Social determinants of health (SDH) impact health outcomes. Medical centers have begun to collect SDH data, urged by government and scientific entities. Provider perspectives on collecting SDH are unknown. The aim is to understand differences in views and preferences according to provider characteristics. A cross-sectional survey of University of Miami clinical faculty was conducted in late 2016. The survey contained 11 questions: 8 demographic and departmental responsibilities questions and 3 Likert scale questions to capture collection and use of SDH perspectives. The main outcome was whether providers thought the benefit of collecting SDH outweighs the burden and risks. In all, 240 faculty members were included. The majority were men (64%), with a mean age of 51 years. Among participants, 53.5% were non-Hispanic white, 32% were Hispanic, 5% were Black/African American, and 5% were Asian. The majority agreed that SDH are important predictors of health outcomes and quality of care (83%). When comparing minority to nonminority faculty, 25% believed that SDH should only be available to PCPs, compared to 8% of nonminorities (P < 0.01). In a multivariate model, belonging to a racial ethnic minority was the only characteristic associated with believing that benefits of collecting SDH outweigh the risks (odds ratio 1.87, 95% confidence interval 1.02- 3.5) after adjusting for age, sex, minority status, health care provider type, type of responsibilities, and department. This study reveals that although most providers of a health system believe social risks impact health outcomes and quality metrics, the buy-in to collect SDH varies according to the racial/ethnic composition of the faculty.

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Introduction As coronavirus disease 2019 (COVID-19) hit the US, there was widespread and urgent implementation of telemedicine programs nationwide without much focus on the impact on patient populations with known existing healthcare disparities. To better understand which populations cannot access telemedicine during the coronavirus disease 2019 pandemic, this study aims to demographically describe and identify the most important demographic predictors of telemedicine visit completion in an urban health system. Methods Patient de-identified demographics and telemedicine visit data ( N = 362,764) between March 1, 2020 and October 31, 2020 were combined with Internal Revenue Service 2018 individual income tax data by postal code. Descriptive statistics and mixed effects logistic regression were used to determine impactful patient predictors of telemedicine completion, while adjusting for clustering at the clinical site level. Results Many patient-specific demographics were found to be significant. Descriptive statistics showed older patients had lower rates of completion ( p < 0.001). Also, Hispanic patients had statistically significant lower rates ( p < 0.001). Overall, minorities (racial, ethnic, and language) had decreased odds ratios of successful telemedicine completion compared to the reference. Discussion While telemedicine use continues to be critical during the coronavirus disease 2019 pandemic, entire populations struggle with access—possibly widening existing disparities. These results contribute large datasets with significant findings to the limited research on telemedicine access and can help guide us in improving telemedicine disparities across our health systems and on a wider scale.

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Background With COVID-19 there was a rapid and abrupt rise in telemedicine implementation often without sufficient time for providers or patients to adapt. As telemedicine visits are likely to continue to play an important role in health care, it is crucial to strive for a better understanding of how to ensure completed telemedicine visits in our health system. Awareness of these barriers to effective telemedicine visits is necessary for a proactive approach to addressing issues. Objective The objective of this study was to identify variables that may affect telemedicine visit completion in order to determine actions that can be enacted across the entire health system to benefit all patients. Methods Data were collected from scheduled telemedicine visits (n=362,764) at the University of Miami Health System (UHealth) between March 1, 2020 and October 31, 2020. Descriptive statistics, mixed effects logistic regression, and random forest modeling were used to identify the most important patient-agnostic predictors of telemedicine completion. Results Using descriptive statistics, struggling telemedicine specialties, providers, and clinic locations were identified. Through mixed effects logistic regression (adjusting for clustering at the clinic site level), the most important predictors of completion included previsit phone call/SMS text message reminder status (confirmed vs not answered) (odds ratio [OR] 6.599, 95% CI 6.483-6.717), MyUHealthChart patient portal status (not activated vs activated) (OR 0.315, 95% CI 0.305-0.325), provider’s specialty (primary care vs medical specialty) (OR 1.514, 95% CI 1.472-1.558), new to the UHealth system (yes vs no) (OR 1.285, 95% CI 1.201-1.374), and new to provider (yes vs no) (OR 0.875, 95% CI 0.859-0.891). Random forest modeling results mirrored those from logistic regression. Conclusions The highest association with a completed telemedicine visit was the previsit appointment confirmation by the patient via phone call/SMS text message. An active patient portal account was the second strongest variable associated with completion, which underscored the importance of patients having set up their portal account before the telemedicine visit. Provider’s specialty was the third strongest patient-agnostic characteristic associated with telemedicine completion rate. Telemedicine will likely continue to have an integral role in health care, and these results should be used as an important guide to improvement efforts. As a first step toward increasing completion rates, health care systems should focus on improvement of patient portal usage and use of previsit reminders. Optimization and intervention are necessary for those that are struggling with implementing telemedicine. We advise setting up a standardized workflow for staff.