Integrating human factors engineering into your pediatric radiology practice.

Human factors engineering involves the study and development of methods aimed at enhancing performance, improving safety, and optimizing user satisfaction. The focus of human factors engineering encompasses the design of work environments and an understanding of human mental processes to prevent errors. In this review, we summarize the history, applications, and impacts of human factors engineering on the healthcare field. To illustrate these applications and impacts, we provide several examples of how successful integration of a human factors engineer in our pediatric radiology department has positively impacted various projects. The successful integration of human factors engineering expertise has contributed to projects including improving response times for portable radiography requests, deploying COVID-19 response resources, informing the redesign of scheduling workflows, and implementation of a virtual ergonomics program for remote workers. In sum, the integration of human factors engineering insight into our department has resulted in tangible benefits and has also positioned us as proactive contributors to broader hospital-wide improvements.

Deep learning-based prediction of rib fracture presence in frontal radiographs of children under two years of age: a proof-of-concept study.

OBJECTIVE: In this proof-of-concept study, we aimed to develop deep-learning-based classifiers to identify rib fractures on frontal chest radiographs in children under 2 years of age. METHODS: This retrospective study included 1311 frontal chest radiographs (radiographs with rib fractures, n = 653) from 1231 unique patients (median age: 4 m). Patients with more than one radiograph were included only in the training set. A binary classification was performed to identify the presence or absence of rib fractures using transfer learning and Resnet-50 and DenseNet-121 architectures. The area under the receiver operating characteristic curve (AUC-ROC) was reported. Gradient-weighted class activation mapping was used to highlight the region most relevant to the deep learning models' predictions. RESULTS: On the validation set, the ResNet-50 and DenseNet-121 models obtained an AUC-ROC of 0.89 and 0.88, respectively. On the test set, the ResNet-50 model demonstrated an AUC-ROC of 0.84 with a sensitivity of 81% and specificity of 70%. The DenseNet-50 model obtained an AUC of 0.82 with 72% sensitivity and 79% specificity. CONCLUSION: In this proof-of-concept study, a deep learning-based approach enabled the automatic detection of rib fractures in chest radiographs of young children with performances comparable to pediatric radiologists. Further evaluation of this approach on large multi-institutional data sets is needed to assess the generalizability of our results. ADVANCES IN KNOWLEDGE: In this proof-of-concept study, a deep learning-based approach performed well in identifying chest radiographs with rib fractures. These findings provide further impetus to develop deep learning algorithms for identifying rib fractures in children, especially those with suspected physical abuse or non-accidental trauma.

Results of a Virtual Multi-Institutional Program for Quality Improvement Training and Project Facilitation.

OBJECTIVE: The purpose of this project was to describe the results of a multi-institutional quality improvement (QI) program conducted in a virtual format. METHODS: Developed at Stanford in 2016, the Realizing Improvement Through Team Empowerment program uses a team-based, project-based improvement approach to QI. The program was planned to be replicated at two other institutions through respective on-site programs but was converted to a multi-institutional virtual format in 2020 in response to the COVID-19 pandemic. The virtual program began in July 2020 and ended in December 2020. The two institutions participated jointly in the cohort, with 10 2-hour training sessions every 2 weeks for a total of 18 weeks. Project progress was monitored using a predetermined project progress scale by the program manager, who provided more direct project support as needed. RESULTS: The cohort consisted of six teams (37 participants) from two institutions. Each team completed a QI project in subjects including MRI, ultrasound, CT, diagnostic radiography, and ACR certification. All projects reached levels of between 3.0 (initial test cycles begun with evidence of modest improvement) and 4.0 (performance data meeting goal and statistical process control criteria for improvement) and met graduation criteria for program completion. DISCUSSION: We found the structured problem-solving method, along with timely focused QI education materials via a virtual platform, to be effective in simultaneously facilitating improvement projects from multiple institutions. The combination of two institutions fostered encouragement and shared learning across institutions.

Using human factors principles to redesign a 3D lab workflow during the COVID-19 pandemic.

BACKGROUND: Like most hospitals, our hospital experienced COVID-19 pandemic-related supply chain shortages. Our additive manufacturing lab's capacity to offset these shortages was soon overwhelmed, leading to a need to improve the efficiency of our existing workflow. We undertook a work system analysis guided by the Systems Engineering Initiative for Patient Safety (SEIPS) construct which is based on human factors and quality improvement principles. Our objective was to understand the inefficiencies in project submission, review, and acceptance decisions, and make systematic improvements to optimize lab operations. METHODS: Contextual inquiry (interviews and workflow analysis) revealed suboptimal characteristics of the system, specifically, reliance on a single person to facilitate work and, at times, fractured communication with project sponsors, with root causes related to the project intake and evaluation process as identified through SEIPS tools. As interventions, the analysis led us to: 1) enhance an existing but underused project submission form, 2) design and implement an internal project scorecard to standardize evaluation of requests, and 3) distribute the responsibility of submission evaluation across lab members. We implemented these interventions in May 2021 for new projects and compare them to our baseline February 1, 2018 through - April 30, 2021 performance (1184 days). RESULTS: All project requests were submitted using the enhanced project submission form and all received a standardized evaluation with the project scorecard. Prior to interventions, we completed 35/79 (44%) of projects, compared to 12/20 (60%) of projects after interventions were implemented. Time to review new submissions was reduced from an average of 58 days to 4 days. A more distributed team responsibility structure permitted improved workflow with no increase in staffing, allowing the Lab Manager to devote more time to engineering rather than administrative/decision tasks. CONCLUSIONS: By optimizing our workflows utilizing a human factors approach, we improved the work system of our additive manufacturing lab to be responsive to the urgent needs of the pandemic. The current workflow provides insights for labs aiming to meet the growing demand for point-of-care manufacturing.

MRI evaluation of pediatric tibial eminence fractures: comparison between conventional and "CT-like" ultrashort echo time (UTE) images.

OBJECTIVE: UTE MRI offers a radiation-free alternative to CT for bone depiction, but data on children is lacking. The purpose of this study was to determine whether UTE images improve detection and characterization of pediatric tibial eminence fractures. METHODS: Fifteen MRIs with UTE from 12 children (10 boys, 2 girls; mean age: 12.6 ± 3.3 years) with tibial eminence fractures (2018-2020) and 15 age-matched MRIs without fractures were included. After randomization, 5 readers reviewed images without and with UTE, at least 1 month apart, and recorded the presence of fracture and preferred images. If fracture is present, radiologists also recorded fragment size, number, and displacement; surgeons assigned Meyers-McKeever grade and management. Disagreements on management were resolved through consensus review. Kappa and intra-class correlation (ICC), sensitivity, and specificity were used to compare agreement between readers and fracture detection between images without and with UTE. RESULTS: For fracture detection, inter-reader agreement was almost perfect (κ-range: 0.91-0.93); sensitivity and specificity were equivalent between images without and with UTE (range: 95-100%). For fracture characterization, UTE improved agreement on size (ICC = 0.88 to 0.93), number (ICC = 0.52 to 0.94), displacement (ICC = 0.74 to 0.86), and grade (ICC = 0.92 to 0.93) but reduced agreement on management (κ = 0.68 to 0.61), leading to a change in consensus management in 20% (3/15). Radiologists were more likely to prefer UTE for fracture and conventional images for non-fracture cases (77% and 77%, respectively, p < 0.001). CONCLUSION: While UTE did not improve diagnosis, it improved agreement on characterization of pediatric tibial eminence fractures, ultimately changing the preferred treatment in 20%.

Artificial intelligence development in pediatric body magnetic resonance imaging: best ideas to adapt from adults.

Emerging manifestations of artificial intelligence (AI) have featured prominently in virtually all industries and facets of our lives. Within the radiology literature, AI has shown great promise in improving and augmenting radiologist workflow. In pediatric imaging, while greatest AI inroads have been made in musculoskeletal radiographs, there are certainly opportunities within thoracoabdominal MRI for AI to add significant value. In this paper, we briefly review non-interpretive and interpretive data science, with emphasis on potential avenues for advancement in pediatric body MRI based on similar work in adults. The discussion focuses on MRI image optimization, abdominal organ segmentation, and osseous lesion detection encountered during body MRI in children.

Can Radiology Technologists be Trained to Measure Leg Length Discrepancies as Accurately as Pediatric Radiologists?

RATIONALE AND OBJECTIVES: Leg length discrepancy studies are labor intensive. They are procedurally simple and represent inefficient use of the radiologists' time and expertise. We hypothesized that radiology technologists could be trained to measure leg length discrepancies, and that their performance would be statistically equivalent to that of board-certified, fellowship-trained pediatric radiologists. MATERIAL AND METHODS: Four radiology technologists were selected to participate in a supervised practice session. They independently measured and calculated leg length discrepancies on 10 randomly selected cases. Their performance was compared to measurements obtained by an experienced pediatric radiologist (reference standard). After 1 week, the technologists repeated their measurements on the same cases, which were resorted to simulate new cases. Intraclass correlation coefficients (ICC) determined interobserver agreement between the technologists and radiologist and intra-observer reliability among the technologists. RESULTS: Among the four technologists, similarity in measurements between session 1 and the reference standard was very high, with ICC values ranging from 0.93 to 0.98 (p < 0.001). The ICC between session 2 and the reference standard was also high, ranging from 0.93 to 0.98 (p < 0.001). Finally, among the four technologists, ICC values between session 1 and session 2 were ≥ 0.96 (p < 0.001). CONCLUSION: Radiology technologists can be rapidly trained to calculate leg length discrepancies as accurately as a board-certified pediatric radiologist. Delegation of this time-consuming task to technologists or radiology assistants will permit radiologists to spend time on more demanding studies, such as studies that require subspecialty training.

Pediatric musculoskeletal pathologies: are there differences in triage of diagnoses and preferences for communication between radiology and orthopedics?

OBJECTIVE: To define the clinical importance of various pediatric musculoskeletal diagnoses, determine preferred communication methods based on the acuity level of findings, and investigate differences between specialties utilizing the Delphi methodology. METHODS: Radiologists, orthopedic surgeons, and sports-medicine pediatricians at a tertiary children's hospital were surveyed (n = 79) twice using REDCap (Research Electronic Data Capture). Surveys were conducted anonymously and at least 1 year apart, first eliciting all potentially non-routine findings and various communication methods (round 1), and later categorizing the acuity (emergent, urgent, or non-urgent) of different diagnosis categories and selecting the preferred communication method (verbal, written electronic messages, and report) and timeframe (round 2). Chi-square, Fisher's exact, and Kruskal-Wallis H tests were used to compare variables between specialties. RESULTS: Round 1 produced 267 entries for non-routine findings (grouped into 19 diagnoses) and 71 for communication methods (grouped into 3 categories). Round 2 found no significant difference in the acuity assignments for the 19 predetermined diagnoses (p = 0.66) between the 3 specialties; however, there was reduced agreement for the top urgent diagnoses within and between specialties. Most pediatricians preferred written electronic messages. The preferred communication timeframe for urgent diagnoses was significantly different (< 2 h for pediatricians, < 4 h for radiologists, and < 8 h for surgeons; p = 0.003) between specialties whereas no difference was found for emergent (p = 1) and non-urgent diagnoses (p = 0.80). CONCLUSION: Acuity assignment for the 19 pediatric-specific musculoskeletal diagnoses was not significantly different between specialties, but the preferred communication timeframe for urgent diagnoses was significantly different, ranging between 2 and 8 h.

Optimizing Radiology Reading Room Design: The Eudaimonia Radiology Machine.

Physical and mental stressors on radiologists can result in burnout. Although current efforts seek to target the issues of burnout and stress for radiologists, the impact of their physical workspace is often overlooked. By combining evidence-based design, human factors, and the architectural concept of the Eudaimonia Machine, we have developed a redesign of the radiology reading room that aims to create an optimal workspace for the radiologist. Informed by classical principles of well-being and contemporary work theory, Eudaimonia integrates concerns for individual wellness and efficiency to create an environment that fosters productivity. This layout arranges a work environment into purposeful spaces, each hosting tasks of varying degrees of intensity. The improved design addresses the radiologist's work requirements while also alleviating cognitive and physical stress, fatigue, and burnout. This new layout organizes the reading room into separate areas, each with a distinct purpose intended to support the range of radiologists' work, from consultation with other health care providers to reading images without interruption. The scientific principles that undergird evidence-based design and human factors considerations ensure that the Eudaimonia Radiology Machine is best suited to support the work of the radiologists and the entire radiology department.

A primer for pediatric radiologists on infection control in an era of COVID-19.

Pediatric radiology departments across the globe face unique challenges in the midst of the current COVID-19 pandemic that have not been addressed in professional guidelines. Providing a safe environment for personnel while continuing to deliver optimal care to patients is feasible when abiding by fundamental recommendations. In this article, we review current infection control practices across the multiple pediatric institutions represented on the Society for Pediatric Radiology (SPR) Quality and Safety committee. We discuss the routes of infectious transmission and appropriate transmission-based precautions, in addition to exploring strategies to optimize personal protective equipment (PPE) supplies. This work serves as a summary of current evidence-based recommendations for infection control, and current best practices specific to pediatric radiologists.

Vitamin C deficiency mimicking inflammatory bone disease of the hand.

BACKGROUND: Severe vitamin C deficiency, or scurvy, encompasses a syndrome of multisystem abnormalities due to defective collagen synthesis and antioxidative functions. Among the more common presentations is a combination of oral or subcutaneous hemorrhage with lower extremity pain, the latter often exhibiting inflammatory bone changes on magnetic resonance imaging (MRI). CASE PRESENTATION: A 12-year-old male with anorexia nervosa presented with asymmetric painful swelling of multiple fingers of both hands. Imaging demonstrated soft tissue and bone marrow edema of several phalanges, without arthritis, concerning for an inflammatory process. Extensive imaging and laboratory evaluations were largely unrevealing, with the exception of a severely low vitamin C level and a moderately low vitamin D level. A diagnosis of scurvy was made and supplementation was initiated. Within 3 weeks of treatment, serum levels of both vitamins normalized and the digital abnormalities resolved on physical exam. CONCLUSIONS: This represents the first description of scurvy manifesting with bone and soft tissue changes limited to the hands. There must be a high index of suspicion for scurvy in children with restricted dietary intake or malabsorption who have bone pain, irrespective of location of the lesions.

The growing issue of burnout in radiology - a survey-based evaluation of driving factors and potential impacts in pediatric radiologists.

BACKGROUND: Burnout in medicine, and specifically radiology, has been receiving more attention. Little data-driven literature is available regarding risk factors/causes to ultimately help guide the development of potential solutions. OBJECTIVE: To survey pediatric radiologists, a cohort with a documented high prevalence of burnout, and to understand the impact of clinical demands on nonclinical tasks and the implications of burnout on mental health. MATERIALS AND METHODS: A survey of Society for Pediatric Radiology (SPR) North America attendings was performed regarding institutional factors contributing to burnout, including call burden, clinical demands, departmental support and administrative/academic tasks. Questions regarding mental health and wellness resources were also included. Generalized linear modeling assuming binomial distribution was used for analyses with SAS 9.4. RESULTS: The response rate was 305/1,282 (24%) with 53% of respondents female. Respondents reported that both the number and complexity of clinical cases have increased since they first started practice as an attending, while the time for interpretation has not changed, P<0.0001. Using a scale of 0 (never), 1 (rarely), 2 (sometimes), 3 (frequently) and 4 (always), covering multiple hospitals (2.2) and administrative tasks (2.4) were the most stressful job factors. For those in administrative roles, the most stressful job factors were job-related tasks affected teaching duties (2.0) and decreased overall job satisfaction (2.0). Of the respondents, 52% said they know a physician affected by work stress-related mental illness and 25% know a physician who has contemplated or committed suicide. While 39% of the respondents have resources available to address burnout, only 33% utilize these resources. CONCLUSION: Increasing clinical demands and additional institutional/departmental factors play a potential role in burnout, which has serious implications for the mental health of pediatric radiologists.

Deep Learning Measurement of Leg Length Discrepancy in Children Based on Radiographs.

Background Radiographic measurement of leg length discrepancy (LLD) is time consuming yet cognitively simple for pediatric radiologists. Purpose To compare deep learning (DL) measurements of LLD in pediatric patients to measurements performed by radiologists. Materials and Methods For this HIPAA-compliant retrospective study, radiographs obtained to evaluate LLD in children between January and August 2018 were identified. LLD was automatically measured by means of image segmentation followed by leg length calculation. On training data, a DL model was trained to segment femurs and tibias on radiographs. The validation set was used to select the optimized model. On testing data, leg lengths were calculated from segmentation masks and compared with measurements from the radiology report. Statistical analysis was performed by using a paired Wilcoxon signed-rank test to compare DL calculations and radiology reports. In addition, the measurement time was manually assessed by a pediatric radiologist and automatically assessed by the DL model on a randomly chosen group of 26 cases; the values were compared with the paired Wilcoxon signed-rank test. Results Radiographs obtained to evaluate LLD in 179 children (mean age ± standard deviation, 12 years ± 3; age range, 5-19 years; 89 boys and 90 girls) were evaluated. Radiographs were randomly divided into training, validation, and testing sets and consisted of studies from 70, 32, and 77 patients, respectively. In the training and validation sets, the DL model showed a high spatial overlap between manual and automatic segmentation masks of pediatric legs (Dice similarity coefficient, 0.94). For the testing set, the correlation between radiology reports and DL-calculated lengths of separated femurs and tibias (r = 0.99; mean absolute error [MAE], 0.45 cm), full pediatric leg lengths (r = 0.99; MAE, 0.45 cm), and full LLD (r = 0.92; MAE, 0.51 cm) was high (P < .001 for all correlations). Calculation time for the DL method per radiograph was faster than the mean time for radiologist manual calculation (1 second vs 96 seconds ± 7, respectively; P < .001). Conclusion A deep learning algorithm measured pediatric leg lengths with high spatial overlap compared with manual measurement at a rate 96 times faster than that of subspecialty-trained pediatric radiologists. © RSNA, 2020 See also the editorial by van Rijn and De Luca in this issue.

'Walk in my shoes': intradepartmental role shadowing to increase workplace collegiality and wellness in a large pediatric radiology department.

BACKGROUND: Our nearly 500-member department implemented the shadowing program "Walk in My Shoes" to improve intradepartmental relationships and build a stronger sense of community. The program provides both clinical and non-clinical employees an opportunity to shadow colleagues in their various roles and learn more about one another's contribution to the overarching mission of caring for children and their families. The goal of this study was to understand the impact of the shadowing program on employee perceptions of various roles. OBJECTIVE: To bridge the gap of understanding among colleagues in order to strengthen workplace interrelatedness, increase understanding of various roles, and decrease preconceived notions about roles, through shadowing. MATERIALS AND METHODS: A preliminary survey distributed to our department in August 2018 assessed the level of interest in new wellness initiatives, including the shadowing program. The survey gauged which roles participants were interested in shadowing. The survey results revealed that 67 employees were interested in the shadowing program. We selected 39 participants and matched them to a coworker in their area of interest. The roles for shadowing included administrator, Child Life specialist, information technologist, medical assistant, nurse, radiologist, researcher and technologist. Participants were required to complete pre- and post-shadowing surveys to assess their experience. Individuals who hosted the shadow experience also completed a survey. RESULTS: A total of 39 clinical and non-clinical staff members participated in the program. We summarized the pre- and post-survey data using median and interquartile range (IQR) and compared the results using the Wilcoxon signed-rank test. The distribution of preconceived notions about each role was not significantly different between the pre- and post-surveys (P=0.094). However, participants' value, understanding of the role they shadowed, and understanding of how the roles relate to each other were significantly greater (P<0.001). In addition, participants showed great interest in shadowing the specific role again (82%) and shadowing another role (92%). Furthermore, almost all hosts would repeat the experience (96%). CONCLUSION: Our study showed that intradepartmental shadowing can improve clinical and non-clinical staff employees' perceptions and understanding of each other's roles in overall patient care, which in turn contributes to the broader initiative of workplace wellness. The enthusiasm and willingness of the hosts were essential for sustainability of the program and demonstrated that this type of program is feasible in a large, busy department.

The Business Case for Evidence-Based Design in Radiology Departments.

Large sums of money are being spent on new construction and remodeling of radiology workspaces, with often disappointing results. Poorly designed radiology environments can contribute to medical error, safety risks to patients and staff, costly inefficiency, and avoidable stress to families and health care workers. Evidence-based design seeks to apply hypothesis-driven, literature-based methodology to the process of creating medical environments that make it easier, not harder, to provide safe and high-quality care to patients. Radiology environments are particularly challenging because of the intimidating size of imaging equipment, the complexity of imaging procedures, and the multiple medical disciplines that often need to work together in shared spaces to provide that care. Evidence-based design projects are currently relatively sparse in the literature. This article provides radiologists and radiology administrators with an introduction to the principles and practice of evidence-based design and a review of the evidence-based design literature specific to radiology workspaces and practice.

Imaging Properties of Additive Manufactured (3D Printed) Materials for Potential Use for Phantom Models.

Over the last few decades, there has been growing interest in the application of additive manufacturing (AM) or 3D printing for medical research and clinical application. Imaging phantoms offer clear benefits in the way of training, planning, and quality assurance, but the model's availability per catalog tend to be suited for general testing purposes only. AM, on the contrary, offers flexibility to clinicians by enabling custom-built phantoms based on specific interests or even individual patient needs. This study aims to quantify the radiographic properties (ultrasound, magnetic resonance imaging, and computed tomography) of common additive manufacturing technologies and to discuss potential opportunities to fabricate imaging phantoms. Test phantoms were composed of samples from the three most common AM styles, namely PolyJet, fused deposition modeling (FDM), and stereolithography (SLA). Test imaging of the phantoms was performed on ultrasound, MRI, and CT and reviewed and evaluated with radiology software. The ultrasound images showed clearly defined upper and lower edges of the material but did not demonstrate distinct differences in internal echogenicity between materials. The MR scans revealed a distinct signal intensity difference between the model (17 grayscale value) and the printer support (778 grayscale value). Finally, the CT images showed a slight variation between the plastic (82 HU) and rubber (145 HU) materials. The radiographic properties of AM offer a clear opportunity to create basic two- or three-material phantoms. These would be high-accuracy and cost-effective models. Although the materials currently available are not suitable for complex multi-material applications as realistic as true human anatomy, one can easily foresee the development of new materials with broader density in the near future.

Imaging of developmental dysplasia of the hip: ultrasound, radiography and magnetic resonance imaging.

Developmental dysplasia of the hip (DDH) describes a broad spectrum of developmental abnormalities of the hip joint that are traditionally diagnosed during infancy. Because the development of the hip joint is a dynamic process, optimal treatment depends not only on the severity of the dysplasia, but also on the age of the child. Various imaging modalities are routinely used to confirm suspected diagnosis, to assess severity, and to monitor treatment response. For infants younger than 4 months, screening hip ultrasound (US) is recommended only for those with risk factors, equivocal or positive exam findings, whereas for infants older than 4-6 months, pelvis radiography is preferred. Following surgical hip reduction, magnetic resonance (MR) imaging is preferred over computed tomography (CT) because MR can not only confirm concentric hip joint reduction, but also identify the presence of soft-tissue barriers to reduction and any unexpected postoperative complications. The routine use of contrast-enhanced MR remains controversial because of the relative paucity of well-powered and validated literature. The main objectives of this article are to review the normal and abnormal developmental anatomy of the hip joint, to discuss the rationale behind the current recommendations on the most appropriate selection of imaging modalities for screening and diagnosis, and to review routine and uncommon findings that can be identified on post-reduction MR, using an evidence-based approach. A basic understanding of the physiology and the pathophysiology can help ensure the selection of optimal imaging modality and reduce equivocal diagnoses that can lead to unnecessary treatment.