A mathematical model for simulation of cardiovascular, renal, and hormonal responses to burn injury and resuscitation.

Introduction: Treating extensive burn injury requires an individually tailored resuscitation protocol that includes hourly-titrated intravenous fluid infusion to avert both hypovolemic shock and edema. Due to the complexity of burn pathophysiology and significant variability in treatment protocols, there is an ongoing effort to optimize burn resuscitation. The goal of this work is to contribute to this effort by developing a mathematical model of burn pathophysiology and resuscitation for in silico testing of burn resuscitation protocols and decision-support systems. Methods: In our previous work, we developed and validated a mathematical model consisting of volume kinetics, burn-induced perturbations, and kidney function. In this work, we expanded our previous mathematical model to incorporate novel mathematical models of cardiovascular system and hormonal system (renin-angiotensin-aldosterone (RAAS) system and antidiuretic hormone) which affect blood volume and pressure regulation. We also developed a detailed mathematical model of kidney function to regulate blood volume, pressure, and sodium levels, including components for glomerular filtration rate, reabsorption rates in nephron tubules, Tubuglomerular feedback, and myogenic mechanisms. We trained and validated the expanded mathematical model using experimental data from 15 pigs and 9 sheep with extensive burns to quantitatively evaluate its prediction accuracy for hematocrit, cardiac output, mean arterial pressure, central venous pressure, serum sodium levels, and urinary output. We then trained and tested the mathematical model using a clinical dataset of 233 human burn patients with demographic data and urinary output measurements. Results: The mathematical model could predict all tested variables very well, while internal variables and estimated parameters were consistent with the literature. Discussion: To the best of our knowledge, this is the first mathematical model of burn injury and resuscitation which is extensively validated to replicate actual burn patients. Hence, this in silico platform may complement large animal pre-clinical testing of burn resuscitation protocols. Beyond its primary purpose, the mathematical model can be used as a training tool for healthcare providers delivering insight into the pathophysiology of burn shock, and offering novel mathematical models of human physiology which can be independently used for other purposes and contexts.

Determining the effectiveness of a virtual service /patient-based education program on patient care and clinical decision-making in nursing: A quasi-experimental study.

OBJECTIVE: The aim of this study was to determine the effectiveness of a virtual service/patient-based program (vSPBP) developed for nursing education and its effect on the development of care plan preparation and clinical decision-making skills. METHODS: The study was conducted in a quasi-experimental design with a sample of fourth-year nursing students. Participants were assigned to the intervention group (n=44) and control group (n=51). The intervention group participated in a full-day vSPBP in addition to clinical training, whereas the control group received only the clinical training. Both groups were evaluated at the end of the intervention for care planning skills and at the beginning, middle, and end of the academic year for clinical decision-making skills. The Modified Simulation Effectiveness Tool (mSET) and focused group interview were used to evaluate the effectiveness of the vSPBP; nursing students' Clinical Decision-Making in Nursing Scale (CDM-NS) and Care Plan Evaluation Form were used to evaluate learning outcomes. Quantitative data were analyzed using the t-test and ANOVA. Qualitative data were analyzed by three researchers, and themes were identified. Ethical permissions were obtained from the relevant units. RESULTS: The total score of the Turkish Version of the mSET was 84.39±12.08 (51-95) and the education program was found to be highly effective. The mean care plan preparation skills scores of the intervention and control groups were 44.84±2.77 and 27.75±4.28 (0-50), respectively, and the total scores of the CDM-NS (at the last measurement) were 147.90±11.28 and 146.42±12.21. While there was a significant difference between the intervention and control groups in the ability to prepare a care plan (p=0.001), there was no difference between the groups in clinical decision-making skills over time (p=0.433), between the second and third measurements over time (p>0.05), but both measurements increased significantly compared with the first measurement (p=0.000). CONCLUSION: The vSPBP was determined to be an effective learning activity for the development of care plan preparation and clinical reasoning skills, as well as effective in closing the gap between theoretical and clinical knowledge and adaptation to the nursing process when applied in an integrated manner with the existing nursing program.

Automated orofacial virtual patient creation: A proof of concept.

OBJECTIVES: To (1) construct a virtual patient (VP) using facial scan, intraoral scan, and low-dose computed tomography scab based on an Artificial intelligence (AI)-approach, (2) quantitatively compare it with AI-refined and semi-automatic registration, and (3) qualitatively evaluate user satisfaction when using virtual patient as a communication tool in clinical practice. MATERIALS AND METHODS: A dataset of 20 facial scans, intraoral scans, and low-dose computed tomography scans was imported into the Virtual Patient Creator platform to create an automated virtual patient. The accuracy of the virtual patients created using different approaches was further analyzed in the Mimics software. The accuracy (% of corrections required), consistency, and time efficiency of the AI-driven virtual patient registration were then compared with the AI-refined and semi-automatic registration (clinical reference). User satisfaction was assessed through a survey of 35 dentists and 25 laypersons who rated the virtual patient's realism and usefulness for treatment planning and communication on a 5-point scale. RESULTS: The accuracy for AI-driven, AI-refined, and semi-automatic registration virtual patient was 85 %, 85 %, and 100 % for the upper and middle thirds of the face, and 30 %, 30 %, and 35 % for the lower third. Registration consistency was 1, 1 and 0.99, and the average time was 26.5, 30.8, and 385 s, respectively (18-fold time reduction with AI). The inferior facial third exhibited the highest registration mismatch between facial scan and computed tomography. User satisfaction with the virtual patient was consistently high among both dentists and laypersons, with most responses indicating very high satisfaction regarding realism and usefulness as a communication tool. CONCLUSION: The AI-driven registration can provide clinically accurate, fast, and consistent virtual patient creation using facial scans, intraoral scans, and low-dose computed tomography scans, enabling interpersonal communication. CLINICAL SIGNIFICANCE: Using AI for automated segmentation and registration of maxillofacial structures leads to clinically efficient and accurate VP creation, opening the doors for its widespread use in diagnosis, treatment planning, and interprofessional and professional-patient communication.

The Effect of Virtual Patient Visits in the Intensive Care Unit on Postpartum Depression.

Background: Maternal postpartum depression negatively affects the baby's emotional, behavioral, and cognitive development and attachment pattern. We aimed to examine the effect of virtual patient visits in neonatal intensive care unit on postpartum depression in mothers. Methods: Research data were obtained from mothers whose preterm infants were hospitalized in the neonatal intensive care unit between April and December 2022. A total of 100 mothers of preterm infants (50 in the virtual patient visit and 50 in the control group) treated in the neonatal intensive care unit of a hospital constituted the sample of the study. Using the Zoom application, virtual patient visits were made for 5 minutes, seven days a week, between mother and the preterm infants, with no nursing intervention implemented for at least 30 minutes. Mothers in the control group saw their babies face to face two days a week. In standard hospital procedure, mothers saw their babies twice a week. Edinburgh postpartum depression scale (EPDS) was administered online to the all mothers before and after the study. Results: The research resulted with statistically significant decreased EPDS scores of the mothers in virtual patient visit group with the pre-study scores. A statistically significant decrease was found compared to the control group (P<0.001). Conclusion: Virtual patient visits between preterm infants in neonatal intensive care unit and their mothers could be effective in preventing or reducing postpartum depression of the mother.

Detection of Internal Hemorrhage via Sequential Inference: An In Silico Feasibility Study.

This paper investigates the feasibility of detecting and estimating the rate of internal hemorrhage based on continuous noninvasive hematocrit measurement. A unique challenge in hematocrit-based hemorrhage detection is that hematocrit decreases in response to hemorrhage and resuscitation with fluids, which makes hemorrhage detection during resuscitation challenging. We developed two sequential inference algorithms for detection of internal hemorrhage based on the Luenberger observer and the extended Kalman filter. The sequential inference algorithms use fluid resuscitation dose and hematocrit measurement as inputs to generate signatures to enable detection of internal hemorrhage. In the case of the extended Kalman filter, the signature is nothing but inferred hemorrhage rate, which allows it to also estimate internal hemorrhage rate. We evaluated the proof-of-concept of these algorithms based on in silico evaluation in 100 virtual patients subject to diverse hemorrhage and resuscitation rates. The results showed that the sequential inference algorithms outperformed naïve internal hemorrhage detection based on the decrease in hematocrit when hematocrit noise level was 1% (average F1 score: Luenberger observer 0.80; extended Kalman filter 0.76; hematocrit 0.59). Relative to the Luenberger observer, the extended Kalman filter demonstrated comparable internal hemorrhage detection performance and superior accuracy in estimating the hemorrhage rate. The analysis of the dependence of the sequential inference algorithms on measurement noise and plant parametric uncertainty showed that small (≤1%) hematocrit noise level and personalization of sequential inference algorithms may enable continuous noninvasive detection of internal hemorrhage and estimation of its rate.

Optimal control of combination immunotherapy for a virtual murine cohort in a glioblastoma-immune dynamics model.

The immune checkpoint inhibitor anti-PD-1, commonly used in cancer immunotherapy, has not been successful as a monotherapy for the highly aggressive brain cancer glioblastoma. However, when used in conjunction with a CC-chemokine receptor-2 (CCR2) antagonist, anti-PD-1 has shown efficacy in preclinical studies. In this paper, we aim to optimize treatment regimens for this combination immunotherapy using optimal control theory. We extend a treatment-free glioblastoma-immune dynamics ODE model to include interventions with anti-PD-1 and the CCR2 antagonist. An optimized regimen increases the survival of an average mouse from 32 days post-tumor implantation without treatment to 111 days with treatment. We scale this approach to a virtual murine cohort to evaluate mortality and quality of life concerns during treatment, and predict survival, tumor recurrence, or death after treatment. A parameter identifiability analysis identifies five parameters suitable for personalizing treatment within the virtual cohort. Sampling from these five practically identifiable parameters for the virtual murine cohort reveals that personalized, optimized regimens enhance survival: 84% of the virtual mice survive to day 100, compared to 60% survival in a previously studied experimental regimen. Subjects with high tumor growth rates and low T cell kill rates are identified as more likely to die during and after treatment due to their compromised immune systems and more aggressive tumors. Notably, the MDSC death rate emerges as a long-term predictor of either disease-free survival or death.

Harnessing Innovative Technologies to Train Nurses in Suicide Safety Planning With Hospital Patients: Formative Acceptability Evaluation of an eLearning Continuing Education Training.

BACKGROUND: Suicide is the 12th leading cause of death in the United States. Health care provider training is a top research priority identified by the National Action Alliance for Suicide Prevention; however, evidence-based approaches that target skill building are resource intensive and difficult to implement. Novel computer technologies harnessing artificial intelligence are now available, which hold promise for increasing the feasibility of providing trainees opportunities across a range of continuing education contexts to engage in skills practice with constructive feedback on performance. OBJECTIVE: This pilot study aims to evaluate the feasibility and acceptability of an eLearning training in suicide safety planning among nurses serving patients admitted to a US level 1 trauma center for acute or intensive care. The training included a didactic portion with demonstration, practice of microcounseling skills with a web-based virtual patient (Client Bot Emily), role-play with a patient actor, and automated coding and feedback on general counseling skills based on the role-play via a web-based platform (Lyssn Advisor). Secondarily, we examined learning outcomes of knowledge, confidence, and skills in suicide safety planning descriptively. METHODS: Acute and intensive care nurses were recruited between November 1, 2021, and May 31, 2022, to participate in a formative evaluation using pretraining, posttraining, and 6-month follow-up surveys, as well as observation of the nurses' performance in delivering suicide safety planning via standardized patient role-plays over 6 months and rated using the Safety Plan Intervention Rating Scale. Nurses completed the System Usability Scale after interacting with Client Bot Emily and reviewing general counseling scores based on their role-play via Lyssn Advisor. RESULTS: A total of 18 nurses participated in the study; the majority identified as female (n=17, 94%) and White (n=13, 72%). Of the 17 nurses who started the training, 82% (n=14) completed it. On average, the System Usability Scale score for Client Bot Emily was 70.3 (SD 19.7) and for Lyssn Advisor was 65.4 (SD 16.3). On average, nurses endorsed a good bit of knowledge (mean 3.1, SD 0.5) and confidence (mean 2.9, SD 0.5) after the training. After completing the training, none of the nurses scored above the expert-derived cutoff for proficiency on the Safety Plan Intervention Rating Scale (≥14); however, on average, nurses were above the cutoffs for general counseling skills per Lyssn Advisor (empathy: mean 4.1, SD 0.6; collaboration: mean 3.6, SD 0.7). CONCLUSIONS: Findings suggest the completion of the training activities and use of novel technologies within this context are feasible. Technologic modifications may enhance the training acceptability and utility, such as increasing the virtual patient conversational abilities and adding automated coding capability for specific suicide safety planning skills. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): RR2-10.2196/33695.

Optimal control of combination immunotherapy for a virtual murine cohort in a glioblastoma-immune dynamics model.

The immune checkpoint inhibitor anti-PD-1, commonly used in cancer immunotherapy, has not been successful as a monotherapy for the highly aggressive brain cancer glioblastoma. However, when used in conjunction with a CC-chemokine receptor-2 (CCR2) antagonist, anti-PD-1 has shown efficacy in preclinical studies. In this paper, we aim to optimize treatment regimens for this combination immunotherapy using optimal control theory. We extend a treatment-free glioblastoma-immune dynamics ODE model to include interventions with anti-PD-1 and the CCR2 antagonist. An optimized regimen increases the survival of an average mouse from 32 days post-tumor implantation without treatment to 111 days with treatment. We scale this approach to a virtual murine cohort to evaluate mortality and quality of life concerns during treatment, and predict survival, tumor recurrence, or death after treatment. A parameter identifiability analysis identifies five parameters suitable for personalizing treatment within the virtual cohort. Sampling from these five practically identifiable parameters for the virtual murine cohort reveals that personalized, optimized regimens enhance survival: 84% of the virtual mice survive to day 100, compared to 60% survival in a previously studied experimental regimen. Subjects with high tumor growth rates and low T cell kill rates are identified as more likely to die during and after treatment due to their compromised immune systems and more aggressive tumors. Notably, the MDSC death rate emerges as a long-term predictor of either disease-free survival or death.

Model-driven engineering for digital twins: a graph model-based patient simulation application.

Introduction: Digital twins of patients are virtual models that can create a digital patient replica to test clinical interventions in silico without exposing real patients to risk. With the increasing availability of electronic health records and sensor-derived patient data, digital twins offer significant potential for applications in the healthcare sector. Methods: This article presents a scalable full-stack architecture for a patient simulation application driven by graph-based models. This patient simulation application enables medical practitioners and trainees to simulate the trajectory of critically ill patients with sepsis. Directed acyclic graphs are utilized to model the complex underlying causal pathways that focus on the physiological interactions and medication effects relevant to the first 6 h of critical illness. To realize the sepsis patient simulation at scale, we propose an application architecture with three core components, a cross-platform frontend application that clinicians and trainees use to run the simulation, a simulation engine hosted in the cloud on a serverless function that performs all of the computations, and a graph database that hosts the graph model utilized by the simulation engine to determine the progression of each simulation. Results: A short case study is presented to demonstrate the viability of the proposed simulation architecture. Discussion: The proposed patient simulation application could help train future generations of healthcare professionals and could be used to facilitate clinicians' bedside decision-making.

Digital intraoral and radiologic records in forensic identification: Match with disruptive technology.

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.

3D virtual patient-Magnetically retained printed stackable system for implant guided placement: Case report.

OBJECTIVE: The aim of this report is to present the complete workflow of 3D virtual patient for planning and performing implant surgery with magnetically retained 3D-printed stackable guides. CLINICAL CONSIDERATIONS: A 3D-printed stackable system was proposed based on bone, dental, and facial references. Initially, a 66-year-old male patient was digitalized through photographs, cone beam computed tomography, and intraoral scans (Virtuo Vivo, Straumann). All files were merged to create a 3D virtual patient in the planning software (coDiagnostiX, Straumann). Sequential stackable guides were designed, printed, and cured. Magnets were inserted into connectors, and the interim protheses received color characterization. Four mounted guides were produced for the specific purposes of pin fixation, bone reduction, implant placement, and immediate provisionalization. After surgery and healing period, patient digital data were updated. Final implant positions were compared to planned values and inconsistencies were clinically acceptable. The mean angular deviation was 5.4° (3.2-7.3) and mean 3D discrepancies were of 0.90 mm (0.46-1.12) at the entry point and 1.68 mm (1.00-2.20) at implant apex. Case follow-up revealed stability, patient's comfort, and no intercurrences. CONCLUSION: Magnetically retained stackable guides provide treatment accuracy and reduce surgical and prosthetic complications. The projected virtual patient enhances decision-making and communication between the multidisciplinary team and the patient, while decreases time and costs. CLINICAL SIGNIFICANCE: Bidimensional diagnosis and freehand implant placement have limitations and outcomes often rely on professionals' expertise. Performing facially driven virtual planning improves treatment predictability. This approach promotes function, esthetic harmony, and patient satisfaction. Implant guided surgery and 3D printed prostheses constitute a reproducible digital workflow that can be implemented into clinical practice to optimize dental care.

Perception of enhanced learning in medicine through integrating of virtual patients: an exploratory study on knowledge acquisition and transfer.

INTRODUCTION: Virtual Patients (VPs) have been shown to improve various aspects of medical learning, however, research has scarcely delved into the specific factors that facilitate the knowledge gain and transfer of knowledge from the classroom to real-world applications. This exploratory study aims to understand the impact of integrating VPs into classroom learning on students' perceptions of knowledge acquisition and transfer. METHODS: The study was integrated into an elective course on "Personalized Medicine in Cancer Treatment and Care," employing a qualitative and quantitative approach. Twenty-two second-year medical undergraduates engaged in a VP session, which included role modeling, practice with various authentic cases, group discussion on feedback, and a plenary session. Student perceptions of their learning were measured through surveys and focus group interviews and analyzed using descriptive statistics and thematic analysis. RESULTS: Quantitative data shows that students highly valued the role modeling introduction, scoring it 4.42 out of 5, and acknowledged the practice with VPs in enhancing their subject matter understanding, with an average score of 4.0 out of 5. However, students' reflections on peer dialogue on feedback received mixed reviews, averaging a score of 3.24 out of 5. Qualitative analysis (of focus-group interviews) unearthed the following four themes: 'Which steps to take in clinical reasoning', 'Challenging their reasoning to enhance deeper understanding', 'Transfer of knowledge ', and ' Enhance Reasoning through Reflections'. Quantitative and qualitative data are cohered. CONCLUSION: The study demonstrates evidence for the improvement of learning by incorporating VPs with learning activities. This integration enhances students' perceptions of knowledge acquisition and transfer, thereby potentially elevating students' preparedness for real-world clinical settings. Key facets like expert role modeling and various authentic case exposures were valued for fostering a deeper understanding and active engagement, though with some mixed responses towards peer feedback discussions. While the preliminary findings are encouraging, the necessity for further research to refine feedback mechanisms and explore a broader spectrum of medical disciplines with larger sample sizes is underscored. This exploration lays a groundwork for future endeavors aimed at optimizing VP-based learning experiences in medical education.

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

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

Digital Health Education for the Future: The SaNuRN (Santé Numérique Rouen-Nice) Consortium's Journey.

SaNuRN is a five-year project by the University of Rouen Normandy (URN) and the Côte d'Azur University (CAU) consortium to optimize digital health education for medical and paramedical students, professionals, and administrators. The project includes a skills framework, training modules, and teaching resources. In 2027, SaNuRN is expected to train a significant portion of the 400,000 health and paramedical professions students at the French national level. Our purpose is to give a synopsis of the SaNuRN initiative, emphasizing its novel educational methods and how they will enhance the delivery of digital health education. Our goals include showcasing SaNuRN as a comprehensive program consisting of a proficiency framework, instructional modules, and educational materials and explaining how SaNuRN is implemented in the participating academic institutions. SaNuRN is a project aimed at educating and training health-related and paramedics students in digital health. The project results from a cooperative effort between URN and CAU, covering four French departments. The project is based on the French National Referential on Digital Health (FNRDH), which defines the skills and competencies to be acquired and validated by every student in the health, paramedical, and social professions curricula. The SaNuRN team is currently adapting the existing URN and CAU syllabi to FNRDH and developing short-duration video capsules of 20 to 30 minutes to teach all the relevant material. The project aims to ensure that the largest student population earns the necessary skills, and it has developed a two-tier system involving facilitators who will enable the efficient expansion of the project's educational outreach and support the students in learning the needed material efficiently. With a focus on real-world scenarios and innovative teaching activities integrating telemedicine devices and virtual professionals, SaNuRN is committed to enabling continuous learning for healthcare professionals in clinical practice. The SaNuRN team introduced new ways of evaluating healthcare professionals by shifting from a knowledge-based to a competencies-based evaluation, aligning with the Miller teaching pyramid and using the Objective Structured Clinical Examination and Script Concordance Test in digital health education. Drawing on the expertise of URN, CAU, and their public health and digital research laboratories and partners, the SaNuRN project represents a platform for continuous innovation, including telemedicine training and living labs with virtual and interactive professional activities. The SaNuRN project provides a comprehensive, personalized 30-hour training package for health and paramedical students, addressing all 70 FNRDH competencies. The program is enhanced using AI and NLP to create virtual patients and professionals for digital healthcare simulation. SaNuRN teaching materials are open-access. The project collaborates with academic institutions worldwide to develop educational material in digital health in English and multilingual formats. SaNuRN offers a practical and persuasive training approach to meet the current digital health education requirements.

Acceptance of virtual patients as a continuous professional development approach among practicing nurses in primary health care settings in a low-income country: a quasi-experimental posttest setup design.

BACKGROUND: Virtual patients are an educational technological approach used in healthcare education. Its distinctive features have rendered virtual patient technology appealing for the training of medical and healthcare students, particularly in the enhancement of clinical reasoning. Virtual patients are less often applied for continuous professional development for practicing healthcare providers, and there is a scarcity of studies exploring this possibility. This study aimed to assess the acceptability of nurses for using virtual patients as a continuous professional development approach. METHOD: The study used a quasi-experimental posttest setup design. The study was conducted in ten primary healthcare settings in Rwanda. Among 76 nurses who consented to participate in the study, 56 completed the intervention and responded to the study questionnaire. Following a one-week program of continuous professional development on four non-communicable diseases, the study used a self-administered questionnaire based on the Technology Acceptance Model 3 to collect data. Descriptive analysis served as the primary method for analyzing participants' responses. The study also used a correlation test to assess the relationship of variables. RESULTS: Across all items in the questionnaire, the median response tended towards either agree or strongly agree, with only a minority number of participants expressing strong disagreement, disagreement, or neutrality. The results indicated a significant positive correlation between perceived usefulness and behavior intention (p < 0.001). CONCLUSION: The findings indicate an acceptability and behavioral intention of adopting virtual patients as an alternative continuous professional development approach among nurses working at health centers in Rwanda or other locations with similar contexts.

Enhancing clinical reasoning for management of non-communicable diseases: virtual patient cases as a learning strategy for nurses in primary healthcare centers: a pre-post study design.

BACKGROUND: In Rwanda, nurses manage all primary care at health centres, and therefore are their clinical reasoning skills important. In this study, a web-based software that allows the creation of virtual patient cases (VP cases) has been used for studying the possibility of using VP cases for the continuous professional development of nurses in primary health care in Rwanda. Previous studies in pre-service education have linked VP cases with the enhancement of clinical reasoning, a critical competence for nurses. This study investigated the feasibility of continuous professional development through VP cases to further train in-service nurses in clinical reasoning. METHOD: The study used a pre-post test design. Initially, seventy-six participants completed a questionnaire as part of the pre-test phase, subsequently invited to engage with all four VP cases, and finally responded to the post-test questionnaire evaluating clinical reasoning skills. Fifty-six participants successfully completed the entire study process and were considered in the analysis. The primary outcomes of this study were evaluated using a paired t-test for the statistical analysis. RESULTS: The results show that the mean score of clinical reasoning increased significantly from the pre-test to the post-test for all four illness areas (p < 0.001). The study findings showed no statistically significant difference in participants' scores based on demographic factors, including whether they worked in urban or rural areas.  CONCLUSION AND RECOMMENDATION: Utilizing VP cases appears to significantly enhance the continuous professional development of nurses, fostering a deliberate learning process that enables them to reflect on how they manage cases and, in turn, refine their clinical reasoning skills. This study strongly recommends incorporating VP cases in the continuous professional development of nurses at the primary health level (health centers). This is especially pertinent in a context where nurses are required to perform diagnostic processes similar to those employed by physicians.

The impact of scaffolded and non-scaffolded suicidal virtual human interaction training on clinician emotional self-awareness, empathic communication, and clinical efficacy.

BACKGROUND: Clinicians working with patients at risk of suicide often experience high stress, which can result in negative emotional responses (NERs). Such negative emotional responses may lead to less empathic communication (EC) and unintentional rejection of the patient, potentially damaging the therapeutic alliance and adversely impacting suicidal outcomes. Therefore, clinicians need training to effectively manage negative emotions toward suicidal patients to improve suicidal outcomes. METHODS: This study investigated the impact of virtual human interaction (VHI) training on clinicians' self-awareness of their negative emotional responses, assessed by the Therapist Response Questionnaire Suicide Form, clinicians' verbal empathic communication assessed by the Empathic Communication and Coding System, and clinical efficacy (CE). Clinical efficacy was assessed by the likelihood of subsequent appointments, perceived helpfulness, and overall interaction satisfaction as rated by individuals with lived experience of suicide attempts. Two conditions of virtual human interactions were used: one with instructions on verbal empathic communication and reminders to report negative emotional responses during the interaction (scaffolded); and the other with no such instructions or reminders (non-scaffolded). Both conditions provided pre-interaction instructions and post-interaction feedback aimed at improving clinicians' empathic communication and management of negative emotions. Sixty-two clinicians participated in three virtual human interaction sessions under one of the two conditions. Linear mixed models were utilized to evaluate the impact on clinicians' negative emotional responses, verbal empathic communication, and clinical efficacy; and to determine changes in these outcomes over time, as moderated by the training conditions. RESULTS: Clinician participants' negative emotional responses decreased after two training sessions with virtual human interactions in both conditions. Participants in the scaffolded condition exhibited enhanced empathic communication after one training session, while two sessions were required for participants in the non-scaffolded condition. Surprisingly, after two training sessions, clinical efficacy was improved in the non-scaffolded group, while no similar improvements were observed in the scaffolded group. CONCLUSION: Lower clinical efficacy after virtual human interaction training in clinicians with higher verbal empathic communication suggests that nonverbal expressions of empathy are critical when interacting with suicidal patients. Future work should explore virtual human interaction training in both nonverbal and verbal empathic communication.

Establishment of a 3D esthetic analysis workflow on 3D virtual patient and preliminary evaluation.

BACKGROUND: In esthetic dentistry, a thorough esthetic analysis holds significant role in both diagnosing diseases and designing treatment plans. This study established a 3D esthetic analysis workflow based on 3D facial and dental models, and aimed to provide an imperative foundation for the artificial intelligent 3D analysis in future esthetic dentistry. METHODS: The established 3D esthetic analysis workflow includes the following steps: 1) key point detection, 2) coordinate system redetermination and 3) esthetic parameter calculation. The accuracy and reproducibility of this established workflow were evaluated by a self-controlled experiment (n = 15) in which 2D esthetic analysis and direct measurement were taken as control. Measurement differences between 3D and 2D analysis were evaluated with paired t-tests. RESULTS: 3D esthetic analysis demonstrated high consistency and reliability (0.973 < ICC < 1.000). Compared with 2D measurements, the results from 3D esthetic measurements were closer to direct measurements regarding tooth-related esthetic parameters (P<0.05). CONCLUSIONS: The 3D esthetic analysis workflow established for 3D virtual patients demonstrated a high level of consistency and reliability, better than 2D measurements in the precision of tooth-related parameter analysis. These findings indicate a highly promising outlook for achieving an objective, precise, and efficient esthetic analysis in the future, which is expected to result in a more streamlined and user-friendly digital design process. This study was registered with the Ethics Committee of Peking University School of Stomatology in September 2021 with the registration number PKUSSIRB-202168136.

The effect of case-based mobile virtual patient application on students' academic achievement in clinical reasoning skills.

This mixed-method study aims to determine the effect of the use of mobile virtual patient application with narrated case-based virtual patients as an assistive technology on students' clinical reasoning skills. It makes a notable contribution by exploring the impact of mobile virtual patient applications on healthcare students' clinical skills and their preparation for real-world patient care. In addition, the accuracy of the analysis results regarding the effect on student achievement was analyzed with a second dataset tool, and thus, aiming to increase reliability by verifying the same research question with a different quantitative analysis technique. In the qualitative part of the study, students' views on the implementation were collected through an open-ended questionnaire and the data were subjected to content analysis. An achievement test was also developed to determine the development of students' clinical reasoning skills, which revealed that each of the learning environments had different outcomes regarding students' achievement and that supporting the traditional environment with the mobile virtual patient application yielded better results for increasing students' achievement. Students' opinions about the mobile virtual patient application and the process also support the increase in academic achievement aimed at measuring clinical reasoning skills. The content analysis showed that the students, who generally reported multiple positive factors related to the application, thought that the stories and cases presented created a perception of reality, and they especially highlighted the contribution of the application to learning the story organization. Based on all these results, it can be said that the application supports clinical reasoning, provides practical experience, improves academic achievement, and contributes positively to motivation.

The chance and challenge of creating virtual patients in prosthodontics.

In the field of prosthodontics, the use of virtual patients for biomimetic restoration holds great promise for various applications. Virtual patients consist of digitized data that encompasses details on the morphology, structure, and spatial relationships within the maxillofacial and intraoral regions. Nonetheless, there are several challenges associated with acquiring digital data, achieving accurate alignment, and recording and transferring dynamic jaw movements. This paper aims to concentrate on the process of constructing virtual patients, highlight the key and challenging aspects of virtual patient construction, and advocate for the extensive adoption and utilization of virtual patient technology.