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.

Automating Angle Measurements on Foot Radiographs in Young Children: Feasibility and Performance of a Convolutional Neural Network Model.

Measurement of angles on foot radiographs is an important step in the evaluation of malalignment. The objective is to develop a CNN model to measure angles on radiographs, using radiologists' measurements as the reference standard. This IRB-approved retrospective study included 450 radiographs from 216 patients (< 3 years of age). Angles were automatically measured by means of image segmentation followed by angle calculation, according to Simon's approach for measuring pediatric foot angles. A multiclass U-Net model with a ResNet-34 backbone was used for segmentation. Two pediatric radiologists independently measured anteroposterior and lateral talocalcaneal and talo-1st metatarsal angles using the test dataset and recorded the time used for each study. Intraclass correlation coefficients (ICC) were used to compare angle and paired Wilcoxon signed-rank test to compare time between radiologists and the CNN model. There was high spatial overlap between manual and CNN-based automatic segmentations with dice coefficients ranging between 0.81 (lateral 1st metatarsal) and 0.94 (lateral calcaneus). Agreement was higher for angles on the lateral view when compared to the AP view, between radiologists (ICC: 0.93-0.95, 0.85-0.92, respectively) and between radiologists' mean and CNN calculated (ICC: 0.71-0.73, 0.41-0.52, respectively). Automated angle calculation was significantly faster when compared to radiologists' manual measurements (3 ± 2 vs 114 ± 24 s, respectively; P < 0.001). A CNN model can selectively segment immature ossification centers and automatically calculate angles with a high spatial overlap and moderate to substantial agreement when compared to manual methods, and 39 times faster.

A proactive learning approach toward building adaptive capacity during COVID-19: A radiology case study.

The COVID-19 pandemic has challenged organizations to adapt under uncertainty and time pressure, with no pre-existing protocols or guidelines available. For organizations to learn to adapt effectively, there is a need to understand the perspectives of the frontline workforce involved in everyday operations. This study implemented a survey-tool to elicit narratives of successful adaptation based on the lived experiences frontline radiology staff at a large multispecialty pediatric hospital. Fifty-eight members of the radiology frontline staff responded to the tool between July and October of 2020. Qualitative analysis of the free-text data revealed five categories of themes that underpinned adaptive capacity of the radiology department during the pandemic: information flow, attitudes and initiative, new and adjusted workflows, availability and utilization of resources, and collaboration and teamwork. Enablers of adaptive capacity included timely and clear communication about procedures and policies from the leadership to frontline staff, and revised workflows with flexible work arrangements, such as remote patient screening. Responses to multiple choice questions in the tool helped identify the main categories of challenges faced by staff, factors that enabled successful adaptation, and resources used. The study demonstrates the use of a survey-tool to proactively identify frontline adaptations. The paper also reports a system-wide intervention resulting directly from a discovery enabled by the findings based on the use of RETIPS in the radiology department. In general, the tool could be used in concert with existing learning mechanisms, such as safety event reporting systems, to inform leadership-level decisions to support adaptive capacity.

A Patch-Based Deep Learning Approach for Detecting Rib Fractures on Frontal Radiographs in Young Children.

Chest radiography is the modality of choice for the identification of rib fractures in young children and there is value for the development of computer-aided rib fracture detection in this age group. However, the automated identification of rib fractures on chest radiographs can be challenging due to the need for high spatial resolution in deep learning frameworks. A patch-based deep learning algorithm was developed to automatically detect rib fractures on frontal chest radiographs in children under 2 years old. A total of 845 chest radiographs of children 0-2 years old (median: 4 months old) were manually segmented for rib fractures by radiologists and served as the ground-truth labels. Image analysis utilized a patch-based sliding-window technique, to meet the high-resolution requirements for fracture detection. Standard transfer learning techniques used ResNet-50 and ResNet-18 architectures. Area-under-curve for precision-recall (AUC-PR) and receiver-operating-characteristic (AUC-ROC), along with patch and whole-image classification metrics, were reported. On the test patches, the ResNet-50 model showed AUC-PR and AUC-ROC of 0.25 and 0.77, respectively, and the ResNet-18 showed an AUC-PR of 0.32 and AUC-ROC of 0.76. On the whole-radiograph level, the ResNet-50 had an AUC-ROC of 0.74 with 88% sensitivity and 43% specificity in identifying rib fractures, and the ResNet-18 had an AUC-ROC of 0.75 with 75% sensitivity and 60% specificity in identifying rib fractures. This work demonstrates the utility of patch-based analysis for detection of rib fractures in children under 2 years old. Future work with large cohorts of multi-institutional data will improve the generalizability of these findings to patients with suspicion of child abuse.

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.

Pediatric Genitourinary 3D Modeling and Printing Using Multiphase Postcontrast Imaging Segmentation.

OBJECTIVE: To describe the development and implementation of a process for creating accurate Pediatric genitourinary 3D modeling and printing with multiphase postcontrast imaging for surgical planning. MATERIALS AND METHODS: Additive manufacturing and 3D model present opportunities to support clinical planning, this manuscript's specific process and considerations for creating pediatric genitourinary 3D modeling to support urology. The process for creating the 3D models and prints covers 3 key aspects from image acquisition, imaging review and selection, and segmentation and modification (as needed). Each step is outlined with the key roles and procedures. RESULTS: The described case had digital and printed models prepared with references to the optimized imaging sequence for 3D modeling of Pediatric genitourinary. Case shared include complex genitourinary reconstruction and Kideny with Wilms tumors. CONCLUSION: The processes described have become a standard of practice for complex kidney tumors and exstrophy planning. The team continues to work on ever-changing improvements to make the best possible models to support clinical and surgical planning.

Artificial Intelligence Curriculum Needs Assessment for a Pediatric Radiology Fellowship Program: What, How, and Why?

RATIONALE AND OBJECTIVES: Artificial intelligence (AI) holds enormous potential for improvements in patient care, efficiency, and innovation in pediatric radiology practice. Although there is a pressing need for a radiology-specific training curriculum and formalized AI teaching, few resources are available. The purpose of our study was to perform a needs assessment for the development of an AI curriculum during pediatric radiology training and continuing education. MATERIALS AND METHODS: A focus group study using a semistructured moderator-guided interview was conducted with radiology trainees' and attending radiologists' perceptions of AI, perceived competence in interpretation of AI literature, and perceived expectations from radiology AI educational programs. The focus group was audio-recorded, transcribed, and thematic analysis was performed. RESULTS: The focus group was held virtually with seven participants. The following themes we identified: (1) AI knowledge, (2) previous training, (3) learning preferences, (4) AI expectations, and (5) AI concerns. The participants had no previous formal training in AI and variability in perceived needs and interests. Most preferred a case-based approach to teaching AI. They expressed incomplete understanding of AI hindered its clinical applicability and reiterated a need for improved training in the interpretation and application of AI literature in their practice. CONCLUSION: We found heterogeneity in perspectives about AI; thus, a curriculum must account for the wide range of these interests and needs. Teaching the interpretation of AI research methods, literature critique, and quality control through implementation of specific scenarios could engage a variety of trainees from different backgrounds and interest levels while ensuring a baseline level of competency in AI.

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.

Neonatal gastrointestinal emergencies: a radiological review.

BACKGROUND: Abdominal emergencies in neonates require surgical management in almost all cases and complications may include bowel perforation, sepsis, shock, and even death. Radiological imaging has become a very important aid in the clinical setting as it shortens time to diagnosis. OBJECTIVE: The objective of this review is to discuss the more prevalent neonatal gastrointestinal emergencies, review appropriate imaging options, and illustrate common radiological presentations of these entities. CONCLUSION: Despite advancements in imaging techniques, it is important to keep in mind that neonates have a higher susceptibility to the adverse effects of ionizing radiation, and therefore radiography and ultrasonography remain the main diagnostic modalities for ruling out the diseases with the worst prognosis. Other modalities (fluoroscopy, computed tomography, and magnetic resonance imaging) may have limited use in very specific conditions. All providers in an emergency department should be familiar with the basic radiological findings that may indicate a gastrointestinal emergency, especially in health institutions that do not have 24-h radiologist coverage.

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%.

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.

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.

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.

Additive manufacturing (3d printing) in response to a pandemic: Lessons learned at the children's hospital of Philadelphia.

The COVID-19 pandemic produced unprecedented challenges to healthcare and medical device manufacturing (e.g. personal protective device and replacement part shortages). Additive manufacturing, 3D printing, and the maker community were uniquely positioned to respond to these needs by providing in-house design and manufacturing to meet the needs of clinicians and hospitals. This paper reviews the pandemic response of Children's Hospital of Philadelphia CHAMP 3D Lab, a point-ofcare3D printing team that supports clinical and research projects across the hospital network. The CHAMP team responded to a variety of COVID-19 healthcare needs including providing protective eyewear and ventilator components, creating a transport hook, and designing a novel transparent facemask. This case series details our response to these needs, describing challenges experienced and lessons learned in overcoming them so that others may learn from our experiences. Challenges to responding to the pandemic included the need to handle urgent pandemic related requests in addition to our standard fare. This required us to not only expand our capacity without additional resources, but also to develop a system of prioritization. Specific changes made included: streamlining workflows, identifying safety review processes, and developing/enlisting a network of collaborators. Further, we consider how to transition to a future, post-pandemic world without losing the cohesive drive of emergency-induced innovation. This paper aims to share what we have learned and to encourage both teams currently engaged in the printing community and those looking to join it.

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.

Cerebral Pulsed Arterial Spin Labeling Perfusion Weighted Imaging Predicts Language and Motor Outcomes in Neonatal Hypoxic-Ischemic Encephalopathy.

Rationale and Objectives: To compare cerebral pulsed arterial spin labeling (PASL) perfusion among controls, hypoxic ischemic encephalopathy (HIE) neonates with normal conventional MRI(HIE/MRI⊕), and HIE neonates with abnormal conventional MRI(HIE/MRI⊖). To create a predictive machine learning model of neurodevelopmental outcomes using cerebral PASL perfusion. Materials and Methods: A total of 73 full-term neonates were evaluated. The cerebral perfusion values were compared by permutation test to identify brain regions with significant perfusion changes among 18 controls, 40 HIE/MRI⊖ patients, and 15 HIE/MRI⊕ patients. A machine learning model was developed to predict neurodevelopmental outcomes using the averaged perfusion in those identified brain regions. Results: Significantly decreased PASL perfusion in HIE/MRI⊖ group, when compared with controls, were found in the anterior corona radiata, caudate, superior frontal gyrus, precentral gyrus. Both significantly increased and decreased cerebral perfusion changes were detected in HIE/MRI⊕ group, when compared with HIE/MRI⊖ group. There were no significant perfusion differences in the cerebellum, brainstem and deep structures of thalamus, putamen, and globus pallidus among the three groups. The machine learning model demonstrated significant correlation (p < 0.05) in predicting language(r = 0.48) and motor(r = 0.57) outcomes in HIE/MRI⊖ patients, and predicting language(r = 0.76), and motor(r = 0.53) outcomes in an additional group combining HIE/MRI⊖ and HIE/MRI⊕. Conclusion: Perfusion MRI can play an essential role in detecting HIE regardless of findings on conventional MRI and predicting language and motor outcomes in HIE survivors. The perfusion changes may also reveal important insights into the reperfusion response and intrinsic autoregulatory mechanisms. Our results suggest that perfusion imaging may be a useful adjunct to conventional MRI in the evaluation of HIE in clinical practice.

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.