Positive patient experiences are associated with illness recovery and adherence to medication. To evaluate the virtual care experience for patients with COVID-19 symptoms as their chief complaints. We conducted a cross-sectional study of the first cohort of patients with COVID-19 symptoms in a virtual clinic. The main end points of this study were visit volume, wait times, visit duration, patient diagnosis, prescriptions received, and satisfaction. Of the 1139 total virtual visits, 212 (24.6%) patients had COVID-19 symptoms. The average wait time (SD) for all visits was 75.5 (121.6) minutes. The average visit duration for visits was 10.5 (4.9) minutes. The highest volume of virtual visits was on Saturdays (39), and the lowest volume was on Friday (19). Patients experienced shorter wait times (SD) on the weekdays 67.1 (106.8) minutes compared to 90.3 (142.6) minutes on the weekends. The most common diagnoses for patients with COVID-19 symptoms were upper respiratory infection. Patient wait times for a telehealth visit varied depending on the time and day of appointment. Long wait times were a major drawback in the patient experience. Based on patient-reported experience, we proposed a list of general, provider, and patient telehealth best practices.

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Black American women experience adverse health outcomes due to anxiety and depression. They face systemic barriers to accessing culturally appropriate mental health care leading to the underutilization of mental health services and resources. Mobile technology can be leveraged to increase access to culturally relevant resources, however, the specific needs and preferences that Black women feel are useful in an app to support management of anxiety and depression are rarely reflected in existing digital health tools. This study aims to assess what types of content, features, and important considerations should be included in the design of a mobile app tailored to support management of anxiety and depression among Black women. Focus groups were conducted with 20 women (mean age 36.6 years, SD 17.8 years), with 5 participants per group. Focus groups were led by a moderator, with notetaker present, using an interview guide to discuss topics, such as participants' attitudes and perceptions towards mental health and use of mental health services, and content, features, and concerns for design of a mobile app to support management of anxiety and depression. Descriptive qualitative content analysis was conducted. Recommendations for content were either informational (e.g., information to find a Black woman therapist) or inspirational (e.g., encouraging stories about overcoming adversity). Suggested features allow users to monitor their progress, practice healthy coping techniques, and connect with others. The importance of feeling “a sense of community” was emphasized. Transparency about who created and owns the app, and how users' data will be used and protected was recommended to establish trust. The findings from this study were consistent with previous literature which highlighted the need for educational, psychotherapy, and personal development components for mental health apps. There has been exponential growth in the digital mental health space due to the COVID-19 pandemic; however, a one-size-fits-all approach may lead to more options but continued disparity in receiving mental health care. Designing a mental health app for and with Black women may help to advance digital health equity by providing a tool that addresses their specific needs and preferences, and increase engagement.

Microbiome data are becoming increasingly available in large health cohorts, yet metabolomics data are still scant. While many studies generate microbiome data, they lack matched metabolomics data or have considerable missing proportions of metabolites. Since metabolomics is key to understanding microbial and general biological activities, the possibility of imputing individual metabolites or inferring metabolomics pathways from microbial taxonomy or metagenomics is intriguing. Importantly, current metabolomics profiling methods such as the HMP Unified Metabolic Analysis Network (HUMAnN) have unknown accuracy and are limited in their ability to predict individual metabolites. To address this gap, we developed a novel metabolite prediction method, and we present its application and evaluation in an oral microbiome study. The new method for predicting metabolites using microbiome data (ENVIM) is based on the elastic net model (ENM). ENVIM introduces an extra step to ENM to consider variable importance (VI) scores, and thus, achieves better prediction power. We investigate the metabolite prediction performance of ENVIM using metagenomic and metatranscriptomic data in a supragingival biofilm multi-omics dataset of 289 children ages 3–5 who were participants of a community-based study of early childhood oral health (ZOE 2.0) in North Carolina, United States. We further validate ENVIM in two additional publicly available multi-omics datasets generated from studies of gut health. We select gene family sets based on variable importance scores and modify the existing ENM strategy used in the MelonnPan prediction software to accommodate the unique features of microbiome and metabolome data. We evaluate metagenomic and metatranscriptomic predictors and compare the prediction performance of ENVIM to the standard ENM employed in MelonnPan. The newly developed ENVIM method showed superior metabolite predictive accuracy than MelonnPan when trained with metatranscriptomics data only, metagenomics data only, or both. Better metabolite prediction is achieved in the gut microbiome compared with the oral microbiome setting. We report the best-predictable compounds in all these three datasets from two different body sites. For example, the metabolites trehalose, maltose, stachyose, and ribose are all well predicted by the supragingival microbiome.

The purpose of this cross-sectional web-based study was to assess the prevalence of perceived stress among dental faculties of Government Dental Colleges across the state of Kerala, India, during the Corona Virus Disease 2019 (COVID-19) pandemic, which has a huge impact on both physical and psychological well-being of persons all over the world. Human transmission of this disease occurs mainly through droplet inhalation and hence, direct contact with mucous membranes and saliva is considered risky. The study was conducted using a five-point scale PSS10 (Perceived Stress Scale) questionnaire. Comparison between study groups (gender and age group) with continuous variables (PSS score) was done using independent t test. The chi-square test was used to compare the age group and gender with PSS categories; low, moderate and high. The mean PSS score of the respondents was 17.43 ± 6.45 with a range from 2 to 35 with women having higher mean stress scores (18.15 ± 6.54) compared to men (16.46 ± 6.24). Participants aged 40 years and less reported higher PSS scores (18.60 ± 6.40) compared to the older age group (16.14 ± 6.30). Since varying amounts of stress are present in ABOUT THE AUTHOR

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Chart checking is a time intensive process with high cognitive workload for physicists. Previous studies have partially automated and standardized chart checking, but limited studies implement data-driven approaches to reduce cognitive workload for quality assurance processes. This study aims to evaluate feature selection methods to improve the interpretability and transparency of machine learning models in predicting the degree of difficulty for a pretreatment physics chart check. We compare chi-square, mutual information, feature importance thresholding, and greedy feature selection for four different classifiers. Random forest has the highest performance with SMOTE oversampling using mutual information for feature selection (accuracy 84.0%, AUC 87.0%, precision 80.0%, recall 80.0%). This study demonstrates that feature selection methods can improve model interpretability and transparency.

Thyroid disease is the second most common endocrine disorder encountered in pregnant women with substantial maternal and fetal implications. Therefore, assessing thyroid status during pregnancy is essential for initiating treatment in newly diagnosed individuals and adjusting doses for those already under treatment. To initiate proper and timely treatment evidence-based recommendations are required for clinical decisionmaking in managing thyroid disorders in pregnant and postpartum women. Keeping this in mind, task force consisted of experts in the fields of endocrinology and thyroid disease was constituted and various published data and guidelines were explored to address screening, diagnosis, and management of hypothyroidism, thyrotoxicosis, GD, thyroid nodules, and post-partum thyroiditis and their related complications during pregnancy. This document provides much-required insights and useful, practical, and accurate guidance that aids a practicing clinician.