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.

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.

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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OBJECTIVE: 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.
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.
CONCLUSIONS: 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.

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.

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.

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.
CONCLUSIONS: 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.

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.

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.
OBJECTIVE: 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, β = 0.11, p = 0.012; PEOU-BI, β = -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.