QA)). Each phase was further divided into a sequence of sub‐processes. Potential failure modes (PFM) were identified from each subprocess and scored in terms of the frequency of occurrence, severity and detectability, and a risk priority number (RPN). High‐risk PFMs were identified based on RPN and were studied for root causes using fault tree analysis.

Results

Our paraspinal SBRT process was characterized by eight simulations, 11 treatment planning, nine delivery, and two machine QA sub‐processes. There were 18, 29, 19, and eight PFMs identified from simulation, planning, treatment, and machine QA, respectively. The median RPN of the PFMs was 62.9 for simulation, 68.3 for planning, 52.9 for delivery, and 22.0 for machine QA. The three PFMs with the highest RPN were: previous radiotherapy outside the institution is not accurately evaluated (RPN: 293.3), incorrect registration between diagnostic magnetic resonance imaging and simulation computed tomography causing incorrect contours (273.0), and undetected patient movement before ExacTrac baseline (217.8). Remedies to the high RPN failures were implemented, including staff education, standardized magnetic resonance imaging acquisition parameters, and an image fusion process, and additional QA on beam steering.

Conclusions

A paraspinal SBRT workflow in a large clinic was evaluated using a multidisciplinary and systematic risk analysis, which led to feasible solutions to key root causes. Treatment planning was a major source of PFMs that systematically affect the safety and quality of treatments. Accurate evaluation of external treatment records remains a challenge.

Evaluation of failure modes and effect analysis for routine risk assessment of lung radiotherapy at a UK center

PURPOSE

Explore the feasibility of adopting failure modes and effects analysis (FMEA) for risk assessment of a high volume clinical service at a UK radiotherapy center. Compare hypothetical failure modes to locally reported incidents.

METHOD

An FMEA for a lung radiotherapy service was conducted at a hospital that treats ~ 350 lung cancer patients annually with radical radiotherapy. A multidisciplinary team of seven people was identified including a nominated facilitator. A process map was agreed and failure modes identified and scored independently, final failure modes and scores were then agreed at a face-to-face meeting. Risk stratification methods were explored and staff effort recorded. Radiation incidents related to lung radiotherapy reported locally in a 2-year period were analyzed to determine their relation to the identified failure modes. The final FMEA was therefore a combination of prospective evaluation and retrospective analysis from an incident learning system.

RESULTS

Thirty-six failure modes were identified for the pre-existing clinical service. The top failure modes varied according to the ranking method chosen. The process required 30 h of combined staff time. Over the 2-year period chosen, 38 voluntarily reported incidents were identified as relating to lung radiotherapy. Of these, 13 were not predicted by the identified failure modes, with six relating to delays in the process, three issues with appointment times, one communication error, two instances of a failure to image, and one technical fault deemed unpredictable by the manufacturer. Four additional failure modes were added to the FMEA following the incident analysis.

CONCLUSION

FMEA can be effectively applied to an established high volume service as a risk assessment method. Facilitation by an individual familiar with the FMEA process can reduce resource requirement. Prospective evaluation of risks should be combined with an incident reporting and learning system to produce a more comprehensive analysis of risk.

Quantifying risk using FMEA: An alternate approach to AAPM TG-100 for scoring failures and evaluating clinical workflow

PURPOSE

Renovation of the brachytherapy program at a leading cancer center utilized methods of the AAPM TG-100 report to objectively evaluate current clinical brachytherapy workflows and develop techniques for minimizing the risk of failures, increasing efficiency, and consequently providing opportunities for improved treatment quality. The TG-100 report guides evaluation of clinical workflows with recommendations for identifying potential failure modes (FM) and scoring them from the perspective of their occurrence frequency O, failure severity S, and inability to detect them D. The current study assessed the impact of differing methods to determine the risk priority number (RPN) beyond simple multiplication.

METHODS AND MATERIALS

The clinical workflow for a complex brachytherapy procedure was evaluated by a team of 15 staff members, who identified discrete FM using alternate scoring scales than those presented in the TG-100 report. These scales were expanded over all clinically relevant possibilities with care to emphasize mitigation of natural bias for scoring near the median range as well as to enhance the overall scoring-system sensitivity. Based on staff member perceptions, a more realistic measure of risk was determined using weighted functions of their scores.

RESULTS

This new method expanded the range of RPN possibilities by a factor of 86, improving evaluation and recognition of safe and efficient clinical workflows. Mean RPN values for each FM decreased by 44% when changing from the old to the new clinical workflow, as evaluated using the TG-100 method. This decreased by 66% when evaluated with the new method. As a measure of the total risk associated with an entire clinical workflow, the integral of RPN values increased by 15% and decreased by 31% with the TG-100 and new methods, respectively.

CONCLUSIONS

This appears to be the first application of an alternate approach to the TG-100 method for evaluating the risk of clinical workflows. It exemplifies the risk analysis techniques necessary to rapidly evaluate simple clinical workflows appropriately.

AAPM Task Group 264: The safe clinical implementation of MLC tracking in radiotherapy

The era of real-time radiotherapy is upon us. Robotic and gimbaled linac tracking are clinically established technologies with the clinical realization of couch tracking in development. Multileaf collimators (MLCs) are a standard equipment for most cancer radiotherapy systems, and therefore MLC tracking is a potentially widely available technology. MLC tracking has been the subject of theoretical and experimental research for decades and was first implemented for patient treatments in 2013. The AAPM Task Group 264 Safe Clinical Implementation of MLC Tracking in Radiotherapy Report was charged to proactively provide the broader radiation oncology community with (a) clinical implementation guidelines including hardware, software, and clinical indications for use, (b) commissioning and quality assurance recommendations based on early user experience, as well as guidelines on Failure Mode and Effects Analysis, and (c) a discussion of potential future developments. The deliverables from this report include: an explanation of MLC tracking and its historical development; terms and definitions relevant to MLC tracking; the clinical benefit of, clinical experience with and clinical implementation guidelines for MLC tracking; quality assurance guidelines, including example quality assurance worksheets; a clinical decision pathway, future outlook and overall recommendations.

Improving radiotherapy safety and efficiency with the customized ARIA oncology information system

OBJECTIVE

To improve safety and efficiency of radiotherapy process by customizing a Varian ARIA oncology information system following the guidelines provided in AAPM TG-100 report.

METHODS

First, failure mode and effects analysis (FMEA) and quality management program were implemented for radiotherapy process. We have customized the visual care path in the ARIA system and set up a series of templates for simulation, prescription, contouring, treatment planning, and multiple checklists. Average time of activities' completion and amount of planning errors were compared before and after the use of the customized ARIA to evaluate its impact on the efficiency and safety of radiotherapy.

RESULTS

Completion time and on-time completion rate of the key activities in the care path are improved. The time of OAR/targets contouring decreases from (1.94±1.51) days to (1.64±1.07) days (p = 0.003), with the on-time completion rate increases from 77.4%to 83.3%(p = 0.048). Treatment planning time decreases from (0.81±0.65) days to (0.55±0.51) days (p < 0.001), with the on-time completion rate increases from 96.6%to 98.3%(p = 0.163). Waiting time of patients decreases from (4.50±1.83) days to (4.04±1.34) days (p < 0.001), with the on-time completion rate increases from 81.9%to 89.7%(p = 0.003). In addition, the average plan error rate decreases from 5.5%(2.9%for safety errors and 2.6%for non-normative errors) to 2.4%(1.6%for safety errors and 0.8%for non-normative errors) (p = 0.029).

CONCLUSION

Our study demonstrates that the customized ARIA system has the potential to promote efficiency and safety in radiotherapy process management. It is beneficial to organize and accelerate the treatment process with more effective communications and fewer errors.

Formulation of objective indices to quantify machine failure risk analysis for interruptions in radiotherapy

Objectives

To evaluate the effect of interruption in radiotherapy due to machine failure in patients and medical institutions using machine failure risk analysis (MFRA).

Material and methods

The risk of machine failure during treatment is assigned to three scores (biological effect, B; occurrence, O; and cost of labor and repair parts, C) for each type of machine failure. The biological patient risk (BPR) and the economic institution risk (EIR) are calculated as the product of B and O (B×O) and C and O (C×O), respectively. The MFRA is performed in two linear accelerators (linacs).

Result

The multileaf collimator (MLC) fault has the highest BPR and second highest EIR. In particular, TrueBeam has a higher BPR and EIR for MLC failures. The total EIR in TrueBeam was significantly higher than that in Clinac iX. The minor interlock had the second highest BPR, whereas a smaller EIR. Meanwhile, the EIR for the LaserGuard fault was the highest, and that for the monitor chamber fault was the second highest. These machine failures occurred in TrueBeam. The BPR and EIR should be evaluated for each linac. Further, the sensitivity of the BPR, it decreased with higher T1/2 and α/β values. No relative difference is observed in the BPR for each machine failure when T1/2 and α/β were varied.

Conclusion

The risk faced by patients and institutions in machine failure may be reduced using MFRA.

Advances in knowledge

For clinical radiotherapy, interruption can occur from unscheduled downtime with machine failures. Interruption causes sublethal damage repair. The current study evaluated the effect of interruption in radiotherapy owing to machine failure on patients and medical institutions using a new method, that is, machine failure risk analysis.

A multi-institutional assessment of COVID-19-related risk in radiation oncology

PURPOSE

The COVID-19 pandemic has presented challenges to delivering safe and timely care for cancer patients. The oncology community has undertaken substantial workflow adaptations to reduce transmission risk for patients and providers. While various control measureshave been proposed and implemented, little is known about their impact on safety of the radiation oncology workflow and potential for transmission. The objective of this study was to assess potential safety impacts of control measures employed during the COVID-19 pandemic.

METHODS

A multi-institutional study was undertaken to assess the risks of pandemic-associated workflow adaptations using failure mode and effects analysis (FMEA). Failure modes were identified and scored using FMEA formalism. FMEA scores were used to identify highest-risk aspects of the radiation therapy process. The impact of control measures on overall risk was quantified. Agreement among institutions was evaluated.

RESULTS

Thirty three failure modes and 22 control measures were identified. Control measures resulted in risk score reductions for 22 of the failure modes, with the largest reductions from screening of patients and staff, requiring use of masks, and regular cleaning of patient areas. The median risk score for all failure modes was reduced from 280 to 168. There was high institutional agreement for 90.3% of failure modes but only 47% of control measures.

CONCLUSIONS

COVID-related risks are similar across oncology practices in this study. While control measures can reducerisk, their use varied. The effectiveness of control measures on risk may guide selection of the highest-impact workflow adaptions to ensure safe care in oncology.

FMEA occurrence values for four failure modes occurring using look-up tables for dose calculations

Purpose

For a number of different treatment types [such as Total Body Irradiation (TBI), etc.] most institutions utilize tables from commissioned databooks to perform the dose calculations. Each time one manually looks up data from a large table and then copies the numbers for a manual calculation, there is potential for errors. While a second check effectively mitigates the potential error from such calculations, information regarding the frequency and nature of such mistakes is important to develop protocols and workflows that avoid related errors.

Methods

Five years’ worth of TBI calculations were reviewed. Each calculation was re‐performed and evaluated against the original calculation and original second check. Any discrepancies were noted and those discrepancies were checked to see if the number was the result of misreading from the look‐up table, a typo, copying/skipping partially redundant steps, or rounding/avoiding interpolation. The number of calculations that contained these various types of discrepancies was tallied and percentages representing the frequency of said discrepancies were derived.

Results

All of the discrepancies only resulted in a monitor unit (MU) calculation difference of <1.7%. Typos, looking up wrong values from tables, rounding/avoiding interpolation, and skipping steps occurred in 10.4% (±3.1%), 6.3% (±2.5%), 53.1% (±5.1%), and 4.2% (±2.0%) of MU calculations, respectively.

Conclusions

While all of the discrepancies only resulted in a monitor unit (MU) calculation difference of <1.7%, this review shows how frequently various discrepancies can occur. Typos and rounding/avoiding interpolation are the steps most likely to potentially cause a miscalculation of MU. To avoid direct human interaction on such a large repetitive scale, creating forms that calculate MU automatically from initial measurement data would reduce the incidences that numbers are written/transcribed and eliminate the need to look up data in a table, thus reducing the chance for error.

Critical Re-Evaluation of a Failure Mode Effect Analysis in a Radiation Therapy Department After 10 Years

PURPOSE

Failure mode effect analysis (FMEA) is a proactive methodology that allows one to analyze a process, regardless of whether an adverse event occurs. In our radiation therapy (RT) department, a first FMEA was performed in 2009. In this paper we critically re-evaluate the RT process after 10 years and present it in terms of a lesson learned.

METHODS AND MATERIALS

A working group (WG), led by a qualified clinical risk engineer, which included radiation oncologists, physicists, a radiation therapist, and a nurse, evaluated the possible failure modes (FMs) of the RT process. For each FM, the estimated frequency of occurrence (O, range 1-4), the expected severity of the damage (S, range 1-5), and the detectability lack (D, range 1-4) were scored. A risk priority number (RPN) was obtained as RPN = OxSxD. The data were compared with the 2009 edition.

RESULTS

In the 2020 analysis, 67 FMs were identified (27 in the 2009 series). The absolute risk values of the previous 3 highest FMs were generally reduced. The patient identification risk (highest value in the 2009 analysis) was reduced from 48.0 to 6.9, becoming the 51st RPN score, thanks to a patient barcode recognition within the bunker. The 2020 highest risk values regarded: (i-2020) the patient's inadequate recollection and reporting of his/her medical history (ie, anamnesis) during the first medical examination and (ii-2020) the incorrect interpretation of tumor and normal tissue in computed tomography images. The WG proposed corrective actions.

CONCLUSIONS

In this single institution experience, the 10-year FMEA analysis showed a reduction in the previous higher RPN values thanks to the corrective actions taken. The new FMs and subsequent RPNs reveal the need for a continuous iterative improvement process.

Physician review of image registration and normal structure delineation

Introduction

Methods

Results

Conclusion

Impact of Simulation-Based Training on Radiation Therapists' Workload, Situation Awareness, and Performance

Purpose

This study aimed to assess the impact of simulation-based training intervention on radiation therapy therapist (RTT) mental workload, situation awareness, and performance during routine quality assurance (QA) and treatment delivery tasks.

Methods and Materials

As part of a prospective institutional review board-approved study, 32 RTTs completed routine QA and treatment delivery tasks on clinical scenarios in a simulation laboratory. Participants, randomized to receive (n = 16) versus not receive (n = 16) simulation-based training had pre- and postintervention assessments of mental workload, situation awareness, and performance. We used linear regression models to compare the postassessment scores between the study groups while controlling for baseline scores. Mental workload was quantified subjectively using the NASA Task Load Index. Situation awareness was quantified subjectively using the situation awareness rating technique and objectively using the situation awareness global assessment technique. Performance was quantified based on procedural compliance (adherence to preset/standard QA timeout tasks) and error detection (detection and correction of embedded treatment planning errors).

Results

Simulation-based training intervention was associated with significant improvements in overall performance (P < .01), but had no significant impact on mental workload or subjective/objective quantifications of situation awareness.

Conclusions

Simulation-based training might be an effective tool to improve RTT performance of QA-related tasks.

A Systems Theoretic Process Analysis of the Medication Use Process in the Operating Room

BACKGROUND

While 4 to 10% of medications administered in the operating room may involve an error, few investigations have prospectively modeled how these errors might occur. Systems theoretic process analysis is a prospective risk analysis technique that uses systems theory to identify hazards. The purpose of this study was to demonstrate the use of systems theoretic process analysis in a healthcare organization to prospectively identify causal factors for medication errors in the operating room.

METHODS

The authors completed a systems theoretic process analysis for the medication use process in the operating room at their institution. First, the authors defined medication-related accidents (adverse medication events) and hazards and created a hierarchical control structure (a schematic representation of the operating room medication use system). Then the authors analyzed this structure for unsafe control actions and causal scenarios that could lead to medication errors, incorporating input from surgeons, anesthesiologists, and pharmacists. The authors studied the entire medication use process, including requesting medications, dispensing, preparing, administering, documenting, and monitoring patients for the effects. Results were reported using descriptive statistics.

RESULTS

The hierarchical control structure involved three tiers of controllers: perioperative leadership; management of patient care by the attending anesthesiologist, surgeon, and pharmacist; and execution of patient care by the anesthesia clinician in the operating room. The authors identified 66 unsafe control actions linked to 342 causal scenarios that could lead to medication errors. Eighty-two (24.0%) scenarios came from perioperative leadership, 103 (30.1%) from management of patient care, and 157 (45.9%) from execution of patient care.

CONCLUSIONS

In this study, the authors demonstrated the use of systems theoretic process analysis to describe potential causes of errors in the medication use process in the operating room. Causal scenarios were linked to controllers ranging from the frontline providers up to the highest levels of perioperative management. Systems theoretic process analysis is uniquely able to analyze management and leadership impacts on the system, making it useful for guiding quality improvement initiatives.

Implementing a new scale for failure mode and effects analysis (FMEA) for risk analysis in a radiation oncology department

PURPOSE

Patients and staffs are endangered by different failure modes during clinical routine in radiation oncology and risks are difficult to stratify. We implemented the method of failure mode and effects analysis (FMEA) via questionnaires in our institution and introduced an adapted scale applicable for radiation oncology.

METHODS

Failure modes in physical treatment planning and daily routine were detected and stratified by ranking occurrence, severity, and detectability in a questionnaire. Multiplication of these values offers the risk priority number (RPN). We implemented an ordinal rating scale (ORS) as a combination of earlier published scales from the literature. This scale was optimized for German radiation oncology. We compared RPN using this ORS versus use of a rather subjective visual analogue rating scale (VRS).

RESULTS

Mean RPN using ORS was 62.3 vs. 67.5 using VRS (p = 0.7). Use of ORS led to improved completeness of questionnaires (91 vs. 79%) and stronger agreement among the experts, especially concerning failure modes during radiation routine. The majority of interviewed experts found the analysis by using the ORS easier and expected a saving of time as well as higher intra- and interobserver reliability.

CONCLUSION

The introduced rating scale together with a questionnaire survey provides merit for conducting FMEA in radiation oncology as results are comparable to the use of VRS and the process is facilitated.

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.

Failure mode and effects analysis for proactive healthcare risk evaluation: A systematic literature review

RATIONALE, AIMS, AND OBJECTIVES

Failure mode and effects analysis (FMEA) is a valuable reliability management tool that can preemptively identify the potential failures of a system and assess their causes and effects, thereby preventing them from occurring. The use of FMEA in the healthcare setting has become increasingly popular over the last decade, being applied to a multitude of different areas. The objective of this study is to review comprehensively the literature regarding the application of FMEA for healthcare risk analysis.

METHODS

An extensive search was carried out in the scholarly databases of Scopus and PubMed, and we only chose the academic articles which used the FMEA technique to solve healthcare risk analysis problems. Furthermore, a bibliometric analysis was performed based on the number of citations, publication year, appeared journals, authors, and country of origin.

RESULTS

A total of 158 journal papers published over the period of 1998 to 2018 were extracted and reviewed. These publications were classified into four categories (ie, healthcare process, hospital management, hospital informatization, and medical equipment and production) according to the healthcare issues to be solved, and analyzed regarding the application fields and the utilized FMEA methods.

CONCLUSION

FMEA has high practicality for healthcare quality improvement and error reduction and has been prevalently employed to improve healthcare processes in hospitals. This research supports academics and practitioners in effectively adopting the FMEA tool to proactively reduce healthcare risks and increase patient safety, and provides an insight into its state-of-the-art.

Using failure mode and effects analysis (FMEA) to generate an initial plan check checklist for improved safety in radiation treatment

PURPOSE

To apply failure mode and effect analysis (FMEA) to generate an effective and efficient initial physics plan checklist.

METHODS

A team of physicists, dosimetrists, and therapists was setup to reconstruct the workflow processes involved in the generation of a treatment plan beginning from simulation. The team then identified possible failure modes in each of the processes. For each failure mode, the severity (S), frequency of occurrence (O), and the probability of detection (D) was assigned a value and the risk priority number (RPN) was calculated. The values assigned were based on TG 100. Prior to assigning a value, the team discussed the values in the scoring system to minimize randomness in scoring. A local database of errors was used to help guide the scoring of frequency.

RESULTS

Twenty-seven process steps and 50 possible failure modes were identified starting from simulation to the final approved plan ready for treatment at the machine. Any failure mode that scored an average RPN value of 20 or greater was deemed "eligible" to be placed on the second checklist. In addition, any failure mode with a severity score value of 4 or greater was also considered for inclusion in the checklist. As a by-product of this procedure, safety improvement methods such as automation and standardization of certain processes (e.g., dose constraint checking, check tools), removal of manual transcription of treatment-related information as well as staff education were implemented, although this was not the team's original objective. Prior to the implementation of the new FMEA-based checklist, an in-service for all the second checkers was organized to ensure further standardization of the process.

CONCLUSION

The FMEA proved to be a valuable tool for identifying vulnerabilities in our workflow and processes in generating a treatment plan and subsequently a new, more effective initial plan checklist was created.

Three-dimensional printing in radiation oncology: A systematic review of the literature

Purpose/objectives

Three‐dimensional (3D) printing is recognized as an effective clinical and educational tool in procedurally intensive specialties. However, it has a nascent role in radiation oncology. The goal of this investigation is to clarify the extent to which 3D printing applications are currently being used in radiation oncology through a systematic review of the literature.

Materials/methods

A search protocol was defined according to preferred reporting items for systematic reviews and meta‐analyses (PRISMA) guidelines. Included articles were evaluated using parameters of interest including: year and country of publication, experimental design, sample size for clinical studies, radiation oncology topic, reported outcomes, and implementation barriers or safety concerns.

Results

One hundred and three publications from 2012 to 2019 met inclusion criteria. The most commonly described 3D printing applications included quality assurance phantoms (26%), brachytherapy applicators (20%), bolus (17%), preclinical animal irradiation (10%), compensators (7%), and immobilization devices (5%). Most studies were preclinical feasibility studies (63%), with few clinical investigations such as case reports or series (13%) or cohort studies (11%). The most common applications evaluated within clinical settings included brachytherapy applicators (44%) and bolus (28%). Sample sizes for clinical investigations were small (median 10, range 1–42). A minority of articles described basic or translational research (11%) and workflow or cost evaluation studies (3%). The number of articles increased over time (P < 0.0001). While outcomes were heterogeneous, most studies reported successful implementation of accurate and cost‐effective 3D printing methods.

Conclusions

Three‐dimensional printing is rapidly growing in radiation oncology and has been implemented effectively in a diverse array of applications. Although the number of 3D printing publications has steadily risen, the majority of current reports are preclinical in nature and the few clinical studies that do exist report on small sample sizes. Further dissemination of ongoing investigations describing the clinical application of developed 3D printing technologies in larger cohorts is warranted.

Estimating dose delivery accuracy in stereotactic body radiation therapy: A review of in-vivo measurement methods

Stereotactic body radiation therapy (SBRT) has been recognized as a standard treatment option for many anatomical sites. Sophisticated radiation therapy techniques have been developed for carrying out these treatments and new quality assurance (QA) programs are therefore required to guarantee high geometrical and dosimetric accuracy. This paper focuses on recent advances on in-vivo measurements methods (IVM) for SBRT treatment. More specifically, all of the online QA methods for estimating the effective dose delivered to patients were compared. Determining the optimal IVM for performing SBRT treatments would reduce the risk of errors that could jeopardize treatment outcome. A total of 89 papers were included. The papers were subdivided into the following topics: point dosimeters (PD), transmission detectors (TD), log file analysis (LFA), electronic portal imaging device dosimetry (EPID), dose accumulation methods (DAM). The detectability capability of the main IVM detectors/devices were evaluated. All of the systems have some limitations: PD has no spatial data, EPID has limited sensitivity towards set-up errors and intra-fraction motion in some anatomical sites, TD is insensitive towards patient related errors, LFA is not an independent measure, DAMs are not always based on measures. In order to minimize errors in SBRT dose delivery, we recommend using synergic combinations of two or more of the systems described in our review: on-line tumor position and patient information should be combined with MLC position and linac output detection accuracy. In this way the effects of SBRT dose delivery errors will be reduced.

Improvements in Physician Clinical Workflow Measures After Implementation of a Dashboard Program

PURPOSE

To determine whether a combination of data-driven, personalized feedback and implementation of a graduated, sequential intervention model improved key measures of physician workflow and quality in radiation treatment planning.

METHODS AND MATERIALS

All radiation oncologists across 3 facilities at a single academic institution were prospectively evaluated on 5 predefined metrics of timeliness and accuracy in the treatment-planning process using a web-based institutional data repository and an institutional incident learning system. The study period encompassed 10 quarters from 2014 to 2016, with 2013 serving as a retrospective baseline. Physicians received quarterly individualized reports of their compliance metrics (a practice labeled the Physician Dashboard), and administrative interventions were initiated if >20% noncompliance with any metric was exceeded within a quarter. Consecutive quarters of noncompliance resulted in escalating interventions, including progress meetings with department leadership, and culminated in financial penalties. Rates of noncompliance were compared before and after implementation of this model.

RESULTS

Three thousand six hundred sixty pre-Dashboard and 9497 post-Dashboard simulations were analyzed. After Dashboard implementation, significant reductions were observed in the rates of simulation orders requiring signature by a covering physician (14.1% vs 7.4%, P < .001), and the submission of plan contours ≥1 day (43.1% vs 23.1%, P < .001) or ≥2 days (30.8% vs 18.3%, P = .002) after the due date. There was some decrease in rates of inaccurate or incomplete plan submissions (6.2% vs 3.9%, P = .08). Seven of the 12 physicians received at least 1 intervention, with only 2 receiving all levels of intervention.

CONCLUSIONS

Regular assessment and targeted feedback using the Physician Dashboard significantly improved radiation oncologist compliance with clinically meaningful treatment planning responsibilities at a high-volume academic center.

An integrated HFMEA-DES model for performance improvement of general hospitals

Purpose

Healthcare failure mode and effect analysis (HFMEA) identifies potential risks and defines preventive actions to reduce the effects of risks. In addition, a discrete event simulation (DES) could evaluate the effects of every improvement scenario. Consequently, a proposed integrated HFMEA-DES model is presented for quality improvement in a general hospital.

Design/methodology/approach

In the proposed model, HFMEA is implemented first. As any risk in the hospital is important and that there are many departments and different related risks, all defined risk factors are evaluated using the risk priority number (RPN) for which related corrective actions are defined based on experts' knowledge. Then, a DES model is designed to determine the effects of selected actions before implementation.

Findings

Results show that the proposed model not only supports different steps of HFMEA but also is highly in accordance with the determination of real priorities of the risk factors. It predicts the effects of corrective actions before implementation and helps hospital managers to improve performances.

Practical implications

This research is based on a case study in a well-known general hospital in Iran.

Originality/value

This study takes the advantages of an integrated HFMEA-DES model in supporting the limitation of HFMEA in a general hospital with a large number of beds and patients. The case study proves the effectiveness of the proposed approach for improving the performances of the hospital resources.

FMEA-guided transition from microSelectron to Flexitron for HDR brachytherapy

PURPOSE

To utilize failure mode and effects analysis (FMEA) to effectively direct the transition from the Elekta microSelectron to the Flexitron high dose-rate afterloader system.

MATERIALS AND METHODS

Our FMEA was performed in two stages. In the first stage, the lead brachytherapy physicists used FMEA to guide the brainstorming sessions and to identify vulnerabilities during this transition. The second stage of FMEA was carried out 2 months after the clinical release of the Flexitron system. The process map was examined again to further refine and improve the entire process.

RESULTS

In the first-stage FMEA, 81 process steps were identified. Moreover, 80 failure modes and their categorized causes were recognized. Checklists and data books containing the corresponding applicator information were verified and updated. Next, based on outcomes of our first-stage FMEA, we chose to implement the commissioning process in two phases. The second stage of FMEA identified error-prone steps in our newly updated processes. This second stage of analysis resulted in the development of new tools and checklist items.

CONCLUSIONS

The two-stage FMEA approach successfully directed the transition to the Flexitron system by identifying the necessary changes in the checklists and workflows for all applicators utilized in our clinic. It also led to the decision to use a two-phase commissioning approach. This allowed for minimization clinical downtime, avoidance of an extra source change, and facilitation of efficient staff training. Additionally, multiple project-level failures were discovered. Our experience and outcomes from this FMEA-guided transition should provide valuable information to the brachytherapy community.

Failure mode and effects analysis of linac-based liver stereotactic body radiotherapy

PURPOSE

Although stereotactic body radiation therapy (SBRT) is an attractive noninvasive approach for liver irradiation, it presents specific challenges associated with respiration-induced liver motion, daily tumor localization due to liver deformation, and poor visualization of target with respect to adjacent normal liver in computed tomography (CT). We aim to identify potential hazards and develop a set of mitigation strategies to improve the safety of our liver SBRT program, using failure mode and effect analysis (FMEA).

MATERIALS AND METHODS

A multidisciplinary group consisting of two physicians, three physicists, two dosimetrists, and two therapists was formed. A process map covering ten major stages of the liver SBRT program from the initial diagnosis to posttreatment follow-up was generated. A total of 102 failure modes (FM), together with their causes and effects, were identified. The occurrence (O), severity (S), and lack of detectability (D) were independently scored using a scale from 1 (lowest risk) to 10 (largest risk). The ranking was done using the risk probability number (RPN) defined as the product of average O, S, and D numbers for each mode. Two fault tree analyses were performed. The failure modes with the highest RPN values as well as highest severity score were considered for investigation and a set of mitigation strategies was developed to address these.

RESULTS

The median RPN (RPN_med ) values for all modes ranged from of 9 to 105 and the highest median S score (S_med ) was 8. Fourteen FMs were identified to be significant by both RPN_med and S_med (top ten RPN_med ranked and highest S_med FMs) and 12 of them were considered for risk mitigation efforts. The remaining two were omitted due to either sufficient checks already in place, or lack of practical mitigation strategies. Implemented measures consisted of five physics tasks, two physician tasks, and three workflow changes.

CONCLUSIONS

The application of FMEA to our liver SBRT program led to the identification of potential FMs and allowed improvement measures to enhance the safety of our clinical practice.

Development and validation of a comprehensive patient-specific quality assurance program for a novel stereotactic radiation delivery system for breast lesions

PURPOSE

The GammaPod is a dedicated prone breast stereotactic radiosurgery (SRS) machine composed of 25 cobalt-60 sources which rotate around the breast to create highly conformal dose distributions for boosts, partial-breast irradiation, or neo-adjuvant SRS. We describe the development and validation of a patient-specific quality assurance (PSQA) system for the GammaPod.

METHODS

We present two PSQA methods: measurement based and calculation based PSQA. The measurements are performed with a combination of absolute and relative dose measurements. Absolute dosimetry is performed in a single point using a 0.053-cc pinpoint ionization chamber in the center of a polymethylmethacrylate (PMMA) breast phantom and a water-filled breast cup. Relative dose distributions are verified with EBT3 film in the PMMA phantom. The calculation-based method verifies point doses with a novel semi-empirical independent-calculation software.

RESULTS

The average (± standard deviation) breast and target sizes were 1263 ± 335.3 cc and 66.9 ± 29.9 cc, respectively. All ion chamber measurements performed in water and the PMMA phantom agreed with the treatment planning system (TPS) within 2.7%, with average (max) difference of -1.3% (-1.9%) and -1.3% (-2.7%), respectively. Relative dose distributions measured by film showed an average gamma pass rate of 97.0 ± 3.2 when using a 3%/1 mm criteria. The lowest gamma analysis pass rate was 90.0%. The independent calculation software had average agreements (max) with the patient and QA plan calculation of 0.2% (2.2%) and -0.1% (2.0%), respectively.

CONCLUSION

We successfully implemented the first GammaPod PSQA program. These results show that the GammaPod can be used to calculate and deliver the predicted dose precisely and accurately. For routine PSQA performed prior to treatments, the independent calculation is recommended as it verifies the accuracy of the planned dose without increasing the risk of losing vacuum due to prolonged waiting times.

Incorporating Human Factors Analysis and Classification System (HFACS) Into Analysis of Reported Near Misses and Incidents in Radiation Oncology

PURPOSE

Human factors analysis and classification system (HFACS) is a framework for investigation into causation of human errors. We herein assess whether radiation oncology professionals, with brief training, can conduct HFACS on reported near misses or safety incidents (NMSIs) in a reliable (eg, with a high level of agreement) and practical (eg, timely and with user satisfaction) manner.

METHODS AND MATERIALS

We adapted a classical HFACS framework by selecting and modifying main headings, subheadings, and nano-codes that were most likely to apply to radiation oncology settings. The final modified HFACS included 3 main headings, 8 subheadings, and 20 nano-codes. The modified HFACS was first tested in a simulated trial on 8 NMSI and was analyzed by 5 to 10 radiation oncology professionals, with 2 endpoints: (1) agreement among participants at the main-heading, subheading, and nano-code level, and (2) time to complete the analysis. We then performed a prospective trial integrating this approach into a weekly NMSI review meeting, with 10 NMSIs analyzed by 8 to 13 radiation oncology professionals with the same endpoints, while also collecting survey data on participants' satisfaction.

RESULTS

In the simulated trial, agreement among participants was 85% on the main headings, 73% on the subheadings, and 70% on the nano-codes. Participants needed, on average, 16.4 minutes (standard deviation, 5.7 minutes) to complete an analysis. In the prospective trial, agreement between participants was 81% on the main headings, 75% on the subheadings, and 74% on the nano-codes. Participants needed, on average, 8.3 minutes (standard deviation, 4.7 minutes) to complete an analysis. The average satisfaction with the proposed HFACS approach was 3.9 (standard deviation 1.0) on a scale from 1 to 5.

CONCLUSIONS

This study demonstrates that, after relatively brief training, radiation oncology professionals were able to perform HFACS analysis in a reliable and timely manner and with a relatively high level of satisfaction.