Hendricksmcmillan4792

Multimorbidity, the coexistence of multiple chronic conditions in an individual, is a growing public health challenge. Amidst the COVID-19 pandemic, physical distancing remains an indispensable measure to limit the spread of the virus. This pertains especially to those belonging to high-risk groups, namely older adults with multimorbidity. In-person visits are discouraged for this cohort; hence, there is a need for an alternative form of consultation such as video consultations to continue the provision of care.

The potential of video consultations has been explored in several studies. However, the emergence of COVID-19 presents us with an unprecedented opportunity to explore the use of this technological innovation in a time when physical distancing is imperative. This study will evaluate the sustainability of video consultations on a micro-, meso-, and macro-level by assessing the views of patients, physicians, and organizational and national policymakers, respectively.

The NASSS (nonadoption, abandone the adoption and sustainability of video consultations for older adults with multimorbidity during the pandemic as well as post COVID-19. The study will yield knowledge that will challenge the current paradigm on how care is being delivered for community-dwelling older adults with multimorbidity. Findings will be shared with administrators in the health care sector in order to enhance the safety and quality of these video consultations to improve patient care for this group of population.

DERR1-10.2196/22679.

DERR1-10.2196/22679.

COVID-19 often causes respiratory symptoms, making otolaryngology offices one of the most susceptible places for community transmission of the virus. Thus, telemedicine may benefit both patients and physicians.

This study aims to explore the feasibility of telemedicine for the diagnosis of all otologic disease types.

A total of 177 patients were prospectively enrolled, and the patient's clinical manifestations with otoendoscopic images were written in the electrical medical records. Asynchronous diagnoses were made for each patient to assess Top-1 and Top-2 accuracy, and we selected 20 cases to conduct a survey among four different otolaryngologists to assess the accuracy, interrater agreement, and diagnostic speed. We also constructed an experimental automated diagnosis system and assessed Top-1 accuracy and diagnostic speed.

Asynchronous diagnosis showed Top-1 and Top-2 accuracies of 77.40% and 86.44%, respectively. In the selected 20 cases, the Top-2 accuracy of the four otolaryngologists was on average 91.25% (SD 7.50%), with an almost perfect agreement between them (Cohen kappa=0.91). The automated diagnostic model system showed 69.50% Top-1 accuracy. Otolaryngologists could diagnose an average of 1.55 (SD 0.48) patients per minute, while the machine learning model was capable of diagnosing on average 667.90 (SD 8.3) patients per minute.

Asynchronous telemedicine in otology is feasible owing to the reasonable Top-2 accuracy when assessed by experienced otolaryngologists. Moreover, enhanced diagnostic speed while sustaining the accuracy shows the possibility of optimizing medical resources to provide expertise in areas short of physicians.

Asynchronous telemedicine in otology is feasible owing to the reasonable Top-2 accuracy when assessed by experienced otolaryngologists. Moreover, enhanced diagnostic speed while sustaining the accuracy shows the possibility of optimizing medical resources to provide expertise in areas short of physicians.

COVID-19 has infected millions of people worldwide and is responsible for several hundred thousand fatalities. The COVID-19 pandemic has necessitated thoughtful resource allocation and early identification of high-risk patients. However, effective methods to meet these needs are lacking.

The aims of this study were to analyze the electronic health records (EHRs) of patients who tested positive for COVID-19 and were admitted to hospitals in the Mount Sinai Health System in New York City; to develop machine learning models for making predictions about the hospital course of the patients over clinically meaningful time horizons based on patient characteristics at admission; and to assess the performance of these models at multiple hospitals and time points.

We used Extreme Gradient Boosting (XGBoost) and baseline comparator models to predict in-hospital mortality and critical events at time windows of 3, 5, 7, and 10 days from admission. Our study population included harmonized EHR data from five hospitalsUC-ROC of 0.78 at 3 days, 0.79 at 5 days, 0.80 at 7 days, and 0.81 at 10 days. Trends in performance on prospective validation sets were similar. At 7 days, acute kidney injury on admission, elevated LDH, tachypnea, and hyperglycemia were the strongest drivers of critical event prediction, while higher age, anion gap, and C-reactive protein were the strongest drivers of mortality prediction.

We externally and prospectively trained and validated machine learning models for mortality and critical events for patients with COVID-19 at different time horizons. VT107 chemical structure These models identified at-risk patients and uncovered underlying relationships that predicted outcomes.

We externally and prospectively trained and validated machine learning models for mortality and critical events for patients with COVID-19 at different time horizons. These models identified at-risk patients and uncovered underlying relationships that predicted outcomes.

Improving women's empowerment is pivotal to public health and development programs; however, inconsistent definitions and lack of cross-cultural measures compromise monitoring efforts.

Data collected in 2017-2018 in Ethiopia, Uganda and two sites in Nigeria were used to develop a cross-cultural index of women's and girls' empowerment in sexual and reproductive health (WGE-SRH). Item development was grounded in qualitative interviews, and informed by a conceptual framework that included domains of existence of choice and exercise of choice related to sex, contraceptive use and pregnancy. Items were pilot tested among 1,229 women aged 15-49 across sites. Psychometric properties were explored to identify crosssite constructs, and logistic regression was used to assess the construct validity of each dimension.

Analyses identified subscales for sexual existence of choice (Cronbach's alphas, 0.71-0.79) and contraceptive existence of choice (0.56-0.78). A pregnancy existence of choice subscale emerged for only two sites (0.