Patient income level and health insurance correlate with differences in health care utilization during the COVID-19 pandemic.

Background: With the coronavirus outbreak of 2019 (COVID-19) came many changes in how health care is accessed and delivered. Perhaps most notable is the massive expansion of telemedicine, especially in the developed world. With pandemic-induced economic and health care system disruptions, it is reasonable to expect changes in how health care services are utilized by different patients. We examined how health care service usage trends changed for various patient demographics from the pre-COVID-19 era to the COVID-19 era. Design and methods: De-identified patient demographics and telemedicine, in-patient, in-person out-patient, radiology/procedures, and emergency department visit data (N = 1,164,719) between January 1st, 2019 and May 31st, 2021 were obtained from UHealth in Miami, Florida, USA. This cross-sectional study employed descriptive statistics and other tools to determine relationships between patient demographics and health system usage. Results: There were significant changes in health care usage and demographics for UHealth services from the pre-COVID-19 era to the COVID-19 era. There was an increase in telehealth visits and a corollary decrease of in-person out-patient visits (p < 0.001) along with increased health care utilization by those with commercial insurance (p < 0.001) during COVID-19. Lower-income patients had increased use of in-person out-patient services (p < 0.001). Non-Hispanic, English-speaking patients and those with higher median incomes had higher telemedicine usage. Conclusions: COVID-19 revealed differences in health care access, particularly telemedicine access, and highlighted differences in vulnerability among patient demographics. These trends are likely multifactorial and reflect changes in patients' preferences and disparities in care access.

Development of an Effective Electronic Medical Record for Student-Run Free Health Fairs Using Research Electronic Data Capture (REDCap) Software.

BACKGROUND: The University of Miami Mitchell Wolfson Sr. Department of Community Service (DOCS) is a student-run organization providing free health care to the medically underserved in South Florida. For a large organization providing care to thousands of people per year, an effective electronic medical record (EMR) is necessary to keep track of patient records. METHODS: A REDCap project was configured in a way that allows it to mimic a basic EMR. This was done by assigning patients medical record numbers, creating repeating events allowing patient results to be seen over time, and incorporating extensive logic to facilitate clinical decision-making. RESULTS: DOCS was able to create a basic EMR using REDCap and has seen success with this approach. DISCUSSION: REDCap is capable of functioning as a robust, basic EMR which can be suitable for any purpose. It is HIPAA-compliant, comprehensive and low-cost, making it suitable for serving underserved populations.

Demographics associated with US healthcare disparities are exacerbated by the telemedicine surge during the COVID-19 pandemic.

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.