Virtual Patient Simulation Research Articles

Using virtual patient simulation with partial task trainer: A quasi-experimental study

BackgroundNursing students need educational approaches that provide sufficient knowledge and practice opportunities to improve their skills. AimTo analyze the benefits of incorporating partial task trainers into virtual patients, focusing on the effectiveness, performance, self-confidence, satisfaction and knowledge scores among senior nursing students in urinary catheterization for patients with acute urinary retention. DesignA randomized, quasi-experimental design. MethodsThe study was conducted at a nursing faculty between April-May 2023 with 71 senior nursing students: 35 in the virtual patient group (Group I) and 36 in the virtual patient and partial task trainer group (Group II). The data were gathered using: Personal Information Form, Student Satisfaction and Self-confidence in Learning Scale, Simulation Effectiveness Tool, Performance Report and Knowledge Report. ResultsThe satisfaction and self-confidence scores for Group I were 4.67 (SD 0.49) and 4.38 (SD 0.48), whereas Group II scored 4.88 (SD 0.22) and 4.70 (SD 0.34), respectively. The differences were statistically significant (p<0.05). For the Simulation Effectiveness Tool, Group I scored 31.05 (SD 3.28) for confidence subdimension and 85.05 (SD 7.37) for the total score, whereas Group II scored 32.57 (SD 2.73 and 88.48 (SD 6.60), respectively. These differences were statistically significant (p<0.05). No significant differences were found between the groups in the prebriefing, learning and debriefing subdimensions of the Simulation Effectiveness Tool (p>0.05). Performance and knowledge scores also showed no significant differences (p>0.05). Effect sizes for all statistically significant differences were moderate. ConclusionsThe results show that using virtual patients with partial task trainers increases students’ satisfaction and self-confidence and is perceived as effective in developing nursing interventions for patients with acute urinary retention.

Technology-based approaches to enhance clinical learning opportunities for student nurses in a nursing education institution in Gauteng

BackgroundClinical learning opportunities and experiences are vital in nursing education to enhance the student nurses’ acquisition and advancement in clinical skills competencies and preparedness to fulfil their professional nursing responsibilities. A paradigm shift of nursing colleges to higher education, the rapid advancement of technology and digitally native students entering the health and nursing profession, require nursing education to upgrade and complement the old teaching and learning pedagogies with new innovative and creative methodologies. The adoption and integration of technology for supported clinical learning will not only enhance clinical competencies and skills development but will also improve the delivery of quality healthcare aligned with the global standards in clinical education and the health profession PurposeThe purpose of the study was to explore and describe the perceptions of nurse educators, clinical facilitators, and operational managers on how clinical learning opportunities could be enhanced through technology. This aimed to outline technology-based approaches to enhance clinical learning opportunities for student nurses. MethodsQualitative, explorative, and descriptive methods were used to achieve the purpose of the study. A purposive sampling method was used to consciously select nurse educators, clinical facilitators, and operational managers as participants. They were able to provide in-depth and rich data on how clinical learning opportunities for student nurses could be enhanced through technology. The demographic information of the participants consisted of twenty-nine female nurse educators, two male nurse educators, nine female clinical facilitators, four female operational managers, and one male operational manager. Data was collected through agenda focus groups and semi-structured individual interviews. A matrix-building method of data analysis as guided by Miles, Huberman, and Saldaña’s framework was used with the development of themes. SettingThe study took place in a public Nursing Education Institution (NEI) in Gauteng that provides education and training for undergraduate nursing programme at a diploma level. ResultsThree themes emanated from the analysis which included promoting the use of web-based learning and interactive video recordings, adopting, and using high-technology systems (virtual and augmented patient simulations), and ensuring accessibility to technology-supported clinical learning resources and the internet. DiscussionThe transition to online learning technology-based approaches will provide accessibility to various clinical learning platforms across time and location to enhance clinical learning opportunities and experiences for student nurses.

Comparison of three artificial intelligence methods for predicting 90% quantile interval of future insulin sensitivity of intensive care patients

Insulin dosing of hyperglycemic patients in the intensive care unit (ICU) is a complex and nonlinear clinical control problem. Recent model-based glycemic control protocols predict a patient-specific and time-specific future insulin sensitivity distribution, which defines the future patient state in response to insulin and nutrition inputs. The prediction methods provide a 90% confidence interval for a future insulin sensitivity distribution for a given time horizon, making the prediction problem more specific compared to common prediction problems where the aim is to predict the expected value of the given stochastic parameter. This study proposes three alternative artificial intelligence-based insulin sensitivity prediction methods to improve the prediction accuracy and make prediction parameters better fit the clinical requirements. The proposed prediction methods use different neural network models: a classification deep neural network model, a Mixture Density Network model, and a Quantile Regression-based model. A large patient data set was used to create the neural network models, including 2357 patients and 92646 blood glucose measurements from three clinical sites (Christchurch, New Zealand, Gyula, Hungary, and Liege, Belgium). Prediction accuracy was assessed by statistical metrics expressing clinical requirements, as well as via validated in-silico virtual patient simulations comparing the clinical performance of a proven glycaemic control protocol using the alternative prediction methods to assess impact on glycemic control performance and thus the need for these alternative models.

The Importance of a Detailed Explanation by a Teacher of the Algorithm of Actions in an Emergency Situation in Simulation Training for the Formation of Sustainable Professional Competence of Medical University Students

The study involved 51 students divided into 2 groups, trained in the algorithm of emergency care for acute coronary syndrome on the virtual patient simulator "BodyInteract". The first group (30 students) followed the algorithm without explanations from the teacher, the second (21 students) — with detailed explanations and the opportunity to ask questions. After 2 months, the assessment on the checklist showed that students with explanations were 5-10% more effective than those who did not receive explanations.

Virtual patients with substance use disorders in healthcare professional education: a scoping review

Background and Objective: Virtual patient simulations are cost-effective methods for training health professionals. Yet, this teaching method is rarely used with clinicians who work or plan on working with people with substance use disorders. This scoping review summarizes the current state of the literature concerning virtual substance use disorder patient simulations in health professionals’ training and provides suggestions for future directions. Methods: Online databases were searched for peer-reviewed articles published between January 2010 and June 2024. Results: Twelve studies were included. The development, administration, and evaluation of performance of the simulations are diverse. Most simulations aim to develop screening, brief interventions or referring skill, they target a variety of health professionals’ disciplines and report positive learning outcomes. Virtual simulations have good acceptance rates from learners. Conclusions: Enhancing the diversity of clinical skills and patient populations portrayed in simulations, alongside adherence to best practices in simulation development and implementation is suggested to optimize training outcomes in this critical area of healthcare education.

Experience of Using a Virtual Patient in the Process of Teaching Hospital Pediatrics to Graduate Students

The paper presents its own data and analysis of the role and effectiveness of the use of the virtual patient simulator “Body interact” in the process of teaching hospital pediatrics to graduating students of the pediatric faculty of the Yaroslavl State Medical University for 3 academic years (period 2021-2024).

Evaluation of Online Virtual Patient Simulation Training for Managing Acute Patients with an Emphasis on Meaningful Learning

Objective: To evaluate the meaningful learning aspect of emergency patient management training conducted using an on-line virtual patient simulator. Methods: Case-based trainings which is a part of curriculum were conducted on-line using a Virtual Patient Simulator (VP) under the guidance of an educator in the years 2019-2020.The course was designed according to FTL(Fascilitation,training,learning) Model. The data in this study consisted of the survey results completed by 2nd year Emergency and First Aid Department students of Acibadem University (n:38/54) before and after the training sessions. The survey used was Keskitalo’s survey .The data were analyzed by using descriptive statistics. The paired-samples t-test was used to compare the differences between the students’ expectations and their experiences. Results: The results obtained indicate that meaningful learning expectations, including concrete, metacognitive, individual and competency-based, characteristics were exceeded by the experiences (p

Evaluating a Virtual Flipped Classroom Approach to Nurse Practitioner Telehealth Competency Development.

Evidence supports the use of multi-modal approaches to develop nurse practitioner (NP) students' telehealth competency. A virtual flipped classroom approach that included eLearning, interactive webinars, and virtual standardized patient (SP) simulations was implemented and evaluated to teach NP students to use telehealth and other connected health technologies to improve care for underserved populations. Analysis of data from multiple-choice quizzes, surveys, and SP evaluations indicated students achieved high levels of knowledge, met the learning objectives, demonstrated above-average competency during telehealth simulations that improved over time, and were highly satisfied with the learning experiences. The educational benefits of a flipped classroom can be realized within a fully online learning experience. Future research should examine the effects of repeated simulation opportunities on telehealth competency development. [J Nurs Educ. 2024;63(8):546-551.].

A virtual patient simulation modeling the neural and perceptual effects of human visual cortical stimulation, from pulse trains to percepts

The field of cortical sight restoration prostheses is making rapid progress with three clinical trials of visual cortical prostheses underway. However, as yet, we have only limited insight into the perceptual experiences produced by these implants. Here we describe a computational model or ‘virtual patient’, based on the neurophysiological architecture of V1, which successfully predicts the perceptual experience of participants across a wide range of previously published human cortical stimulation studies describing the location, size, brightness and spatiotemporal shape of electrically induced percepts in humans. Our simulations suggest that, in the foreseeable future the perceptual quality of cortical prosthetic devices is likely to be limited by the neurophysiological organization of visual cortex, rather than engineering constraints.

Analysis of the efficiency of theoretical knowledge and practical skills acquisition by intern doctors using the “Body Interact” virtual patient simulator in the conditions of today’s challenges

The aim of the work is to evaluate the efficiency of theoretical knowledge and practical skills acquisition by intern doctors using the body interact virtual patient simulator in distance (offline) and traditional (online) form of education. Materials and methods. The study involved 22 interns in the second year of study of the specialty “Internal Diseases”. 17 of them studied in offline format, 5 – in distance (online) format. The efficiency of theoretical knowledge acquisition was evaluated based on the results of the Integrated License exam “KROK 3. General Medical Training”. The efficiency of practical skills acquisition was determined by passing scenarios of providing medical care using the virtual patient simulator “Body Interact”. Results. It has been established that the effectiveness of traditional (offline) and distance (online) learning is comparable in terms of theoretical knowledge acquisition which is evidenced by the comparative results of passing the Integrated License exam “KROK 3. General Medical Training”. The results of passing the state license integrated exam “KROK 3. General medical training” and the virtual patient “Body Interact” had no correlation, which indicates the insufficient representativeness of the test assessment regarding the practical skills acquisition. Traditional (offline) learning is more effective in practical skills acquisition, which is confirmed by the better results of passing scenarios of providing medical care using the virtual patient program “Body Interact” in the group of traditional form of education compared to the group of online learning. Conclusions. Utilizing virtual patient simulation technology such as “Body Interact” in the training of intern doctors not only enhances the acquisition of theoretical educational material but also offers an opportunity to practice practical skills. This is particularly valuable in light of resource constraints resulting from COVID-19 and martial law. Implementing the “Body Interact” virtual patient technology into daily educational practices, especially in the context of distance learning, is advisable.

ChatGPT, a Friend or a Foe in Medical Education: A Review of Strengths, Challenges, and Opportunities

Background: ChatGPT is a large-scale language model that utilizes artificial intelligence (AI) to answer a broad range of scientific inquiries, create clinical scenarios, and evaluate educational programs. While its use offers numerous advantages, it also presents several challenges. Objectives: The aim of this study is to conduct a comprehensive review of ChatGPT's functionality and explore the benefits, challenges, existing solutions, and future prospects of using AI in medical education. Method: A comprehensive literature review was conducted using PubMed, Scopus, Web of Science, and Google Scholar. The search phrases used were ChatGPT, Artificial Intelligence, Chatbot, Medical Education, and large language models (LLMs). Results: The application of ChatGPT in medical education offers several advantages, such as enhanced quality of interaction between medical students and patients, improved education quality, enhanced research opportunities, personalized learning, virtual patient simulations, and cost-effectiveness. However, there are also critical challenges, such as ethical and transparency concerns, limited access to reliable databases, restricted information availability after 2021, limited development of students' critical thinking ability, and the risk of generating AI hallucinations. Conclusions: Artificial intelligence models have become a popular tool for researchers to access scientific resources, comprehend articles and textbooks, and create scientific texts. ChatGPT has been used extensively in medicine and medical education in a short period. It is essential to weigh the benefits and challenges, use expert supervision, conduct frequent assessments, and provide feedback reviews to guarantee its efficacy. Although this technology should not replace human labor, it is essential to prepare for the changes brought by AI and create appropriate guidelines and curricula by reviewing existing solutions and conducting extensive studies.

Simulation-Based Telemedicine Training Program for Residents in Physical Medicine and Rehabilitation: A Virtual Standardized Patient Activity

Background: The COVID-19 pandemic has ushered in a new era of healthcare delivery, emphasizing the crucial role of telemedicine. This study aimed to address the training needs of physical medicine and rehabilitation (PM&amp;R) residents in telemedicine through an Interactive Virtual Telemedicine training program utilizing standardized patient methodology. Methods: A tailored virtual standardized patient simulation module was developed for PM&amp;R residents. Prior to the training, residents completed pre-training questionnaires. The training consisted of (1) a 2-hour classroom didactic session covering telemedicine strategies for conducting physical examinations and triaging, as well as anticipated challenges. (2) a 2-hour zoom telemedicine simulation training exposed residents to three distinct patient scenarios (sickle cell crisis, acute low back pain, and neurorehabilitation). (3) a three-stage debriefing tool was employed for post-training reflection. Results: Eight PM&amp;R residents, predominantly males, with varying prior exposure to telemedicine, participated in the study. Post-training evaluations indicated that residents overwhelmingly found the simulation-based telemedicine training valuable and worth the didactic time invested. Data analysis demonstrated a significant increase in residents’ knowledge and confidence in telemedicine skills. Discussion: The study’s findings suggest that the telemedicine training program effectively enhanced residents’ understanding and confidence in telemedicine skills. Future research endeavors could involve a larger and more diverse group of residents to further assess training effectiveness and gather a wider array of perspectives on telemedicine. Expanding the use of standardized patients in telemedicine simulation training holds promise for advancing medical education in this evolving healthcare landscape.

Effectiveness of a virtual patient simulation training on improving provider engagement in suicide safer care.

Health care providers have a critical opportunity to mitigate the public health problem of suicide. Virtual patient simulations (VPS) allow providers to learn and practice evidence-based suicide prevention practices in a realistic and risk-free environment. The purpose of this study was to test whether receiving VPS training increases the likelihood that providers will engage in effective suicide safer care practices. Behavioral health and non-behavioral health providers (N = 19) at a Federally Qualified Health Center who work with patients at risk for suicide received the VPS training on risk assessment, safety planning, and motivation to engage in treatment. Providers' electronic health records were compared 6months pre- and post-VPS training on their engagement in suicide safer care practices of screening, assessment, safety planning, and adding suicide ideation to the problem list. Most behavioral health providers were already engaging in evidence-based suicide prevention care prior to the VPS training. Findings demonstrated the VPS training may impact the likelihood that non-behavioral health providers engage in suicide safer care practices. VPS training in evidence-based suicide prevention practices can optimize and elevate all health care providers' skills in suicide care regardless of role and responsibility, demonstrating the potential to directly impact patient outcomes.

Impact of myocardial phenotype on atrioventricular delay optimization at rest and during exercise: insights from a virtual patient study

Abstract Background The usefulness of atrioventricular delay (AVD) optimization in cardiac resynchronization therapy (CRT) for heart failure (HF) patients with left bundle branch block (LBBB) and reduced left ventricular ejection fraction (LVEF) is still debated. Yet, studies have not explored the role of non-electrical myocardial disease substrates in determining optimal AVD settings among patients. Purpose We first hypothesized that acute response to AVD optimization might be modulated by various myocardial disease substrates. Furthermore, we hypothesized that cardiac function is more sensitive to changes in AV coupling during exercise and that this sensitivity is also modulated by underlying myocardial disease substrates. Methods We used the CircAdapt model of the human heart and circulation to simulate different types of LV remodeling that can be found in CRT candidates. First, an LBBB-like pattern of mechanical activation was imposed by delaying LV free wall (LVFW) and interventricular septal (IVS) activation relative to right ventricular free wall activation. Then, two types of LV remodeling were simulated by LV dilation with preserved or impaired myocardial contractility, resulting in LVEF &amp;lt;35%. To investigate the effect of diastolic dysfunction, we increased passive myocardial stiffness of the IVS and LVFW on both substrates with LV remodeling until a mean left atrial pressure (mLAP) of 14 mmHg was reached. CRT was simulated by resynchronizing IVS and LVFW. AVD optimization was conducted by gradually reducing the paced AVD from 220 ms to 40 ms in steps of 20 ms. In each simulation, the optimal paced AVD at rest (cardiac output (CO) and heart rate (HR) of 4.2 L/min and 80 bpm, respectively) was assessed using stroke volume (SV) and mLAP which is used as a measure of LV filling pressure. To investigate the relative effect of these myocardial phenotypes during AVD optimization under exercise, CO and HR were increased step-wise. Results The gain in SV by AVD optimization was larger in the simulations with healthy myocardium than in those with hypo-contractile and stiff myocardium (Figure 1: top-row). However, mLAP was comparably decreased by AVD optimization in healthy and diseased myocardium (Figure 1: bottom row). During exercise, the optimum AVD shifted to shorter values (Figure 2: top-row), and mLAP was much more sensitive to AVD, particularly in the presence of hypo-contractile and stiff myocardium (Figure 2: bottom row). Conclusion Virtual patient simulations show that both myocardial hypocontractility and stiffness dampen the effect of AVD optimization on SV. Furthermore, simulations suggest that patients benefit more from AVD optimization during exercise than during resting conditions. These mechanistic insights may help to gain more benefit from AVD optimization for patients undergoing CRT.AVD optimization at restAVD optimization during exercise

Abstract PO2-02-01: Online Virtual Patient Simulation CME Improves Clinical Decision-Making In The Management Of Patients With HR+/HER2- Early Breast Cancer

Abstract Background: The goal of this study is to assess whether an online, virtual patient simulation (VPS) continuing medical educational (CME) activity, improved the performance of oncologists in ordering appropriate diagnostic tests and managing and counselling patients with HR+/HER2- early breast cancer (EBC) at high risk of recurrence. Methods: Two VPS educational activities were developed, the first VPS consisted of one patient case, focused on diagnosing patients with high risk of recurrence, while the other VPS consisted of 2 patient cases focused on the management and counselling of patients with HR+/HER2- EBC with high risk of recurrence. Physicians could make clinical decisions using complete open-field entries, based on the 3 performance based learning objectives. After each decision, learners received clinical guidance (CG) based on current evidence and expert recommendations. Pre- and post-CG clinical decisions were compared using a a McNemar's test to determine P values (P &amp;lt; .05 is significant). Rationales for clinical decisions were collected in real time. Data were collected between November 2021 and August 2022 and are reported here as % point change per performance learning objective, P value. Results: A total of 922 oncologists were included in the analysis. There was a significant improvement across the 3 performance learning objectives analysed. Please see table below for the results. Conclusions: This analysis demonstrated the educational impact of an immersive, online VPS education which allowed physicians to improve evidence-based clinical decisions regarding diagnosing patients with HR+/HER2- early breast cancer at high risk of recurrence and managing and counselling them regarding their treatment. Results Citation Format: Nabil Dorkhom, Urvi Patel, Stephanie Baumhover, Jacob Cohen. Online Virtual Patient Simulation CME Improves Clinical Decision-Making In The Management Of Patients With HR+/HER2- Early Breast Cancer [abstract]. In: Proceedings of the 2023 San Antonio Breast Cancer Symposium; 2023 Dec 5-9; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2024;84(9 Suppl):Abstract nr PO2-02-01.

500 Hyponatremia Virtual Patient Simulator: An innovative educational tool with generative artificial intelligence and physiologic models

500 Hyponatremia Virtual Patient Simulator: An innovative educational tool with generative artificial intelligence and physiologic models

The Role of Chatbot GPT Technology in Undergraduate Dental Education.

This comprehensive article explores the transformative role of Chatbot GPT, based on the GPT-3 architecture, in revolutionizing dental education. The focus is on its impact across various facets, including personalized learning pathways, integration into virtual patient simulation scenarios, 24/7 accessibility, multilingual support, interactive dental dictionary functionality, evidence-based learning, and assessment and evaluation of dental students. The objective is to showcase how Chatbot GPT enhances educational experiences, promotes inclusivity, and aligns with contemporary pedagogical principles. Chatbot GPT emerges as a powerful ally in dental education, offering personalized learning experiences, risk-free clinical simulations, continuous accessibility, multilingual support, instant terminology assistance, evidence-based learning resources, and real-time assessment capabilities. Its adaptability caters to diverse learning needs, fostering a learner-centered approach and promoting lifelong learning for both dental students and practitioners. As a versatile tool, Chatbot GPT not only transforms the educational journey but also serves as a valuable asset for continuous professional development in the dynamic landscape of dentistry.

Computational Simulation of Virtual Patients Reduces Dataset Bias and Improves Machine Learning-Based Detection of ARDS from Noisy Heterogeneous ICU Datasets.

Goal: Machine learning (ML) technologies that leverage large-scale patient data are promising tools predicting disease evolution in individual patients. However, the limited generalizability of ML models developed on single-center datasets, and their unproven performance in real-world settings, remain significant constraints to their widespread adoption in clinical practice. One approach to tackle this issue is to base learning on large multi-center datasets. However, such heterogeneous datasets can introduce further biases driven by data origin, as data structures and patient cohorts may differ between hospitals. Methods: In this paper, we demonstrate how mechanistic virtual patient (VP) modeling can be used to capture specific features of patients' states and dynamics, while reducing biases introduced by heterogeneous datasets. We show how VP modeling can be used for data augmentation through identification of individualized model parameters approximating disease states of patients with suspected acute respiratory distress syndrome (ARDS) from observational data of mixed origin. We compare the results of an unsupervised learning method (clustering) in two cases: where the learning is based on original patient data and on data derived in the matching procedure of the VP model to real patient data. Results: More robust cluster configurations were observed in clustering using the model-derived data. VP model-based clustering also reduced biases introduced by the inclusion of data from different hospitals and was able to discover an additional cluster with significant ARDS enrichment. Conclusions: Our results indicate that mechanistic VP modeling can be used to significantly reduce biases introduced by learning from heterogeneous datasets and to allow improved discovery of patient cohorts driven exclusively by medical conditions.

Virtual Patient Simulations in Nursing Education: A Descriptive Systematic Review

Background Virtual patients commonly train students in clinical competence in nursing education. This review aimed to evaluate the sample characteristics and sampling method, technological design of the virtual patients, duration of simulation methods, comparison methods used in control groups, outcomes of simulation interventions, and quality of the included randomized controlled studies. Methods Five databases were searched using English-language keywords between 1995 and 2019. The Turkish Quality Assessment Tool for Quantitative Studies was used to evaluate the quality of randomized controlled trials. Of the 4235 studies identified, 10 randomized controlled trials reporting virtual patients in nursing education were reviewed. Results The total number of participants in the review was 787. The virtual patient methods used in all studies differed. The duration was different in all the studies. Standardized patients, traditional education, facilitated mannequin-based simulation, written descriptions of the skills, low-, medium-, and high-fidelity simulation, and virtual simulation without training methods were used in the control groups for the comparison. Six studies indicated that virtual patients increased students’ knowledge, skills, self-efficacy, self-confidence, nursing competencies, and satisfaction levels in clinical practice, while four studies found no significant difference between the groups. The quality was poor in four of 10 studies, moderate in two of 10 studies, and strong in four of 10 studies. Conclusions This study demonstrated that the virtual patient methods used in studies varied in terms of technological design. The duration of simulation in the studies varied from less than 30 minutes to more than two months. The virtual patients could improve knowledge, skill acquisition, self-efficacy, self-confidence, and nursing competency compared with low-fidelity simulation, didactic education, no-intervention, or no-education studies. The quality results of the studies show that most of them had weak or strong research designs.

*SMARTSIM* A multicenter prospective randomized trial of 3D virtual reality versus traditional patient simulation.

Simulations are an integral part of paramedic education. Technological advancements have introduced three-dimensional virtual reality patient simulations (3DVRS), offering a low-cost, accessible alternative. This study compares the impact of 3DVRS and traditional simulation on paramedic students. Students selected from a convenience sample of 11 cohorts in 10 accredited programs distributed across the United States were allocated to 2 groups using a stratified random sampling. One group received simulations via 3DVRS, the second used traditional in-person simulation. Students were exposed to 6 scenarios over 2h from the National Association of Emergency Medical Technicians (NAEMT) Advanced Medical Life Support (AMLS) program. Altered mental status scenarios were selected a priori by the research team containing approximately 30 potential differential diagnoses. A 50-item posttest was administered using validated cognitive items provided by Fisdap. A multicenter prospective randomized trial of 174 paramedic students was undertaken from April until August of 2022. The traditional simulation group was comprised of 88 students and the 3DVRS group had 86 students. A Mann-Whitney U test (U=4064.5, n 1=88, n 2=86, p=0.396) detected no statistical difference between two distributions or median exam score (70%), the range of values and interquartile range (IQR) for both groups: TS IQR=64-75 (range, 32-82); 3DVRS IQR=64-76 (range, 34-86). No difference in exam scores using 3DVRS versus traditional simulation was detected. Paramedic programs may have an effective new option when incorporating simulation with 3DVRS, potentially reducing financial and real-estate resources required with in-person simulations. Larger studies are needed to truly evaluate the impact and usability of virtual reality on paramedic education.