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DOI: 10.1055/s-0041-1730026
Disparities in Telemedicine Access: A Cross-Sectional Study of a Newly Established Infrastructure during the COVID-19 Pandemic
Funding This work was supported by the NCI Cancer Center Support Grant P30 CA014520, grant UL1TR000427 to UW ICTR from NIH/NCATS and grant T32 DC009401 from the NIH/NIDCD.
Abstract
Background The COVID-19 pandemic led to dramatic increases in telemedicine use to provide outpatient care without in-person contact risks. Telemedicine increases options for health care access, but a “digital divide” of disparate access may prevent certain populations from realizing the benefits of telemedicine.
Objectives The study aimed to understand telemedicine utilization patterns after a widespread deployment to identify potential disparities exacerbated by expanded telemedicine usage.
Methods We performed a cross-sectional retrospective analysis of adults who scheduled outpatient visits between June 1, 2020 and August 31, 2020 at a single-integrated academic health system encompassing a broad range of subspecialties and a large geographic region in the Upper Midwest, during a period of time after the initial surge of COVID-19 when most standard clinical services had resumed. At the beginning of this study period, approximately 72% of provider visits were telemedicine visits. The primary study outcome was whether a patient had one or more video-based visits, compared with audio-only (telephone) visits or in-person visits only. The secondary outcome was whether a patient had any telemedicine visits (video-based or audio-only), compared with in-person visits only.
Results A total of 197,076 individuals were eligible (average age = 46 years, 56% females). Increasing age, rural status, Asian or Black/African American race, Hispanic ethnicity, and self-pay/uninsured status were significantly negatively associated with having a video visit. Digital literacy, measured by patient portal activation status, was significantly positively associated with having a video visit, as were Medicaid or Medicare as payer and American Indian/Alaskan Native race.
Conclusion Our findings reinforce previous evidence that older age, rural status, lower socioeconomic status, Asian race, Black/African American race, and Hispanic/Latino ethnicity are associated with lower rates of video-based telemedicine use. Health systems and policies should seek to mitigate such barriers to telemedicine when possible, with efforts such as digital literacy outreach and equitable distribution of telemedicine infrastructure.
Protection of Human and Animal Subject
This study was exempted from the University of Wisconsin Institutional Review Board (IRB) review.
Publikationsverlauf
Eingereicht: 02. Februar 2021
Angenommen: 13. April 2021
Artikel online veröffentlicht:
09. Juni 2021
© 2021. Thieme. All rights reserved.
Georg Thieme Verlag KG
Rüdigerstraße 14, 70469 Stuttgart, Germany
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References
- 1 Ryu S. Telemedicine: opportunities and developments in member states: report on the second global survey on eHealth 2009 (global observatory for eHealth series, volume 2). Healthc Inform Res 2012; 18 (02) 153-155
- 2 Wisconsin Budget Project. Pulling apart 2017: focus on Wisconsin's 1%. Accessed 2017 at: http://www.wisconsinbudgetproject.org/pulling-apart-2017-focus-on-wisconsins-1
- 3 Hosworth A, Ruhter J, Samson LW. et al. Medicare Beneficiary Use of Telehealth Visits: Early Data from the Start of COVID-19 Pandemic. Washington, DC: Office of the Assistant Secretary for Planning and Evaluation, U.S. Department of Health and Human Services; 2020
- 4 Ekeland AG, Bowes A, Flottorp S. Effectiveness of telemedicine: a systematic review of reviews. Int J Med Inform 2010; 79 (11) 736-771
- 5 Zanaboni P, Wootton R. Adoption of telemedicine: from pilot stage to routine delivery. BMC Med Inform Decis Mak 2012; 12 (01) 1
- 6 Mitchell UA, Chebli PG, Ruggiero L, Muramatsu N. The Digital divide in health-related technology use: the significance of race/ethnicity. Gerontologist 2019; 59 (01) 6-14
- 7 Khairat S, Haithcoat T, Liu S. et al. Advancing health equity and access using telemedicine: a geospatial assessment. J Am Med Inform Assoc 2019; 26 (8-9): 796-805
- 8 Nouri S, Khoong EC, Lyles CR, Karliner L. Addressing equity in telemedicine for chronic disease management during the Covid-19 pandemic. NEJM Catal 2020; DOI: 10.1056/CAT.20.0123.
- 9 Douthit N, Kiv S, Dwolatzky T, Biswas S. Exposing some important barriers to health care access in the rural USA. Public Health 2015; 129 (06) 611-620
- 10 Federal Communications Commission. 2016 Broadband Progress Report. Washington, DC: 2016
- 11 Hirko KA, Kerver JM, Ford S. et al. Telehealth in response to the COVID-19 pandemic: implications for rural health disparities. J Am Med Inform Assoc 2020; 27 (11) 1816-1818
- 12 Kind AJH, Buckingham WR. Making neighborhood-disadvantage metrics accessible - the neighborhood atlas. N Engl J Med 2018; 378 (26) 2456-2458
- 13 Blumenthal SJ, Kagen J. MSJAMA. The effects of socioeconomic status on health in rural and urban America. JAMA 2002; 287 (01) 109-109
- 14 Lion KC, Brown JC, Ebel BE. et al. Effect of telephone vs video interpretation on parent comprehension, communication, and utilization in the pediatric emergency department: a randomized clinical trial. JAMA Pediatr 2015; 169 (12) 1117-1125
- 15 Viers BR, Lightner DJ, Rivera ME. et al. Efficiency, satisfaction, and costs for remote video visits following radical prostatectomy: a randomized controlled trial. Eur Urol 2015; 68 (04) 729-735
- 16 Manganello J, Gerstner G, Pergolino K, Graham Y, Falisi A, Strogatz D. The relationship of health literacy with use of digital technology for health information: implications for public health practice. J Public Health Manag Pract 2017; 23 (04) 380-387
- 17 Pérez-Stable EJ, Jean-Francois B, Aklin CF. Leveraging advances in technology to promote health equity. Med Care 2019; 57 (Suppl 6 Suppl 2): S101-S103
- 18 Weiss D, Rydland HT, Øversveen E, Jensen MR, Solhaug S, Krokstad S. Innovative technologies and social inequalities in health: a scoping review of the literature. PLoS One 2018; 13 (04) e0195447
- 19 U.S. Census Bureau. Population and Housing Unit Counts, CPH-2–51, Wisconsin. Washington, D.C.: U.S. Government Printing Office; 2012
- 20 Campos-Castillo C, Anthony D. Racial and ethnic differences in self-reported telehealth use during the COVID-19 pandemic: a secondary analysis of a U.S. survey of internet users from late March. J Am Med Inform Assoc 2020; DOI: 10.1093/jamia/ocaa221.
- 21 Eberly LA, Kallan MJ, Julien HM. et al. Patient characteristics associated with telemedicine access for primary and specialty ambulatory care during the COVID-19 pandemic. JAMA Netw Open 2020; 3 (12) e2031640-e2031640
- 22 Hron JD, Parsons CR, Williams LA, Harper MB, Bourgeois FC. Rapid implementation of an inpatient telehealth program during the COVID-19 pandemic. Appl Clin Inform 2020; 11 (03) 452-459
- 23 Ong SY, Stump L, Zawalich M. et al. Inpatient telehealth tools to enhance communication and decrease personal protective equipment consumption during disaster situations: a case study during the COVID-19 pandemic. Appl Clin Inform 2020; 11 (05) 733-741
- 24 Gordon WJ, Henderson D, DeSharone A. et al. Remote patient monitoring program for hospital discharged COVID-19 patients. Appl Clin Inform 2020; 11 (05) 792-801
- 25 Knighton AJ, Ranade-Kharkar P, Brunisholz KD. et al. Rapid implementation of a complex, multimodal technology response to COVID-19 at an integrated community-based health care system. Appl Clin Inform 2020; 11 (05) 825-838
- 26 Chunara R, Zhao Y, Chen J. et al. Telemedicine and healthcare disparities: a cohort study in a large healthcare system in New York City during COVID-19. J Am Med Inform Assoc 2021; 28 (01) 33-41
- 27 Grange ES, Neil EJ, Stoffel M. et al. Responding to COVID-19: the UW medicine information technology services experience. Appl Clin Inform 2020; 11 (02) 265-275
- 28 Mehrotra A, Ray K, Brockmeyer DM, Barnett ML, Bender JA. Rapidly converting to “virtual practices”: outpatient care in the era of Covid-19. NEJM Catal 2020; DOI: 10.1056/CAT.20.0091.
- 29 Wisconsin Department of Health Services. COVID-19 (Coronavirus Disease). Accessed December 15, 2020 at: https://www.dhs.wisconsin.gov/covid-19/index.html
- 30 United States Department of Agriculture. Rural-urban continuum codes. Accessed 2020 at: https://www.ers.usda.gov/data-products/rural-urban-continuum-codes/
- 31 Charlson ME, Pompei P, Ales KL, MacKenzie CR. A new method of classifying prognostic comorbidity in longitudinal studies: development and validation. J Chronic Dis 1987; 40 (05) 373-383
- 32 Area Deprivation Index. Accessed 2020 at: https://www.neighborhoodatlas.medicine.wisc.edu
- 33 Storey JD. False discovery rate. Int Encycloped Stat Sci 2011; 1: 504-508
- 34 Kruse CS, Bouffard S, Dougherty M, Parro JS. Telemedicine use in rural Native American communities in the era of the ACA: a systematic literature review. J Med Syst 2016; 40 (06) 145
- 35 Lopez III L, Hart III LH, Katz MH. Racial and ethnic health disparities related to COVID-19. JAMA 2021; 325 (08) 719-720
- 36 Yehia BR, Winegar A, Fogel R. et al. Association of race with mortality among patients hospitalized with coronavirus disease 2019 (COVID-19) at 92 US hospitals. JAMA Netw Open 2020; 3 (08) e2018039-e2018039
- 37 Ramsetty A, Adams C. Impact of the digital divide in the age of COVID-19. J Am Med Inform Assoc 2020; 27 (07) 1147-1148