Publications by authors named "Raymond W Sze"

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.

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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, = 653) from 1231 unique patients (median age: 4 m). Patients with more than one radiograph were included only in the training set.

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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.

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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.

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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.

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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).

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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.

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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.

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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. A total of 73 full-term neonates were evaluated.

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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.

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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.

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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.

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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.

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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.

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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.

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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.

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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.

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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.

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Peer learning represents a shift away from traditional peer review. Peer learning focuses on improvement of diagnostic performance rather than on suboptimal performance. The shift in focus away from random selection and toward identification of cases with valuable teaching points can encourage more active radiologist engagement in the learning process.

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During the last 15 years, peer review has been widely incorporated into radiology quality improvement programs. However, current implementations are variable and carry concerns, including subjectivity of numerical scores and a sense of merely satisfying regulatory requirements. The Society for Pediatric Radiology (SPR) Quality and Safety Committee sought to evaluate the state of peer review programs in pediatric radiology practices, including implementation methods, perceived functions, strengths and weaknesses, and opportunities for improvement.

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Machine learning, a subfield of artificial intelligence, is a rapidly evolving technology that offers great potential for expanding the quality and value of pediatric radiology. We describe specific types of learning, including supervised, unsupervised and semisupervised. Subsequently, we illustrate two core concepts for the reader: data partitioning and under/overfitting.

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The concepts behind the patient experience and patient- and family-centered care have their roots in the 1980s. Prioritization and implementation of programs to improve the patient experience have received increased attention since the passage of legislation tying health insurance reimbursement to patient satisfaction surveys. Radiology has joined these efforts with the Radiology 3.

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