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OBJECTIVE: To determine the effect of patient immigrant status on physician trainees’ diabetes treatment decisions. METHODS: Participants were 140 physician trainees (‘providers’). Providers viewed videos and vignettes of virtual patients differing in immigrant status (born in Mexico or U.S.; other characteristics held constant). Analyses were completed at the group and individual levels. RESULTS: Providers were less likely to refer foreign-born (vs. U.S.-born) patients to endocrinology. Individual-level results showed an almost even split between treatment ratings for foreign-born vs. U.S.-born patients for three decisions (take no action, add oral hypoglycemic agent, add/switch to insulin), explaining why group-level differences for these ratings did not emerge (i.e., they were cancelled out). CONCLUSION: Physician trainees are less likely to refer foreign-born patients to endocrinology. Half of individual-level decisions were influenced by patient immigrant status, but group-level analyses mask these differences. Systematic treatment differences based on non-relevant factors could lead to adverse outcomes for immigrants.

Medical students are considered as 'essential workers' within the National Health Service (NHS) and the delivery of clinical experience is essential to their learning and progression into the workforce. The COVID-19 pandemic impacted the delivery of clinical placements in primary care; GPs are currently delivering the majority of consultations using telephone or video methods and difficulties in attaining placement experience are being encountered by medical students. Virtual remote consultations are an appropriate adjunct to conventional face-to-face patient encounters and could facilitate students to attain core learning outcomes. This article describes some of the approaches that enable remote (home) virtual patient encounters in Primary Care for medical students. These are categorised as methods that a) enable remote access into GP clinical systems, b) enable remote access into individual patient consultations and c) enable an observational-only experience. Key considerations are highlighted to enable safe and effective implementation of remote virtual consultations, along with the advantages and disadvantages of each method. These include patient consent, confidentiality, data sharing and protection, professionalism, student agreements and data gathering templates. It is hoped that sharing of these methods of virtual consulting will support the ongoing delivery of Primary Care education across medical schools.

This study uses bibliometrics and social network analysis methods to investigate the characteristics of the most cited research papers in the Web of Science (WoS) database in virtual patient (VP) s...

Through unfolding patient case studies, case-based learning has the potential to facilitate nurse practitioner (NP) student application of didactic knowledge to clinical practice, thus narrowing the theory-practice gap. The purpose of this study was to investigate NP student experience using virtual patient cases to supplement clinical learning. Bloom's cognitive taxonomy served as the framework. Seven students enrolled in the final semester of a distance family NP program completed 45 virtual patient cases over an 8-week period. Case topics covered a variety of primary care problems. Usability of the virtual learning platform was measured by the System Usability Scale. Clinical learning value was measured by the Student Satisfaction and Self-Confidence in Learning Scale. Students rated their perceived ability to manage common primary care problems at the start and end of the 8-week period. Students found the virtual learning platform highly usable and were satisfied and self-confident with their learning. Student perceived ability to manage common primary care problems increased for all virtual case topics from start to end of course. This study provides further evidence that well-designed virtual patient cases have the potential to effectively supplement NP student clinical learning. These results are important as NP faculty look for alternative forms of clinical learning to supplement direct care clinical experiences.

Background : As the pandemic continues to unfold, effective, technology-based solutions are needed to help patients with atrial fibrillation (AF) maintain their health and well-being during the outbreak of COVID-19 Methods : This single-center, pilot study investigated the effects of a 4-week (8 sessions) virtual AF self-management program Questionnaires were completed at baseline and 1 week after the intervention, and assessed AF knowledge, adherence to self-management behaviors, mental health, physical function, and disease-specific quality of life in patients with AF Secondary outcomes included knowledge of COVID-19, intervention acceptability and satisfaction Results : Of 68 patients who completed baseline questionnaires, 57 participated in the intervention and were included in the analysis (mean age of 73 4±10 0 years, 60% male) Adherence to AF self-monitoring behaviors, including monitoring their heart rate (p < 0 001), heart rhythm (p = 0 003), and blood pressure (p = 0 013) were significantly improved at the end of the intervention compared with baseline Symptom identification (p = 0 007) and management (p < 0 001) also improved Reductions in sleep disturbance (p < 0 001), anxiety (p = 0 014) and depression (p = 0 046) were also observed Misinformation and inaccurate beliefs about COVID-19 were significantly reduced at the end of the intervention compared with baseline Conclusions : This pilot study suggests that a virtual patient education program could have beneficial effects on adherence to guideline-recommend self-care of AF, emotional wellbeing, physical function and knowledge of COVID-19 in patients with AF Future randomized studies in larger samples are needed to determine the clinical benefits of the intervention This article is protected by copyright All rights reserved

Останнє десятиріччя характеризується стрімким використанням великої кількості віртуальних технологій у різних сферах діяльності людини. Упровадження інноваційних технологій у навчальні процеси в системі вищої освіти є актуальною проблемою її модернізації й оптимізації на шляху реформаційних змін у процесі європейської інтеграції. У статті розглянуто питання щодо навчання студентів із використання інформаційної технології «Віртуальний пацієнт». Описано розгляд клінічного завдання з теми «Ведення пацієнтів із захворюваннями щитоподібної залози» для студентів шостого курсу медичного факультету з використанням інтерактивного методу ”Case-study”. Використання інформаційних технологій під час навчання студентів сприяє підвищенню оптимальності й ефективності навчання, формуванню професійної компетентності майбутніх лікарів.

Recruitment maneuvers (RMs) following with positive-end-expiratory-pressure (PEEP) have proved effective in recruiting lung volume and preventing alveoli collapse. To date, standards for optimal patient-specific PEEP are unknown, resulting in variability in care and reduced outcomes, both indicating the need for personalized care. This research extends a well-validated virtual patient model by adding novel elements to model, which is able to utilize bedside available respiratory data, without increasing modelling complexity, to predict patient-specific lung distension and thus to minimise barotrauma risk. Prediction accuracy and robustness are validated against clinical data from 18 volume controlled ventilation (VCV) patients at 7 different baseline PEEP levels (0 to 12cmH2O), where predictions were made up to 12cmH2O of PEEP ahead. Using an exponential basis function set for prediction yields an absolute median peak inspiratory pressure prediction error of 1.50cmH2O for 623 prediction cases. Comparing predicted and clinically measured distension prediction in VCV demonstrated consistent, robust high accuracy with R2=0.90 (623 predictions), which is a measurable improvement in prediction error compared to predictions without using the proposed distension function (R2=0.82). Moreover, the R2 value increases to 0.93-0.95 if only clinically relevant ΔPEEP steps (2-6cmH2O) are considered with an overall median absolute error in peak pressure prediction of 1.04cmH2O. Overall, the results demonstrate the potential and significance for accurately capturing distension mechanics, allowing better risk assessment, as well as extending and more fully validating this virtual mechanical ventilation patient model.

Introduction: Medical education today is equipped with an armamentarium of newer online-based methods of correspondence courses, computerized virtual patient simulation, many open online courses in medical sciences, and tele-learning. The sudden, unplanned change from conventional teaching to online teaching during COVID-19 poses unique challenges and opportunities for teachers and learners, both. Many themes and principles have emerged for good online teaching learning and assessment practices (GOTLAP). Materials and Methods: The present study, involving 392 MBBS first year students from two universities, was conducted with an aim of comparing students' perception regarding online and offline teaching methodology, and online v/s offline method of assessment and to recommend the principles of GOTLAP. Data collected were analyzed by Strength, Weakness, Opportunity, and Threat (SWOT) analysis to provide a focused measure on how students perceive the program of online teaching and assessment. Results: In the present study, majority of the students (approximately 49.6%) have shown preference for offline teaching methodology, 22.9% has shown similar preference for both methods, while 27.5% has shown preference for the offline teaching method. SWOT analysis applied on qualitative data is a useful tool for assessing our present status in online learning and laying a ground work for formulating GOTLAP and a plan of future strategy. Conclusions: The GOTLAP principles can effectively pave way for the incorporation of blended learning (currently underutilized) in undergraduate medical education.

© 2021 Editorial Council for the Journal of Prosthetic DentistryStatement of problem: A patient 3-dimensional virtual representation aims to facilitate the integration of facial references into treatment planning or prosthesis design procedures, but the accuracy of the virtual patient representation remains unclear. Purpose: The purpose of the present observational clinical study was to determine and compare the accuracy (trueness and precision) of a virtual patient obtained from the superimposition procedures of facial and intraoral digital scans guided by 2 scan body systems. Material and methods: Ten participants were recruited. An intraoral digital scan was completed (TRIOS 4). Four fiduciary markers were placed in the glabella (Gb), left (IOL) and right infraorbital canal (IOR), and tip of the nose (TN). Two digitizing procedures were completed: cone beam computed tomography (CBCT) (i-CAT FLX V-Series) and facial scans (Face Camera Pro Bellus) with 2 different scan body systems: AFT (ScanBodyFace) and Sat 3D (Sat 3D). For the AFT system, a reference facial scan was obtained, followed by a facial scan with the participant in the same position as when capturing the CBCT scan. For the Sat 3D system, a reference facial scan was recorded, followed by a facial scan with the patient in the same position as when capturing the CBCT scan. The patient 3-dimensional representation for each scan body system was obtained by using a computer program (Matera 2.4). A total of 14 interlandmark distances were measured in the CBCT scan and both 3-dimensional patient representations. The discrepancies between the CBCT scan (considered the standard) and each 3-dimensional representation of each patient were used to analyze the data. The Kolmogorov-Smirnov test revealed that trueness and precision values were not normally distributed (P

The study examined the level of clinical thinking and perception of students when learning on Body Interact. A questionnaire was used for students in the direction of "general medicine", after the completion of the cycle and analysis of clinical scenarios. The results showed that more than 80% of students noted a positive impact on the development of clinical skills and contributes to the development of clinical thinking. Training Center according to the schedule of practical classes in the 2019–2020 academic year, took part in the survey. All students who completed a practical lesson with the use of a virtual patient were asked to complete an online questionnaire. Modern medical education offers a wide range of simulation technologies for the development of clinical skills, while virtual interactive systems are an innovative solution with limited opportunities for working with real patients.

Receding Horizon Control (RHC), also known as Model Predictive Control (MPC) is one of the most intensively researched areas of control algorithms applied in the artificial pancreas concept. Nevertheless, MPC algorithms have not yet been implemented in commercially available insulin pumps, mainly due to their high computational demand, their less robust nature, and their instability on account of model’s uncertainty. In this paper, we present a robust adjustable RHC. The proposed RHC controller was tested under known food inputs by applying a high degree of parameter uncertainty to the virtual patient implemented in the controller to test the robustness of the architecture. A particle swarm optimization method was applied to tune the controller. The so-called identifiable virtual patient (IVP) model was used in the tests, supplemented with food absorption and continuous glucose monitoring sensor model. The implementation was performed in Julia. The results showed that the proposed RHC is sufficiently robust under high food intake and parameter uncertainty.

Positive-end-expiratory-pressure (PEEP) have proved effective in recruiting lung volume and keeping alveoli open. However, there is no standard means to find an optimal patient-specific PEEP, creating variability in care and outcomes. There is thus a need for personalized approaches to find the best PEEP and optimise care. This research extends a well-validated virtual patient model with a newly proposed function to predict lung distension, while the impact on outcome of two different elastance identification strategies are discussed and compared. A prior studied and effective exponential basis function set is used as the general model, while elastance are identified using overlapped and separate methods, respectively. In this approach, model with overlapped elastance identification and proposed distension function yields an absolute median peak inspiratory pressure (PIP) prediction error of 1.50cmH2O for 623 prediction cases. Comparison between clinically measurement and model prediction for PIP yields R2=0.90 across 623 predictions in total, while R2=0.87 with separate elastance identification. Furthermore, both elastance identification methods are an improvement compared to predictions without proposed distension function (R2=0.82). Validation is fulfilled with 18 volume controlled ventilation patients respiratory data at 7 different baseline PEEP levels (0-12cmH2O) with a maximal PEEP prediction interval of 12cmH2O. Overall, the results demonstrate the impact of elastance identification methods, as well as the potential and significance for accurately capturing distension mechanics, which thus providing guidance for clinical care and insights for predictive lung mechanics modelling.

Purpose During the COVID-19 pandemic, there have been particular concerns regarding the related impact on specialist tumour services. Neuroendocrine tumour (NET) services are delivered in a highly specialised setting, typically delivered in a small number of centres that fulfil specific criteria as defined by the European Neuroendocrine Tumour Society (ENETS). We aimed to address the COVID-19-related impact on specialist NET tumour services in England and other countries. Methods Electronic survey addressing patient access and delivery of care distributed to all ENETS Centres of Excellence (CoE) in England and matching number of ENETS CoE elsewhere. Semi-quantitative and qualitative analyses of survey responses were performed. Results Survey response of ENETS CoE in England was 55% (6/11). Responses from six non-UK ENETS CoE elsewhere were received and analysed in a similar manner. Relevant disruption of various NET services was noted across all responding Centres, which included delayed patient appointments and investigations, reduced availability of treatment modalities including delayed surgical treatment and a major negative impact on research activities. The comparison between English and non-UK ENETS CoE suggested that the former had significantly greater concerns related to future research funding (p = 0.014), whilst having less disruption to multidisciplinary meetings (p = 0.01). A trend was also noted towards virtual patient appointments in ENETS CoE in England vs. elsewhere (p = 0.092). Conclusions Restoration of highly specialised NET services following COVID-19 and planning for future service delivery and research funding must take account of the severe challenges encountered during the pandemic.

Aim. To formulate the methodology for developing the interactive virtual computer simulations with a rating assessment of trainees’ decisions and the capability of remote access.Material and Methods. The methods of knowledge engineering were used to extract and formalize expert knowledge about the structure, significance, and relevance of clinical diagnostic information. The materials for creating virtual computer simulations were based on texts from the archival medical records, laboratory data, and multimedia results of instrumental methods of study. A three-tier network architecture was applied to provide the capabilities of remote access. It was organizationally represented by three components: a client layer, a business logic layer, and a data layer. Data transfer was provided by the Web protocols while microservices represented the infrastructure.Results. The information was formalized and structured after expert analysis and identification of significant diagnostic and prognostic data. The process included defining the domain model, identifying the aggregates and connections between them, and designing software and user interfaces. Possible solutions for trainees are now presented in the form of interactive reference lists. The artifacts of the user’s work are saved in the storage represented by the module for working with the server file system and the object-relational database management system. Each task module contains static and interactive blocks of information. The purpose of static blocks is to provide trainees with the necessary information for making clinical and diagnostic decisions. The interactive blocks allow selecting one or more solutions from the list. The content and sequence of further information presentation are determined by the trainees’ answers to the questions of an interactive block. Trainees’ decision-making competencies are evaluated using the rating system. The final personal rating is calculated as a multiplication of all coefficients related to the trainees’ decisions. This approach integrates a rating system with the trajectory chosen by the trainee for task completion.Conclusions. The distance learning technologies, developed for clinical disciplines in this study, are quite new and innovative. The repository of virtual computer simulations is under development to improve the methodological support of remote clinical training.

Virtual reality (VR) is commonplace for training, yet simulated physical activities in VR do not require trainees to engage and contract the muscle groups normally engaged in physical lifting. This paper presents a muscle activity-driven interface to elicit the sensation of forceful, physical exertions when lifting virtual objects. Users contracted and attained predefined muscle activity levels that were calibrated to user-specific muscle activity when lifting the physical counterpart. The overarching goal is to engage the appropriate muscles, and thereby encourage and elicit behaviors normally seen in the physical environment. Activities of 12 key muscles were monitored using electromyography (EMG) sensors while they performed a three-part patient lifting task in a Cave Automatic Virtual Environment. Participants reported higher task mental loads and less physical loads for the virtual lift than the physical lift. Findings suggest the potential to elicit sensation of forceful exertion via EMG feedback but needed fine-tuning to offset perceived workload.

Background: Reports on using virtual patients to assess counselling skills is scarce. Aim: This paper describes the feasibility and acceptability of assessing patient counselling skills of pharmacy students using a virtual patient simulator. Description: In this innovative method, a high quality simulator ‘Virtual Patient Learning’ (VPL) was developed at Gulf Medical University (GMU) and was used to assess the counselling skills of 15 pharmacy graduate students. Counselling skills were measured using a four-domain scoring rubric of 1 to 5 marks followed by instant feedback for improvements. Student and faculty satisfaction scores were collected based on the feasibility and acceptability of the assessment method. Evaluation: The average counselling skills score for all students was 68.4 (85.5%) out of 80 (range 54-76), with a standard deviation of 5.8. The overall student agreement on the feasibility and acceptability of the assessment method was 92.8%; it was 100% agreement for faculty. Conclusion: The use of a high quality VPL simulator in assessing counselling skills was deemed feasible and acceptable for students and faculty. The assessment was repeated among 30 Doctor of Pharmacy (Pharm.D.) graduates with similar outcomes. The virtual counselling method will be used in the programme exit exams, as well as in students entering their experiential year. Further studies are required to assess its validity and reliability with more students.

Background and purpose Therapeutic decision-making is a critical part of the skill set required by practicing pharmacists. A potentially straightforward way to teach and evaluate decision-making skills is use of real-time simulations. This study aimed to evaluate pharmacy students' perceptions of using a real-time simulation game to treat their own virtual patient. Educational activity and setting Second professional year (Y2) and fourth professional year (Y4) pharmacy students were provided an opportunity to use a virtual patient simulation in their courses. Student perceptions of the game and use of the game for decision-making were evaluated by a questionnaire. The qualitative findings were analysed using an inductive thematic analysis. Findings The questionnaire was completed by 120 Y2 and 115 Y4 students. Four themes arose from the analysis that reflected the students' perceptions of autonomy in their learning and practice, the difficulty in committing to a decision, and having fun while learning. Summary This work provides an initial exploration of the perceptions of pharmacy students about playing a real-time, autonomous, simulation-based game for treating a patient. Future research should focus on more detailed evaluation of this game for learning therapeutic decision-making.

Background The goal of this exploratory study is to analyse which psychological characteristics of students are related to the effectiveness of learning clinical interview skills with the use of a virtual patient (VP). Methods The sample consisted of 29 final-year clinical psychology students. The authors’ VP tool was used for measuring and teaching clinical interview skills: building contact with the patient, gathering important information, and identifying the students’ mistakes. Psychological questionnaires were used to measure the students’ psychological features: need for cognitive closure, ability to achieve cognitive structure, beliefs in the changeability of human traits, level of hope, intelligence, positive vs negative affect, and academic knowledge. Results The most important aspect of the diagnostician’s psychological features which substantially influence effectiveness of learning interview skills is belief in the stability or changeability of human traits and the need to achieve cognitive closure. Participants who have a belief in human changeability are able to perform the task correctly even without training, while those who believe in human stability improve only slightly with training. Students with lower need of cognitive closure successfully learned to build a good relation with the patient. Conclusions The study allows a better understanding of the phenomena occurring during the learning of clinical interview skills with the use of a VP.

Mechanical ventilation (MV) is a core treatment for patients suffering from respiratory disease and failure. However, MV settings are not standardized due to significant inter- and intra- patient variability in response to care, leading to variability in outcome. There is thus a need to personalize MV settings. This research significantly extends a single compartment lung mechanics model with physiologically relevant basis functions, and uses it to identify patient-specific lung mechanics and predict response to changes in MV settings. Nonlinear evolution of pulmonary elastance over positive end expiratory pressure (PEEP) is modelled by a newly proposed, physiologically relevant and simplified compensatory function to enable prediction of pulmonary response for both volume-controlled ventilation (VCV) and pressure-controlled ventilation (PCV), and identified as patient-specific using each patient’s data at a baseline PEEP. Predictions at higher PEEP levels test the validity of the proposed models based on errors in predicted peak inspiratory pressure (PIP) in two VCV trials and volume (PIV) in one PCV trial. A total of 210 prediction cases over 36 patients (22 VCV; 14 PCV) yielded absolute predicted PIP errors within 1.0 cmH2O (2.3%) and 3.3 cmH2O (7.3%) for 90% cases in VCV, while predicted PIV errors are within 0.073 L (16.8%) for 85% cases in PCV. In conclusion, a novel deterministic virtual patient model is presented, able to offer accurate prediction of pulmonary response across a wide range of PEEP changes for the two main MV modes used clinically, enabling predictive decision support in real-time to safely personalize and optimize care.

Like it happens in other tumours, glioma cells co-evolve in a microenvironment consisting of bona fide tumour cells as well as a range of parenchymal cells, which produces numerous signalling molecules. Recently, the results of an in silico experiment suggested that a combination therapy that would target multiple key cytokines at the same time may be more effective for suppressing the growth of a tumour. The in silico experiments also showed that the optimal combination therapy is very much dependent on a patient’s molecular profile. In this work, we employ evolutionary algorithms for designing optimal combination therapy tailored to the patient’s tumour microenvironment. Experiments were performed using a state-of-the-art glioma microenvironment model, capable of imitating many characteristics of human glioma development, and many virtual patient profiles. Results show that the therapies designed by the presented memetic algorithm were very effective in impeding tumour growth and were tailored to the patient’s personal tumour microenvironment.

Virtual patient simulation (VPS) is an online computer simulation system that contains patients' clinical situation to train and realistically assess clinical abilities with supplemental animation, demonstration of anatomy, ECG tracing, etc. It can be used for teaching, learning, and assessment supports learning. Until now, it is difficult to ascertain how many VPS systems have been used today in the world, both for learning and assessment, especially in cardiology medicine field. We searched for all research using the term "virtual patient in medical education, virtual patient in cardiology & virtual patient simulation in cardiology" in the title or research abstract from 2014 until 2019. In this current review, we will describe the use of VPS for learning and assessment purposes in cardiology medicine field.Â

An exponential rise in patient data provides an excellent opportunity to improve the existing health care infrastructure. In the present work, a method to enable cardiovascular digital twin is proposed using inverse analysis. Conventionally, accurate analytical solutions for inverse analysis in linear problems have been proposed and used. However, these methods fail or are not efficient for nonlinear systems, such as blood flow in the cardiovascular system (systemic circulation) that involves high degree of nonlinearity. To address this, a methodology for inverse analysis using recurrent neural network for the cardiovascular system is proposed in this work, using a virtual patient database. Blood pressure waveforms in various vessels of the body are inversely calculated with the help of long short-term memory (LSTM) cells by inputting pressure waveforms from three non-invasively accessible blood vessels (carotid, femoral and brachial arteries). The inverse analysis system built this way is applied to the detection of abdominal aortic aneurysm (AAA) and its severity using neural networks.

With the introduction of cone beam computed tomography (CBCT) into dentistry in the 1990s, radiologists have become more frequently involved in dental implant planning. This article describes the information that should be included in a radiology report to achieve a successful implantation. The justification to use CBCT during the preoperative planning phase is based on the need to evaluate patient-specific anatomy in detail (general condition of the jaw, bone quantity, and bone quality), the application of more advanced surgical techniques (maxillary sinus augmentation procedure, zygomatic implants), and the integrated presurgical planning and virtual patient approach. Postoperatively, CBCT is used when implant retrieval is anticipated and two-dimensional radiographs have not provided sufficient information, for evaluation of graft healing, or to assess complications, mostly related to neurovascular trauma.

Interventions designed to limit the spread of coronavirus disease 2019 (COVID-19) are having profound effects on the delivery of health care, but data showing the impact on oncology clinical trial enrollment, treatment, and monitoring are limited. We prospectively tracked relevant data from oncology clinical trials at Dana-Farber Cancer Institute from January 1, 2018, to June 30, 2020, including the number of open trials, new patient enrollments, in-person and virtual patient visits, dispensed investigational infusions, dispensed or shipped oral investigational agents, research biopsies, and blood samples. We ascertained why patients came off trials and determined on-site clinical research staffing levels. We used 2-sided Wilcoxon rank sum tests to assess the statistical significance of the reported changes. Nearly all patients on interventional treatment trials were maintained, and new enrollments continued at just under one-half the prepandemic rate. The median number of investigational prescriptions shipped to patients increased from 0 to 74 (range = 22-107) per week from March to June 2020. The median number of telemedicine appointments increased from 0 to 107 (range = 33-267) per week from March to June 2020. Research biopsies and blood collections decreased dramatically after Dana-Farber Cancer Institute implemented COVID-19–related policies in March 2020. The number of research nurses and clinical research coordinators on site also decreased after March 2020. Substantial changes were required to safely continue clinical research during the pandemic, yet we observed no increases in serious adverse events or major violations related to drug dosing. Lessons learned from adapting research practices during COVID-19 can inform industry sponsors and governmental agencies to consider altering practices to increase operational efficiency and convenience for patients.

The MDA/ALS Clinic at the University of California, San Francisco (UCSF) have recently transitioned our in-person multidisciplinary clinics to a successful, virtual patient experience. Here, we outline our new clinic model, presenting detailed information about our clinics’ virtual workflow and our experiences with this transition. In this way, we hope to demonstrate the feasibility of a large-scale virtual multidisciplinary clinic and assist other clinics (both local and academic) as they transition their care of patients virtually within the COVID-19 environment.

Background The Coronavirus Disease 19 (COVID-19) pandemic brought significant disruption to in-hospital medical training. Virtual reality simulating the clinical environment has the potential to overcome this issue and can be particularly useful to supplement the traditional in-hospital medical training during the COVID-19 pandemic, when hospital access is banned for medical students. The aim of this study was to assess medical students’ perception on fully online training including simulated clinical scenarios during COVID-19 pandemic. Methods From May to July 2020 when in-hospital training was not possible, 122 students attending the sixth year of the course of Medicine and Surgery underwent online training sessions including an online platform with simulated clinical scenarios (Body Interact™) of 21 patient-based cases. Each session focused on one case, lasted 2 h and was divided into three different parts: introduction, virtual patient-based training, and debriefing. In the same period, adjunctive online training with formal presentation and discussion of clinical cases was also given. At the completion of training, a survey was performed, and students filled in a 12-item anonymous questionnaire on a voluntary basis to rate the training quality. Results were reported as percentages or with numeric ratings from 1 to 4. Due to the study design, no sample size was calculated. Results One hundred and fifteen students (94%) completed the questionnaire: 104 (90%) gave positive evaluation to virtual reality training and 107 (93%) appreciated the format in which online training was structured. The majority of participants considered the platform of virtual reality training realistic for the initial clinical assessment (77%), diagnostic activity (94%), and treatment options (81%). Furthermore, 97 (84%) considered the future use of this virtual reality training useful in addition to the apprenticeship at patient’s bedside. Finally, 32 (28%) participants found the online access difficult due to technical issues. Conclusions During the COVID-19 pandemic, online medical training including simulated clinical scenarios avoided training interruption and the majority of participant students gave a positive response on the perceived quality of this training modality. During this time frame, a non-negligible proportion of students experienced difficulties in online access to this virtual reality platform.

Background. Simulation has revolutionized teaching and learning. However, traditional manikins are limited in their ability to exhibit emotions, movements, and interactive eye gaze. As a result, students struggle with immersion and may be unable to authentically relate to the patient. Intervention. We developed a new type of patient simulator called the Physical-Virtual Patients (PVP) which combines the physicality of manikins with the richness of dynamic visuals. The PVP uses spatial Augmented Reality to rear project dynamic imagery (e.g., facial expressions, ptosis, pupil reactions) on a semi-transparent physical shell. The shell occupies space and matches the dimensions of a human head. Methods. We compared two groups of third semester nursing students (N=59) from a baccalaureate program using a between-participant design, one group interacting with a traditional high-fidelity manikin versus a more realistic PVP head. The learners had to perform a neurological assessment. We measured authenticity, urgency, and learning. Results. Learners had a more realistic encounter with the PVP patient (p=0.046), they were more engaged with the PVP condition compared to the manikin in terms of authenticity of encounter and cognitive strategies. The PVP provoked a higher sense of urgency (p=0.002). There was increased learning for the PVP group compared to the manikin group on the pre and post-simulation scores (p=0.027). Conclusion. The realism of the visuals in the PVP increases authenticity and engagement which results in a greater sense of urgency and overall learning.

Fractional flow reserve is the current reference standard in the assessment of the functional impact of a stenosis in coronary heart disease. In this study, three models of artificial intelligence of varying degrees of complexity were compared to fractional flow reserve measurements. The three models are the multivariate polynomial regression, which is a statistical method used primarily for correlation; the feed-forward neural network; and the long short-term memory, which is a type of recurrent neural network that is suited to modelling sequences. The models were initially trained using a virtual patient database that was generated from a validated one-dimensional physics-based model. The feed-forward neural network performed the best for all test cases considered, which were a single vessel case from a virtual patient database, a multi-vessel network from a virtual patient database, and 25 clinically invasive fractional flow reserve measurements from real patients. The feed-forward neural network model achieved around 99% diagnostic accuracy in both tests involving virtual patients, and a respectable 72% diagnostic accuracy when compared to the invasive fractional flow reserve measurements. The multivariate polynomial regression model performed well in the single vessel case, but struggled on network cases as the variation of input features was much larger. The long short-term memory performed well for the single vessel cases, but tended to have a bias towards a positive fractional flow reserve prediction for the virtual multi-vessel case, and for the patient cases. Overall, the feed-forward neural network shows promise in successfully predicting fractional flow reserve in real patients, and could be a viable option if trained using a large enough data set of real patients.