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In 2020, a new coronavirus swept the world, and the advent of this disease has a huge impact on our social and economic development. Due to the limited medical resources and regional differences, this model of virtual medicine becomes more valuable. In this paper, we create a virtual medical space based on a metaverse in order to investigate whether the medical model can be freely transformed between virtual and reality. In this process, I first describe different scenarios of virtual medical care in mixed reality, and then we use one of them as an example to develop a medical device. Then we designed the software and hardware of the product and performed the user experience, it includes the interaction and usage scenarios that affect the user. Finally, this medical device will be demonstrated by user experience and feedback. © 2023 The authors and IOS Press.
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Objective: To compare the efficacy of the combination of abidol, lopinavir/ritonavir plus recombinant interferon alpha-2b (rIFNalpha-2b) and the combination of lopinavir/ritonavir plus rIFNalpha-2b for patients with COVID-19 in Zhejiang province. Method(s): A multicenter prospective study was carried out to compare the efficacy of triple combination antiviral therapy and dual combination antiviral therapy in 15 medical institutions of Zhejiang province during January 22 to February 16, 2020. All patients were treated with rIFNalpha-2b (5 million U, 2 times/d) aerosol inhalation, in addition 196 patients were treated with abidol (200 mg, 3 times/d) + lopinavir/ritonavir (2 tablets, 1 time/12 h) (triple combination group) and 41 patients were treated with lopinavir/ritonavir (2 tablets, 1 time/12 h) (dual combination group). The patients who received triple combination antiviral therapy were further divided into three subgroups: <48 h, 3-5 d and >5 d according the time from the symptom onset to medication starting. The therapeutic efficacy was compared between triple combination group and dual combination group, and compared among 3 subgroups of patients receiving triple combination antiviral therapy. SPSS 17.0 software was used to analyze the data. Result(s): The virus nucleic acid-negative conversion time in respiratory tract specimens was (12.2+/-4.7) d in the triple combination group, which was shorter than that in the dual combination group [(15.0+/-5.0) d] (t=6.159, P<0.01). The length of hospital stay in the triple combination group [12.0 (9.0, 17.0) d] was also shorter than that in the dual combination group [15.0 (10.0, 18.0) d] (H=2.073, P<0.05). Compared with the antiviral treatment which was started within after the symptom onset of in the triple combination group, the time from the symptom onset to the viral negative conversion was 13.0 (10.0, 17.0), 17.0 (13.0, 22.0) and 21.0 (18.0, 24.0) d in subgroups of 48 h, 3-5 d and >5 d, respectively (Z=32.983, P<0.01), while the time from antiviral therapy to viral negative conversion was (11.8+/-3.9), (13.5+/-5.1) and (11.2+/-4.3) d, respectively(Z=6.722, P<0.05). Conclusion(s): The triple combination antiviral therapy of abidol, lopinavir/litonavir and rIFNalpha-2b shows shorter viral shedding time and shorter hospitalization time, compared with the dual combination antiviral therapy;and the earlier starting triple combination antiviral therapy will result in better antiviral efficacy.Copyright © 2020 by the Chinese Medical Association.
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Since most schools and learners had no choice but to learn online during the pandemic, online learning became the mainstream learning mode rather than a substitute for traditional face-to-face learning. Given this enormous change in online learning, we conducted a systematic review of 191 of the most recent online learning studies published during the COVID-19 era. The systematic review results indicated that the themes regarding "courses and instructors” became popular during the pandemic, whereas most online learning research has focused on "learners” pre-COVID-19. Notably, the research topics "course and instructors” and "course technology” received more attention than prior to COVID-19. We found that "engagement” remained the most common research theme even after the pandemic. New research topics included parents, technology acceptance or adoption of online learning, and learners' and instructors' perceptions of online learning. © 2023, The Online Learning Consortium. All rights reserved.
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Objective: To analyze the risk factors of fatal outcome in patients with severe COVID-19. Method(s): The clinical characteristics of 107 patients with severe COVID-19 admitted in Renmin Hospital of Wuhan University from February 12 to March 12, 2020 were retrospectively analyzed. During the hospitalization 49 patients died (fatal group) and 58 patients survived (survival group). The clinical characteristics, baseline laboratory findings were analyzed using R and Python statistical software. The risk factors of fatal outcome in patients with severe COVID-19 were analyzed with multivariate logistic regression. Result(s): Univariate analysis showed that the two groups had statistically significant differences in age, clinical classification, dry cough, dyspnea and laboratory test indicators (P<0.05 or <0.01). The random forest model was used to rank the significance of the statistically significant variables in the univariate analysis, and the selected variables were included in the binary logistic regression model. After stepwise regression analysis, the patient's clinical type, age, neutrophil count, and the proportion of CD3 cells are independent risk factors for death in severe COVID-19 patients. Dry cough is an independent protective factor for the death of severe COVID-19 patients. Conclusion(s): COVID-19 patients with fatal outcome are more likely to have suppressed immune function, secondary infection and inflammatory factor storm. These factors may work together in severe patients, leading to intractable hypoxemia and multiple organ dysfunction and resulting in fatal outcome of patients. The study indicates that timely intervention and treatment measures against above factors may be effective to save the lives of patients with severe COVID-19.Copyright © 2020 by the Chinese Medical Association.
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Objective: To analyze the risk factors of fatal outcome in patients with severe COVID-19. Method(s): The clinical characteristics of 107 patients with severe COVID-19 admitted in Renmin Hospital of Wuhan University from February 12 to March 12, 2020 were retrospectively analyzed. During the hospitalization 49 patients died (fatal group) and 58 patients survived (survival group). The clinical characteristics, baseline laboratory findings were analyzed using R and Python statistical software. The risk factors of fatal outcome in patients with severe COVID-19 were analyzed with multivariate logistic regression. Result(s): Univariate analysis showed that the two groups had statistically significant differences in age, clinical classification, dry cough, dyspnea and laboratory test indicators (P<0.05 or <0.01). The random forest model was used to rank the significance of the statistically significant variables in the univariate analysis, and the selected variables were included in the binary logistic regression model. After stepwise regression analysis, the patient's clinical type, age, neutrophil count, and the proportion of CD3 cells are independent risk factors for death in severe COVID-19 patients. Dry cough is an independent protective factor for the death of severe COVID-19 patients. Conclusion(s): COVID-19 patients with fatal outcome are more likely to have suppressed immune function, secondary infection and inflammatory factor storm. These factors may work together in severe patients, leading to intractable hypoxemia and multiple organ dysfunction and resulting in fatal outcome of patients. The study indicates that timely intervention and treatment measures against above factors may be effective to save the lives of patients with severe COVID-19.Copyright © 2020 by the Chinese Medical Association.
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Objective: To analyze the risk factors of fatal outcome in patients with severe COVID-19. Method(s): The clinical characteristics of 107 patients with severe COVID-19 admitted in Renmin Hospital of Wuhan University from February 12 to March 12, 2020 were retrospectively analyzed. During the hospitalization 49 patients died (fatal group) and 58 patients survived (survival group). The clinical characteristics, baseline laboratory findings were analyzed using R and Python statistical software. The risk factors of fatal outcome in patients with severe COVID-19 were analyzed with multivariate logistic regression. Result(s): Univariate analysis showed that the two groups had statistically significant differences in age, clinical classification, dry cough, dyspnea and laboratory test indicators (P<0.05 or <0.01). The random forest model was used to rank the significance of the statistically significant variables in the univariate analysis, and the selected variables were included in the binary logistic regression model. After stepwise regression analysis, the patient's clinical type, age, neutrophil count, and the proportion of CD3 cells are independent risk factors for death in severe COVID-19 patients. Dry cough is an independent protective factor for the death of severe COVID-19 patients. Conclusion(s): COVID-19 patients with fatal outcome are more likely to have suppressed immune function, secondary infection and inflammatory factor storm. These factors may work together in severe patients, leading to intractable hypoxemia and multiple organ dysfunction and resulting in fatal outcome of patients. The study indicates that timely intervention and treatment measures against above factors may be effective to save the lives of patients with severe COVID-19.Copyright © 2020 by the Chinese Medical Association.
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Objective: To compare the efficacy of the combination of abidol, lopinavir/ritonavir plus recombinant interferon alpha-2b (rIFNalpha-2b) and the combination of lopinavir/ritonavir plus rIFNalpha-2b for patients with COVID-19 in Zhejiang province. Method(s): A multicenter prospective study was carried out to compare the efficacy of triple combination antiviral therapy and dual combination antiviral therapy in 15 medical institutions of Zhejiang province during January 22 to February 16, 2020. All patients were treated with rIFNalpha-2b (5 million U, 2 times/d) aerosol inhalation, in addition 196 patients were treated with abidol (200 mg, 3 times/d) + lopinavir/ritonavir (2 tablets, 1 time/12 h) (triple combination group) and 41 patients were treated with lopinavir/ritonavir (2 tablets, 1 time/12 h) (dual combination group). The patients who received triple combination antiviral therapy were further divided into three subgroups: <48 h, 3-5 d and >5 d according the time from the symptom onset to medication starting. The therapeutic efficacy was compared between triple combination group and dual combination group, and compared among 3 subgroups of patients receiving triple combination antiviral therapy. SPSS 17.0 software was used to analyze the data. Result(s): The virus nucleic acid-negative conversion time in respiratory tract specimens was (12.2+/-4.7) d in the triple combination group, which was shorter than that in the dual combination group [(15.0+/-5.0) d] (t=6.159, P<0.01). The length of hospital stay in the triple combination group [12.0 (9.0, 17.0) d] was also shorter than that in the dual combination group [15.0 (10.0, 18.0) d] (H=2.073, P<0.05). Compared with the antiviral treatment which was started within after the symptom onset of in the triple combination group, the time from the symptom onset to the viral negative conversion was 13.0 (10.0, 17.0), 17.0 (13.0, 22.0) and 21.0 (18.0, 24.0) d in subgroups of 48 h, 3-5 d and >5 d, respectively (Z=32.983, P<0.01), while the time from antiviral therapy to viral negative conversion was (11.8+/-3.9), (13.5+/-5.1) and (11.2+/-4.3) d, respectively(Z=6.722, P<0.05). Conclusion(s): The triple combination antiviral therapy of abidol, lopinavir/litonavir and rIFNalpha-2b shows shorter viral shedding time and shorter hospitalization time, compared with the dual combination antiviral therapy;and the earlier starting triple combination antiviral therapy will result in better antiviral efficacy.Copyright © 2020 by the Chinese Medical Association.
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Since the onset of the COVID-19 pandemic, social media users have shared their personal experiences related to the viral infection. Their posts contain rich information of symptoms that may provide useful hints to advancing the knowledge body of medical research and supplement the discoveries from clinical settings. Identification of symptom expressions in social media text is challenging, partially due to lack of annotated data. In this study, we investigate utilizing few-shot learning with generative pre-trained transformer language models to identify COVID-19 symptoms in Twitter posts. The results of our approach show that large language models are promising in more accurately identifying symptom expressions in Twitter posts with small amount of annotation effort, and our method can be applied to other medical and health applications where abundant of unlabeled data is available. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.
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Covid-19 has become a world pandemic for years. With the appearance of mutations, immune escape has become a problem, reducing the effectiveness of vaccines and antibodies. To reveal the mechanism of immune escape, we analyze the geometrical properties of the receptor-binding domain in the SARS-CoV-2 spike protein, which plays a vital role in the immune reaction. Several important variants are taken as examples, and the wild type model is prepared as a reference. The computational method is applied to simulate the behaviors of the models, and alpha shape algorithm is employed to extract geometrical data of the protein surface. Average moving distance of the surface atoms is used to quantify their activity. Our results show that the mutations changed the properties of the protein. The variants have different distributions of active sites, which may change the specific antigenicity and influence the binding abilities of drugs and antibodies. This study explains the mechanism of immune escape of SARS-CoV-2, and provides a geometrical method to find potential new target sites for the design of drugs and vaccines. © 2022 IEEE.
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PurposeBased on text content analysis using big data, this study aims to explore differences in guest perceptions of peer-to-peer accommodations before and after COVID-19 to provide suggestions for the development of these properties in China postpandemic. Design/methodology/approachA guest perception dictionary was established by collecting Ctrip customer reviews of peer-to-peer accommodations. After data cleaning, thematic word analysis and semantic association network analysis were used to explore perceptions and thematic differences before and after COVID-19. FindingsThis research constructed a multidimensional framework of guest-perceived values for peer-to-peer accommodation in the context of COVID-19. The findings showed that the emphasis on functionality in peer-to-peer accommodation changed;perceived emotional values associated with peer-to-peer stays were more complex;perceived social values decreased, host-guest interactions were reduced and online communication became a stronger trend;tourist preferences for types of experiences changed, and people changed their destination selections;perceived conditional value was reflected in perceived risks, and the perceptions of environmental health, service and physical risks increased. Research limitations/implicationsThis research has constructed a multidimensional framework of tourist perceived value on the basis of peer-to-peer accommodation context and epidemic background and has thus shown the changes in tourist perceived value of peer-to-peer accommodation before and after COVID-19. Originality/valueTo the best of authors' knowledge, this research constitutes the first attempt to explore the perceptual differences for peer-to-peer accommodations before and after COVID-19 based on an extensive data set of online reviews from multiple provinces of China.
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INTRODUCTION: Severe pneumonia is a common indication for admission to the pediatric intensive care unit (PICU) and a leading cause of morbidity and mortality. The lack of epidemiology and outcome data from Asia is a barrier to improving outcomes of severe pneumonia in the region. METHOD(S): This is a prospective multicenter cohort study carried out from April 2019 to April 2022. Fifteen PICUs participated in this study under the Pediatric Acute & Critical Care Medicine Asian Network. Epidemiological, microbiological and outcome data were collected up to hospital discharge. Univariate logistic regression analysis were conducted to explore the association between potential risk factors and severe outcomes [acute respiratory distress syndrome (ARDS) and PICU mortality]. Multivariable analysis was performed withforward stepwise logistic regression adjusted for sites and COVID-19 pandemic including variables with p< 0.05 in univariate model. RESULT(S): There were 786 children with severe pneumonia in PICU with mean (standard deviation) age 2.8 (3.9) years. 384/786 (48.9%) had comorbidities;126/786 (16.0%) had a history of prematurity (gestational age < 37 weeks). Admission Pediatric Index of Mortality 3 (PIM3) and Pediatric Logistic Organ Dysfunction 2 (PELOD2) score were 16.2 (22.9) and 4.1(4.6). A sole viral or bacterial pathogen was identified in 179/786 (22.4%) and 165/786 (21.0%). Co-infections occurred in 114/786 (14.5%) patients. ARDS and mortality occurred in 156/786 (20.1%) and 70/786(8.9%) patients. In the multivariable model, risk factors for ARDS included PIM3 [adjusted odds ratio (aOR) [95% confidence interval (CI)] of 1.02 (1.01, 1.03)], PELOD2 [aOR 1.08 (95%CI 1.02, 1.13)] and involvement of 4 quadrants on chest-x-ray, [aOR 2.69 (95%CI 1.39, 5.18)]. Risk factors for mortality included PIM 3 [aOR 1.03 (95%CI 1.01, 1.04)], involvement of 4 quadrants on chest-x-ray [aOR 2.72 (95%CI 1.10, 6.73)], bacterial [aOR 2.61 (95%CI 1.00, 6.82)], fungus or mycobacterium [aOR 12.30 (95%CI 1.45, 104.57)] and co-infections [aOR 2.72 (95%CI 1.10, 10.35)]. CONCLUSION(S): The rate of ARDS and mortality in severe pneumonia admitted to PICU in Asia was high. Risk factors for poor outcomes were admission severity scores, generalized X-ray involvement and identification of bacteria, fungus/mycobacteria or co-infections.
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Background. We developed a COVID-Influenza Combination (CIC) vaccine, comprising recombinant SARS-CoV-2 Spike (rS) and quadrivalent influenza hemagglutinin (HA) protein nanoparticles (qNIV), and Matrix-MTM adjuvant. rS/Matrix-M previously demonstrated efficacy against COVID-19 in Phase 3 trials, while qNIV/ Matrix-M previously demonstrated induction of broadly cross-reactive antibodies. Here we report preliminary safety and immunogenicity results of a first-ever Phase 1/2 CIC dose-finding trial. Methods. Seropositive (COVID-19 vaccinated >= 8 weeks prior) participants (N=642) aged 50-70 years were randomized equally, to receive two intramuscular doses, 56 days apart, to 1 of 14 different dose/formulations of CIC using a design of experiments approach (dose range: rS 2.5-22.5ug, HA5-60ug;and 50ug Matrix-M), or to 1 of 2 reference formulations of either standalone rS with Matrix-M [2 doses] or qNIV with Matrix-M [1 dose only]. Pre- and post-vaccination (Days 0, 28, 56, 70, 84, 182) immunogenicity assessments including SARS-CoV-2 anti-S IgG and influenza HAI antibodies to vaccine-homologous strains. Reactogenicity was assessed 7 days following each dose, and safety outcomes assessed through Day 70. Multiple regression was used to create predictive models to assess antibody response surfaces and for dose optimization. Results. All CIC formulations were well tolerated, with a reactogenicity and safety profile generally comparable to standalone rS or qNIV. Regression modelling of post-first dose responses revealed that both rS and HA antigens in a CIC formulation modestly interfered with each other, however, interference was overcome with dose adjustment across a range of rS/HA doses. Specifically, higher rS dose ( >20ug), in a dose dependent fashion, overcame HA interference, closely matching standalone rS IgG reference responses (GMEU 16,818), whereas lower, intermediate HA dose overcame rS interference, closely matching standalone HA reference HAI responses for H3N2 (GMT 145), H1N1 (GMT 134), and B-Victoria (GMT 66);while modestly (at least 34%) lower than the reference B-Yamagata response (GMT 101). Conclusion. CIC formulations were well tolerated and immunogenic, with various dose combinations achieving response comparable to standalone vaccines.
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Background. It is known that the pandemic increased the risk of severe illness from SARS-CoV-2 infection in immunocompromised patients as compared to the general public. To combat this issue, telemedicine was utilized in various settings, but there is very little data on the adequate continuity of care (COC) and sustainability of telehealth throughout the pandemic. There is even less data on the effects of the pandemic in special populations, such as people living with human immunodeficiency virus (PLWH) and within the correctional setting. The purpose of this study was to investigate rates of HIV virologic suppression (VS) and the sustainability of telemedicine throughout the SARS-CoV-2 pandemic. Methods. This was a retrospective, pre-post cohort study of PLWH who received antiretroviral therapy (ART) within Illinois Department of Corrections (IDOC) / University of Illinois Chicago telemedicine clinic between 3/2019 and 3/2021. Patients who were released from IDOC or reincarcerated during the study period were excluded. The primary endpoint compared the rate of HIV VS pre-, peri- and post-SARS-CoV-2 restrictions. Secondary endpoints included change in immunologic function pre-, peri- and post-SARS-CoV-2 restrictions, incidence of SARS-CoV-2 infection, number of hospitalizations, development of virologic failure, and change in ART post-pandemic. Other factors known to influence COC were also collected. Results. Of 320 patients screened, 225 were included. The majority were Black males (73.7%) and 95.1% had a CD4 T-cell count > 200 cells/mm3 at baseline. Approximately 88% of patients were on a single tablet regimen, with most receiving bictegravir/emtricitabine/tenofovir alafenamide. Rates of HIV VS are summarized in Figure 1 and secondary outcomes are summarized in table 1. Factors associated with disruption of COC included non-adherence to appointments (62.6%) and ART (4.4%). Conclusion. Results demonstrated that PLWH within IDOC had sustained VS and COC throughout the SARS-CoV-2 pandemic with telemedicine. These results describe how the pandemic impacted PLWH in a correctional setting and future studies could contribute to creating national guidance for telemedicine models to streamline clinical practice.
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Introduction/Objective: In response to the sudden COVID-19 pandemic, the Chinese government has restricted student visas for international students as part of an emergency "zero COVID" plan. As a result of these border closures, most international students enrolled in Chinese medical universities have not returned to campus for more than 2.5 years and have continued their medical education online. Basic medical science study has continued relatively smoothly compared to clinical apprenticeships, which focus more on clinical traineeship and practice. How to turn the crisis caused by the pandemic into an opportunity to improve the level of basic medical science education and level of pathology interest in our school is the topic explored in this paper. Methods/Case Report: The methods proposed in this study include the intelligence of artificial intelligence technology and adaptive teaching tools for teaching students in accordance with their aptitude, interactive microscopic slides and gross pathology recognition methods based on computer graphics technology, and Internet-based group learning and large group discussions. All the advantages of traditional classroom teaching are therefore included and some overcome the shortcomings of traditional classroom teaching. Results (if a Case Study enter NA): Grades in 2019-2020 school year basic medical science courses were obtained. In this school year, the first semester was traditional education on campus and the second semester was online using the method described in this paper. Second semester grades were 17.2% higher than the first. USMLE Step 1 performance was another measured outcome because it focuses on basic science and pathology. A good outcome on this exam was obtained and the teaching method proposed in this paper is verified. Conclusion(s): The author is preparing to expand the sample size to further verify, improve, and perfect the method proposed in this paper, so as to promote the depth and breadth of basic medical education and continuing success from education into success in pathology careers.
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Chest radiographs are widely used in the medical domain and at present, chest X-radiation particularly plays an important role in the diagnosis of medical conditions such as pneumonia and COVID-19 disease. The recent developments of deep learning techniques led to a promising performance in medical image classification and prediction tasks. With the availability of chest X-ray datasets and emerging trends in data engineering techniques, there is a growth in recent related publications. Recently, there have been only a few survey papers that addressed chest X-ray classification using deep learning techniques. However, they lack the analysis of the trends of recent studies. This systematic review paper explores and provides a comprehensive analysis of the related studies that have used deep learning techniques to analyze chest X-ray images. We present the state-of-the-art deep learning based pneumonia and COVID-19 detection solutions, trends in recent studies, publicly available datasets, guidance to follow a deep learning process, challenges and potential future research directions in this domain. The discoveries and the conclusions of the reviewed work have been organized in a way that researchers and developers working in the same domain can use this work to support them in taking decisions on their research. (c) 2022 Elsevier B.V. All rights reserved.
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Purpose: This paper aims to explore students’ work-integrated learning experience. Particularly, students’ application of knowledge and improvement of multiple skills in work-integrated learning, their influence on positive industry image change due to the COVID-19 pandemic, and students’ desired career prospect were explored. Design/methodology/approach: A questionnaire survey with valid responses from 168 undergraduate students in hospitality and tourism management was conducted in four colleges/universities in Guangdong, China in 2021. PLS-SEM method was used to analyze the data. Findings: It was found that students’ multiple skills had improved through application of knowledge in work-integrated learning. Skill improvement helped form positive industry image change and shape future career prospect. However, such positive industry image change did not impact students’ career prospect directly. Originality/value: Today, work-integrated learning has become one of the most valuable opportunities for students in hospitality and tourism management to gain industry experience. However, recent literature has largely examined the negative impacts of COVID-19, whereas few studies have examined the positive aspects of work-integrated learning. © 2022, Jing Bill Xu, Pimtong Tavitiyaman, Xinyan Zhang and Mingfang Zhu.
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The outbreak of the COVID-19 pandemic at the end of 2019 has caused a profound impact on economic development. The catering, logistics and tourism industries have suffered a huge blow. This paper selects the catering industry as the research object, selects the 2019 and 2020 annual reports of five representative listed catering companies, classifies and summarizes the stated criteria for determination of the occurrence of self-interest attribution, calculates the degree of self-interest attribution, and compares and analyzes whether the self-interest attribution behavior of the five case companies before and after the COVID-19 pandemic stands out or amplifies the self-interest attribution behavior of the companies. The case studies showed that the degree of self-interest attribution was higher in the poor-performing companies, and that the impact of the COVID-19 pandemic on the self-interest behavior of restaurant companies was prominent, and that the poor external environment was more likely to lead to a higher degree of self-interest attribution behavior. © 2022 IEEE.
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BACKGROUND: One strategy to aid patients in managing their diabetes is group visits (GVs) that include group education and individual medical visits with a provider. Though in-person GVs have been shown to benefit patients, few studies have evaluated virtual diabetes GVs. METHODS: In this single-arm trial, adult patients with A1c ≥ 8% from six community health centers were recruited to participate in monthly virtual GVs for 6 months. Patients completed surveys about diabetes self-care, support, distress, and the group visit concept before and after they participated in GVs. Health center staff trained to lead the GVs completed surveys pre and post virtual GVs. Linear mixed effects models were used to adjust cohort-based association and model the survey data over time for the time trend effect. RESULTS: Forty-five patients enrolled in the study and thirty-eight patients completed the baseline survey. The average age was 55 (range of 36-83) and 65% of enrollees were female. 63% were black/African American, 32% were white/Caucasian, and 8% were Hispanic/Latino. Thirty-four patients attended one or more GVs and twenty-one patients completed the post GV survey. Overall satisfaction with the virtual GVs was high with 18/20 (90%) of participants being very satisfied and 20/21 (95%) saying they would attend GVs in the future. Most participants agreed that GVs helped improve diabetes self-management skills (78%), motivated them to achieve health goals (89%), and introduced them to others living with diabetes (78%). Barriers to participation were the timing of the GVs and access to a computer, tablet, phone, and internet. Patients had an increase in their diabetes knowledge (mean (SD): 3.2/ 5 (0.9) to 3.6/5 (0.7), p= 0.02) and diabetes support (3.5/5 (0.64) to 4.1/5 (0.7), p <0.001) as well as decreased diabetes distress (2.9/6 (1.5) to 1.2/6 (0.5), p=0.03) from baseline to 6 months. Thirty-five staff enrolled in the study and seventeen completed a post GV survey. Most staff agreed that GVs provided patients with social support and more frequent contact with medical providers. Staff largely agreed that virtual GVs increased opportunity for teamwork and collaboration (94%), care coordination (82%), and understanding of patients (94%). However, only 5/17 (29%) and 3/17 (18%) staff members agreed that virtual GVs increased provider productivity or led to higher reimbursement, respectively. Staff cited other priorities at the health center, difficulty recruiting patients, and concerns about access to technology as the biggest barriers to implementing virtual GVs. CONCLUSIONS: Virtual GVs show promise as evidenced by high patient satisfaction and improvements in support, distress, and diabetes knowledge in patients. Staff also perceived virtual GV benefits to patients, staff, and health centers despite concerns about logistics such as productivity, reimbursement, and the health center's ability to continue visits virtually.