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Patient characteristics associated with telemedicine access for primary and specialty ambulatory care during the COVID-19 pandemic.
Open accessAbstract
Importance: The coronavirus disease 2019 (COVID-19) pandemic has required a shift in health care delivery platforms, necessitating a new reliance on telemedicine. Objective: To evaluate whether inequities are present in telemedicine use and video visit use for telemedicine visits during the COVID-19 pandemic. Design, Setting, and Participants: In this cohort study, a retrospective medical record review was conducted from March 16 to May 11, 2020, of all patients scheduled for telemedicine visits in primary care and specialty ambulatory clinics at a large academic health system. Age, race/ethnicity, sex, language, median household income, and insurance type were all identified from the electronic medical record. Main Outcomes and Measures: A successfully completed telemedicine visit and video (vs telephone) visit for a telemedicine encounter. Multivariable models were used to assess the association between sociodemographic factors, including sex, race/ethnicity, socioeconomic status, and language, and the use of telemedicine visits, as well as video use specifically. Results: A total of 148 402 unique patients (86 055 women [58.0%]; mean [SD] age, 56.5 [17.7] years) had scheduled telemedicine visits during the study period; 80 780 patients (54.4%) completed visits. Of 78 539 patients with completed visits in which visit modality was specified, 35 824 (45.6%) were conducted via video, whereas 24 025 (56.9%) had a telephone visit. In multivariable models, older age (adjusted odds ratio [aOR], 0.85 [95% CI, 0.83-0.88] for those aged 55-64 years; aOR, 0.75 [95% CI, 0.72-0.78] for those aged 65-74 years; aOR, 0.67 [95% CI, 0.64-0.70] for those aged ≥75 years), Asian race (aOR, 0.69 [95% CI, 0.66-0.73]), non-English language as the patient's preferred language (aOR, 0.84 [95% CI, 0.78-0.90]), and Medicaid insurance (aOR, 0.93 [95% CI, 0.89-0.97]) were independently associated with fewer completed telemedicine visits. Older age (aOR, 0.79 [95% CI, 0.76-0.82] for those aged 55-64 years; aOR, 0.78 [95% CI, 0.74-0.83] for those aged 65-74 years; aOR, 0.49 [95% CI, 0.46-0.53] for those aged ≥75 years), female sex (aOR, 0.92 [95% CI, 0.90-0.95]), Black race (aOR, 0.65 [95% CI, 0.62-0.68]), Latinx ethnicity (aOR, 0.90 [95% CI, 0.83-0.97]), and lower household income (aOR, 0.57 [95% CI, 0.54-0.60] for income <$50 000; aOR, 0.89 [95% CI, 0.85-0.92], for $50 000-$100 000) were associated with less video use for telemedicine visits. These results were similar across medical specialties. Conclusions and Relevance: In this cohort study of patients scheduled for primary care and medical specialty ambulatory telemedicine visits at a large academic health system during the early phase of the COVID-19 pandemic, older patients, Asian patients, and non-English-speaking patients had lower rates of telemedicine use, while older patients, female patients, Black, Latinx, and poorer patients had less video use. Inequities in accessing telemedicine care are present, which warrant further attention.
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“Notably, during the COVID-19 pandemic, video telehealth was generally used less frequently among older adults, rural residents, and racial and ethnic minority patients.50,51”
From Mobile Health Intervention in Patients With Type 2 Diabetes: A Randomized Clinical Trial (2023) · cites this paper as [50]
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Cites
Charlson, M. E., Pompei, P., Ales, K. L., & MacKenzie, C. R. (1987) · Journal of Chronic Diseases · doi:10.1016/0021-9681(87)90171-8 · Cited by 49,742
Unequal treatment: confronting racial and ethnic disparities in health care
(2003) · Choice Reviews Online · doi:10.5860/choice.40-5843 · Cited by 7,043
Capps, L. (2003) · JAMA · doi:10.1001/jama.290.18.2487-b · Cited by 4,558
Garg, S., Kim, L., Whitaker, M., O’Halloran, A., Cummings, C., Holstein, R., Prill, M., Chai, S. J., Kirley, P. D., Alden, N. B., et al. (2020) · MMWR. Morbidity and Mortality Weekly Report · doi:10.15585/mmwr.mm6915e3 · Cited by 2,335
COVID-19 and African Americans
Yancy, C. W. (2020) · JAMA · doi:10.1001/jama.2020.6548 · Cited by 1,932
The Impact of COVID-19 on Gender Equality
Alon, T., Doepke, M., Olmstead-Rumsey, J., & Tertilt, M. (2020) · doi:10.3386/w26947 · Cited by 1,706
Telehealth transformation: COVID-19 and the rise of virtual care
Wosik, J., Fudim, M., Cameron, B., Gellad, Z. F., Cho, A., Phinney, D., Curtis, S., Roman, M., Poon, E. G., Ferranti, J., et al. (2020) · Journal of the American Medical Informatics Association · doi:10.1093/jamia/ocaa067 · Cited by 1,581
COVID-19 exacerbating inequalities in the US
Dorn, A. V., Cooney, R. E., & Sabin, M. L. (2020) · The Lancet · doi:10.1016/s0140-6736(20)30893-x · Cited by 1,315
Critical Race Theory, Race Equity, and Public Health: Toward Antiracism Praxis
Ford, C. L., & Airhihenbuwa, C. O. (2010) · American Journal of Public Health · doi:10.2105/ajph.2009.171058 · Cited by 995
Back to the Future: Achieving Health Equity Through Health Informatics and Digital Health
Brewer, L. C., Fortuna, K. L., Jones, C., Walker, R., Hayes, S. N., Patten, C. A., & Cooper, L. A. (2020) · JMIR mHealth and uHealth · doi:10.2196/14512 · Cited by 367
Anthony, D. L., Campos‐Castillo, C., & Lim, P. S. (2018) · Health Affairs · doi:10.1377/hlthaff.2018.05117 · Cited by 271
Telemedicine Outpatient Cardiovascular Care During the COVID-19 Pandemic
Eberly, L. A., Khatana, S. A. M., Nathan, A. S., Snider, C. K., Julien, H. M., Deleener, M. E., & Adusumalli, S. (2020) · Circulation · doi:10.1161/circulationaha.120.048185 · Cited by 243
Self-citationIn situ monitoring of health in older adults: technologies and issues
Kang, H. G., Mahoney, D. F., Hoenig, H., Hirth, V. A., Bonato, P., Hajjar, I., Lipsitz, L. A., & for the Center for Integration of Medicine and Innovative Technology Working Group on Advanced Approaches to Physiologic Monitoring for the Aged (2010) · Journal of the American Geriatrics Society · doi:10.1111/j.1532-5415.2010.02959.x · Cited by 227
Asian Americans’ reports of their health care experiences: Results of a national survey
Ngo-Metzger, Q., Legedza, A. T. R., & Phillips, R. S. (2004) · Journal of General Internal Medicine · doi:10.1111/j.1525-1497.2004.30143.x · Cited by 215
Trends in Seniors' Use of Digital Health Technology in the United States, 2011-2014.
Levine, D., Lipsitz, S., & Linder, J. (2016) · Journal of the American Medical Association (JAMA) · doi:10.1001/jama.2016.9124 · Cited by 209
Designing Telemedicine Systems for Geriatric Patients: A Review of the Usability Studies
Narasimha, S., Madathil, K. C., Agnisarman, S., Rogers, H., Welch, B. M., Ashok, A., Nair, A., & McElligott, J. T. (2016) · Telemedicine Journal and e-Health · doi:10.1089/tmj.2016.0178 · Cited by 180
Barriers to Health Care Access in 13 Asian American Communities
Lee, S. (2010) · American Journal of Health Behavior · doi:10.5993/ajhb.34.1.3 · Cited by 148
George, S. M., Hamilton, A., & Baker, R. (2009) · Telemedicine and e-Health · doi:10.1089/tmj.2008.0152 · Cited by 73
Determinants of Low Health Literacy Among Asian-American and Pacific Islanders in California
Becerra, M. B., Becerra, B. J., Daus, G. P., & Martin, L. R. (2015) · Journal of Racial and Ethnic Health Disparities · doi:10.1007/s40615-015-0092-0 · Cited by 31
(2020) · doi:10.1056/cat.20.0123
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The State of Telehealth Before and After the COVID-19 Pandemic
Shaver, J. (2022) · Primary Care: Clinics in Office Practice · doi:10.1016/j.pop.2022.04.002 · Cited by 863
Rochester, C. L., Alison, J. A., Carlin, B. W., Jenkins, A. R., Cox, N. S., Bauldoff, G. S., Bhatt, S. P., Bourbeau, J., Burtin, C., Camp, P. G., et al. (2023) · American Journal of Respiratory and Critical Care Medicine · doi:10.1164/rccm.202306-1066st · Cited by 475
Wearable Devices in Cardiovascular Medicine
Hughes, A. N., Shandhi, M. M. H., Master, H., Dunn, J., & Brittain, E. L. (2023) · Circulation Research · doi:10.1161/circresaha.122.322389 · Cited by 341
Changes in Access to Health Services during the COVID-19 Pandemic: A Scoping Review
Pujolar-Díaz, G., Oliver-Anglès, A., Vargas, Í., & Vázquez, M. L. (2022) · International Journal of Environmental Research and Public Health · doi:10.3390/ijerph19031749 · Cited by 317
Virtual care use before and during the COVID-19 pandemic: A repeated cross-sectional study
Bhatia, R. S., Chu, C., Pang, A., Tadrous, M., Stamenova, V., & Cram, P. (2021) · CMAJ Open · doi:10.9778/cmajo.20200311 · Cited by 272
Metadata
- Authors
- Eberly, L. A., Kallan, M. J., Julien, H. M., Haynes, N., Khatana, S. A. M., Nathan, A. S., Snider, C., Chokshi, N. P., Eneanya, N. D., Takvorian, S. U., Anastos-Wallen, R., Chaiyachati, K., Ambrose, M., O’Quinn, R., Seigerman, M., Goldberg, L. R., Leri, D., Choi, K., Gitelman, Y., Kolansky, D. M., Cappola, T. P., Ferrari, V. A., Hanson, C. W., Deleener, M. E., & Adusumalli, S.
- Affiliations
- Hospital of the University of Pennsylvania (US)
- University of Pennsylvania (US)
- University of Pennsylvania Health System (US)
- Office of the Chief Medical Information Officer, University of Pennsylvania Health System, Philadelphia
- Corresponding author
- Adusumalli, Srinath
- ORCID
- Eberly, Lauren A.
- Kallan, Michael J.
- Julien, Howard M.
- Haynes, Norrisa
- Khatana, Sameed Ahmed M.
- Nathan, Ashwin S.
- Snider, Christopher
- Chokshi, Neel P.
- Eneanya, Nwamaka D.
- Takvorian, Samuel U.
- Chaiyachati, Krisda
- Ambrose, Marietta
- O’Quinn, Rupal
- Seigerman, Matthew
- Goldberg, Lee R.
- Leri, Damien
- Kolansky, Daniel M.
- Cappola, Thomas P.
- Ferrari, Victor A.
- Hanson, C. William
- Adusumalli, Srinath
- Venue
- JAMA Network Open
- Publisher
- American Medical Association (AMA)
- Published
- 2020-12-29
- Volume
- 3
- Issue
- 12
- Pages
- e2031640
- ISSN
- 2574-3805 (ISSN-L)
- PMC
- PMC7772717
- OpenAlex
- W3114602681
- Cited by
- 740
- References
- 19
- Citations by year
- 2026: 54 · 2025: 79 · 2024: 117 · 2023: 170 · 2022: 202 · 2021: 115 · 2020: 1
- FWCI
- 91.50
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- cc-by
- Locations
- JAMA Network Open (publishedVersion, cc-by)
- Europe PMC (PubMed Central) (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- Funders
- Agency for Healthcare Research and Quality (K12 HS026372)
- National Heart, Lung, and Blood Institute (T32HL007843)
- Topics
- Health Sciences › Medicine › Public Health, Environmental and Occupational Health › Telemedicine and Telehealth Implementation
- Health Sciences › Medicine › Oncology › COVID-19 and healthcare impacts
- Health Sciences › Medicine › Public Health, Environmental and Occupational Health › Healthcare Systems and Technology
- Keywords
- telemedicine, video consultation, telephone visits, COVID-19 pandemic, health care access disparities, sociodemographic disparities, primary care, language barriers, older adults, racial and ethnic disparities, socioeconomic status, insurance type, telehealth utilization, academic health system
- MeSH terms
- COVID-19, SARS-CoV-2, Adult, Age Factors, Aged, Ambulatory Care, Asian, Black or African American, Female, Medicare, Health Services Accessibility, Hispanic or Latino, Humans, Income, Language, Male, Medicaid, Middle Aged, Primary Health Care, Sex Factors, Telephone, United States, Telemedicine, Videoconferencing, Healthcare Disparities, Secondary Care, Secondary Health Care, Tertiary Healthcare, Tertiary Health Care
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