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Community Factors Associated With Telemedicine Use During the COVID-19 Pandemic

Educational Objective
To identify the key insights or developments described in this article
1 Credit CME

While telemedicine use grew rapidly during the COVID-19 pandemic, there was substantial geographic variation in uptake.1,2 What drives this variation is unclear. To understand drivers of telemedicine use, we examined the association of county-level telemedicine use with community factors among individuals with commercial or Medicare Advantage insurance.

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Article Information

Accepted for Publication: March 24, 2021.

Published: May 18, 2021. doi:10.1001/jamanetworkopen.2021.10330

Open Access: This is an open access article distributed under the terms of the CC-BY License. © 2021 Patel SY et al. JAMA Network Open.

Corresponding Author: Ateev Mehrotra, MD, MPH, Department of Health Care Policy, Harvard Medical School, 180A Longwood Ave, Boston, MA 02115 (mehrotra@hcp.med.harvard.edu).

Author Contributions: Dr Patel had full access to all of the data in the study and takes responsibility for the integrity of the data and the accuracy of the data analysis.

Concept and design: Patel, Rose, Barnett, Mehrotra.

Acquisition, analysis, or interpretation of data: Patel, Rose, Huskamp, Uscher-Pines, Mehrotra.

Drafting of the manuscript: Patel.

Critical revision of the manuscript for important intellectual content: All authors.

Statistical analysis: Patel, Rose, Barnett.

Obtained funding: Uscher-Pines, Mehrotra.

Administrative, technical, or material support: Patel, Barnett, Huskamp, Mehrotra.

Supervision: Patel, Rose, Huskamp, Uscher-Pines.

Conflict of Interest Disclosures: None reported.

Funding/Support: This project was supported by the Commonwealth Fund, National Institute on Aging of the National Institutes of Health (K23 AG058806-01) and the National Institute of Mental Health (R01 MH112829-01, T32MH019733).

Role of the Funder/Sponsor: The funders had no role in the design and conduct of the study; collection, management, analysis, and interpretation of the data; preparation, review, or approval of the manuscript; and decision to submit the manuscript for publication.

Additional Contributions: We thank Rebecca Shyu, University of Missouri, for contributing to data cleaning, data analysis, and manuscript preparation efforts. She was not compensated.

References
1.
Patel  SY , Mehrotra  A , Huskamp  HA , Uscher-Pines  L , Ganguli  I , Barnett  ML .  Trends in outpatient care delivery and telemedicine during the COVID-19 pandemic in the US.   JAMA Intern Med. 2021;181(3):388-391. doi:10.1001/jamainternmed.2020.5928 PubMedGoogle ScholarCrossref
2.
Patel  SY , Mehrotra  A , Huskamp  HA , Uscher-Pines  L , Ganguli  I , Barnett  ML .  Variation in telemedicine use and outpatient care during the COVID-19 pandemic in the United States.   Health Aff (Millwood). 2021;40(2):349-358. doi:10.1377/hlthaff.2020.01786 PubMedGoogle ScholarCrossref
3.
Centers for Medicare & Medicaid Services. List of telehealth services. Updated April 30, 2020. Accessed July 15, 2020. https://www.cms.gov/Medicare/Medicare-General-Information/Telehealth/Telehealth-Codes
4.
Bureau of Health Workforce and HRSA. Area Health Resources Files. Updated July 31, 2020. Accessed November 1, 2020. https://data.hrsa.gov/topics/health-workforce/ahrf
5.
van der Laan  MJ , Rose  S .  Targeted Learning: Causal Inference for Observational and Experimental Data. Springer-Verlag; 2011. doi:10.1007/978-1-4419-9782-1
6.
Morton  H . Broadband 2020 legislation. National Conference of State Legislatures. Updated October 8, 2020. Accessed November 15, 2020. https://www.ncsl.org/research/telecommunications-and-information-technology/broadband-2020-legislation.aspx
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