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Background: At the University of Tasmania (UTAS), pharmacy education traditionally relies on placements to provide students with hands-on experience. However, these placements have become increasingly limited due to logistical challenges and growing student numbers. Computer-based simulation (CBS) has the potential to offer a scalable, effective alternative to enhance learning and critical thinking. However, integrating CBS in pharmacy education faces several barriers that must be addressed for successful implementation. Objective: To gain insight into pharmacy educators' and students' views regarding the barriers, and potential solutions, to integrating CBS in pharmacy practice education. Methods: This mixed-methods case study involved semi-structured interviews with pharmacy educators and quantitative surveys with pharmacy students. The data underwent thematic coding for interview transcripts and statistical analysis for survey responses. The findings were integrated by examining convergence, complementarity, and discrepancy, revealing insights into how pharmacy students and educators perceive implementation barriers and improvement strategies for CBS. Results: Ten interviews were conducted, and 75 survey responses were collected, with a 62.5% response rate. Key barriers to CBS integration included educators' heavy workload, scepticism about CBS's educational value, and general integration challenges. Students, however, showed high acceptance of CBS, with 70.7% agreeing that CBS could assess their knowledge, 69.3% emphasising its role in developing problem-solving skills, and 80% viewing CBS as a complement to classroom study. Proposed solutions for enhancing CBS uptake included additional institutional support by appointing dedicated simulation technicians, leveraging champions to advocate for CBS, and aligning CBS with educational objectives. Conclusion: A significant gap between students' readiness and educators' hesitancy to use CBS in pharmacy education was identified. While students are eager to adopt new technologies, educators expressed reservations, primarily due to workload concerns and uncertainties about the efficacy of CBS. The feedback from educators suggests that institutions may see improved uptake by employing dedicated support personnel and initiating targeted training programs. Future research should focus on exploring barriers and facilitators, using larger and more diverse samples, and gaining deeper insights into decision-makers' perspectives to enhance the integration and efficacy of CBS in pharmacy education.

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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.

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The generation of synthetic patient data that reflect the statistical properties of real data plays a fundamental role in today's world because of its potential to (i) be enable proprietary data access for statistical and research purposes and (ii) increase available data (e.g., in low-density regions-i.e., for patients with under-represented characteristics). Generative methods employ a family of solutions for generating synthetic data. The objective of this research is to benchmark numerous state-of-the-art deep-learning generative methods across different scenarios and clinical datasets comprising patient covariates and several pharmacokinetic/pharmacodynamic endpoints. We did this by implementing various probabilistic models aimed at generating synthetic data, such as the Multi-layer Perceptron Conditioning Generative Adversarial Neural Network (MLP cGAN), Time-series Generative Adversarial Networks (TimeGAN), and a more traditional approach like Probabilistic Autoregressive (PAR). We evaluated their performance by calculating discriminative and predictive scores. Furthermore, we conducted comparisons between the distributions of real and synthetic data using Kolmogorov-Smirnov and Chi-square statistical tests, focusing respectively on covariate and output variables of the models. Lastly, we employed pharmacometrics-related metric to enhance interpretation of our results specific to our investigated scenarios. Results indicate that multi-layer perceptron-based conditional generative adversarial networks (MLP cGAN) exhibit the best overall performance for most of the considered metrics. This work highlights the opportunities to employ synthetic data generation in the field of clinical pharmacology for augmentation and sharing of proprietary data across institutions.

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BACKGROUND: Although history taking is fundamental for diagnosing medical conditions, teaching and providing feedback on the skill can be challenging due to resource constraints. Virtual simulated patients and web-based chatbots have thus emerged as educational tools, with recent advancements in artificial intelligence (AI) such as large language models (LLMs) enhancing their realism and potential to provide feedback. OBJECTIVE: In our study, we aimed to evaluate the effectiveness of a Generative Pretrained Transformer (GPT) 4 model to provide structured feedback on medical students' performance in history taking with a simulated patient. METHODS: We conducted a prospective study involving medical students performing history taking with a GPT-powered chatbot. To that end, we designed a chatbot to simulate patients' responses and provide immediate feedback on the comprehensiveness of the students' history taking. Students' interactions with the chatbot were analyzed, and feedback from the chatbot was compared with feedback from a human rater. We measured interrater reliability and performed a descriptive analysis to assess the quality of feedback. RESULTS: Most of the study's participants were in their third year of medical school. A total of 1894 question-answer pairs from 106 conversations were included in our analysis. GPT-4's role-play and responses were medically plausible in more than 99% of cases. Interrater reliability between GPT-4 and the human rater showed "almost perfect" agreement (Cohen κ=0.832). Less agreement (κ<0.6) detected for 8 out of 45 feedback categories highlighted topics about which the model's assessments were overly specific or diverged from human judgement. CONCLUSIONS: The GPT model was effective in providing structured feedback on history-taking dialogs provided by medical students. Although we unraveled some limitations regarding the specificity of feedback for certain feedback categories, the overall high agreement with human raters suggests that LLMs can be a valuable tool for medical education. Our findings, thus, advocate the careful integration of AI-driven feedback mechanisms in medical training and highlight important aspects when LLMs are used in that context.

Asunto(s)

Anamnesis , Simulación de Paciente , Estudiantes de Medicina , Humanos , Estudios Prospectivos , Anamnesis/métodos , Anamnesis/normas , Estudiantes de Medicina/psicología , Femenino , Masculino , Competencia Clínica/normas , Inteligencia Artificial , Retroalimentación , Reproducibilidad de los Resultados , Educación de Pregrado en Medicina/métodos

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Ineffective communication is implicated in 80% of medical errors, costing the United States approximately $12 billion annually. Teaching communication skills is a component of nursing curricula linked to improved patient outcomes. Simulation-based experience (SBE) is a strategy for healthcare professionals to learn communication skills. Providing nurses with the ability to practice nurse-nurse, nurse-physician, nurse-patient, and team communication skills in a psychologically safe learning environment provides an opportunity for skill development and meaningful self-reflection. The multiple modalities for SBE support needed communication techniques for skill development and acquisition to improve patient outcomes.

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Competencia Clínica , Comunicación , Entrenamiento Simulado , Humanos , Competencia Clínica/normas , Entrenamiento Simulado/métodos , Curriculum/normas , Estados Unidos , Simulación de Paciente , Educación en Enfermería , Relaciones Enfermero-Paciente

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BACKGROUND: Virtual patients (VPs) are widely used in health professions education. When they are well integrated into curricula, they are considered to be more effective than loosely coupled add-ons. However, it is unclear what constitutes their successful integration. The aim of this study was to identify and synthesise the themes found in the literature that stakeholders perceive as important for successful implementation of VPs in curricula. METHODS: We searched five databases from 2000 to September 25, 2023. We included qualitative, quantitative, mixed-methods and descriptive case studies that defined, identified, explored, or evaluated a set of factors that, in the perception of students, teachers, course directors and researchers, were crucial for VP implementation. We excluded effectiveness studies that did not consider implementation characteristics, and studies that focused on VP design factors. We included English-language full-text reports and excluded conference abstracts, short opinion papers and editorials. Synthesis of results was performed using the framework synthesis method with Kern's six-step model as the initial framework. We appraised the quality of the studies using the QuADS tool. RESULTS: Our search yielded a total of 4808 items, from which 21 studies met the inclusion criteria. We identified 14 themes that formed an integration framework. The themes were: goal in the curriculum; phase of the curriculum when to implement VPs; effective use of resources; VP alignment with curricular learning objectives; prioritisation of use; relation to other learning modalities; learning activities around VPs; time allocation; group setting; presence mode; VPs orientation for students and faculty; technical infrastructure; quality assurance, maintenance, and sustainability; assessment of VP learning outcomes and learning analytics. We investigated the occurrence of themes across studies to demonstrate the relevance of the framework. The quality of the studies did not influence the coverage of the themes. CONCLUSIONS: The resulting framework can be used to structure plans and discussions around implementation of VPs in curricula. It has already been used to organise the curriculum implementation guidelines of a European project. We expect it will direct further research to deepen our knowledge on individual integration themes.

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Curriculum , Humanos , Educación de Pregrado en Medicina , Simulación de Paciente , Participación de los Interesados , Empleos en Salud/educación

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Clinical reasoning is considered one of the most important competencies but is not included in most healthcare curricula. The number and diversity of patient encounters are the decisive factors in the development of clinical reasoning competence. Physical real patient encounters are considered optimal, but virtual patient cases also promote clinical reasoning. A high-volume, low-fidelity virtual patient library thus can support clinical reasoning training in a safe environment and can be tailored to the needs of learners from different health care professions. It may also stimulate interprofessional understanding and team shared decisions. Implementation will be challenged by tradition, the lack of educator competence and prior experience as well as the high-density curricula at medical and veterinary schools and will need explicit address from curriculum managers and education leads.

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AIMS: Patients with persistent atrial fibrillation (AF) experience 50% recurrence despite pulmonary vein isolation (PVI), and no consensus is established for secondary treatments. The aim of our i-STRATIFICATION study is to provide evidence for stratifying patients with AF recurrence after PVI to optimal pharmacological and ablation therapies, through in silico trials. METHODS AND RESULTS: A cohort of 800 virtual patients, with variability in atrial anatomy, electrophysiology, and tissue structure (low-voltage areas, LVAs), was developed and validated against clinical data from ionic currents to electrocardiogram. Virtual patients presenting AF post-PVI underwent 12 secondary treatments. Sustained AF developed in 522 virtual patients after PVI. Second ablation procedures involving left atrial ablation alone showed 55% efficacy, only succeeding in the small right atria (<60 mL). When additional cavo-tricuspid isthmus ablation was considered, Marshall-PLAN sufficed (66% efficacy) for the small left atria (<90 mL). For the bigger left atria, a more aggressive ablation approach was required, such as anterior mitral line (75% efficacy) or posterior wall isolation plus mitral isthmus ablation (77% efficacy). Virtual patients with LVAs greatly benefited from LVA ablation in the left and right atria (100% efficacy). Conversely, in the absence of LVAs, synergistic ablation and pharmacotherapy could terminate AF. In the absence of ablation, the patient's ionic current substrate modulated the response to antiarrhythmic drugs, being the inward currents critical for optimal stratification to amiodarone or vernakalant. CONCLUSION: In silico trials identify optimal strategies for AF treatment based on virtual patient characteristics, evidencing the power of human modelling and simulation as a clinical assisting tool.

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Antiarrítmicos , Fibrilación Atrial , Ablación por Catéter , Venas Pulmonares , Recurrencia , Fibrilación Atrial/cirugía , Fibrilación Atrial/tratamiento farmacológico , Fibrilación Atrial/fisiopatología , Fibrilación Atrial/diagnóstico , Humanos , Ablación por Catéter/métodos , Venas Pulmonares/cirugía , Venas Pulmonares/fisiopatología , Antiarrítmicos/uso terapéutico , Resultado del Tratamiento , Modelos Cardiovasculares , Simulación por Computador , Potenciales de Acción , Medición de Riesgo , Atrios Cardíacos/fisiopatología , Atrios Cardíacos/cirugía , Masculino , Anisoles/uso terapéutico , Selección de Paciente , Femenino , Modelación Específica para el Paciente , Persona de Mediana Edad , Pirrolidinas/uso terapéutico , Electrocardiografía , Toma de Decisiones Clínicas

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Recently, immunotherapies for antitumoral response have adopted conditionally activated molecules with the objective of reducing systemic toxicity. Amongst these are conditionally activated antibodies, such as PROBODY® activatable therapeutics (Pb-Tx), engineered to be proteolytically activated by proteases found locally in the tumor microenvironment (TME). These PROBODY® therapeutics molecules have shown potential as PD-L1 checkpoint inhibitors in several cancer types, including both effectiveness and locality of action of the molecule as shown by several clinical trials and imaging studies. Here, we perform an exploratory study using our recently published quantitative systems pharmacology model, previously validated for triple-negative breast cancer (TNBC), to computationally predict the effectiveness and targeting specificity of a PROBODY® therapeutics drug compared to the non-modified antibody. We begin with the analysis of anti-PD-L1 immunotherapy in non-small cell lung cancer (NSCLC). As a first contribution, we have improved previous virtual patient selection methods using the omics data provided by the iAtlas database portal compared to methods previously published in literature. Furthermore, our results suggest that masking an antibody maintains its efficacy while improving the localization of active therapeutic in the TME. Additionally, we generalize the model by evaluating the dependence of the response to the tumor mutational burden, independently of cancer type, as well as to other key biomarkers, such as CD8/Treg Tcell and M1/M2 macrophage ratio. While our results are obtained from simulations on NSCLC, our findings are generalizable to other cancer types and suggest that an effective and highly selective conditionally activated PROBODY® therapeutics molecule is a feasible option.

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Artificial Intelligence has the potential to reshape the landscape of clinical trials through innovative applications, with a notable advancement being the emergence of synthetic patient generation. This process involves simulating cohorts of virtual patients that can either replace or supplement real individuals within trial settings. By leveraging synthetic patients, it becomes possible to eliminate the need for obtaining patient consent and creating control groups that mimic patients in active treatment arms. This method not only streamlines trial processes, reducing time and costs but also fortifies the protection of sensitive participant data. Furthermore, integrating synthetic patients amplifies trial efficiency by expanding the sample size. These straightforward and cost-effective methods also enable the development of personalized subject-specific models, enabling predictions of patient responses to interventions. Synthetic data holds great promise for generating real-world evidence in clinical trials while upholding rigorous confidentiality standards throughout the process. Therefore, this study aims to demonstrate the applicability and performance of these methods in the context of onco-hematological research, breaking through the theoretical and practical barriers associated with the implementation of artificial intelligence in medical trials.

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Background: Computer simulators of human metabolism are powerful tools to design and validate new diabetes treatments. However, these platforms are often limited in the diversity of behaviors and glycemic conditions they can reproduce. Replay methodologies leverage field-collected data to create ad hoc simulation environments representative of real-life conditions. After formal validations of our method in prior publications, we demonstrate its capacity to reproduce a recent clinical trial. Methods: Using the replay methodology, an ensemble of replay simulators was generated using data from a randomized crossover clinical trial comparing the hybrid closed loop (HCL) and fully closed loop (FCL) control modalities in automated insulin delivery (AID), creating 64 subject/modality pairs. Each virtual subject was exposed to the alternate AID modality to compare the simulated versus observed glycemic outcomes. Equivalence tests were performed for time in, below, and above range (TIR, TBR, and TAR) and high and low blood glucose indices (HBGI and LBGI) considering equivalence margins corresponding to clinical significance. Results: TIR, TAR, LBGI, and HBGI showed statistical and clinical equivalence between the original and the simulated data; TBR failed the equivalence test. For example, in the HCL mode, simulated TIR was 84.89% versus an observed 84.31% (P = 0.0170, confidence interval [CI] [-3.96, 2.79]), and for FCL mode, TIR was 76.58% versus 77.41% (P = 0.0222, CI [-2.54, 4.20]). Conclusion: Clinical trial data confirm the prior in silico validation of the UVA replay method in predicting the glycemic impact of modified insulin treatments. This in vivo demonstration justifies the application of the replay method to the personalization and adaptation of treatment strategies in people with type 1 diabetes.

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BACKGROUND: History taking and clinical reasoning are important skills that require knowledge, cognition and meta-cognition. It is important that a trainee must experience multiple encounters with different patients to practice these skills. However, patient safety is also important, and trainees are not allowed to handle critically ill patients. To address this issue, a randomized controlled trial was conducted to determine the effectiveness of using Virtual Patients (VP) versus Standardized Patients (SP) in acquiring clinical reasoning skills in ophthalmology postgraduate residents. METHODS: Postgraduate residents from two hospitals in Lahore, Pakistan, were randomized to either the VP group or the SP group and were exposed to clinical reasoning exercise via the VP or SP for 30 min after the pretest. This was followed by a posttest. One month after this activity, a follow-up posttest was conducted. The data were collected and analysed using IBM-SPSS version 25. Repeated measures ANOVA was used to track the effect of learning skills over time. RESULTS: The mean age of the residents was 28.5 ± 3 years. The male to female ratio was 1:1.1. For the SP group, the mean scores were 12.6 ± 3.08, 16.39 ± 3.01 and 15.39 ± 2.95, and for the VP group, the mean scores were 12.7 ± 3.84, 16.30 ± 3.19 and 15.65 ± 3.18 for the pretest, posttest and follow-up posttest, respectively (p value < 0.00). However, the difference between the VP and SP groups was not statistically significant (p = 0.896). Moreover, there was no statistically significant difference between the VP and SP groups regarding the retention of clinical reasoning ability. In terms of learning gain, compared with the VP group, the SP group had a score of 51.46% immediately after clinical reasoning exercise as compared to VP group, in which it was 49.1%. After one month, it was 38.01 in SP and 40.12% in VP group. CONCLUSION: VPs can be used for learning clinical reasoning skills in postgraduate ophthalmology residents in a safe environment. These devices can be used repeatedly without any risk to the real patient. Although similarly useful, SP is limited by its nonavailability for repeated exercises.

Asunto(s)

Competencia Clínica , Razonamiento Clínico , Internado y Residencia , Oftalmología , Humanos , Oftalmología/educación , Masculino , Femenino , Adulto , Simulación de Paciente , Pakistán , Educación de Postgrado en Medicina , Evaluación Educacional , Anamnesis/normas

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BACKGROUND: Using virtual patients integrated in simulators expands students' training opportunities in healthcare. However, little is known about the usability perceived by students and the factors/determinants that predict the acceptance and use of clinical virtual simulation in nursing education. OBJECTIVES: To identify the factors/determinants that predict the acceptance and use of clinical virtual simulation in learning in nursing education. METHODS: Observational, cross-sectional, analytical study of the use of clinical virtual simulation in nursing to answer the research question: What factors/determinants predict the acceptance and use of a clinical virtual simulator in nursing education? We used a non-probabilistic sampling, more specifically a convenience sample of nursing degree students. The data were collected through a questionnaire adapted from the Technology Acceptance Model 3. In technology and education, the Technology Acceptance Model is a theoretical model that predicts the acceptance of the use of technology by users. RESULTS: The sample comprised 619 nursing students, who revealed mean values of perceived usefulness (M = 5.34; SD = 1.19), ease of use (M = 4.74; SD = 1.07), and intention to use the CVS (M = 5.21; SD = 1.18), in a Likert scale of seven points (1-the worst and 7 the best possible opinion). This study validated the use of Technology Acceptance Model 3 adapted and tested the related hypotheses, showing that the model explains 62% of perceived utility, 32% of ease of use, and 54% of intention to use the clinical virtual simulation in nursing by nursing students. The adequacy of the model was tested by analysis of the direct effects of the relationships between the internal constructs (PU-BI, ß = 0.11, p = 0.012; PEOU-BI, ß = -0.11, p = 0.002) and the direct relations between some of the constructs internal to the Technology Acceptance Model 3 and the external determinants Relevance for learning and Enjoyability. In the proposed model, the external constructs that best predicted perceived usefulness, ease of use, and behaviour intention to use the clinical virtual simulation in nursing were Relevance for learning and Enjoyability. CONCLUSIONS: These study results allowed us to identify relevance for learning and enjoyability as the main factors/determinants that predict the acceptance and use of clinical virtual simulation in learning in nursing.

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Educación en Enfermería , Estudiantes de Enfermería , Humanos , Estudios Transversales , Educación en Enfermería/métodos , Simulación por Computador , Modelos Teóricos

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A FHIR based platform for case-based instruction of health professions students has been developed and field tested. The system provides a non-technical case authoring tool; supports individual and team learning using digital virtual patients; and allows integration of SMART Apps into cases via its simulated EMR. Successful trials at the University of Queensland have led to adoption at the University of Melbourne.

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Educación Profesional , Aprendizaje , Humanos

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OBJECTIVES: Dizziness is a common medical symptom that is frequently misdiagnosed. While virtual patient (VP) education has been shown to improve diagnostic accuracy for dizziness as assessed by VPs, trainee performance has not been assessed on human subjects. The study aimed to assess whether internal medicine (IM) interns after training on a VP-based dizziness curriculum using a deliberate practice framework would demonstrate improved clinical reasoning when assessed in an objective structured clinical examination (OSCE). METHODS: All available interns volunteered and were randomized 2:1 to intervention (VP education) vs. control (standard clinical teaching) groups. This quasi-experimental study was conducted at one academic medical center from January to May 2021. Both groups completed pre-posttest VP case assessments (scored as correct diagnosis across six VP cases) and participated in an OSCE done 6 weeks later. The OSCEs were recorded and assessed using a rubric that was systematically developed and validated. RESULTS: Out of 21 available interns, 20 participated. Between intervention (n=13) and control (n=7), mean pretest VP diagnostic accuracy scores did not differ; the posttest VP scores improved for the intervention group (3.5 [SD 1.3] vs. 1.6 [SD 0.8], p=0.007). On the OSCE, the means scores were higher in the intervention (n=11) compared to control group (n=4) for physical exam (8.4 [SD 4.6] vs. 3.9 [SD 4.0], p=0.003) and total rubric score (43.4 [SD 12.2] vs. 32.6 [SD 11.3], p=0.04). CONCLUSIONS: The VP-based dizziness curriculum resulted in improved diagnostic accuracy among IM interns with enhanced physical exam skills retained at 6 weeks post-intervention.

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Mareo , Internado y Residencia , Humanos , Mareo/diagnóstico , Mareo/etiología , Curriculum , Examen Físico , Evaluación Educacional

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BACKGROUND: The emergence of the COVID-19 pandemic has posed a significant ethical dilemma in the allocation of scarce, life-saving medical equipment to critically ill patients. It remains uncertain whether medical students are equipped to navigate this complex ethical process. OBJECTIVE: This study aimed to assess the ability and confidence of medical students to apply principles of medical ethics in allocating critical medical devices through the scenario of virtual patients. METHODS: The study recruited third- and fourth-year medical students during clinical rotation. We facilitated interactions between medical students and virtual patients experiencing respiratory failure due to COVID-19 infection. We assessed the students' ability to ethically allocate life-saving resources. Subsequently, we analyzed their written reports using thematic analysis to identify the ethical principles guiding their decision-making. RESULTS: We enrolled a cohort of 67 out of 71 medical students with a mean age of 34 (SD 4.7) years, 60% (n=40) of whom were female students. The principle of justice was cited by 73% (n=49) of students while analyzing this scenario. A majority of them expressed hesitancy in determining which patient should receive life-saving resources, with 46% (n=31) citing the principle of nonmaleficence, 31% (n=21) advocating for a first-come-first-served approach, and 25% (n=17) emphasizing respect for patient autonomy as key influencers in their decisions. Notably, medical students exhibited a lack of confidence in making ethical decisions concerning the distribution of medical resources. A minority, comprising 12% (n=8), proposed the exploration of legal alternatives, while 4% (n=3) suggested medical guidelines and collective decision-making as potential substitutes for individual ethical choices to alleviate the stress associated with personal decision-making. CONCLUSIONS: This study highlights the importance of improving ethical reasoning under time constraints using virtual platforms. More than 70% of medical students identified justice as the predominant principle in allocating limited medical resources to critically ill patients. However, they exhibited a lack of confidence in making ethical determinations and leaned toward principles such as nonmaleficence, patient autonomy, adherence to legal and medical standards, and collective decision-making to mitigate the pressure associated with such decisions.

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COVID-19 , Estudiantes de Medicina , Humanos , Femenino , Adulto , Masculino , COVID-19/epidemiología , Pandemias , Enfermedad Crítica , Beneficencia

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Virtual patients are increasingly used in undergraduate psychiatry education. This article reports on a systematic review aimed at providing an overview of different approaches in this context, describing their effectiveness, and thematically comparing learning outcomes across different undergraduate programs. The authors searched PubMed, PsycInfo, CINAHL, and Scopus databases for articles published between 2000 and January 2021. Quantitative and qualitative studies that reported on outcomes related to learners' knowledge, skills, and attitudes following an intervention with virtual patients in undergraduate psychiatry education were reviewed. Outcomes were thematically compared, and a narrative synthesis of the different outcomes and effectiveness was provided. Of 7856 records identified, 240 articles were retrieved for full-text review and 46 articles met all inclusion criteria. There were four broad types of virtual patient interventions: case-based presentation (n = 17), interactive virtual patient scenarios (n = 14), standardized virtual patients (n = 10), and virtual patient videogames (n = 5). The thematic analysis revealed that virtual patients in psychiatry education have been used for learners to construe knowledge about symptomatology and psychopathology, develop interpersonal and clinical communicative skills, and to increase self-efficacy and decrease stigmatizing attitudes towards psychiatric patients. In comparison with no intervention, traditional teaching, and text-based interventions, virtual patients were associated with higher learning outcomes. However, the results did not indicate any superiority of virtual patients over non-technological simulation. Virtual patients in psychiatry education offer opportunities for students from different health disciplines to build knowledge, practice skills, and improve their attitudes towards individuals with mental illness. The article discusses methodological shortcomings in the reviewed literature. Future interventions should consider the mediating effects of the quality of the learning environment, psychological safety, and level of authenticity of the simulation.

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Aprendizaje , Psiquiatría , Humanos , Estudiantes , Actitud , Competencia Clínica

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BACKGROUND: At the Faculty of Medicine of the National University of Malaysia, a virtual patient software program, DxR Clinician, was utilised for the teaching of neurocognitive disorder topics during the psychiatry posting of undergraduate medical students in a modified team-based learning (TBL) module. This study aimed to explore medical students' learning experiences with virtual patient. METHODS: Ten students who previously underwent the learning module were recruited through purposive sampling. The inclusion criteria were: (a) Fourth-year medical students; and (b) Completed psychiatry posting with the new module. Students who dropped out or were unable to participate in data collection were excluded. Two online focus group discussions (FGDs) with five participants each were conducted by an independent facilitator, guided by a questioning route. The data were transcribed verbatim and coded using the thematic analysis approach to identify themes. RESULTS: Three main themes of their learning experience were identified: (1) fulfilment of the desired pedagogy (2), realism of the clinical case, and (3) ease of use related to technical settings. The pedagogy theme was further divided into the following subthemes: level of entry for students, flexibility of presentation of content, provision of learning guidance, collaboration with peers, provision of feedback, and assessment of performance. The realism theme had two subthemes: how much the virtual patient experience mimicked an actual patient and how much the case scenario reflected real conditions in the Malaysian context. The technical setting theme entailed two subthemes: access to the software and appearance of the user interface. The study findings are considered in the light of learning formats, pedagogical and learning theories, and technological frameworks. CONCLUSIONS: The findings shed light on both positive and negative aspects of using virtual patients for medical students' psychiatry posting, which opens room for further improvement of their usage in undergraduate psychiatry education.

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Psiquiatría , Estudiantes de Medicina , Humanos , Aprendizaje , Grupos Focales , Estudiantes de Medicina/psicología , Psiquiatría/educación , Programas Informáticos

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Due to contact restrictions imposed because of the COVID-19 pandemic, we created a novel digital course on the Moodle learning platform for winter term in 2020. In the clinical pathology course (CPC) with hematological content, third-year students were able to work independently on 10 extra digital cases of internal medicine involving eight different animal species as a compensation for the reduction in traditional microscopy exercises. Each case presented was initiated using an anamnesis, also the participants to generate a differential blood count based on digitized leukocytes, previously been photographed using a microscope camera. The cases were successive and increased in complexity, for example through the increase in the number of different cell types to be differentiated. The participants had the opportunity to evaluate the course through a final module to rate user-friendliness and acceptance. The total results of the participants in 2021 were analyzed descriptively, focusing on success rates, time spent on the tasks, and number of attempts. A total of 237 (= 96%) of 247 students completed all cases, each assessing 1033 photographed blood cells in sum. The mean processing time was 22.48 min for a differentiation and the students spent an average of 1.48 attempts on it. A voluntary feedback form was completed by 192 (= 78%) students, with more than 95% rating the course positively in 12 evaluation questions, and 29 of 33 comments (= 87.88%) providing positive statements in a comment box. Suggestions for improvement primarily included more explanations on erythrocyte morphologies, followed by adjusting the difficulty level and improving the presentational set-up. Slight improvements in results, time spent on processing the tasks, and the number of attempts indicated an achievement of routine and confidence during the course and were associated with an increase of competency. The positive feedback showed a high acceptance of the digital format and students evaluated the course as improving the quality of teaching when combined with practical exercises.