Telehealth access,willingness,and barriers during the COVID-19 pandemic among a nationally representative diverse sample of U.S. adults with and without chronic health conditions

Abstract

Introduction

During the COVID-19 pandemic, telehealth services represented a critical tool in maintaining continuity and access to care for adults in the USA. However, despite improvements in access and utilization during the pandemic, disparities in telehealth utilization have persisted. It is unclear what role access and willingness to use telehealth play in telehealth disparities.

Methods

We used data from the nationally representative COVID-19's Unequal Racial Burden (CURB) survey, an online survey conducted between December 2020 and February 2021, n = 5500. Multivariable Poisson regression was used to estimate the prevalence of perceived telehealth access and willingness to use telehealth services among adults with and without chronic conditions.

Results

Overall, 60.1% of adults with and 38.7% of adults without chronic conditions reported having access to telehealth. After adjustment, adults with chronic conditions were more likely to report telehealth access (adjusted prevalence ratio [aPR] = 1.35, 95% confidence interval [CI] = 1.21–1.50). Most adults with and without chronic conditions reported being willing to use telehealth services (85.1% and 79.8%, respectively), and no significant differences in willingness were observed across chronic condition status (aPR = 1.03, 95% CI = 0.95–1.13). Perceived telehealth access appeared to be a predictor of telehealth willingness in both groups (chronic conditions: aPR = 1.22, 95% CI = 0.97–1.54; no chronic conditions: aPR = 1.37, 95% CI = 1.22–1.54). The prevalence of perceived barriers to telehealth was low, with the majority reporting no barriers (chronic conditions = 51.4%; no chronic conditions = 61.4%).

Discussion

Perceived access to telehealth was associated with telehealth willingness. Investing in approaches that increase telehealth accessibility and awareness is needed to improve access to telehealth for adults with and without chronic conditions.

Keywords

Telehealth healthcare access chronic condition COVID-19 racial-ethnic disparities

Introduction

With federal regulators expanding telehealth policies and increasing insurance reimbursement for telehealth services during the COVID-19 pandemic, telehealth became an integral part of ensuring continued access to healthcare,^1–3 especially for patients in areas with fewer healthcare providers and who travel longer distances to in-person healthcare facilities.^1,4–6 However, both before^7,8 and during the first year of the pandemic,^9,10 studies have shown that disparities in telehealth access and utilization exist (and persist) across race-ethnicity, rurality, and socioeconomic status.^7–10 For example, using data from our COVID-19's Unequal Racial Burden (CURB) survey, we previously found that while rural adults were just as willing to use telehealth as non-rural adults, they were less likely to report having access to telehealth services in early 2021.¹¹ While marginalized communities, especially those with chronic conditions,¹² would benefit from access and utilization of telehealth services, questions remain about how perceived access and willingness to use telehealth services were impacted during the pandemic.

Chronic conditions are the leading cause of disability and account for 7 in 10 U.S. deaths,¹³ and racial-ethnic disparities in the prevalence of chronic conditions have been well documented.^14–17 Telehealth can maintain continuity of healthcare,¹⁸ which is important for effective chronic care management to improve quality of life.¹⁹ Studies during the pandemic have found that adults with chronic conditions are willing to learn and use telehealth for their medical care,^20,21 and at least one study has found those with chronic conditions were more likely to use telehealth.²²

Yet to date, most of the telehealth research during the pandemic has focused on utilization,^23–26 as opposed to access or willingness to use telehealth services. Telehealth utilization studies during the pandemic have also largely been restricted to Medicare beneficiaries^24,26 or to Black/African American, Latino, and White adults,^24–26 limiting generalizability to other populations. A comprehensive assessment of telehealth access and willingness using nationally representative cohorts that include American Indian/Alaska Native, Asian, Native Hawaiian/Pacific Islander, and multiracial communities are still needed. Thus, the purpose of this study was to (1) assess differences in perceived access and willingness to use telehealth services, (2) assess racial-ethnic differences in perceived access and willingness to use telehealth, and (3) identify barriers to telehealth, among a nationally representative diverse cohort of adults with and without chronic conditions living in the USA.

Methods

Study design and participants

This study used data collected from the CURB online survey administered by YouGov, a consumer research firm that uses a proprietary, opt-in survey panel comprised of over 1.8 million U.S. residents to conduct nationally representative online surveys. Panel members are recruited through a variety of methods to ensure diversity, and then matched to a theoretical target sample for each survey.

For this study, the target sample was drawn from the 2018 American Community Survey 1-year sample data and included 500 American Indian/Alaska Native, 1000 Asian, 1000 Black/African American, 1000 Latino (including 500 Spanish-speaking), 500 Native Hawaiian/Pacific Islander, 1000 White, and 500 multiracial adults aged ≥18 years (n = 5500 total). English- and Spanish-speaking status among Latino participants was assigned based on survey language preference; 87.6% of Latino participants who took the survey in Spanish also self-reported poor English-speaking skills. Members who completed the online survey were then proximity-matched and weighed to obtain a nationally representative sample within each racial-ethnic group (e.g. Asian participants were weighted to represent all Asian adults living in the USA).

Surveys were completed between December 8, 2020, and February 17, 2021 and were offered in both English and Spanish (Latino participants only). More information about the CURB survey development and sampling design have been published elsewhere.^27,28 The National Institutes of Health Office of Institutional Review Board Operations determined that this study does not qualify as human subjects research because data were de-identified (IRB# 000166).

Perceived access, willingness, and barriers for telehealth

To measure perceived access to telehealth, participants were asked: “Does the place where you usually go for care offer telehealth (virtual) visits?” Response choices included: no, yes, and don’t know. To measure willingness, participants were asked: “Would you be willing to meet with a doctor or nurse by smartphone, telephone, tablet, or computer?” with response options being yes with video, yes without video, and no. Responses were dichotomized into yes (with or without video) versus no for analyses.

We measured perceived telehealth barriers by asking the question: “Do you have any of the following concerns about telehealth or virtual visits? (Check all that apply).” Response options included: (1) I do not have an internet connection; (2) My smartphone has a limited data plan; (3) I may have problems with the quality of the audio or video; (4) I may have problems using the application for the virtual visit; (5) I worry that I may not feel comfortable using the new technology; (6) I do not have a telephone, smartphone, tablet, or computer that will work with the application; (7) I may not have a private place to do a telehealth visit; (8) I have vision or hearing problems that may make it hard to have a telehealth visit; (9) They may not speak my language or have an interpreter available; (10) Other (write-in response); and (11) I have no concerns about using telehealth.

Chronic conditions

Participants were also asked: “Has a medical doctor ever told you that you have any of the following conditions? (check all that apply).” Response choices included: (1) chronic obstructive pulmonary disease (COPD); (2) heart conditions (such as heart failure, coronary artery disease, or cardiomyopathies); (3) type 2 diabetes; (4) chronic kidney disease or dialysis; (5) sickle cell disease; (6) cancer in the past year; (7) immunocompromised state (weakened immune system) from solid organ transplant; (8) obesity; (9) COVID-19; and (10) none of the above. Individuals who selected any of the above conditions besides COVID-19 and obesity were classified as having a chronic condition.

Statistical analyses

Descriptive statistics and chi-square tests were used to compare perceived access to telehealth, willingness to use telehealth, and perceived telehealth barriers across race-ethnicity and chronic condition status. Multivariable Poisson regression was used to estimate differences in the perceived access to telehealth (yes vs. no/don’t know) and willingness to use telehealth between adults with and without chronic conditions. Multivariable Poisson regression was also used to assess racial-ethnic differences in perceived access and willingness to use telehealth, among individuals with and without chronic conditions. Separate models were run for individuals with and without chronic conditions. All models were adjusted for age, gender, urbanicity, census division, health insurance, self-reported physical health, education, family annual income, and perceived access to telehealth (telehealth willingness models only).

We also performed several sensitivity analyses. First, among those with at least one chronic condition, we used multivariable Poisson regression, adjusting for the covariates mentioned above, to assess whether specific chronic conditions were associated with perceived access or willingness. A single model was used to estimate the independent associations. Second, among those with at least one chronic condition, we modeled the association between the number of chronic conditions (multimorbidity vs. single comorbidity) on perceived access and willingness to use telehealth.

All analyses were conducted in SAS version 9.4 (SAS Institute, Inc., Cary, NC) and weighted to obtain nationally representative estimates within each racial-ethnic group. Robust variance estimates were used to account for survey weighting in all models.

Results

Overall, 1152 participants (21.0%) reported having at least one chronic condition. The prevalence of chronic conditions, stratified by sociodemographics, is reported in Supplemental Table 1. American Indian/Alaska Native adults (32.0%) had a higher prevalence of chronic conditions, compared to all other racial-ethnic groups (15.2%–23.5%), P < 0.0001. Increasing age, lower education, and poorer self-reported physical health were also associated with higher prevalence of chronic conditions, P < 0.0001 for all (Supplemental Table 1). No other meaningful differences in the prevalence of chronic conditions across sociodemographics were observed.

Perceived access to telehealth

Adults with chronic conditions were significantly more likely to report having access to telehealth, compared to those without chronic conditions (60.1% vs. 38.7%, P < 0.0001; Figure 1). Almost half of all adults without chronic conditions were unsure about their telehealth access (44.3%). This trend was also consistent across race ethnicity.

Figure 1.

Prevalence of perceived access to telehealth among adults (a) with chronic conditions and (b) without chronic conditions, overall and stratified by race ethnicity, weighted to be nationally representative within racial-ethnic groups.

After adjusting for sociodemographics, adults with chronic conditions were still more likely to report having access to telehealth services, compared to adults without any chronic conditions (adjusted prevalence ratio [aPR] = 1.35, 95% confidence interval [CI] = 1.21–1.50, Supplemental Table 2). When stratified by chronic condition status, no racial-ethnic differences in perceived telehealth access were observed among adults with chronic conditions; however, among adults without chronic conditions, Spanish-speaking Latino adults (aPR = 1.40, 95% CI = 1.11–1.75) were more likely to report access to telehealth services, compared to White adults (Table 1).

Table 1.

Adjusted associations between sociodemographics and the prevalence of perceived access and willingness to use telehealth among adults with and without chronic condition status, weighted to be nationally representative within each racial-ethnic group.

	Perceived access to telehealth		Willing to use telehealth
	Chronic condition(s) aPR (95% CI)^a	No Chronic Conditions aPR (95% CI)^a	Chronic condition(s) aPR (95% CI)^a	No chronic conditions aPR (95% CI)^a
Race ethnicity
American Indian/ Alaskan Native	1.11 (0.80, 1.55)	1.19 (0.96, 1.48)	0.86 (0.65, 1.13)	1.02 (0.87, 1.20)
Asian	1.05 (0.76, 1.45)	0.86 (0.72, 1.03)	0.92 (0.71, 1.21)	1.07 (0.94, 1.21)
Black/African American	1.05 (0.76, 1.44)	1.07 (0.89, 1.28)	1.03 (0.80, 1.32)	1.07 (0.94, 1.22)
Latino
English-speaking	1.02 (0.70, 1.49)	1.08 (0.87, 1.34)	0.96 (0.71, 1.30)	1.06 (0.91, 1.24)
Spanish-speaking	1.15 (0.76, 1.74)	1.40 (1.11, 1.75)	0.95 (0.67, 1.34)	1.15 (0.98, 1.36)
Native Hawaiian/ Pacific Islander	0.94 (0.65, 1.37)	1.12 (0.90, 1.40)	0.98 (0.73, 1.31)	1.06 (0.91, 1.25)
White	1.00 (ref)	1.00 (ref)	1.00 (ref)	1.00 (ref)
Multiracial	0.96 (0.64, 1.43)	0.90 (0.73, 1.12)	0.90 (0.65, 1.24)	1.09 (0.94, 1.27)
Age group
18–29	1.00 (0.69, 1.44)	0.89 (0.76, 1.04)	0.98 (0.74, 1.31)	1.00 (0.90, 1.12)
30–39	1.15 (0.83, 1.60)	0.97 (0.83, 1.14)	0.95 (0.73, 1.25)	1.03 (0.92, 1.15)
40–49	1.00 (ref)	1.00 (ref)	1.00 (ref)	1.00 (ref)
50–59	1.25 (0.95, 1.65)	1.00 (0.83, 1.19)	0.98 (0.78, 1.22)	1.03 (0.91, 1.17)
60–69	1.02 (0.76, 1.35)	0.98 (0.81, 1.19)	0.91 (0.72, 1.15)	1.02 (0.88, 1.17)
≥70	0.93 (0.67, 1.29)	1.04 (0.80, 1.36)	0.95 (0.74, 1.23)	0.85 (0.69, 1.05)
Gender
Men	1.00 (ref)	1.00 (ref)	1.00 (ref)	1.00 (ref)
Women	1.05 (0.88, 1.26)	1.21 (1.09, 1.35)	1.04 (0.90, 1.20)	1.00 (0.93, 1.08)
Transgender or non-binary^b	1.05 (0.57, 1.93)	1.28 (0.91, 1.81)	1.11 (0.68, 1.81)	0.96 (0.75, 1.23)
Highest education
Less than high school	1.07 (0.77, 1.49)	0.84 (0.66, 1.06)	0.92 (0.70, 1.23)	0.86 (0.73, 1.01)
High school graduate/ GED	0.90 (0.70, 1.16)	0.78 (0.66, 0.91)	0.95 (0.78, 1.17)	0.92 (0.83, 1.03)
Some college/ vocational	0.94 (0.74, 1.21)	0.94 (0.82, 1.08)	1.03 (0.84, 1.26)	0.96 (0.87, 1.06)
College graduate or more^c	1.00 (ref)	1.00 (ref)	1.00 (ref)	1.00 (ref)
Family annual income^d
<$20,000	0.72 (0.52, 1.00)	0.66 (0.54, 0.81)	0.95 (0.72, 1.25)	0.90 (0.78, 1.03)
$20,000–$59,999	0.82 (0.63, 1.09)	0.82 (0.70, 0.96)	0.95 (0.75, 1.20)	0.96 (0.86, 1.08)
$60,000–$99,999	0.87 (0.65, 1.15)	0.99 (0.85, 1.16)	1.00 (0.79, 1.28)	1.00 (0.89, 1.12)
≥$100,000	1.00 (ref)	1.00 (ref)	1.00 (ref)	1.00 (ref)
Health insurance
Any private insurance	1.00 (ref)	1.00 (ref)	1.00 (ref)	1.00 (ref)
Public insurance only	1.01 (0.83, 1.23)	0.98 (0.86, 1.11)	1.03 (0.87, 1.21)	1.03 (0.94, 1.13)
Uninsured	0.83 (0.60, 1.14)	0.52 (0.44, 0.62)	0.92 (0.71, 1.20)	0.94 (0.85, 1.05)
Poor/fair physical health	0.97 (0.81, 1.15)	1.19 (1.04, 1.35)	1.00 (0.87, 1.16)	1.06 (0.97, 1.16)
Rurality
Big city	1.28 (1.03, 1.61)	1.07 (0.93, 1.22)	0.96 (0.79, 1.16)	1.01 (0.92, 1.12)
Smaller city	0.99 (0.76, 1.29)	1.01 (0.87, 1.18)	1.00 (0.81, 1.22)	1.02 (0.92, 1.14)
Suburban area	1.00 (ref)	1.00 (ref)	1.00 (ref)	1.00 (ref)
Small town	1.04 (0.77, 1.41)	0.94 (0.78, 1.13)	0.99 (0.77, 1.26)	1.00 (0.88, 1.14)
Rural area	0.95 (0.69, 1.33)	0.96 (0.79, 1.17)	0.95 (0.73, 1.23)	0.96 (0.84, 1.10)
Census division
New England	0.98 (0.55, 1.74)	0.88 (0.63, 1.24)	0.88 (0.54, 1.42)	1.02 (0.81, 1.28)
Middle Atlantic	0.95 (0.67, 1.34)	1.11 (0.92, 1.35)	0.98 (0.74, 1.30)	1.01 (0.88, 1.16)
East North Central	1.24 (0.90, 1.72)	0.98 (0.79, 1.20)	0.96 (0.73, 1.27)	1.00 (0.86, 1.15)
West North Central	0.88 (0.54, 1.43)	0.91 (0.67, 1.22)	0.88 (0.59, 1.30)	1.12 (0.92, 1.36)
South Atlantic	1.00 (ref)	1.00 (ref)	1.00 (ref)	1.00 (ref)
East South Central	0.86 (0.55, 1.33)	0.82 (0.60, 1.12)	0.88 (0.63, 1.25)	1.00 (0.81, 1.22)
West South Central	1.13 (0.82, 1.56)	1.01 (0.84, 1.23)	1.08 (0.83, 1.40)	1.05 (0.92, 1.19)
Mountain	1.16 (0.83, 1.64)	0.98 (0.80, 1.21)	0.94 (0.70, 1.26)	1.00 (0.87, 1.16)
Pacific	1.04 (0.79, 1.38)	1.10 (0.93, 1.30)	1.08 (0.87, 1.35)	1.06 (0.95, 1.20)
Perceived access to telehealth
Yes	-	-	1.22 (0.97, 1.54)	1.37 (1.22, 1.54)
Don’t know	-	-	1.00 (0.78, 1.29)	1.25 (1.11, 1.40)
No	-	-	1.00 (ref)	1.00 (ref)

Abbreviations: aPR, adjusted prevalence ratio; CI, confidence interval.

Adjusted for age, gender, urbanicity, household income, census division, insurance status, self-reported physical health, and education level; willingness to use telehealth was adjusted for access to telehealth services.

Includes individuals who identified as non-binary, gender fluid, gender queer, other, and none.

Includes bachelor's degree, master's degree, and doctoral or postgraduate education.

Collected by YouGov at enrollment into panel and updated every 6 months.

When examining specific chronic conditions, only adults with chronic kidney disease (aPR = 1.34, 95% CI = 1.03–1.76), compared to those without chronic kidney disease, were more likely to report having access to telehealth (Table 2). Having more than one chronic condition, compared to only one, was also associated with having increased perceived access to telehealth (aPR = 1.32, 95% CI = 1.08–1.61).

Table 2.

Adjusted associations between specific chronic conditions and having multiple chronic conditions, perceived access to telehealth, and willingness to use telehealth services.

	Perceived Access to Telehealth	Willing to Use Telehealth
	aPR (95% CI)^a	aPR (95% CI)^a
Chronic conditions^b
COPD	1.18 (0.94, 1.48)	1.00 (0.83, 1.21)
Heart conditions	1.18 (0.95, 1.46)	1.04 (0.87, 1.25)
Type 2 diabetes	1.18 (0.96, 1.44)	0.97 (0.81, 1.16)
Chronic kidney disease	1.34 (1.03, 1.76)	0.96 (0.76, 1.22)
Sickle cell disease	1.08 (0.68, 1.72)	0.92 (0.62, 1.37)
Cancer in the past year	1.11 (0.79, 1.55)	0.98 (0.75, 1.29)
Immunocompromised	1.08 (0.73, 1.59)	1.04 (0.75, 1.43)
Multimorbidity	1.32 (1.08, 1.61)	1.01 (0.85, 1.20)

Abbreviations: aPR, adjusted prevalence ratio; CI, confidence interval; COPD, chronic obstructive pulmonary disease.

Analyses were weighted to be nationally representative within racial-ethnic groups. All analyses were restricted to those with at least one chronic condition.

Compared to adults without that chronic condition; all analyses were restricted to adults with at least one chronic condition.

Willingness to use telehealth

Smaller differences were observed for telehealth willingness, with most adults with and without chronic conditions being willing to use telehealth services (85.1% and 79.8%, respectively, Figure 2). Similar trends were observed across race ethnicity. After adjustment, no significant differences in telehealth willingness were observed between those with and without chronic conditions (aPR = 1.03, 95% CI = 0.95–1.13, Supplemental Table 2). Additionally, no differences in willingness to use telehealth were observed across type of chronic condition or multimorbidity status (Table 2).

Figure 2.

Prevalence of willingness to use telehealth among adults with chronic conditions and without chronic conditions, stratified by race ethnicity, weighted to be nationally representative within racial-ethnic groups.

Among both adults with and without chronic conditions, adults who reported having access to telehealth, compared to no access, appeared more willing to use telehealth (with chronic conditions: aPR = 1.22, 95% CI = 0.97–1.54; without chronic conditions: aPR = 1.37, 95% CI = 1.22–1.54), although estimates were imprecise (Table 1). Among adults without chronic conditions, those who were unsure about their access, compared to those having no access, were also more willing to use telehealth (aPR = 1.25, 95% CI = 1.11–1.40).

Perceived barriers to telehealth

The prevalence to each perceived telehealth barrier was relatively low among both adults with and without chronic conditions (Figure 3). Among adults with chronic conditions, the three most common were: (1) problems with using the telehealth application (15.6%); (2) feeling uncomfortable with technology (13.6%); and (3) problems with audio/video quality (12.8%). Overall, 51.4% of adults with chronic conditions and 61.4% of adults without chronic conditions reported no barriers to telehealth use.

Figure 3.

Prevalence of perceived telehealth barriers, stratified by chronic condition status. Results were weighted to be nationally representative within racial-ethnic groups. Overall, 51.4% of adults with chronic conditions and 61.4% of adults without chronic conditions reported no perceived barriers (data not shown).

Similar trends in perceived barriers to telehealth were observed across racial-ethnic groups, with a few notable exceptions (Supplemental Tables 3 and 4). First, Spanish-speaking Latino adults (with and without chronic conditions) were more likely to report having language concerns and/or having no available interpreter (chronic conditions: 10.4% vs. 0%–4.0%; no chronic conditions: 18.3% vs. 0.7–2.8%) compared to other racial-ethnic groups, P < 0.0001 for both. While the overall prevalence of perceived barriers was relatively low, only 33.1% of Spanish-speaking Latino adults and 37.3% of American Indian/Alaska Native adults with chronic conditions reported having no concerns (other racial-ethnic groups: 45.7%–60.2%, P = 0.0009 and P = 0.0001, respectively, Supplemental Table 3). Among those without chronic conditions, multiracial adults were more likely to report problems with audio/video quality (21.1% vs. 10.2–15.9% P < 0.0001) and not having a private place for the telehealth visit (14.2% vs. 3.5–10.2%, P < 0.0001, Supplemental Table 4).

Discussion

Using data from a nationally representative, diverse sample of adults living in the USA, we found that adults with chronic conditions were more likely to report having access to telehealth services compared to adults without chronic conditions; however, minimal differences were seen in the willingness to use telehealth services between these two groups. Perceived access to telehealth services was also one of the strongest predictors of willingness to use telehealth. No racial-ethnic disparities in perceived access or willingness to use telehealth were observed, although perceived access was associated with higher household income, private insurance, and higher levels of education. While perceived barriers to telehealth were relatively low among most racial-ethnic groups, only two in three American Indian/Alaska Native and Spanish-speaking Latino adults reported at least one barrier to using telehealth. Taken together, these results suggest that efforts should be directed toward increasing access and raising awareness of telehealth access to addressing racial-ethnic disparities in telehealth utilization.

Compared to other studies that have found racial-ethnic minorities and other marginalized communities to be less likely to utilize telehealth services during the pandemic,^29–31 our study did not find disparities in reported telehealth access. Historically, racial-ethnic minorities and other marginalized communities have experienced disparities in healthcare access and utilization, including telemedicine and telehealth services.^32,33 For instance, when primary care transitioned to entirely remote during the first year of the COVID-19 pandemic, there was evidence that non-Hispanic White patients, English-speaking patients, and patients with private insurance were overrepresented in telehealth patient visits for chronic disease management.^12,34 Other studies have also found that racial-ethnic minorities and other marginalized communities were less likely to utilize telehealth services during the pandemic.^29–31 Moving forward, evaluating telehealth utilization and addressing disparities remains important for the continuity of care for patients with chronic conditions.³⁵

Although willingness to use telehealth seemed to increase from the pandemic,^36,37 privacy concerns, out-of-pocket costs, and the degree of trust in the healthcare system have still been reported as barriers to access and use.^38,39 While we found that roughly half of adults reported no barriers to telehealth, adopting strategies to improve telehealth platforms are still needed. For instance, educational campaigns that address digital literacy and incorporate technological solutions (e.g. ensuring health professionals receive appropriate training to deliver telehealth services and providing tablets that will facilitate the use of telehealth)^1,3,40,41 that improve access may be fundamental to ensuring equitably patient-centered telehealth care for populations vulnerable to communication barriers.

Disparities in perceived telehealth barriers, even after the expansion of telehealth services during the pandemic, have also been described.^42–44 In our study, we found that Spanish-speaking Latino and American Indian/Alaska Native adults were more likely to report perceived barriers to telehealth services, especially those with chronic conditions. Studies among Latino adults have consistently found that they face unique challenges to accessing technology-enabled resources due to language barriers and gaps in digital literacy.^32,45,46 At least one study prior to the pandemic also found that Latino adults experience more barriers to accessing or using technologies for health purposes.⁴⁷ Interestingly, we found that Spanish-speaking Latinos were more likely to report having access to telehealth, which may be linked to a high satisfaction of telehealth service as well as cultural perceptions of healthcare and physicians within the Latino community.⁴⁸ It is also possible the telehealth systems being used have built-in language interpretation which can better assist those with limited English proficiency,⁴⁹ allowing Spanish-speaking Latino adults to overcome potential language barriers with visual cues and reduced concerns with audio quality, consistent with literature showing better communication outcomes with video interpreters over phone interpreters.^49,50 This may also explain why relatively few Spanish-speaking Latino adults reported language concerns as a barrier to telehealth (10.4% and 18.3% among those with and without chronic conditions).

There is a lack of research in telehealth access, willingness, and barriers within American Indian/Alaska Native populations, with most prior studies finding that they have lower access to broadband internet services.^51,52 Similarly, we found that American Indian/Alaska Native adults were more likely to report having barriers to telehealth. They also appeared to be less willing to use telehealth compared to other racial-ethnic groups. This differs from a study that found 93% of American Indian adults reported being willing to use telehealth⁵¹ and a report that found telehealth use among American Indian/Alaskan beneficiaries to be higher than White and Black adults.⁵³ Given the limited literature on telehealth and potential barriers among the American Indian/Alaska Native population, more research is needed to assess the health challenges that may exist within this community and if telehealth could potentially reduce adverse health outcomes.

This study is not without limitations. First, the CURB survey was conducted online and individuals with low broadband access, no access to technology, and/or have lower digital literacy would be less inclined to participate. This potential selection bias may mean that we are underestimating the prevalence of telehealth barriers. Second, because the survey was administered in English and Spanish (Latino participants only), non-English-speaking adults and non-Spanish-speaking Latinos were more likely to be excluded. This may have also created a language barrier, particularly for non-English-speaking individuals in other racial-ethnic categories, limiting the generalizability of these results. This may have particularly impacted our Asian adult cohort, as 12.3% reported limited English proficiency, compared to the national estimates of 31.9% of Asian adults having a limited English proficiency.⁵⁴ Access to telehealth was self-reported, and we did not have information to whether their usual healthcare location offered telehealth services, and if so, what services were available. Lastly, this study was conducted in late 2020, and changes in telehealth access, willingness, and barriers may have occurred since the survey was conducted. Despite these limitations, this study is one of the first to conduct a nationally representative and comprehensive assessment of perceived access, willingness, and barriers to telehealth after its expansion during the COVID-19 pandemic among a diverse population of adults with and without chronic conditions.

In conclusion, this study suggests that willingness to use telehealth services is dependent on its accessibility. Given that willingness to use telehealth services is strong across all sociodemographic groups, targeting barriers, increasing awareness, and increasing access to telehealth are critical to promote telehealth use and reduce racial-ethnic disparities. Given that telehealth continues to be an important tool to provide timely and accessible healthcare services to adults in the USA, investing into strategies to improve telehealth services will maintain high-quality care and could mitigate health disparities that arise due to the COVID-19 pandemic, especially among adults with chronic health conditions.

Supplemental Material

sj-docx-1-jtt-10.1177_1357633X231199522 - Supplemental material for Telehealth access, willingness, and barriers during the COVID-19 pandemic among a nationally representative diverse sample of U.S. adults with and without chronic health conditions

Supplemental material, sj-docx-1-jtt-10.1177_1357633X231199522 for Telehealth access, willingness, and barriers during the COVID-19 pandemic among a nationally representative diverse sample of U.S. adults with and without chronic health conditions by Randy Le , Izabelle Mendez, Stephanie A Ponce, Alexis Green, Sherine El-Toukhy, Anna M Nápoles and Paula D Strassle in Journal of Telemedicine and Telecare

Footnotes

Acknowledgements

The datasets analyzed during the current study are available from the corresponding author upon reasonable request.

Declaration of conflicting interests

The authors declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The authors disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This work was supported by the Division of Intramural Research, National Institute on Minority Health and Health Disparities, National Institutes of Health.

ORCID iDs

Randy Le

Paula D. Strassle

Supplemental material

Supplemental material for this article is available online.

References

Bustamante

Silver

Martinez

, et al. Equity gaps in telehealth use to manage chronic conditions during COVID-19. UCLA Latino Policy & Politics Institute University of California, Los Angeles, 2023.

Volk

Palanker

O’Brien

, et al. States’ actions to expand telemedicine access during COVID-19 and future policy considerations. The Commonwealth Fund, 2021.

Smith

Thomas

Snoswell

, et al. Telehealth for global emergencies: implications for coronavirus disease 2019 (COVID-19). J Telemed Telecare 2020; 26: 309–313.

Dorsey

Topol

. State of telehealth. N Engl J Med 2016; 375: 154–161.

Powell

Stone

Hollander

. Patient and health system experience with implementation of an enterprise-wide telehealth scheduled video visit program: mixed-methods study. JMIR Med Inform 2018; 6: 10.

Hollander

Carr

. Virtually perfect? Telemedicine for COVID-19. N Engl J Med 2020; 382: 1679–1681.

Perzynski

Roach

Shick

, et al. Patient portals and broadband internet inequality. J Am Med Inform Assoc 2017; 24: 927–932.

Roberts

Mehrotra

. Assessment of disparities in digital access among Medicare beneficiaries and implications for telemedicine. JAMA Intern Med 2020; 180: 1386–1389.

Adepoju

Chae

Ojinnaka

, et al. Utilization gaps during the COVID-19 pandemic: racial and ethnic disparities in telemedicine uptake in federally qualified health center clinics. J Gen Intern Med 2022; 37: 1191–1197.

10.

Zhang

Shi

Han

, et al. Disparities in telehealth utilization during the COVID-19 pandemic: findings from a nationally representative survey in the United States. J Telemed Telecare 2021. Online ahead of print. doi:10.1177/1357633x211051677

11.

El-Toukhy

Quintero

, et al. Disparities in telehealth access, not willingness to use services, likely explain rural telehealth disparities. J Rural Health 2023; 39: 617–624.

12.

Nouri

Khoong

Lyles

, et al. Addressing equity in telemedicine for chronic disease management during the COVID-19 pandemic. NEJM Catal Innov Care Deliv 2020; 1. doi:https://doi.org/10.1056/CAT.20.0123

13.

Partnership to Fight Chronic Disease Disease. Almanac of Chronic Disease. 2008. http://www.fightchronicdisease.org/sites/default/files/docs/2009AlmanacofChronicDisease_updated81009.pdf

14.

Davis

Penha

Mbowe

, et al. Prevalence of single and multiple leading causes of death by race/ethnicity among people aged 60 to 70 years, 2017. https://www.cdc.gov/pcd/issues/2017/16_0241.htm.

15.

Quiñones

Botoseneanu

Markwardt

, et al. Racial/ethnic differences in multimorbidity development and chronic disease accumulation for middle-aged adults. PLoS One 2019; 14: e0218462.

16.

Boersma

Black

Ward

. Prevalence of multiple chronic conditions among US adults, 2018. Prev Chronic Dis 2020; 17: E106.

17.

Hacker

Briss

Richardson

, et al. COVID-19 and chronic disease: the impact now and in the future. 2021.

18.

Georgeff

. Digital technologies and chronic disease management. Aust Fam Physician. 2014; 43: 842–846.

19.

Hamar

Wells

Gandy

, et al. The impact of a proactive chronic care management program on hospital admission rates in a German health insurance society. Popul Health Manag 2010; 13: 339–345.

20.

Haque

MMA

Jahan

Khair

, et al. Perceptions about telemedicine among populations with chronic diseases amid COVID-19: data from a cross-sectional survey. Int J Environ Res Public Health 2022; 19: 4250.

21.

Lan

Chen

. Telehealth care system for chronic disease management of middle-aged and older adults in remote areas. Health Informatics J 2022; 28: 14604582221141835.

22.

Blavin

Hill

Smith

, et al. How adults with chronic health conditions experience telehealth. Urban Institute, 2023. https://www.urban.org/research/publication/how-adults-chronic-health-conditions-experience-telehealth

23.

Harju

Neufeld

. Telehealth utilization during the COVID-19 pandemic: a preliminary selective review. Telemed Rep 2022; 3: 38–47.

24.

Park

Silverman

, et al. Accessibility and utilisation of telehealth services among older adults during COVID-19 pandemic in the United States. Health Soc Care Community 2022; 30: e2657–e2669.

25.

Almandoz

Xie

Schellinger

, et al. Telehealth utilization among multi-ethnic patients with obesity during the COVID-19 pandemic. J Telemed Telecare 2023; 29: 530–539.

26.

Hamadi

Zhao

Haley

, et al. Medicare and telehealth: the impact of COVID-19 pandemic. J Eval Clin Pract 2022; 28: 43–48.

27.

Strassle

Stewart

Quintero

, et al. COVID-19-related discrimination among racial/ethnic minorities and other marginalized communities in the United States. Am J Public Health 2022; 112: 453–466.

28.

Nápoles

Stewart

Strassle

, et al. Racial/ethnic disparities in intent to obtain a COVID-19 vaccine: a nationally representative United States survey. Prev Med Rep 2021; 24: 101653.

29.

Demeke

Merali

Marks

, et al. Trends in use of telehealth among health centers during the COVID-19 pandemic – United States, June 26-November 6, 2020. MMWR Morb Mortal Wkly Rep 2021; 70: 240–244.

30.

Eberly

Kallan

Julien

, et al. Patient characteristics associated with telemedicine access for primary and specialty ambulatory care during the COVID-19 pandemic. JAMA Netw Open 2020; 3: e2031640.

31.

Koonin

Hoots

Tsang

, et al. Trends in the use of telehealth during the emergence of the COVID-19 pandemic – United States, January-March 2020. MMWR Morb Mortal Wkly Rep 2020; 69: 1595–1599.

32.

Anaya

Hernandez

, et al. Meeting them where they are on the web: addressing structural barriers for Latinos in telehealth care. J Am Med Inform Assoc 2021; 28: 2301–2305.

33.

Sarkar

Karter

Liu

, et al. Social disparities in internet patient portal use in diabetes: evidence that the digital divide extends beyond access. J Am Med Inform Assoc 2011; 18: 318–321.

34.

Darrat

Tam

Boulis

, et al. Socioeconomic disparities in patient use of telehealth during the coronavirus disease 2019 surge. JAMA Otolaryngol–Head Neck Surg 2021; 147: 287–295.

35.

Udegbe

Clapp

Bryant

. Disparities from bedside to “webside”: barriers to achieving equity in telemedicine in obstetrics. AJOG Glob Rep 2023; 3: 100159.

36.

Fischer

Predmore

Roth

, et al. Use of and willingness to use video telehealth through the COVID-19 pandemic. Health Aff 2022; 41: 1645–1651.

37.

Predmore

Roth

Breslau

, et al. Assessment of patient preferences for telehealth in post-COVID-19 pandemic health care. JAMA Network Open 2021; 4: e2136405.

38.

Orrange

Patel

Mack

, et al. Patient satisfaction and trust in telemedicine during the COVID-19 pandemic: retrospective observational study. JMIR Hum Factors 2021; 8: e28589.

39.

Kato-Lin

Thelen

. Privacy concerns and continued use intention of telemedicine during COVID-19. Telemed J E Health 2022; 28: 1440–1448.

40.

Thomas

Haydon

Mehrotra

, et al. Building on the momentum: sustaining telehealth beyond COVID-19. J Telemed Telecare 2022; 28: 301–308.

41.

Gallegos-Rejas

Thomas

Kelly

, et al. A multi-stakeholder approach is needed to reduce the digital divide and encourage equitable access to telehealth. J Telemed Telecare 2023; 29: 73–78.

42.

Naimark

Elwood

McCobb

, et al. Barriers to accessing video-based telehealth appointments at a community veterinary clinic during the COVID-19 pandemic. Front Vet Sci 2022; 9: 878220.

43.

Chen

Amaize

Barath

. Evaluating telehealth adoption and related barriers among hospitals located in rural and urban areas. J Rural Health 2021; 37: 801–811.

44.

Chang

Lai

Gupta

, et al. Rapid transition to telehealth and the digital divide: implications for primary care access and equity in a post-COVID era. Milbank Q 2021; 99: 340–368.

45.

Agate

. Unlocking the power of telehealth: increasing access and services in underserved, urban areas. Harvard Journal of Hispanic Policy 2017; 29: 85–96.

46.

Rodriguez

Saadi

Schwamm

, et al. Disparities in telehealth use among California patients with limited English proficiency. Health Aff 2021; 40: 487–495.

47.

Mitchell

Chebli

Ruggiero

, et al. The digital divide in health-related technology use: the significance of race/ethnicity. Gerontologist 2019; 59: 6–14.

48.

Payvandi

Parsons

Bourgeois

, et al. Inpatient telehealth experience of patients with limited English proficiency: cross-sectional survey and semistructured interview study. JMIR Form Res 2022; 6: e34354.

49.

Samuels-Kalow

Jaffe

Zachrison

. Digital disparities: designing telemedicine systems with a health equity aim. Emerg Med J 2021; 38: 474–476.

50.

Lion

Brown

Ebel

, 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: 1117–1125.

51.

Pruitt

Chapin

Eakin

, et al. Telehealth during COVID-19: suicide prevention and American Indian communities in Montana. Telemed J E Health 2022; 28: 325–333.

52.

Graves

Mackelprang

Amiri

, et al. Barriers to telemedicine implementation in Southwest tribal communities during COVID-19. J Rural Health 2021; 37: 239–241.

53.

Samson

Tarazi

Turrini

, et al. Medicare beneficiaries’ use of telehealth in 2020: trends by beneficiary characteristics and location. ASPE: U.S. Department of Health and Human Services. 2021. https://aspe.hhs.gov/sites/default/files/documents/a1d5d810fe3433e18b192be42dbf2351/medicare-telehealth-report.pdf

54.

AAPI Data. State of Asian Americans, Native Hawaiians, and Pacific Islanders in the United States. AAPI Data, 2022. https://aapidata.com/wp-content/uploads/2022/06/State-AANHPIs-National-June2022.pdf

Supplementary Material

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