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The COVID-19 pandemic increased the use of digital tools in health care (eg, patient portal, telemedicine, and web-based scheduling). Studies have shown that older individuals, racial/ethnic minority groups, or populations with lower educational attainment or income have lower rates of using digital health tools. Digitalization of health care may exacerbate already existing access barriers in these populations.
This study evaluated how use of digital tools to asynchronously communicate with clinicians, schedule appointments, and view medical records changed near the beginning of the pandemic.
Using 2020 Health Information National Trends Survey (HINTS) data, we examined internet use and 7 digital health technology use outcomes (electronic communication with a provider, electronic appointment scheduling, electronic test result viewing, patient portal access, portal use to download health records, portal use for patient-provider communication, and portal use to view test results). The HINTS surveyors designated surveys received after March 11, 2020, as postpandemic responses. Using weighted logistic regression, we investigated the impact of the pandemic after adjusting for sociodemographic traits (age, race/ethnicity, income, education, and gender), digital access (having ever used the internet and smartphone/tablet ownership), and health-related factors (insurance coverage, caregiver status, having a regular provider, and chronic diseases). To explore differences in changes in outcomes among key sociodemographic groups, we tested for significant interaction terms between the pandemic variable and race/ethnicity, age, income, and educational attainment.
There were 3865 respondents (1437 prepandemic and 2428 postpandemic). Of the 8 outcomes investigated, the pandemic was only significantly associated with higher odds (adjusted odds ratio 1.99, 95% CI 1.18-3.35) of using electronic communication with a provider. There were significant interactions between the pandemic variable and 2 key sociodemographic traits. Relative to the lowest income group (<US $20,000), the highest income group (≥US $75,000) had increased growth in the odds of ever having used the internet in postpandemic responses. Compared to the most educated group (postbaccalaureates), groups with lower educational attainment (high school graduates and bachelor’s degree) had lower growth in the odds of using electronic communication with a provider in postpandemic responses. However, individuals with less than a high school degree had similar growth to the postbaccalaureate group in using electronic communication with a provider.
Our study did not show a widespread increase in use of digital health tools or increase in disparities in using these tools among less advantaged populations in the early months of the COVID-19 pandemic. Although some advantaged populations reported a greater increase in using the internet or electronic communication with a provider, there were signs that some less advantaged populations also adapted to an increasingly digital health care ecosystem. Future studies are needed to see if these differences remain beyond the initial months of the pandemic.
In response to the COVID-19 public health emergency, many American health centers transitioned to telemedicine almost overnight, with most visits conducted over the phone or video and only a limited number of visits were conducted in person [
Many of these differences in the use of digital health tools stem from structural factors—including the cost of internet access, broadband infrastructure, and digital literacy skills [
Although much of the focus on digital health equity since the start of the COVID-19 pandemic has been on web-based visits (or telemedicine) and increasingly remote patient monitoring tools, digital tools support a variety of other health-related tasks. Digital health technologies have been defined to include “mobile health (mHealth), health information technology, wearable devices, telehealth and telemedicine and personalized medicine” [
The Health Information National Trends Survey (HINTS) is a nationally administered annual survey from the National Cancer Institute, which collects information about health communication, including patients’ use of technology for health care–related tasks outside of web-based visits [
Using HINTS data, we investigated whether disparities increased in the use of digital tools to conduct health care–related tasks after the start of the COVID-19 public health emergency. (Within this paper, we will use the term “disparity” to describe differences between groups.)
We focused on 4 sociodemographic factors previously documented to be associated with disparities in using digital health tools: age, race/ethnicity, education, and income [
Details about the HINTS administration and design are publicly available [
We selected 8 dichotomous (yes/no) outcome variables from questions about having ever used the internet and the use of digital tools for health-related tasks (see
To guide our analysis, we conceptualized the predictors that could impact each of these outcomes. In addition to having the pandemic as a key predictor variable in all models, we identified 3 groups of predictors (sociodemographic traits, digital access, and health-related factors) drawn from prior literature and described below [
The pandemic was a key predictor variable that indicated if the survey response occurred after (survey received after March 11, 2020) or before the COVID-19 pandemic. This designation was made by the HINTS surveyors.
The sociodemographic traits included in the model were age (18-34, 35-49, 50-64, 65-74, and ≥75 years), race/ethnicity (Asian, Black, Hispanic of any race, non-Hispanic White, and other), education (less than a high school degree, high school graduate, some college, bachelor’s degree, and postbaccalaureate), income (<US $20,000, US $20,000-$34,999, US $35,000-$49,999, US $50,000-$74,999, and >US $75,000), and gender (male and female). All predictors were categorical variables. Missing values in the income data were imputed and supplied by the HINTS data set. For the logistic regression models, the reference groups for age, race/ethnicity, education, income, and gender were the following, respectively: aged 18-34 years, non-Hispanic White, postbaccalaureate education, income<US $20,000, and male.
There were 2 dichotomous variables included in this group: owns a tablet or smartphone and having ever used the internet. Having ever used the internet was an outcome in 1 model but was included as a covariate in the other models.
There were 3 dichotomous health care–related variables: functions as a caregiver for another individual, has access to a regular provider, and has insurance. We also included 1 categorical variable: the number of chronic diseases (0, 1, 2, or ≥3) based on self-reported diagnoses of depression, hypertension, diabetes, heart disease, or lung disease, with 0 chronic diseases used as the reference value.
We report descriptive statistics of predictor variables, covariates, and outcomes unweighted. To infer population-level statistics, we report weighted proportions using weights provided by the HINTS data set. Using weight adjusted survey data, we constructed bivariate and multivariable logistic regression models for each of the 8 outcomes. The models for all outcomes used all the predictor and covariate variables listed above; we did not conduct variable selection, since all variables have been shown to impact these outcomes in the literature.
To determine the impact of the pandemic, we focused on the pandemic variable and the interaction terms between the pandemic variable and the 4 sociodemographic traits of interest (race/ethnicity, age, education, and income). The Wald test was used to evaluate the interaction between pandemic status and these 4 sociodemographic traits. Interactions at
All analyses were performed using R statistical software (version 4.1.0; R Foundation for Statistical Computing). To adjust for complex survey design, we used a survey adjustment via the
We used
Of the 3865 survey respondents, 1437 responded before the pandemic indicator and 2428 responded post the pandemic.
Traits of included participants (N=3865).
Trait, variablea | 2020, prepandemic (n=1437), n (weighted %b) | 2020, postpandemic (n=2428), n (weighted %b) | 2020, total, n (weighted %b) | ||
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Female | 804 (47.71) | 1400 (51.59) | 2204 (50.22) |
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18-34 | 151 (19.21) | 333 (28.89) | 484 (25.47) |
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35-49 | 212 (21.93) | 491 (26.37) | 703 (24.80) |
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50-64 | 433 (31.88) | 709 (24.25) | 1142 (26.95) |
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65-74 | 361 (13.81) | 508 (10.42) | 869 (11.62) |
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≥75 | 237 (9.74) | 303 (7.61) | 540 (8.36) |
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Asian | 51 (3.84) | 110 (5.37) | 161 (4.83) |
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Black | 135 (7.70) | 346 (11.75) | 481 (10.32) |
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Hispanic | 170 (11.86) | 426 (17.84) | 596 (15.73) |
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White | 904 (7.37) | 1229 (7.31) | 2133 (7.34) |
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Other | 49 (4.25) | 70 (2.45) | 119 (3.09) |
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<20,000 | 258 (15.13) | 506 (17.38) | 764 (16.58) |
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20,000-34,999 | 189 (11.02) | 302 (11.73) | 491 (11.48) |
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35,000-49,999 | 180 (11.74) | 336 (12.74) | 516 (12.39) |
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50,000-74,999 | 257 (17.44) | 392 (17.98) | 649 (17.79) |
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≥75,000 | 547 (43.66) | 880 (39.67) | 1427 (41.08) |
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Less than a high school degree | 90 (7.01) | 183 (8.25) | 273 (7.81) |
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High school graduate | 251 (19.69) | 454 (23.09) | 705 (21.89) |
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Some college | 415 (38.61) | 666 (37.82) | 1081 (38.10) |
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Bachelor’s degree | 358 (19.97) | 621 (17.32) | 979 (18.26) |
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Postbaccalaureate | 285 (12.05) | 399 (10.70) | 684 (11.18) |
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Having ever used the internet | 1187 (87.09) | 1961 (85.09) | 3148 (85.80) | |
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Owns a tablet or smartphone | 1210 (87.63) | 2029 (88.58) | 3239 (88.25) | |
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Has insurance | 1352 (90.43) | 2252 (89.42) | 3604 (89.78) | |
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Has a regular provider | 1046 (69.60) | 1582 (56.91) | 2628 (61.39) | |
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Is a caregiver | 198 (14.53) | 378 (16.66) | 576 (15.91) | |
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0 | 506 (42.58) | 922 (45.45) | 1428 (44.44) |
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1 | 356 (20.76) | 550 (18.98) | 906 (19.61) |
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2 | 335 (20.58) | 560 (21.88) | 895 (21.42) |
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≥3 | 229 (15.71) | 362 (12.79) | 591 (13.82) |
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Electronic communication with a provider | 659 (48.22) | 1141 (45.61) | 1800 (46.53) | |
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Electronic means to make Appointments | 680 (48.78) | 1211 (48.73) | 1891 (48.75) | |
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Electronic means to view test results | 634 (45.67) | 995 (39.42) | 1629 (41.63) | |
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Having ever accessed their patient portal | 605 (41.18) | 948 (38.57) | 1553 (39.49) | |
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Patient portal to message a providerc | 350 (57.85) | 570 (60.13) | 920 (59.24) | |
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Patient portal to view test resultsc | 530 (87.60) | 819 (86.39) | 1349 (86.53) | |
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Patient portal to download health recordsc | 171 (28.26) | 284 (29.95) | 455 (29.30) |
aEach variable had less than 10% missing data.
bThe percentage rates were calculated using weighted data to represent the US population.
cPatient portal tasks were only asked of those who had accessed the patient portal. Therefore, the proportions are reported only out of those that reported having ever accessed their patient portal.
All bivariate models and multivariable analysis are shown in
After accounting for other variables, the pandemic variable was only significant for using electronic means to communicate with a provider. Postpandemic respondents had higher odds (adjusted odds ratio [aOR] 1.99, 95% CI 1.18-3.35;
The interaction between the pandemic variable and 4 sociodemographic variables (age, race/ethnicity, education, and income) was only significant for 2 outcomes: having ever used the internet and electronic communication with a provider (see
For the outcome related to internet use, there was a significant interaction between the pandemic variable and income (see
The use of electronic communication with a provider was notably increased in the highest educational attainment group (postbaccalaureate). As seen in
Odds of having ever used the internet before (pre) and after (post) the pandemic among income groups (in US $).
Odds of using electronic communication with a provider among different education groups before (pre) and after (post) the pandemic. Bacc: baccalaureate; HS: high school; Postbacc: postbaccalaureate.
Overall, we found mixed results on how the pandemic affected internet use, the use of digital tools to communicate with clinicians or schedule appointments, and patient portal use. For most of the outcomes, there were no significant differences before and after the pandemic in the early months of the pandemic and no significant changes in disparities in the uptake of digital health tools.
Consistent with prior literature, we did find that populations with a history of digital exclusion (older, lower income, lower educational attainment, and racial/ethnic minority groups) continue to have lower odds of using the internet and a variety of digital health tools. These disparities, particularly in telemedicine use, have been repeatedly documented since the start of the pandemic [
With this focus in mind, we did find that immediately after the pandemic, after adjusting for other factors, there were increased odds overall in the use of electronic communication with a provider. One reason for this finding may be that the policies enacted by the Centers for Medicare and Medicaid Services to incentivize the use of telehealth [
Our study had mixed findings on how differences in the uptake of these digital tools were immediately impacted by the public health emergency. Immediately after the start of the pandemic, the highest income group (≥US $75,000) had a greater rate of growth in having ever used the internet than the lowest income group (<US $20,000), suggesting a widening of the disparity between income groups. This finding may reflect that higher income earners were more likely to have jobs that could be performed remotely through the internet than lower income groups [
In contrast to the findings among income groups, there was some suggestion of the gaps closing between groups with different levels of educational attainment. Both the lowest educational attainment respondents (less than high school) and highest educational attainment respondents (postbaccalaureate) had similar rates of growth in the use of electronic communication tools (eg, smartphones, internet, and email) with their doctors. However, the bachelor’s degree holders and high school graduates had decreases in the odds of using electronic communication with their doctors after the pandemic, which were significantly different from the most educated group. Together, these findings suggest that although some disparities in the use of electronic communication with clinicians were closing, others were widening. It is worth highlighting that the most vulnerable group from an educational attainment perspective (less than high school education) had a larger growth in using electronic communication tools with their clinician relative to most other respondents, which defies a frequent pattern of innovations disseminating the most slowly to the most disadvantaged.
Given the rapid move of health care to telehealth settings [
We believe it important to specifically highlight that we found no changes in any of the patient portal tasks, despite patient portals being the primary digital health tool that has been adopted by health systems to increase patient engagement and care accessibility. Many health care systems already had patient portals in place and tried to use their patient portals to address health care needs during the pandemic; however, studies have repeatedly showed the significant barriers to using a patient portal, including the lack of technical skills, usability, privacy concerns, and the lack of physician encouragement [
This study has several limitations. Since the 2020 HINTS responses were collected in a 5-month period between February and June 2020, the results only reflect the early impact of the pandemic. In addition, most outcome questions inquired about electronic communication over the last 12 months, hence outcomes may be less sensitive to the immediate behavior changes resulting from the pandemic. For patient portal–related outcomes, the sample size was limited to respondents who had accessed their patient portal; therefore, there may have been inadequate power to detect statistically significant changes in patient portal use. Although the survey weights are designed to extrapolate these data to the American population, owing to the limited sample size in some subgroups, there may not be enough variability to accurately evaluate the outcomes. For example, all Asian individuals in the postpandemic group reported the use of the patient portal for viewing a test result (
Our study finds that early within the pandemic, there was not widespread increase in the use of digital health tools or in disparities in the use of digital health tools. Although these data were only from the first 3 months of the pandemic, we did find an increase in odds of using electronic communication with a provider after the pandemic and some mixed results on whether preexisting inequities between groups in the use of digital health increased. Despite health care systems’ reliance on patient portals to increase patient access and engagement, we did not see changes in the use of patient portals during the early stages of the pandemic. These early data from the pandemic support the need to explicitly study a wide range of digital health care–related tasks. Changes in the use of 1 digital task may not translate to other health care–related digital tasks.
Survey questions and outcome variables.
Unadjusted bivariate models.
Multivariable models.
adjusted odds ratio
Health Information National Trends Survey
We thank Charles McCulloch for his advice on this study. Research reported in this publication was supported by the National Heart Lung and Blood Institute of the National Institutes of Health (NIH) under award K12HL138046 and K23HL157750 (ECK); the National Center for Advancing Translational Sciences of the NIH under award KL2TR001870 (ECK); and the National Cancer of the NIH under award K24CA212294 (US). The content is solely the responsibility of the authors and do not necessarily represent the official views of the NIH.
BZ contributed to the conception and design of the study, analysis of the data, and drafting the work. NAR contributed to the design of the study, interpretation of the data, and critically revising the work. AW contributed to the analysis of the data and drafting of the work. US contributed to the interpretation of the data and critically revising the work. ECK contributed to the conception and design of the study, interpretation of the data, and critically revising the work. All authors approved the final version of the manuscript.
US has received funding from AppliedVR, InquisitHealth, RecoverX, and Somnology (research contracts). She has also received funding from The Doctors Company (gift), the American Medical Association's Equity and Innovation Advisory Group (honoraria), and Hopelab (grant).