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• Student behaviors contained information about their practice of clinical competency. • Students exercised clinical judgment, person-centered care and professionalism. • Students' performance in gerontology versus mental health scenarios were significantly different. Few studies have connected nursing students' performance in simulations to clinical competencies exemplified in The Essentials and the NCJMM. This retrospective study examined the effectiveness of screen-based virtual patient simulations (VPS) in facilitating ten undergraduate prelicensure students' practice of clinical judgment, person-centered care, and professionalism. Multimodal transcripts capturing patient interactions, exam actions, and documentation in a gerontology and mental health scenario were analyzed using Transmodal Ordered Network Analysis (T/ONA). We examined the strength and order of connections students made to recognizing cues, analyzing cues, diagnosing and prioritizing hypotheses, generating solutions, evaluating outcomes, subjective assessment, therapeutic communication, and care-management and coordination. Both scenarios afforded opportunities for students to gather comprehensive data and make evidence-based and patient-centered care decisions. However, students' patterns of connection making were significantly different for virtual patients who presented palliative care versus mental health care needs. The assurance for nursing students' practice readiness can be realized through careful implementation of diverse assignment types and scenarios in VPS. T/ONA provides a novel and systematic approach for studying student behaviors in simulations. [ABSTRACT FROM AUTHOR]

• This pilot study found statistically significant improvements in clinical reasoning and communication variables, suggesting that VPS is an effective strategy to impact nursing students' ability to effectively assess and interact with patients. • The improvements in clinical reasoning and communication were made without a significant difference in the time it took to complete the VPSs, implying more efficient patient interactions. Clinical reasoning and effective communication are foundational to patient outcomes, and therefore, key concepts in nursing education. There is a growing body of evidence to support virtual patient simulation in nursing and other health professions, but to date none that focuses specifically on whether virtual simulation impacts clinical reasoning in on-campus baccalaureate nursing health assessment courses. First semester prelicensure baccalaureate nursing students. A quantitative, quasi-experimental design with a pre-test post-test methodology grounded in Experiential Learning Theory was used. The Student Performance Index score measured clinical reasoning and simulation software automatically calculated communication variables. In the sample of 19 students, a two-tailed paired samples t -test found statistically significant improved outcomes for most variables. Virtual patient simulation positively impacts clinical reasoning and communication skills and is an appropriate pedagogical tool for nurse educators. [ABSTRACT FROM AUTHOR]

Virtual patients (VPs) are a safe and standardised method of simulating clinical environments but few studies have explored health care professional's experiences of learning via a VP. This study explored how users experienced and used a VP that aimed to teach the user to deliver non-vitamin K oral anticoagulant patient education. The study used semi-structured interviews with pharmacists and pre-registration trainees from a wider research study. Interview topics were based on key areas concerning VP use. Interviews were audio-recorded and transcribed verbatim before being analysed using the framework approach to thematic analysis. Ethical approval was granted by Keele University. There was variation in the type and nature of use of the VP and in the reported learning, which included reinforcement of knowledge, an opportunity to promote reflection, and acquisition and application of knowledge to clinical, patient-facing interactions. The VP was seen as an adjunct to other education and training. The majority of users indicated that they used the VP more than once. Some users seemed to have gamified their learning with a drive to achieve perfect feedback rather than true engagement with the learning, whereas for others the learning appeared to be deep with a reflective focus. The VP offered an educational use as experiential learning, although the users experienced the VP differently; commonly the VP facilitated learning via reinforcement of pre-existing knowledge. The users reported that the VP had value as an adjunct to other education and training resources. [ABSTRACT FROM AUTHOR]

While clinical dentistry has seamlessly integrated the digital revolution, there is a gap in the technological capabilities of forensic dentistry.The study aimed to compare the superimposition accuracy of two different three-dimensional record formats, namely the intraoral scanner and cone beam computer tomography, in the context of forensic identification.The sample consisted of randomly selected adults (n=10) of both sexes aged between 20 and 50 years. Following the acquisition of data using the Medit i700 wireless scanner and the iCAT Tomograph with InVivo software, the records were analysed and compared through superimposition using Medit Scan Clinic software to assess the technical precision of anatomical identification details.The results obtained through the superimposition of dental and bone records following intra- and inter-observer analysis enabled an accurate comparison and identification of an individual. This method can differentiate between positive and negative matches, achieving exclusion results and offering a potential solution to overcoming the absence of a standardisation procedure in human identification. [ABSTRACT FROM AUTHOR]

Background: Antitachycardia pacing (ATP) success rates as low as 50% for fast ventricular tachycardias (VTs) have been reported providing an opportunity for improved ATP to decrease shocks.Objective: The purpose of this study was to determine how a new automated antitachycardia pacing (AATP) therapy would perform compared with traditional burst ATP using computer modeling to conduct a virtual study.Methods: Virtual patient scenarios were constructed from magnetic resonance imaging and electrophysiological (EP) data. Cardiac EP simulation software (CARPEntry) was used to generate reentrant VT. Simulated VT exit sites were physician adjudicated against corresponding clinical 12-lead electrocardiograms. Burst ATP comprised 3 sequences of 8 pulses at 88% of VT cycle length, with each sequence decremented by 10 ms. AATP was limited to 3 sequences, with each sequence learning from the previous sequences.Results: Two hundred fifty-nine unique ATP scenarios were generated from 7 unique scarred hearts. Burst ATP terminated 145 of 259 VTs (56%) and accelerated 2.0%. AATP terminated 189 of 259 VTs (73%) with the same acceleration rate. The 2 dominant ATP failure mechanisms were identified as (1) insufficient prematurity to close the excitable gap; and (2) failure to reach the critical isthmus of the VT. AATP reduced failures in these categories from 101 to 63 (44% reduction) without increasing acceleration.Conclusion: AATP successfully adapted ATP sequences to terminate VT episodes that burst ATP failed to terminate. AATP was successful with complex scar geometries and EP heterogeneity as seen in the real world. [ABSTRACT FROM AUTHOR]

Background: A psychiatric diagnosis involves the physician's ability to create an empathic interaction with the patient in order to accurately extract semiology (i.e., clinical manifestations). Virtual patients (VPs) can be used to train these skills but need to be evaluated in terms of accuracy, and to be perceived positively by users.Methods: We recruited 35 medical students who interacted in a 35-min psychiatric interview with a VP simulating major depressive disorders. Semiology extraction, verbal and non-verbal empathy were measured objectively during the interaction. The students were then debriefed to collect their experience with the VP.Results: The VP was able to simulate the conduction of a psychiatric interview realistically, and was effective to discriminate students depending on their psychiatric knowledge. Results suggest that students managed to keep an emotional distance during the interview and show the added value of emotion recognition software to measure empathy in psychiatry training. Students provided positive feedback regarding pedagogic usefulness, realism and enjoyment in the interaction.Limitations: Our sample was relatively small. As a first prototype, the measures taken by the VP would need improvement (subtler empathic questions, levels of difficulty). The face-tracking technique might induce errors in detecting non-verbal empathy.Conclusion: This study is the first to simulate a realistic psychiatric interview and to measure both skills needed by future psychiatrists: semiology extraction and empathic communication. Results provide evidence that VPs are acceptable by medical students, and highlight their relevance to complement existing training and evaluation tools in the field of affective disorders. [ABSTRACT FROM AUTHOR]

• The scope of digital dentistry reaches far beyond intraoral scanners and CAD/CAM systems. • Additive manufacturing is expected to serve as an important platform for new materials innovation and development. • While disruptive, digital dentistry has created advantages for patients, clinicians, and researchers. Summarizing the new state of the art of digital dentistry, opens exploration of the type and extent of innovations and technological advances that have impacted – and improved – dentistry. The objective is to describe advances and innovations, the breadth of their impact, disruptions and advantages they produce, and opportunities created for material scientists. On-line data bases, web searches, and discussions with industry experts, clinicians, and dental researchers informed the content. Emphasis for inclusion was on most recent publications along with innovations presented at trade shows, in press releases, and discovered through discussions leading to web searches for new products. Digital dentistry has caused disruption on many fronts, bringing new techniques, systems, and interactions that have improved dentistry. Innovation has spurred opportunities for material scientists' future research. With disruptions intrinsic in digital dentistry's new state of the art, patient experience has improved. More restoration options are available delivering longer lifetimes, and better esthetics. Fresh approaches are bringing greater efficiency and accuracy, capitalizing on the interest, capabilities, and skills of those involved. New ways for effective and efficient inter-professional and clinician-patient interactions have evolved. Data can be more efficiently mined for forensic and epidemiological uses. Students have fresh ways of learning. New, often unexpected, partnerships have formed bringing further disruption — and novel advantages. Yes, digital dentistry has been disruptive, but the abundance of positive outcomes argues strongly that it has not been destructive. [ABSTRACT FROM AUTHOR]

Virtual patients (VPs) have long been used in health care education. However, their high cost restricts their widespread use, and personal use of VPs has not yet been achieved. In addition, the quality of VPs, such as validity and accuracy, has rarely been reported, although the effects of VPs have been widely discussed. "Sun Yat-sen University Nursing Education Platform" (SYSU-NEP) is a software program designed for use on WeChat that provides VPs for nursing history-taking sessions. SYSU-NEP is characterized by ease of use, accessibility, and capacity to facilitate autonomous learning. This study aimed to examine the quality of the 13 VPs in SYSU-NEP based on content completeness and accuracy. This was a retrospective data analysis study. The data were derived from 570 history-taking sessions involving conversations generated by 57 baccalaureate nursing students and 13 VPs. Among the conversations, the students' queries without a response and the queries with an incorrect response were analyzed. The content completeness of each VP was defined as the percentage of queries with a response. The accuracy was defined as the percentage of queries with a correct response. There were 13,312 student queries. The content completeness of each VP ranged from 73.7% to 97.3% (median: 83%; interquartile range: 78.1%–88.3%). The content accuracy of each VP ranged from 85.7% to 98.8% (median: 97.7%; interquartile range: 95.7%–98%). The Present Conditions part had the highest rate of queries without a response and with an incorrect response. The content completeness and accuracy of the VPs were satisfactory but had room for improvement. The queries without a response and with an incorrect response were also identified, which is helpful for the continuous improvement of the VPs. [ABSTRACT FROM AUTHOR]

Knowledge related to the use of virtual patients (VP) in pharmacy education is limited in relation to student satisfaction with this learning technique. This project aimed to assess students' confidence and impressions in using their communication skills with a VP and to evaluate their skills using this technology. This explanatory mixed-methods study was conducted with first-year doctor of pharmacy students. Fourteen survey items that addressed confidence in student abilities and VP impressions were analyzed. Mann-Whitney U tests were conducted to compare pre- and post- scores. Student reflections were reviewed to obtain primary themes and concerns. Descriptive statistics were used for student grades. Out of 205 students, 203 completed the pre-survey and 163 completed the post-survey. Responses regarding pre-post confidence indicated statistically significant improvement (p < 0.001) for 8 of 10 survey items as follows: understand history taking, conduct an organized interview, elicit subjective information, ask follow-up questions, ask questions related to severity, document subjective data, document objective data, and organize an interview. Regarding their impressions toward VPs, student responses were improved and statistically significant (p < 0.001) for 1 of 4 survey items (tools are easy to use). Forty-two students stressed the need to improve their written communication skills in the self-reflection. The average grade for subjective data was 31.48% and for objective data was 93.66%. This VP program improved student confidence in their verbal and written communication skills despite low subjective data scores. [ABSTRACT FROM AUTHOR]

• Research and clinical studies have shown that excessive pressure and volume in mechanical ventilation can lead to ventilator induced lung injury (VILI). • Development and validation of predictive virtual patient or digital twins. • Models used have been previously validated within this area of research. • Model predicts peak volumes and pressures as well as elastance in both VCV and PCV ventilation modes. • Clinical application of these models would increase the efficacy of patient care within invasive mechanical ventilation. Recruitment maneuvers with subsequent positive-end-expiratory-pressure (PEEP) have proven effective in recruiting lung volume and preventing alveoli collapse. However, determining a safe, effective, and patient-specific PEEP is not standardized, and this more optimal PEEP level evolves with patient condition, requiring personalised monitoring and care approaches to maintain optimal ventilation settings. This research examines 3 physiologically relevant basis function sets (exponential, parabolic, cumulative) to enable better prediction of elastance evolution for a virtual patient or digital twin model of MV lung mechanics, including novel elements to model and predict distension elastance. Prediction accuracy and robustness are validated against recruitment maneuver data from 18 volume-controlled ventilation (VCV) patients at 7 different baseline PEEP levels (0 to 12 cmH 2 O) and 14 pressure-controlled ventilation (PCV) patients at 4 different baseline PEEP levels (6 to 12 cmH 2 O), yielding 623 and 294 prediction cases, respectively. Predictions were made up to 12 cmH 2 O of added PEEP ahead, covering 6 × 2 cmH 2 O PEEP steps. The 3 basis function sets yield median absolute peak inspiratory pressure (PIP) prediction error of 1.63 cmH 2 O for VCV patients, and median peak inspiratory volume (PIV) prediction error of 0.028 L for PCV patients. The exponential basis function set yields a better trade-off of overall performance across VCV and PCV prediction than parabolic and cumulative basis function sets from other studies. Comparing predicted and clinically measured distension prediction in VCV demonstrated consistent, robust high accuracy with R2 = 0.90–0.95. The results demonstrate recruitment mechanics are best captured by an exponential basis function across different mechanical ventilation modes, matching physiological expectations, and accurately capture, for the first time, distension mechanics to within 5–10 % accuracy. Enabling the risk of lung injury to be predicted before changing ventilator settings. The overall outcomes significantly extend and more fully validate this digital twin or virtual mechanical ventilation patient model. [ABSTRACT FROM AUTHOR]

Abstract Introduction Non-technical skills (NTS) are fundamental requirements for health professional graduates for safe and competent practice. Technology enhanced simulation, including virtual patients (VPs), is suggested to be effective in developing NTS. Purpose The purpose of this paper is to explore what participants learnt about NTS following interactions with the VPs in the case study. Design Case study methodology was used to explore learning by undergraduate nursing students related to NTS following interactions with VPs. First- (n = 40) and third-year (n = 31) undergraduate nursing students from two universities in Victoria, Australia agreed to participate in focus groups and interviews. These were audio-recorded, transcribed and then underwent framework analysis. A priori codes in the framework used NTS categories including communication, situation awareness, teamwork, decision-making skills, leadership, managing stress and coping with fatigue. Findings Overall, students in both years and universities reported that interactions with VPs developed knowledge and skills across all categories of NTS to varying degrees. Third-year students suggested that interactions with VPs enabled development of knowledge and skills, as well as practise of selected NTS in the clinical setting. Conclusion Interactions with VPs across the curriculum may assist undergraduate nursing students in developing knowledge, skill and practice of NTS categories including communication, situation awareness, teamwork, decision-making skills and duty, advocacy and empathy. [ABSTRACT FROM AUTHOR]

Abstract Background History taking is difficult for nursing students with little clinical experience. Virtual patients (VPs) can supplement traditional teaching to some extent. However, the high cost of VPs limits their widespread personal use. "Sun Yat-sen University Nursing Education Platform" (SYSU-NEP) is a software program designed on WeChat that can provide VPs for nursing history-taking sessions. SYSU-NEP is characterized by ease of use, accessibility and capacity to facilitate autonomous learning. Objective To assess history-taking instruction for baccalaureate nursing students after VP training using WeChat. Method This was a retrospective analysis of the effectiveness of SYSU-NEP, which provided 13 VPs for history-taking instruction based on the Functional Health Pattern framework. Each VP session consisted of 17 parts, and data were derived from conversations between the baccalaureate nursing students and the VPs from September 2016 to January 2017. The effectiveness of history taking was assessed by the hit rate for each of the 17 VP parts. Hit rates in this case identify the completeness of the students' patient history taking. Furthermore, software usage by the students was assessed by an automated survey on SYSU-NEP. Results Fifty-seven students participated in 570 VP history-taking sessions. Of the 17 VP parts, the five parts with the lowest hit rates were personal history (median: 0.00, interquartile range: 0.00–0.00), Coping/Stress and Self-Perception/Self-Concept (median: 0.00, interquartile range: 0.00–0.00), Cognitive Perception (median: 0.00, interquartile range: 0.00–2.50), Activity/Exercise (median: 0.00, interquartile range: 0.00–3.00) and Sexual-Reproductive (median: 0.00, interquartile range: 0.00–12.81). Thirty-six students participated in the survey voluntarily and thirty-five students (97%) believed that the software was helpful for learning about patient history taking. Conclusions SYSU-NEP provides a platform for nursing students for history taking self-practice. Students' weaknesses were identified clearly through data analysis. The students were enthusiastic about the usefulness of the software. [ABSTRACT FROM AUTHOR]

Highlights • Titrating PEEP to minimum elastance is a lung protective strategy in mechanical ventilation. • Clinical studies have shown excessive PIP can be dangerous. • The ability of this model to predict PIP could reduce the risk of titrating PEEP in VCV. • Development and validation of predictive in-silico models is presented. • Future use in virtual patients could improve clinician confidence and patient safety in delivering care. Abstract Background and Objectives Mechanical ventilation (MV) is a primary therapy for patients with acute respiratory failure. However, poorly selected ventilator settings can cause further lung damage due to heterogeneity of healthy and damaged alveoli. Varying positive-end-expiratory-pressure (PEEP) to a point of minimum elastance is a lung protective ventilator strategy. However, even low levels of PEEP can lead to ventilator induced lung injury for individuals with highly inflamed pulmonary tissue. Hence, models that could accurately predict peak inspiratory pressures after changes to PEEP could improve clinician confidence in attempting potentially beneficial treatment strategies. Methods This study develops and validates a physiologically relevant respiratory model that captures elastance and resistance via basis functions within a well-validated single compartment lung model. The model can be personalised using information available at a low PEEP to predict lung mechanics at a higher PEEP. Proof of concept validation is undertaken with data from four patients and eight recruitment manoeuvre arms. Results Results show low error when predicting upwards over the clinically relevant pressure range, with the model able to predict peak inspiratory pressure with less than 10% error over 90% of the range of PEEP changes up to 12 cmH 2 O. Conclusions The results provide an in-silico model-based means of predicting clinically relevant responses to changes in MV therapy, which is the foundation of a first virtual patient for MV. [ABSTRACT FROM AUTHOR]

Background and Objective Objective Structured Clinical Examination (OSCE) has been used in many areas of healthcare training over the years. However, it constantly needs to be upgraded and enhanced due to technological and teaching changes. We aim at implementing an integrative OSCE method which employs informatics via the virtual patient within the pharmacy education curriculum at Taipei Medical University to enhance the pharmacy students’ competence for using and disseminating information and to also improve critical thinking and clinical reasoning. Methods We propose an integrated pharmacy OSCE which uses standardized patients and virtual patients (DxR Clinician). To evaluate this method, we designed four simulated stations and pilot tested with 19 students in the first year of the Master in Clinical Pharmacy program. Three stations were simulated as the inpatient pharmacy: 1) History and lab data collection; 2) Prescription review; 3) Calling physician to discuss potential prescription problems. The fourth was simulated as the patient ward station to provide patient education. A satisfaction questionnaire was administered at the end of the study. Results Students rated their ability of 2.84, 2.37, 2.37, and 3.63 of 5 for each of the four stations, with the second and third being the most difficult stations. The method obtained an average rating of 4.32 of 5 for relevance, 4.16 for improving clinical ability, 4.32 for practicality in future healthcare work, and 4.28 for willing to have another similar learning experience. Conclusion The integration of Virtual Patient in this study reveals that this assessment method is efficient and practical in many aspects. Most importantly, it provides the test taker with a much closer real-life clinical encounter. Although it is in many ways more difficult, it also provides for better “learning from mistakes” opportunities for test-takers. [ABSTRACT FROM AUTHOR]

Introduction To compare online learning tools, looped, branch serious game (SG) and linear text-based scenario (TBS), among a sample of Belgian and Swiss pharmacy students. Methods Open randomized controlled study. The lesson was based on the case of a benign cough in a healthy child. A randomized sample of 117 students: only the Swiss students had attended a previous lecture on coughs. Participation rate, pre- and post-experience Likert scales and students’ clinical knowledge were measured. Results Our primary hypothesis was demonstrated: students favored the SG even if navigation was rated as more complex, and students who performed the SG better understood the aim of pharmacist triage in case of cough. The influence of the SG appeared to be linked to the presence of a previous lecture in the curriculum. Discussion and conclusion SG and TBS are effective to teach pharmacist triage. Higher SG complexity should be used to teach the aim of pharmacist triage in the case of a specific disease and could be an alternative to simulated patients. A simpler TBS does not require a previous lecture and a debriefing to be fully effective. [ABSTRACT FROM AUTHOR]

• Virtual patients (VP) provide a means of synthetic generation of clinical data. • Time-varying mechanical ventilation (MV) VPs are synthesised using a stochastic model. • The model is used to generate a respiratory elastance (E rs) VP profile. • Virtual trials show the VPs capture E rs trends within and beyond the retrospective patient cohort. • Facilitates virtual development of bedside MV protocols via robust virtual trials. Healthcare datasets are plagued by issues of data scarcity and class imbalance. Clinically validated virtual patient (VP) models can provide accurate in-silico representations of real patients and thus a means for synthetic data generation in hospital critical care settings. This research presents a realistic, time-varying mechanically ventilated respiratory failure VP profile synthesised using a stochastic model. A stochastic model was developed using respiratory elastance (E rs) data from two clinical cohorts and averaged over 30-minute time intervals. The stochastic model was used to generate future E rs data based on current E rs values with added normally distributed random noise. Self-validation of the VPs was performed via Monte Carlo simulation and retrospective E rs profile fitting. A stochastic VP cohort of temporal E rs evolution was synthesised and then compared to an independent retrospective patient cohort data in a virtual trial across several measured patient responses, where similarity of profiles validates the realism of stochastic model generated VP profiles. A total of 120,000 3-hour VPs for pressure control (PC) and volume control (VC) ventilation modes are generated using stochastic simulation. Optimisation of the stochastic simulation process yields an ideal noise percentage of 5–10% and simulation iteration of 200,000 iterations, allowing the simulation of a realistic and diverse set of E rs profiles. Results of self-validation show the retrospective E rs profiles were able to be recreated accurately with a mean squared error of only 0.099 [0.009–0.790]% for the PC cohort and 0.051 [0.030–0.126]% for the VC cohort. A virtual trial demonstrates the ability of the stochastic VP cohort to capture E rs trends within and beyond the retrospective patient cohort providing cohort-level validation. VPs capable of temporal evolution demonstrate feasibility for use in designing, developing, and optimising bedside MV guidance protocols through in-silico simulation and validation. Overall, the temporal VPs developed using stochastic simulation alleviate the need for lengthy, resource intensive, high cost clinical trials, while facilitating statistically robust virtual trials, ultimately leading to improved patient care and outcomes in mechanical ventilation. [ABSTRACT FROM AUTHOR]

Background Face-to-face instruction, paper-based case-studies and clinical placements remain the most commonly used teaching methods for therapeutics curricula. Presenting clinical content in a didactic manner presents challenges in engaging learners and developing their clinical reasoning skills which may be overcome by inclusion of the virtual patient (VP). Currently there is limited literature examining the use of the VP in therapeutics teaching and learning. This review aimed to determine the role of VPs in therapeutics education, specifically the impact on student experiences, performance, and clinical skills. Methods A search of primary literature was conducted with search terms including virtual patient, education, health, AND learning. Boolean operators were applied to include studies from health relevant fields with article titles and abstracts vetted. Results Nine of the 21 included studies were control-matched, and all but one compared VPs to traditional teaching. VPs enhanced the learning experience in all 17 studies that measured this outcome. Fourteen studies measured performance and clinical skills and 12 found VPs were beneficial, while two did not. The VP was not superior to traditional teaching in all studies, but the VP appeared beneficial to the student learning experience. Discrepancy was found between the impact of VPs on short- and long-term knowledge. Implications The VP appears to enhance the student learning experience and has a role in therapeutics education, however a blended-learning (BL) approach may be required to account for individual learning styles. Additional investigation is required to clarify the efficacy of the VP, particularly as a component of BL, on longer-term knowledge retention. [ABSTRACT FROM AUTHOR]

• Predictive computational models are useful to complement clinical trials. • Disease models that infer causality in pathophysiology have been produced. • Quantitative System Pharmacology models are being enhanced with artificial intelligence (AI) and machine learning. • Virtual patients and digital twins provide representations of individual patients. • Regulations are being established to support the use of these predictive models. Computational models are being explored to simulate in silico the efficacy and safety of drug candidates and medical devices. Disease models that are based on patients' profiling data are being produced to represent interactomes of genes or proteins and to infer causality in the pathophysiology, which makes it possible to mimic the impact of drugs on relevant targets. Virtual patients designed from medical records as well as digital twins are generated to simulate specific organs and to predict treatment efficacy at the individual patient level. As the acceptance of digital evidence by regulators grows, predictive artificial intelligence (AI)-based models will support the design of confirmatory trials in humans and will accelerate the development of efficient drugs and medical devices. [ABSTRACT FROM AUTHOR]

Model-based decision support could be used to tailor insulin treatment to patients suffering from stress hyperglycemia, while avoiding hypoglycemia. This work combines a previously published glucose and insulin model with a subcutaneous insulin delivery model, herein simplified using Markov Chain Monte Carlo optimization and Kullback–Liebler distance, to capture fast-acting and regular insulin using two shared and one type-specific fitted parameter. Glucose data from a critical care population ( N =48) receiving subcutaneous insulin are fit to within finger stick glucose measurement error of 5% using a regularized, time-varying parameter. The resulting virtual patient cohort provides a basis on which automated insulin delivery systems can be tested. [ABSTRACT FROM AUTHOR]

Screen-based simulation (SBS) and 3-dimensional virtual reality (3D VR) applied to pediatric emergency medicine (PEM) leverage digital technology to represent patients, populations, situations, and/or environments. SBS and 3D VR provide realistic, dynamic, safe opportunities for training and testing provider competency, systems-based practice, and environments. SBS engages the user to interact with a computer or mobile device screen to access and respond to content. Virtual reality adds the element of movement and/or allows manipulation of the SBS. 3D VR uses head-mounted ocular technology to create an environment in which the user is immersed as an active participant. SBS and 3D VR types include virtual patients, virtual worlds, screen-based haptic trainers, and resource management simulators. Advantages of SBS and 3D VR simulation over mannequin-based simulation are that it is infinitely replicable; can be distributed widely to devices simultaneously; is portable; enables tracking, storing, and analysis of massive amounts of usage and performance data; and does not require the presence of a live instructor. Disadvantages include cost, technical problems, and lower fidelity. We provide examples of types of SBS and 3D VR relevant to PEM that are currently available and in development; PEM-specific uses of SBS and 3D VR for training and testing, as well as uses for research to determine best practices for acquiring and retaining PEM knowledge and skills; and strategies for integrating SBS and 3D VR into training and PEM practice. [ABSTRACT FROM AUTHOR]

Background Virtual reality technology can range from bedside learning to computerized tools accessed exclusively online; yet there is little evidence of the effectiveness of online virtual simulation for teaching diagnostic reasoning to health care providers. To address this gap, an examination of virtual simulation encounters in the online classroom was undertaken to inform education of a variety of health care providers, including nurse practitioners, registered nurses, and physicians. Method Arskey and O'Malley's (2005) framework was the methodology used to conduct the scoping review. Result Twelve studies published between 2008 and 2015 were identified from a search of 14 databases. The study sample included physicians and medical students (seven), baccalaureate nursing students (two), associate degree nursing students (one), and providers from other fields such as pharmacists, physiotherapists, sports medicine, forensics, and veterinarians. Conclusion Online virtual simulation was comparable or superior to traditional simulation methods where increased engagement with learning occurred in a safe environment with convenient access. [ABSTRACT FROM AUTHOR]

Background Technology enhanced simulation such as virtual patients offer flexible, reproducible, and accessible learning experiences for student to develop nontechnical skills. Method Integrative review methods were used to synthesize empirical and theoretical literature to provide a comprehensive understanding of virtual patients in curricula relative to nontechnical skills. Results Twenty-eight articles were included in the review. Results suggest interactions with virtual patients develop communication, teamwork, and decision-making. Additional themes related to transfer of learning to practice, socialization into roles, and authenticity emerged. Conclusions The educational design of the simulation experience, sequencing learning activities surrounding the virtual patient, and authenticity of the virtual patient interaction are identified as important factors for consideration. [ABSTRACT FROM AUTHOR]

While the use of virtual characters in medical education is becoming more and more commonplace, an understanding of the role they can play in empathetic communication skills training is still lacking. This paper presents a study aimed at building this understanding by determining if students can respond to a virtual patient’s statement of concern with an empathetic response. A user study was conducted at the University of Florida College of Medicine in which early stage medical students interacted with virtual patients in one session and real humans trained to portray real patients (i.e., standardized patients) in a separate session about a week apart. During the interactions, the virtual and ‘real’ patients presented the students with empathetic opportunities which were later rated by outside observers. The results of pairwise comparisons indicate that empathetic responses made to virtual patients were rated as significantly more empathetic than responses made to standardized patients. Even though virtual patients may be perceived as artificial, the educational benefit of employing them for training medical students’ empathetic communications skills is that virtual patients offer a low pressure interaction which allows students to reflect on their responses. [ABSTRACT FROM AUTHOR]

Background Acquisition of knowledge and skills by nurse practitioner students before real-life practice is a familiar nursing education challenge. Methods Virtual clinical simulations (VCSs) using Digital Clinical Experience™ were introduced in an advanced health appraisal course to explore how these VCSs affected students' learning and perception of their simulation experience as a learning strategy in an online course using a quasi-experimental design. Differences for intervention (VCS) and control groups (no VCS) were measured. Results No significant difference was revealed in course grades, integrated performance proficiency scores, or in National League of Nursing simulation scores. Conclusion The integration of VCSs into current standardized online course content as an adjunctive learning experience warrants additional evaluation. [ABSTRACT FROM AUTHOR]

Purpose The communication skills of pharmacists are essential for the identification and reduction of patient́s drug related problems. Therefore, Pharmacy courses started the process of teaching Pharmaceutical Care to students in order to improve their communication skills. The use of virtual patients (VP) has been a widely used technique in health care courses, but many of the VP tools in Pharmacy field are in English and do not have clinical cases that are common in tropical countries, such as Brazil. The aim of this work is to describe the PharmaVP system, developed with the purpose of training Latin America students in Pharmaceutical Care. The main differential of PharmaVP is the availability in three languages (Portuguese, English and Spanish) and the possibility of clinical case evolution, simulating several visits made by the patient. Methods The system was developed according to an incremental and interactive methodology, well suited for conducting multidisciplinary projects. Real clinical cases were collected from a Pharmaceutical Care program and added in PharmaVP to simulate the virtual patients. Then, 31 students of a Pharmacy course were trained and invited to participate of the evaluation study. They used the software and answered adapted instruments that assess the students' acceptance of, use of, learning of, and satisfaction with the system. Results The results showed that the students found the cases realistic and learned significantly using the software. Another positive point is that the application process of PharmaVP did not consume much time. Discussion We can conclude that the virtual patient tool contributed to the development of the skills required for the practice of Pharmaceutical Care, but should be used as complementary technique. [ABSTRACT FROM AUTHOR]

Summary Background The number of men diagnosed with prostate cancer is increasing and internationally there are high incidence rates. It is important that nurses and healthcare professionals are enabled to provide appropriate care to those men affected by prostate cancer and their families. Despite this need, there is recognition that many professionals feel ill prepared and lack knowledge in a number of areas. This paper presents the development of a Virtual Patient (VP) online resource to support practitioner learning. Aim To develop five online VP simulation scenarios to meet the learning needs of nurses and health-care professionals caring for men with prostate cancer. Method Topic areas for the VPs were taken from previous work exploring the needs of health care professionals working with men with prostate cancer. An initial scoping exercise involving nursing practitioners, students and a prostate cancer charity confirmed the focus of the case study scenarios. Service users and specialist practitioners reviewed an outline of each case study to ensure fidelity of the simulations scenarios. Cases were entered into UChoose, a web based interactive VP player and authoring tool. The final case studies were reviewed by a sample of both registered and non-registered nurses and nursing students. Results The majority of respondents reported an increase in knowledge and suggested that they would recommend the resource to others. A number of positive aspects of the resource were highlighted. Respondents also commented about areas of weakness, a number of which have been addressed subsequently. Conclusions The VP case studies provided an opportunity to develop knowledge and confidence in caring for men with prostate cancer. The mode of delivery and the content was acceptable for less experienced and knowledgeable staff. [ABSTRACT FROM AUTHOR]

• Model-based mechanical ventilation (MV) treatments require clinical trials prior to implementation. • A virtual patient (VP) framework is developed for testing model-based decision support. • A VP platform of 100 virtual patients were generated from retrospective data. • A virtual trial demonstrates efficacy of VPs for prospective protocol testing. • Potential to safely and rapidly design, develop and optimise new MV protocols. Model-based and personalised decision support systems are emerging to guide mechanical ventilation (MV) treatment for respiratory failure patients. However, model-based treatments require resource-intensive clinical trials prior to implementation. This research presents a framework for generating virtual patients for testing model-based decision support, and direct use in MV treatment. The virtual MV patient framework consists of 3 stages: 1) Virtual patient generation, 2) Patient-level validation, and 3) Virtual clinical trials. The virtual patients are generated from retrospective MV patient data using a clinically validated respiratory mechanics model whose respiratory parameters (respiratory elastance and resistance) capture patient-specific pulmonary conditions and responses to MV care over time. Patient-level validation compares the predicted responses from the virtual patient to their retrospective results for clinically implemented MV settings and changes to care. Patient-level validated virtual patients create a platform to conduct virtual trials, where the safety of closed-loop model-based protocols can be evaluated. This research creates and presents a virtual patient platform of 100 virtual patients generated from retrospective data. Patient-level validation reported median errors of 3.26% for volume-control and 6.80% for pressure-control ventilation mode. A virtual trial on a model-based protocol demonstrates the potential efficacy of using virtual patients for prospective evaluation and testing of the protocol. The virtual patient framework shows the potential to safely and rapidly design, develop, and optimise new model-based MV decision support systems and protocols using clinically validated models and computer simulation, which could ultimately improve patient care and outcomes in MV. [ABSTRACT FROM AUTHOR]

Abstract: The closed loop control of blood glucose levels in a nonlinear glucose–insulin regulatory system is considered in this paper. Based on the subcutaneous glucose sensor readings, a control algorithm is designed and implemented. A mathematical model characterizing the ultradian oscillatory nature of the glucose–insulin regulatory system of diabetic patients is considered and an estimation based model predictive control scheme with physiological and actuator constraints is implemented. An in silico preclinical testing is done to corroborate the control algorithm using the UVa/Padova virtual patient software. [Copyright &y& Elsevier]

• An in-silico upper limb model has been built using the NEUROID platform. The multiscale model has a modular architecture and facilitates the reuse of existing models in NEURON and Opensim, making it feasible to quickly curate and integrate models to build larger sub-systems and systems. • A notable feature in the spinal cord model is that all elements from ion channels to neurons are placed within anatomical coordinates. This opens the possibility of simulating anatomical interventions and their resultant movement behaviors. • By means of spinal cord stimulations and reminiscent of descending cortical activations, emergence of common upper limb movements were demonstrated. • The movements produced by stimulation of various spinal levels produced a map of various degrees of movement along the spinal cord. This map was in agreement with known experimental results. Virtual patients and physiologies allow experimentation, design, and early-stage clinical trials in-silico. Virtual patient technology for human movement systems that encompasses musculoskeleton and its neural control are few and far in between. Our major goal is to create a neuro- musculoskeletal upper limb in-silico model, which is modular in architecture and generates movement as an emergent phenomenon out of a multiscale co-simulation of spinal cord neural control and musculoskeletal dynamics. The model is developed on the NEUROiD movement simulation platform that enables a co-simulation of popular neural simulator NEURON and the musculoskeletal simulator OpenSim. We further characterized and demonstrated the use of this model in generating a range of commonly observed upper limb movements by means of a spatio-temporal stimulation pattern delivered to the cervical spinal cord. We were able to characterize the model based on proprioception (Ia, Ib and II fibers), afferent conduction delay and inital postures of the musculoskeletal system. A smooth movement was achieved in all the considered experiments. The generated movements in all degrees of freedom were reproduced in accordance with the previous experimental studies. In this work, design and development of the upper limb model was described in a modular fashion, while reusing existing models and modules. We believe this work enables a first and small step towards an in-silico paradigms for understanding upper limb movement, disease pathology, medication, and rehabilitation. [ABSTRACT FROM AUTHOR]

Abstract: Introduction - Simulation is the gold standard for training in aviation and trauma medicine. Now it is available for diseases diagnosis and treatment. With simulation, clinicians can practice their art in a realistic, consequence free environment, combining both theory and practice in a comprehensive program. Method and materials - In the virtual clinical environment of “VPsoft mentor” software, the practitioner reviews a patient''s history, orders laboratory tests and procedures, interprets findings, renders diagnoses, and prescribes medication. Every diagnostic or treatment decision the clinician makes for the patient is analyzed by E-mentor, providing instant feedback.Results - This software have been used in our academic centre in Arak, as part of residency evidence based learning in a schedule of the small group teaching which, by comparing two groups of case and control, we detected meaningful differences in the Objective Structured Clinical Examination (OSCE) evaluation scores between them.Discussion / Conclusion – Expert systems-based virtual patient simulation shows promise as a mechanism for assessing practitioner skill, detecting skill gaps and for electronic mentoring. Using CD- based clinical simulations it is possible to augment clinical sciences training globally at low cost with high clinician acceptance, kin order to track improvement over time. [Copyright &y& Elsevier]

The Faculty of Health and Social Care Sciences at Kingston University/St George's University of London (KU/SGUL) provides a module to train registered nurses to qualify as independent nurse prescribers. During the programme the participants engage in an online learning activity using a virtual patient (VP). The aim of this VP activity is to enable students to consolidate their learning and to practice the range of skills that the students have been developing related to prescribing. The activity was designed by the module leader and was run as a pilot on two groups of students (n = 34). An evaluative study was undertaken on the value of this blended learning activity to the student and their prescribing practice. This paper presents the development, implementation and evaluation of the VP activity. Findings showed that the VP activity was perceived as being particular useful for helping them to learn the skills of patient history/assessment, decision-making and prescription writing. The VP was also perceived as being helpful in developing their confidence. [ABSTRACT FROM AUTHOR]

Abstract: Purpose: Modern teaching concepts for undergraduate medical students in Germany include problem based learning as a major component of the new licensing regulations for physicians. Here we describe the usage of a web-based virtual outpatient clinic in the teaching curriculum of undergraduate medical students, its effect on learning success, and student reception. Methods: Fifth year medial students were requested to examine 7 virtual orthopaedic patients which had been created by the authors using the Inmedea-Simulator. They also had to take a multiple-choice examination on two different occasions and their utilisation of the simulator was analysed subjectively and objectively. Results: One hundred and sixty students took part in the study. The average age was 24.9 years, 60% were female. Most of the participants studied on their own using their private computer with a fast internet-connection at home. The average usage time was 263min, most of the students worked with the system in the afternoon, although a considerable number used it late in the night. Regarding learning success, we found that the examination results were significantly better after using the system (7.66 versus 8.37, p <0.0001). Eighty percent of the students enjoyed dealing with the virtual patients emphasizing the completeness of patient cases, the artistic graphic design and the expert comments available, as well as the good applicability to real cases. Eighty-seven percent of the students graded the virtual orthopaedic clinic as appropriate to teach orthopaedic content. Conclusion: Using the Inmedea-Simulator is an effective method to enhance students’ learning efficacy. The way the system was used by the students emphasises the advantages of the internet-like free time management and the implementation of multimedia-based content. [Copyright &y& Elsevier]

History-taking is an essential skill for nurses. In nursing education, it is necessary to objectively assess history-taking skills in a way that accurately reflects differences among students. Current history-taking evaluation methods lack objectivity, consistency and standardization, which makes it difficult to identify factors that influence history-taking skill. A virtual standardized patient (VSP) can provide history-taking practice with repeatability and consistency. It can make objective and standardized assessment possible by eliminating the subjectivity of different teachers and patients. To evaluate the history-taking skills of nursing undergraduates using a VSP, and to explore its independent influencing factors. A cross-sectional design was employed with a sample of convenience from 3 universities. All 174 nursing undergraduates had their history-taking level evaluated using one VSP via computer or mobile terminal. For each query raised by the students, the VSP could give a preset response. The responses were assigned corresponding scores according to their importance, with a total score of 100. The students' general demographics were documented, and their self-efficacy, critical thinking and communication skills were measured online with the Nursing History-taking Self-Efficacy Questionnaire, Yoon's Critical Thinking Disposition Instrument and the Supportive Communicative Scale. Univariate analysis, Pearson correlation analysis and multiple linear regression were performed. The mean total history-taking score was 58.53 ± 14.32. History-taking scores were well discriminated among the students tested, with 10% of the students scoring above 80, 37% between 60 and 80, 22% between 50 and 60 and 31% scored below 50. The history-taking level of the nursing students was significantly associated with ethnicity, previous academic performance, and Supportive Communicative Scale scores. Using the VSP as a history-taking assessment method is an effective way to achieve a relatively objective, standardized and consistent assessment of history-taking education. Ethnicity, previous academic performance and supportive communication skills independently influenced the students' history-taking level. Mature history-taking skills require not only solid theoretical knowledge but good communication skills. [ABSTRACT FROM AUTHOR]

Over the past year, the occurrence of COVID-19 pandemic has challenged clinical education for health care students, due to the possibility of exposure to the virus and increased spread of the disease. Clinical training of midwifery students in gynecologic problems, based on the Iran midwifery education curriculum, was also disrupted during this pandemic. This study was aimed at designing, implementing and evaluating a virtual clinical training protocol for midwifery internship in a Gynecology course. A semi-experimental study. Faculty of Nursing and Midwifery, Mazandaran University of Medical Sciences, Sari , Iran. Forty-seven midwifery interns in Gynecology course were recruited during two semesters in 2020. Five steps based on the Analysis, Design, Development, Implementation, and Evaluation (ADDIE) model were taken, which included 1) educational and skills needs assessment, 2) design, 3) development via focused group interviews and brainstorming with the presence of the midwifery department members in three sessions, 4) implementation including pretest and posttest, webinar, uploading the information of virtual patients, questions and correct answers, and 5) evaluation including knowledge assessment by a designed questionnaire and skills evaluation by the modified-Mini-CEX checklist. Data were analyzed using mean, standard deviation and paired t -test. After training, a significant increase (p < 0.001) was observed in scores of knowledge and interview skills, clinical judgment, consultation, efficiency, professionalism, clinical competence and total score of clinical skills. Training for gynecological diseases through virtual clinic promoted knowledge and clinical skills of midwifery interns. To enhance education, a virtual clinic may be used in crisis situations and in combination with teaching under normal circumstances by strengthening the infrastructure and removing barriers. [ABSTRACT FROM AUTHOR]

As technological advances revolutionize the process of novel target identification in drug discovery, the problem of validating this ever-growing number of targets against predicted clinical efficacy in humans is creating a bottleneck. All methods of novel target identification rely on partial and isolated models of human disease. For example, methods such as differential gene expression (comparing the upregulation of a particular gene in several sick versus healthy patients) and high-throughput compound screening (identifying compounds that hit a pathway that is thought to be involved in a disease process) are important research that intimate target involvement in a particular disease process, but such ‘hints’ lack specificity for predicting the clinical efficacy of a target. Given that current target identification methods are an imperfect predictor of clinical efficacy and that moving all targets forward through to development is prohibitive in terms of cost and time - how can rational choices between novel targets be made? [Copyright &y& Elsevier]

In nursing education, knowledge and competence in conducting physical assessments are crucial. Therefore, physical assessment knowledge has become a fundamental and essential education program for nursing students. However, most of the current nursing courses are taught via a didactic teaching approach, making it difficult for students to think deeply about relevant issues due to the lack of interaction and context. This may, in turn, have an impact on learning effectiveness and clinical judgment. A virtual patient (VP)-based social learning approach is proposed to enhance nursing students' performance and clinical judgment in education programs. A quasi-experiment method was adopted to evaluate the effectiveness of the proposed approach. A total of 40 senior nursing students participated in the study (VP-based social learning approach group = 20, control group = 20). The study involved a pre- and post-test to examine students' learning achievements, self-efficacy, and communication skills. The experimental group adopted the VP-based learning approach, while the control group adopted the conventional didactic learning approach. ANCOVA was employed to compare the performances of the two groups. The experimental results indicate that using a VP for learning can enhance students' learning achievements, self-efficacy, and communication skills. In addition, based on the analysis of the results, students generally believed that learning with a VP makes learning more enjoyable. The VP-based social learning approach would be an effective strategy to train nursing students who have limited opportunities to experience real clinical situations, in particular during the COVID-19 pandemic. • A virtual patent (VP)-based social learning approach is proposed for nursing training. • A quasi-experiment was conducted in nursing training. • The proposed approach enhanced the students' learning achievements, self-efficacy, and communication skills. • The approach could be applied to other nursing training courses, in particular, during the COV-19 pandemic. [ABSTRACT FROM AUTHOR]

• Clinical studies have shown excessive pressure and volume in mechanical ventilation leads to unintended lung injury. • Development and validation of a predictive in-silico models or digital clones is presented. • These models and methods are built on well-validated nonlinear mechanics models. • This model very accurately predicts peak volumes and pressures in multiple common ventilation modes. • The model very accurately predicts the volume retained when raising PEEP. • Clinical use of virtual patients would improve clinician confidence and patient safety in MV care. Mechanical ventilation (MV) is a core intensive care unit (ICU) therapy. Significant inter- and intra- patient variability in lung mechanics and condition makes managing MV difficult. Accurate prediction of patient-specific response to changes in MV settings would enable optimised, personalised, and more productive care, improving outcomes and reducing cost. This study develops a generalised digital clone model, or in-silico virtual patient, to accurately predict lung mechanics in response to changes in MV. An identifiable, nonlinear hysteresis loop model (HLM) captures patient-specific lung dynamics identified from measured ventilator data. Identification and creation of the virtual patient model is fully automated using the hysteresis loop analysis (HLA) method to identify lung elastances from clinical data. Performance is evaluated using clinical data from 18 volume-control (VC) and 14 pressure-control (PC) ventilated patients who underwent step-wise recruitment maneuvers. Patient-specific virtual patient models accurately predict lung response for changes in PEEP up to 12 cmH 2 O for both volume and pressure control cohorts. R2 values for predicting peak inspiration pressure (PIP) and additional retained lung volume, V frc in VC, are R2=0.86 and R2=0.90 for 106 predictions over 18 patients. For 14 PC patients and 84 predictions, predicting peak inspiratory volume (PIV) and V frc yield R2=0.86 and R2=0.83. Absolute PIP, PIV and V frc errors are relatively small. Overall results validate the accuracy and versatility of the virtual patient model for capturing and predicting nonlinear changes in patient-specific lung mechanics. Accurate response prediction enables mechanically and physiologically relevant virtual patients to guide personalised and optimised MV therapy. [ABSTRACT FROM AUTHOR]

Abstract: Background: The Digital Clinical Experience™ is a simulation software that allows nursing students to interview and examine virtual patient and then write self-reflections on their performance. Methods: A secondary data analysis was conducted on 130 master of science in nursing students' performance in the Digital Clinical Experience™ using a framework of situated cognition and transformative learning theory. Employing a within-stage mixed-model design, content analysis of structured self-reflections was conducted using Cook's Reflection Rating Rubric. Multiple regression was performed using self-reflection scores as the dependent variable; independent variables included time spent in simulation, lines of dialog, and primary and secondary clinical items discovered. Results: Critical self-reflection indicating transformative learning was predicted by the number of secondary clinical items students uncover during their virtual patient interview. Yet, this level of self-reflection did not occur for students who discovered only the primary clinical findings. Conclusions: This study presents evidence that virtual patient simulations can provide transformative learning experiences in nursing. However, to facilitate a transformative learning experience, these simulations must allow students to explore the presentation, history, and backstory of the virtual patient in depth, beyond superficial clinical findings. [Copyright &y& Elsevier]