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Background: A digital workflow for implant-supported fixed complete prostheses (ISFCP) using photogrammetry (PG), virtual articulator (VA), and virtual facebow (VF) data remains a challenge., Methods: The novel ISFCP technique included four steps: (1) formation of a dynamic virtual patient, (2) integration of PG data, (3) fabrication of a diagnostic ISFCP, and (4) fabrication of a definitive ISFCP and test of the deviation., Results: Dynamic virtual patients were formed by integrating PG, VA, and VF data. The cumulative root mean square deviation between the designed data and actual definitive prosthesis was 140.4 µm., Conclusions: The novel technique for ISFCP fabrication described in this paper can help optimise the clinical efficiency and quality of ISFCP but requires an initial learning curve., Clinical Significance: This technique provides a direct workflow, using PG, VA, and VF data, to fabricate ISFCP based on the provisional restoration., Competing Interests: Declaration of Competing Interest The authors declare that they have no conflicts of interest with the contents of this article. The study was approved by the ethical committee of the Jinan Military General Hospital, and informed consent was obtained from the patient., (Copyright © 2023 Elsevier Ltd. All rights reserved.)

Objectives: The present study aimed to investigate the trueness and precision of an intraoral transfer element (IOTE) designed for matching intraoral and facial scans., Methods: A mannequin head with a maxillary model in position was used. Cone beam computed tomography (CBCT) was used to construct the master model (MM). A digital impression of the maxillary arch was taken using an intraoral scanner, and the IOTE was used to record the maxillary arch position. Twenty facial scans with and twenty without the IOTE in place were performed with a handheld device (Ipad Pro, Apple) using a dedicated software. Digital mounting was performed to build a scan model (SM). Using software for data processing, the three-dimensional deviation between the MM and SM at the central left incisor (#9#), and the left and right first molars (#3#, #14#) was calculated as trueness and linear deviation precision. . Angular deviation was also calculated at the occlusal plane., Results: The linear deviation trueness at #9#, #3# and #14# was 0.3 ± 0.12 mm, 1.07 ± 0.28 mm, and 0.18 ± 0.34 mm, respectively, whereas the precision was 1.17 ± 0.4 mm, 0.43 ± 0.12 mm, and 0.64 ± 0.28 mm, respectively. Trueness of the angular deviation at the occlusal plane was 2.17 ± 0.46°, whereas the angular deviation precision was 0.64 ± 0.28°., Conclusions: Based on in vitro findings the proposed IOTE design is accurate and suitable for clinical use., Clinical Significance: Direct virtual mounting is a reliable technique in vitro; however, in vivo tests are required., Competing Interests: Declaration of Competing Interest Rome, 21st January 2022. All the authors declare no conflict of interest. Luigi Canullo on behalf of others., (Copyright © 2023. Published by Elsevier Ltd.)

Background: The capacity of health care professionals to perform clinical procedures safely and competently is crucial as it will directly impact patients' outcomes. Given the ability of head-mounted virtual reality to simulate the authentic clinical environment, this platform should be suitable for nurses to refine their clinical skills for knowledge and skills acquisition. However, research on head-mounted virtual reality in learning clinical procedures is limited., Objective: The objectives of this study were (1) to describe the design of a head-mounted virtual reality system and evaluate it for education on clinical procedures for nursing students and (2) to explore the experience of nursing students using head-mounted virtual reality for learning clinical procedures and the usability of the system., Methods: This usability study used a mixed method approach. The stages included developing 3D models of the necessary instruments and materials used in intravenous therapy and subcutaneous injection procedures performed by nurses, followed by developing the procedures using the Unreal Engine (Epic Games). Questionnaires on the perception of continuance intention and the System Usability Scale were used along with open-ended questions., Results: Twenty-nine nursing students took part in this questionnaire study after experiencing the immersive virtual reality (IVR) intervention. Participants reported largely favorable game perception and learning experience. Mean perception scores ranged from 3.21 to 4.38 of a maximum score of 5, while the mean system usability score was 53.53 of 100. The majority found that the IVR experience was engaging, and they were immersed in the game. The challenges encountered included unfamiliarity with the new learning format; technological constraints, such as using hand controllers; and physical discomfort., Conclusions: The conception of IVR for learning clinical procedures through deliberate practice to enhance nurses' knowledge and skills is promising. However, refinement of the prototypes is required to improve user experience and learning. Future research can explore other ways to use IVR for better education and health care purposes., (©Siew Tiang Lau, Rosalind Chiew Jiat Siah, Khairul Dzakirin Bin Rusli, Wen Liang Loh, John Yin Gwee Yap, Emily Ang, Fui Ping Lim, Sok Ying Liaw. Originally published in JMIR Serious Games (https://games.jmir.org), 30.08.2023.)

Background: Simulation use is rapidly expanding, with technologies like virtual patients (VPs) and computer-based simulation (CBS) allowing for educators to equip pharmacy students with the necessary skills that are aligned with the demands and expectations of a practicing pharmacy professional. These technologies enable pharmacy students to be exposed to challenging or infrequent patient case scenarios in an authentic pharmacy setting. This allows for the reinforcing of care processes and for techniques and crucial skills to be applied., Aim of the Study: To consolidate the existing evidence regarding the utilization of VPs and CBS in preparing and supporting students in pharmacy experiential education and evaluate the effectiveness of these approaches in enhancing student pharmacists' learning outcomes, including knowledge, skills, confidence, enjoyment, and engagement., Methods: Five electronic databases were searched using combined keyword and indexing terms (when available) with Boolean operators for the literature search. Studies that reported or investigated the use of VPs and CBS in pharmacy experiential education were included. Data on study design, demographics of participants, information on the interventions, course/skills, primary and secondary outcomes, and qualitative findings were extracted., Results: A total of 911 unique articles were initially identified and filtered down to 19 articles fitting within the inclusion criteria. The selected 19 articles involved student pharmacists (Y1-Y5) and pre-registered pharmacists from ten countries. Simulation tools were used in various pharmacy courses, including Advanced Pharmacy Practice Experience (APPE), Advanced Pharmaceutical Care II, and Medication Management. Implementing these tools in pharmacy experiential education demonstrated a statistically significant improvement in student knowledge ( p  < 0.05). Most students agreed/strongly agreed that practicing with virtual patient cases enhanced their clinical reasoning, counseling skills, confidence in communication, and attitudes toward the courses., Conclusions: This systematic review supports the use of VPs and CBS in pharmacy experiential education and provides practical recommendations for educators including selecting suitable tools, implementing them strategically within courses, integrating them with existing activities, and considering financial and IT support., Competing Interests: All authors declare no conflict of interests., (© 2023 The Authors.)

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]

This paper presents the domain of information sciences, applied informatics and biomedical engineering, proposing to develop methods for an automated detection of similarities between two particular virtual learning environments - virtual patients at Akutne.cz and the OPTIMED curriculum management system - in order to provide support to clinically oriented stages of medical and healthcare studies. For this purpose, the authors used large amounts of text-based data collected by the system for mapping medical curricula and through the system for virtual patient authoring and delivery. The proposed text-mining algorithm for an automated detection of links between content entities of these systems has been successfully implemented by the means of a web-based toolbox. [ABSTRACT FROM AUTHOR]

Introduction: The success rate of pharmaceutical Research & Development (R&D) is much lower compared to other industries such as micro-electronics or aeronautics with the probability of a successful clinical development to approval in central nervous system (CNS) disorders hovering in the single digits (7%). Areas covered: Inspired by adjacent engineering-based industries, we argue that quantitative modeling in CNS R&D might improve success rates. We will focus on quantitative techniques in early clinical development, such as PharmacoKinetic-PharmacoDynamic modeling, clinical trial simulation, model-based meta-analysis and the mechanism-based physiology-based pharmacokinetic modeling, and quantitative systems pharmacology. Expert commentary: Mechanism-based computer modeling rely less on existing clinical datasets, therefore can better generalize than Big Data analytics, including prospectively and quantitatively predicting the clinical outcome of new drugs. More specifically, exhaustive post-hoc analysis of failed trials using individual virtual human trial simulation could illuminate underlying causes such as lack of sufficient functional target engagement, negative pharmacodynamic interactions with comedications and genotypes, and mismatched patient population. These insights are beyond the capacity of artificial intelligence (AI) methods as they are many more possible combinations than subjects. Unlike ‘black box’ approaches in AI, mechanism-based platforms are transparent and based on biologically sound assumptions that can be interrogated. [ABSTRACT FROM AUTHOR]

Abstract: Objectives: This study aimed to measure students’ perceptions of virtual patient scenarios (VPs) for developing ethical reasoning skills and to explore features in VP design that are necessary to promote professionalism. Methods: Sixty‐five dental students participated in learning sessions that involved collaborative practice with five VPs (four high fidelity and one low fidelity), followed by reflection sessions. Students’ perceptions towards the use of VPs in developing ethical reasoning skills were assessed using a questionnaire that involved 10 closed and three open‐ended questions. Results: High‐fidelity VPs were perceived as significantly better for developing ethical reasoning skills than low‐fidelity VPs. Analyses of answers to open‐ended questions revealed two new features that are specific for VPs intended for teaching professionalism, which are VP dramatic structure and how it should end. Conclusion: VPs intended for teaching professionalism need to have high fidelity, follow a specific dramatic structure and should include multiple plausible endings. The use of VPs as part of a collaborative activity that is followed by a reflection session is perceived as an effective tool for the development of ethical reasoning skills in dental education. [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]

• 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]

Nowadays cardiac simulations are becoming increasingly sophisticated. This trend, part of the maturing field of computational medicine, has provided medical students and cardiologists alike with a new tool for education and research – their very own virtual “patient”. The CircAdapt biophysical model of the human heart and circulation (www.circadapt.org) allows the creation of a virtual “patient” for the study of the cardiovascular system and circulatory haemodynamics under diverse physiological and pathophysiological conditions in real time. The interactive CircAdapt model with its modular design based on established physical and physiologial principles allows dynamic monitoring of blood flow velocities, pressures and volumes in the heart and blood vessels, and across valves and shunts. As an educational tool, the CircAdapt model enables medical students and residents in cardiology, neonatology and intensive care medicine to analyze complex situations while improving their comprehension of cardiovascular physics and (patho)physiology. Moreover, the CircAdapt model has been successfully utilized as a research tool for cardiac resynchronization therapy as well as for various cardiovascular pathologies (e.g. pulmonary arterial hypertension, LBBB). All in all the CircAdapt perspective is as follows: bridge education and research - from classroom to bedside – to foster the future of clinical practice.