Telehealth utilization in U.S. Medicare beneficiaries aged 65 years and older during the COVID-19 pandemic

Abstract

Background The COVID-19 pandemic has become a serious public health concern for older adults and amplified the value of deploying telehealth solutions. The purpose of this study was to investigate telehealth offered by providers among U.S. Medicare beneficiaries aged 65 years and older during the COVID-19 pandemic. Methods This cross-sectional study analyzed Medicare beneficiaries aged 65 years and older using data from the Medicare Current Beneficiary Survey, Winter 2021 COVID-19 Supplement ($$n=9,185$$ n = 9 , 185 ). We identified variables that were associated with telehealth offered by primary care physicians and beneficiaries’ access to the Internet through a multivariate classification analysis utilizing Random Forest machine learning techniques. Findings For study participants interviewed by telephone, 81.06% of primary care providers provided telehealth services, and 84.62% of the Medicare beneficiaries had access to the Internet. The survey response rates for each outcome were 74.86% and 99.55% respectively. The two outcomes were positively correlated ($$\chi ^2=268.58,\,p<.001$$ χ 2 = 268.58 , p < . 001 ). The Our machine learning model predicted the outcomes accurately utilizing 44 variables. Residing area and race/ethnicity were most informative for predicting telehealth coverage, and Medicare-Medicaid dual eligibility and income were most informative for predicting Internet access. Other strong correlates included age, ability to access basic needs and certain mental and physical health conditions. Interactions were found among statuses of residing area, age, Medicare Advantage and heart conditions that intensified the disparity of outcomes. Conclusions We found that telehealth offered by providers likely increased during the COVID-19 pandemic for older beneficiaries, providing important access to care for certain subgroups. Policymakers must continue to identify effective means of delivering telehealth services, modernize the framework of regulatory, accreditation and reimbursement, and address disparities in access to telehealth with a particular focus on underserved communities.

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“…We could try to use machine learning approaches. Nowadays the machine learning and deep learning models have been used in many fields, including agriculture ( 6 ), environment ( 7 – 9 ) and medical field ( 10 ). We utilized Random Forest machine learning techniques for the multivariate classification analysis.…”