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Extended reality (XR), encompassing technologies such as virtual reality, augmented reality, and mixed reality, has rapidly gained prominence in health care. However, existing XR research often lacks rigor, proper controls, and standardization. To address this and to enhance the transparency and quality of reporting in early-phase clinical evaluations of XR applications, we present the \"Reporting for the early-phase clinical evaluation of applications using extended reality\" (RATE-XR) guideline. We conducted a 2-round modified Delphi process involving experts from diverse stakeholder categories, and the RATE-XR is therefore the result of a consensus-based, multistakeholder effort. The guideline comprises 17 XR-specific (composed of 18 subitems) and 14 generic reporting items, each with a complementary Explanation & Elaboration section. The items encompass critical aspects of XR research, from clinical utility and safety to human factors and ethics. By offering a comprehensive checklist for reporting, the RATE-XR guideline facilitates robust assessment and replication of early-stage clinical XR studies. It underscores the need for transparency, patient-centeredness, and balanced evaluation of the applications of XR in health care. By providing an actionable checklist of minimal reporting items, this guideline will facilitate the responsible development and integration of XR technologies into health care and related fields.

Synergistic learning combining computational thinking (CT) and STEM has proven to be an effective method for advancing learning and understanding in a number of STEM domains and simultaneously helping students develop important CT concepts and practices. We adopt a design-based approach to develop, evaluate, and refine our Collaborative, Computational STEM (C2STEM) learning environment. The system adopts a novel paradigm that combines visual model building with a domain-specific modeling language (DSML) to scaffold learning of high school physics using a computational modeling approach. In this paper, we discuss the design principles that guided the development of our open-ended learning environment (OELE) using a learning-by-modeling and evidence-centered approach for curriculum and assessment design. Students learn by building models that describe the motion of objects, and their learning is supported by scaffolded tasks and embedded formative assessments that introduce them to physics and CT concepts. We have also developed preparation for future learning (PFL) assessments to study students’ abilities to generalize and apply CT and science concepts and practices across problem solving tasks and domains. We use mixed quantitative and qualitative analysis methods to analyze student learning during a semester-long study run in a high school physics classroom. We document some of the lessons learned from this study and discuss directions for future work.

Abstract Study Objectives To examine the cost-effectiveness and potential net monetary benefit (NMB) of a fully automated digital cognitive behavioral therapy (CBT) intervention for insomnia compared with no insomnia treatment in the United States (US). Similar relative comparisons were made for pharmacotherapy and clinician-delivered CBT (individual and group). Methods We simulated a Markov model of 100,000 individuals using parameters calibrated from the literature including direct (treatment) and indirect costs (e.g. insomnia-related healthcare expenditure and lost workplace productivity). Health utility estimates were converted into quality-adjusted life years (QALYs) and one QALY was worth $50,000. Simulated individuals were randomized equally to one of five arms (digital CBT, pharmacotherapy, individual CBT, group CBT, or no insomnia treatment). Sensitivity was assessed by bootstrapping the calibrated parameters. Cost estimates were expressed in 2019 US dollars. Results Digital CBT was cost beneficial when compared with no insomnia treatment and had a positive NMB of $681.06 (per individual over 6 months). Bootstrap sensitivity analysis demonstrated that the NMB was positive in 94.7% of simulations. Relative to other insomnia treatments, digital CBT was the most cost-effective treatment because it generated the smallest incremental cost-effectiveness ratio (−$3,124.73). Conclusions Digital CBT was the most cost-effective insomnia treatment followed by group CBT, pharmacotherapy, and individual CBT. It is financially prudent and beneficial from a societal perspective to utilize automated digital CBT to treat insomnia at a population scale.

Our objective is to propose a method capable of disentangling the magnitude, the speed, and the duration or decay rate of the time course of response to rapid antidepressant therapies. To this end, we introduce a computational model of the time course of response to a single treatment with a rapid antidepressant. Numerical simulation is used to evaluate whether model parameters can be accurately estimated from observed data. Finally, we compare our computational modelling-based approach with linear mixed effects modelling in terms of their ability to detect changes in the magnitude and time-course of response to rapid antidepressant therapies in simulated randomized trials. Simulation experiments show that the parameters of our computational model can be accurately recovered using nonlinear least squares. Parameter estimation accuracy is stable over noise levels reaching as high as 25% of the true antidepressant effect magnitude. Comparison of our approach to mixed effects modelling using simulated randomized controlled trial data demonstrates an inability of linear mixed models to disentangle effect magnitude and time course, while our computational model accurately separates these response components. Our modelling approach may accurately identify the (A) magnitude, (B) speed, and (C) durability or decay rate of response to rapid antidepressant therapies. Future studies should fit this model to data from real clinical trials, and use resulting parameter estimates to uncover predictors and causes of different elements of the temporal course of antidepressant response.

Purpose Traditional didactic lecture-based models in stomatology education, which rely on passive learning through lectures and observation, have limitations in fostering clinical reasoning. This study aimed to assess the effectiveness of Computer-based Case Simulations (CCS) in enhancing the clinical reasoning skills of non-dental medical undergraduates, using the Mini-Clinical Evaluation Exercise (Mini-CEX) as an outcome measure. Methods The study involved 328 non-dentistry medical undergraduates enrolled in four different educational programs: Bilingual, Pediatrics, Clinical Medicine I and Clinical Medicine II. Both the control and intervention group completed a Mini-CEX prior to training to establish a baseline. The control group received traditional didactic training (lectures + passive clinical observation), while the intervention group underwent CCS. Educational effectiveness was evaluated via theoretical test scores and Mini-CEX assessments. Results A pre-clerkship survey revealed that non-dentistry undergraduates prioritized learning about various dental diseases and developing clinical diagnostic and therapeutic thinking skills over the technical and procedural skills involved in the delivery of patient care. The intervention group, demonstrated significantly higher theoretical test scores compared with the control group across all classes (Bilingual Class: 98.1 ± 1.22 vs. 97.3 ± 0.97, Cohen’s d = 1.129; Pediatric Class: 97.9 ± 0.85 vs. 96.5 ± 1.35, Cohen’s d = 1.072; Clinical Medicine Class Ⅰ: 98.0 ± 0.91 vs. 97.0 ± 1.08, Cohen’s d = 1.000; Clinical Medicine ClassⅡ: 99.2 ± 1.04 vs. 97.7 ± 1.74, Cohen’s d = 1.432; all P  < 0.05). There was no significant difference in the Mini-CEX score between the groups before the clerkship ( P  > 0.05). Although both groups showed improvements in Mini-CEX scores post-clerkship, the intervention group exhibited a significantly greater increase (Cohen’s d > 0.5, P  < 0.01), indicating superior clinical skill development. Conclusion The results suggest that Computer-based case simulations (CCS) were associated with enhanced clinical knowledge and superior development of clinical reasoning skills in non-dentistry medical undergraduates compared to traditional methods, as measured by theoretical examination and Mini-CEX assessment. Future research should explore the long-term retention of clinical reasoning and the feasibility of scaling CCS in resource-limited settings.

Background Although healthcare providers (HCPs) are the most trusted source of vaccine information, there is a paucity of easily accessible, multidisciplinary educational tools on vaccine communication for them. Virtual simulation games (VSGs) are innovative yet accessible and effective tools in healthcare education. The objectives of our study were to develop VSGs to increase HCP confidence and self-efficacy in vaccine communication, advocacy, and promotion, and evaluate the VSGs’ effectiveness using a pre-post self-assessment pilot study. Methods A multidisciplinary team of experts in medicine, nursing, pharmacy, and simulation development created three VSGs for HCP learners focused on addressing conversations with vaccine hesitant individuals. We evaluated the VSGs with 24 nursing students, 30 pharmacy students, and 18 medical residents who completed surveys and 6-point Likert scale pre-post self-assessments to measure changes in their confidence and self-efficacy. Results There were no significant differences in baseline confidence and self-efficacy across the three HCP disciplines, despite varied levels of education. Post-VSG confidence and self-efficacy (median: 5) were significantly higher than pre-VSG (median: 4–5) for all three HCP disciplines ( P  ≤ 0.0005), highlighting the effectiveness of the VSGs. Medical residents reported significantly lower post-VSG confidence and self-efficacy than nursing and pharmacy learners despite completing the most significant amount of education. Conclusions Following the completion of the VSGs, learners in medicine, nursing, and pharmacy showed significant improvement in their self-assessed confidence and self-efficacy in holding vaccine conversations. The VSGs as an educational tool, in combination with existing clinical immunization training, can be used to increase HCP confidence and engagement in vaccine discussions with patients, which may ultimately lead to increased vaccine confidence among patients.

In the Industry 4.0 era, healthcare services have experienced more dual interventions that integrate lean and six sigma with simulation modeling. This systematic review, which focuses on evidence-based practice and complies with the PRISMA guidelines, aims to evaluate the effects of these dual interventions on healthcare services and provide insights into which paradigms and tools produce the best results. Our review identified 4018 studies, of which 39 studies met the inclusion criteria and were selected. The predominantly positive results reported in 73 outcomes were mostly related to patient flow: length of stay, waiting time, and turnaround time. In contrast, there is little reported evidence of the impact on patient health and satisfaction, staff wellbeing, resource use, and savings. Discrete event simulation stands out in 74% of the interventions as the main simulation paradigm. Meanwhile, 66% of the interventions utilized lean, followed by lean-six sigma with 28%. Our findings confirm that dual interventions focus mainly on utilization and access to healthcare services, particularly on either patient flow problems or problems concerning the allocation of resources; however, most interventions lack evidence of implementation. Therefore, this study promotes further research and encourages practical applications including the use of Industry 4.0 technologies.

We present an overview of the Conference on Transformative Opportunities for Modeling in Neurorehabilitation held in March 2023. It was supported by the Disability and Rehabilitation Engineering (DARE) program from the National Science Foundation’s Engineering Biology and Health Cluster. The conference brought together experts and trainees from around the world to discuss critical questions, challenges, and opportunities at the intersection of computational modeling and neurorehabilitation to understand, optimize, and improve clinical translation of neurorehabilitation. We organized the conference around four key, relevant, and promising Focus Areas for modeling: Adaptation & Plasticity, Personalization, Human-Device Interactions, and Modeling ‘In-the-Wild’. We identified four common threads across the Focus Areas that, if addressed, can catalyze progress in the short, medium, and long terms. These were: (i) the need to capture and curate appropriate and useful data necessary to develop, validate, and deploy useful computational models (ii) the need to create multi-scale models that span the personalization spectrum from individuals to populations, and from cellular to behavioral levels (iii) the need for algorithms that extract as much information from available data, while requiring as little data as possible from each client (iv) the insistence on leveraging readily available sensors and data systems to push model-driven treatments from the lab, and into the clinic, home, workplace, and community. The conference archive can be found at (dare2023.usc.edu). These topics are also extended by three perspective papers prepared by trainees and junior faculty, clinician researchers, and federal funding agency representatives who attended the conference.

Children with epilepsy are often presented to Community Emergency Departments (CEDs) for acute treatment of status epilepticus (SE). Timely medical management is imperative to prevent morbidity and mortality, and adherence to evidence-based guidelines improves outcomes for high stakes/low frequency events. Barriers to guideline adherent management in the CED setting are understudied; in-situ simulation (ISS) can be used to identify gaps in care for events such as pediatric SE. The primary objective was to assess for deviations from evidence-based guidelines in the management of pediatric SE. A secondary objective was to explore potential barriers to practice within the evidence-based guidelines. We conducted a prospective observational ISS pilot study examining representative CED teams caring for a simulated child in SE. The primary outcome was overall adherence to the pediatric SE guidelines as measured by 12 metrics: 5 non-pharmacologic (for example: delays in vital sign assessment, failure to time seizure) and 7 pharmacologic (for example: incorrect benzodiazepine dose, delay in benzodiazepine administration or escalation to antiseizure medication). Additional metrics including provider knowledge (recognition of status epilepticus) and resources (antiseizure medications stocked) were analyzed as process measures. We enrolled 4 interprofessional teams at 4 participating ED sites. Overall, 0 of the 4 teams adhered to all 12 metrics. A barrier to timely administration of benzodiazepines for two of the sites came from attempting IV access repeatedly. No team referenced an up-to-date treatment algorithm based on current evidence-based guidelines. Standardized ISS scenarios identified variability in adherence to the pediatric SE guideline across a pilot sample of local CEDs. Barriers to guideline-adherent care occurred at both individual and systems levels. The study was limited in scope to 4 pilot sites.