The COVID-19 pandemic led to a rapid and widespread transition to synchronous real-time audio-based and/or video-based telemedicine.1 While telemedicine use was infrequent among Medicaid beneficiaries and other vulnerable patient populations before the pandemic, its use had grown over time before the outbreak of COVID-19.2–4 Earlier work has identified disparities in access to telemedicine services resulting in lower use for racial and ethnic minorities, rural populations, and Medicaid beneficiaries.3,5–7 However, whether this increased reliance on telemedicine has exacerbated or alleviated existing disparities in telemedicine access among vulnerable populations is not yet known8–11 despite concern over longstanding inequalities in social, economic, and geographic resources.12–14 For example, internet connectivity varies widely depending on where patients live and the type of internet coverage plan they can access.7,15 Low-income households and households with a Black head of household are substantially less likely to have access to a broadband connection, potentially limiting access to telemedicine services.16 Broadband penetration tends to decrease as areas become more rural making it more difficult to access telemedicine, but utilization of telemedicine services may still be lower in urban areas because of greater availability of providers.17,18

Our objective in this paper was to characterize both the changes in outpatient evaluation and management (E&M) service use associated with the COVID-19 pandemic and the uptake of telemedicine services for Medicaid beneficiaries in the state of Louisiana. Outpatient E&M services exhibit a relatively high degree of substitutability between in-person and telemedicine modalities and have thus been the focus of prior research on COVID-19 and telemedicine use.19–22 We examined these outcomes separately by race, ethnicity, and rurality to highlight pre-COVID-19 disparities in telemedicine use within Louisiana’s Medicaid population and to determine how the pandemic impacted existing disparities. While recent work has quantified the transition to telemedicine during the COVID-19 pandemic in other settings, our focus on telemedicine uptake and equity in the Medicaid program is novel and Louisiana serves as an instructive setting for analyzing disparities among vulnerable populations.12,14,23–31 Louisiana ranks 49th among states in real median household income.32 Nearly one-third of Louisianans received their health insurance coverage through Medicaid in 2020, placing Louisiana second nationally in Medicaid population share.33 Geographic and racial disparities in access to health care were also pronounced in Louisiana before the pandemic.34

Louisiana’s first case of COVID-19 was confirmed on March 9, 2020, and over the next several weeks, Louisiana experienced the fastest growth rate of COVID-19 infections of any state in the United States.35 Before the outbreak of COVID-19 in Louisiana, Medicaid providers were paid to deliver telemedicine services at parity with in-person services.36 However, by early April 2020, the Louisiana Department of Health (LDH) further encouraged telemedicine use by directing providers to deliver services using telemedicine when appropriate, relaxing restrictions on the provision of certain telemedicine services to Medicaid beneficiaries (including substance use disorder treatment services and physical, occupational, and speech therapy services), and promulgating a series of memos providing billing guidance for Medicaid telemedicine services.35 By June 1, 2020, Louisiana Medicaid expanded coverage to audio-only services and further clarified telemedicine billing guidance to providers [eg, requiring modifier “95” and place of service “02” codes to be appended to Current Procedural Terminology (CPT) codes and prohibiting the use of non-face-to-face E&M CPT codes 99441-3].37 These modifications to Louisiana Medicaid telemedicine policy, similar to what several other states enacted during the pandemic, will remain in place at least through the end of the Public Health Emergency.38

Policymakers lack a solid understanding of the consequences (both intended and unintended) of the rapid transition to telemedicine during the early stages of the COVID-19 pandemic. Our study adds to a growing evidence base examining the association between COVID-19 and telemedicine adoption on health equity and disparities in access to care. We caution that we were unable to account for all potential confounders that could explain distinct patterns of service utilization and telemedicine uptake by race, ethnicity, and rurality among Louisiana Medicaid beneficiaries during the COVID-19 pandemic. However, documenting differential care patterns is a critical first step toward further assessments of the pandemic’s equity effects. Socially disadvantaged populations including racial and ethnic minorities and people living in low-income households face barriers to accessing health care services, especially through modalities of care that rely on technology.39–43 Barriers include, but are not limited to, lack of appropriate technology, poor digital literacy, non-English language preference, and lack of reliable internet coverage.9,44 These issues are compounded in Louisiana which ranks 46th of the 50th states in the share of households with a broadband internet subscription.16

METHODOLOGY Data

We used Medicaid claims data from the state of Louisiana for outpatient E&M services delivered between January 2018 and December 2020. Eligibility for Louisiana Medicaid included Modified Adjusted Gross Income (MAGI)-based criteria (eg, adults, pregnant women, children under age 19) and non-MAGI-based criteria (eg, aged, disabled, etc.). We restricted our sample to claims for outpatient E&M services to focus on care with a high degree of substitutability between in-person and telemedicine delivery. We further restricted our sample to include a balanced panel of individuals continuously enrolled in Louisiana Medicaid over the sample period to avoid issues with compositional changes resulting from increased enrollment because of COVID-19 and excluded dual-eligibles as we lack access to Medicare claims. Outpatient E&M services were identified using the CPT codes: 99201–99205 (“Office or other outpatient visits for the evaluation and management of a new patient,” ranging from 10 to 60 min of visit time), 99211–99215 (“Office or other outpatient visits for the evaluation and management of an established patient,” ranging from 5 to 40 min of visit time), and 99241–99245 (“Office consultation for a new or established patient,” ranging from straightforward to high complexity).

We examined changes in telemedicine use by race, ethnicity, and rurality. We created three mutually exclusive race and ethnicity categories that included non-Hispanic Black, non-Hispanic White, and Hispanic. Approximately 95% of the Medicaid beneficiaries in our sample identified as 1 of these 3 race and ethnicity groups. Our rural/urban designations consisted of either rural or urban county of residence as defined by the National Center for Health Statistics where categories including large central metro, large fringe metro, medium metro, and small metro were classified as urban and micropolitan and noncore areas were classified as rural.45 Our final analytic sample included claims for 850,821 Medicaid beneficiaries, 53.1% of whom were non-Hispanic Black, 7.5% were Hispanic, and 39.4% were non-Hispanic White. 31.2% of our sample was comprised of beneficiaries living in rural counties and 68.8% lived in urban counties.

Finally, we calculated the share of beneficiaries in the various subgroups in each month of our sample with at least one of the following diagnoses: cancer, chronic kidney disease, chronic obstructive pulmonary disease, heart failure, and stroke. We used these measures of disease burden as controls in our regression models (described below).

Outcomes

We aggregated daily E&M claims to the monthly level to minimize random variation in the data and to provide a clear picture of trends in service use over our sample period. We then divided monthly E&M claims by the number of Medicaid beneficiaries in each subgroup of our balanced panel and multiplied the ratio by 1000 using claim modifiers (GT, GQ, 95) or a place of service code (02) to designate a telemedicine modality for service delivery. We followed this procedure for each separate race, ethnicity, and urban/rural category and then calculated differences in average monthly E&M service use across each combination of race/ethnicity and geography to estimate COVID-related changes in service use between the various subgroups (eg, Hispanic-to-non-Hispanic Black, non-Hispanic White-to-non-Hispanic Black, non-Hispanic White-to-Hispanic, and rural-to-urban).

Statistical Analysis

We used interrupted time series (ITS) models to estimate outpatient E&M service use and telemedicine use across race/ethnicity groups and rural/urban county of residence. All models included a linear monthly trend term that captured the pre-COVID-19 trend in total outpatient E&M and telemedicine E&M claim volume. Models also included indicators for April 2020 and July 2020, the peaks of the first and second waves of COVID-19 infections in Louisiana, and for December 2020 after those peaks had subsided. We included interactions between the monthly time trend and the April 2020 and July 2020 indicators to estimate trend changes in the dependent variables associated with the first 2 waves of COVID-19 infections in Louisiana. Lastly, we included month indicators to account for any seasonality in E&M service use and differences in the measure of disease burden described above to control for potential confounding because of health status changes between groups. See the Supplementary Appendix, Supplemental Digital Content 1, https://links.lww.com/MLR/C544 for the formal specification of the ITS regression model and detailed description.

We estimated separate models for each combination of race/ethnicity and for rural beneficiaries compared with urban beneficiaries. We conducted Cumby-Huizinga tests for autocorrelation, which led to the inclusion of a 2-period lag term in the models, and estimated all models using ordinary least squares. Our study followed the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) reporting guidelines and was classified as exempt research by the Tulane Institutional Review Board.

RESULTS

Before examining differences in E&M telemedicine use by race, ethnicity, and rurality/urbanicity, we first summarize overall patterns of outpatient E&M care before and during the pandemic (Supplemental Fig. 1, Supplemental Digital Content 2, https://links.lww.com/MLR/C545 and Supplemental Table 1, Supplemental Digital Content 3, https://links.lww.com/MLR/C546). Total E&M service use fell dramatically during April 2020, coinciding with the first wave of COVID-19 infections in Louisiana and following the implementation of various mitigation strategies including stay-at-home orders. Hispanic beneficiaries experienced the largest relative decline in service use (53% reduction from baseline average) while relative declines were similar for those living in rural and urban areas (43.9% and 44.0% reductions, respectively). Outpatient E&M service use rose in May and June 2020 before falling again during the second wave of COVID-19 infections in July 2020. Service use rebounded through December 2020, but remained below prepandemic levels for all of the subgroups we studied. Outpatient E&M telemedicine use increased dramatically relative to baseline levels for all subgroups in our analysis during April 2020, however the substitution toward telemedicine was not sufficient to offset diminished in-person E&M claim volume. While E&M telemedicine use remained elevated compared with prepandemic use rates through December 2020, there was no substantial uptick in telemedicine use during the second COVID-19 wave in July 2019.

Figure 1A plots differences in total outpatient E&M claims (both in-person and telemedicine) per 1000 Medicaid beneficiaries by race and ethnicity. E&M service use rates were higher for non-Hispanic White beneficiaries compared with non-Hispanic Black or Hispanic beneficiaries in the pre-COVID period (non-Hispanic White-to-non-Hispanic Black ratio=105.1 claims per 1000 beneficiaries, 95% CI: 96.7–113.6; non-Hispanic White-to-Hispanic ratio=75.4 claims per 1000 beneficiaries, 95% CI: 73.2–77.7) and Hispanic beneficiaries exhibited higher use rates than non-Hispanic Black beneficiaries (Hispanic-to-non-Hispanic Black ratio=29.7 claims per 1000 beneficiaries, 95% CI: 20.3–39.1). Figure 1B plots differences in outpatient telemedicine E&M claims per 1000 Medicaid beneficiaries by race and ethnicity. The largest post-COVID gap in telemedicine use emerged between non-Hispanic White and Hispanic beneficiaries. Non-Hispanic White beneficiaries also increased their telemedicine use relative to non-Hispanic Black beneficiaries during the pandemic.

FIGURE 1:

Differences in evaluation and management (E&M) service use by race/ethnicity and rurality/urbanicity before and during the COVID-19 pandemic. This figure plots monthly differences in the ratio of E&M claims per 1000 beneficiaries enrolled in Louisiana Medicaid by race, ethnicity, and geography. Figures 1A and C include all outpatient E&M claims, while Figures 1B, D include only claims for outpatient E&M services delivered through telemedicine. The vertical line represents March 2020, the month when stay-at-home orders were introduced in Louisiana and the Louisiana Department of Health expanded access to telemedicine services for Medicaid beneficiaries.

Estimates in Table 1 from our corresponding ITS models confirm the graphical evidence indicating growing gaps in telemedicine use between non-Hispanic White beneficiaries and Hispanic and non-Hispanic Black beneficiaries. Prior the pandemic, non-Hispanic Whites averaged 1.5 more monthly E&M telemedicine claims per 1000 beneficiaries compared with Hispanic (95% CI: 0.4–2.6) and non-Hispanic Black beneficiaries (95% CI: 0.6–2.3). By April 2020, the gap in E&M telemedicine use between non-Hispanic Whites and Hispanics had increased to 42.3 claims per 1000 beneficiaries (95% CI: 39.1–45.5) and to 24.9 claims per 1000 beneficiaries (95% CI: 22.3–27.4) between non-Hispanic Whites and non-Hispanic Black beneficiaries. Hispanic and non-Hispanic Black beneficiaries had similar rates of E&M telemedicine use before the pandemic, but telemedicine use among Hispanic beneficiaries grew at a slower rate than among non-Hispanic Black beneficiaries in April 2020 and a gap of 17.4 claims per 1000 beneficiaries (95% CI: 16.7–18.1) emerged. Gaps in telemedicine use between non-Hispanic White and non-Hispanic Black beneficiaries and between non-Hispanic White and Hispanic beneficiaries narrowed between April 2020 and December 2020, but remained sizeable.

TABLE 1 - ITS Estimates of E&M Use by Race/Ethnicity and Geography (1) (2) (3) (4) (5) (6) Outcomes Baseline trend (1/2018–2/2020) April 2020 change from baseline April 2020 to June 2020 trend July 2020 change from baseline July 2020 to November 2020 trend December 2020 change from baseline Race and Ethnicity—all E&M claims  Hispanic/Black ratio 29.7 [20.3, 39.1] −52.8 [−69.5, −36.0] 2.7 [1.7, 3.7] −42.3 [−59.2, −25.3] 4.0 [2.3, 5.7] −43.7 [−60.4, −27.0]  White/Black ratio 105.1 [96.7, 113.6] −58.2 [−74.9, −41.5] 10.2 [8.6, 11.7] −66.6 [−86.4, −46.9] 10.2 [6.1, 14.2] −35.8 [−53.2, −18.5]  White/Hispanic ratio 75.4 [73.2, 77.7] −5.4 [−12.0, 1.2] 7.5 [4.9, 10.0] −24.4 [−35.9, −12.8] 6.1 [2.8, 9.4] 7.9 [0.1, 15.6] Race and ethnicity—telemedicine claims  Hispanic/Black ratio −0.0 [−0.3, 0.2] −17.4 [−18.1, −16.7] 3.1 [2.7, 3.4] −11.9 [−14.4, −9.3] 0.4 [−0.6, 1.3] −11.7 [−13.0, −10.4]  White/Black ratio 1.5 [0.6, 2.3] 24.9 [22.3, 27.4] −7.9 [−8.9, −6.9] 16.8 [13.4, 20.2] 0.7 [−0.1, 1.5] 20.3 [17.7, 23.0]  White/Hispanic ratio 1.5 [0.4, 2.6] 42.3 [39.1, 45.5] −11.0 [−12.4, −9.5] 28.7 [22.9, 34.4] 0.4 [−1.4, 2.1] 32.0 [28.2, 35.8] Geography—all E&M claims  Rural/urban ratio 59.8 [54.0, 65.6] −25.8 [−37.7, −13.8] 6.0 [5.4, 6.6] −26.1 [−41.1, −11.0] 7.3 [3.3, 11.2] −5.2 [−17.8, 7.5] Geography—telemedicine claims  Rural/urban ratio 0.4 [0.3, 0.5] 5.3 [4.0, 6.6] −3.6 [−4.8, −2.5] 5.8 [4.7, 7.0] −0.6 [−1.0, −0.3] 4.9 [4.0, 5.8]

Regression estimates are from an interrupted time series specification that includes a monthly time trend, indicators for April 2020, July 2020, and December 2020, interactions between the trend term and the April and July 2020 indicators, calendar month indicators, and differences in the monthly share of beneficiaries in each subgroup with a diagnosis of cancer, chronic kidney disease, chronic obstructive pulmonary disease, heart failure, and stroke. Column 1 reports differences in baseline average monthly use ratios per 1000 beneficiaries from January 2018 through February 2020 by race/ethnicity and geography. Column 2 reports the coefficient estimate for the April 2020 indicator. Column 3 reports the sum of the monthly time trend and the coefficient estimate of the interaction between the trend term and the April 2020 indicator. Column 4 reports the difference between the estimate for July 2020 utilization and the baseline average. Column 5 reports the sum of the monthly time trend and the coefficient estimate of the interaction between the trend term and the July 2020 indicator. Column 6 reports the difference between the estimate for December 2020 utilization and the baseline average. Data for each regression model are comprised of 36-month-year level observations. Cumby-Huizinga tests for autocorrelation led to the inclusion of a maximum lag of order 2.

E&M indicates evaluation and management; ITS, interrupted time series.

Figures 1C, D plot differences in total outpatient E&M service use and outpatient telemedicine E&M service use by urbanicity/rurality. Rural beneficiaries used outpatient E&M services at a slightly higher rate than urban beneficiaries before the pandemic (59.8 more monthly claims, on average, per 1000 beneficiaries, 95% CI: 54.0–65.6). Outpatient E&M service use for rural beneficiaries fell slightly relative to urban beneficiaries during the peak waves of the pandemic, but the difference had returned to prepandemic levels by December 2020. Outpatient E&M telemedicine use was similar for rural and urban beneficiaries before the COVID-19 pandemic, grew somewhat for rural beneficiaries compared with urban beneficiaries during the March 2020 and July 2020 COVID-19 waves [5.3 claims per 1000 beneficiaries (95% CI: 4.0–6.6) and 5.8 claims per 1000 beneficiaries (95% CI: 4.7–7.0)], and stayed slightly elevated for rural beneficiaries relative to urban beneficiaries through December 2020 [4.9 claims per 1000 (95% CI: 4.0–5.8)].

DISCUSSION

Outpatient E&M service use for Louisiana Medicaid beneficiaries fell precipitously during the first 2 waves of COVID-19 infections in Louisiana—April 2020 and July 2020. Telemedicine use increased across all racial and ethnic groups and for both rural and urban beneficiaries compared with prepandemic averages. These findings are consistent with prior work that has also documented reduced in-person visits and increased reliance on telemedicine during the pandemic for the commercially insured and Medicare populations.1,19,23,31

E&M service use remained below baseline levels for all race and ethnicity groups in our study through December 2020. However, Hispanic beneficiaries experienced the largest relative decline in service use from baseline levels to December 2020 (107.6 claims per 1000 beneficiaries or 31% relative to baseline, 95% CI: 64.4–150.8). This finding can be partially explained by the relatively low levels of telemedicine take-up among Hispanic beneficiaries compared with non-Hispanic Black and non-Hispanic White beneficiaries in Louisiana Medicaid. Several studies have documented lower telemedicine use for Hispanics during the COVID-19 pandemic in settings that were not specific to the Medicaid population.10,46–48 Whether this finding is due primarily to difficulties accessing Spanish-language telemedicine services is not yet clear.49

We found that non-Hispanic Black Louisiana Medicaid beneficiaries saw smaller relative declines in E&M service use after the onset of the pandemic compared with Hispanic and non-Hispanic White beneficiaries. This finding is attributable to both an increased use of telemedicine among non-Hispanic Black beneficiaries and a smaller reduction in in-person service use compared with Hispanic and non-Hispanic White beneficiaries. Like other studies, we found that non-Hispanic Black individuals had lower absolute rates of telemedicine use than White individuals throughout the pandemic,49 however, relative increases in telemedicine use from baseline levels were larger for non-Hispanic Black beneficiaries in Louisiana Medicaid than for non-Hispanic White beneficiaries and remained so through December 2020. Non-Hispanic Black beneficiaries experienced a 28-fold increase in outpatient E&M telemedicine use by December 2020 (53.0 claims per 1000 beneficiaries above baseline level of 1.9, 95% CI: 46.7–59.2) compared with a 22-fold increase for non-Hispanic White beneficiaries (73.3 claims per 1000 beneficiaries above baseline level of 3.3, 95% CI: 64.4–82.1).

We also explored differences in outpatient E&M service use and telemedicine visits for rural and urban Louisiana Medicaid beneficiaries. Baseline outpatient E&M service use was slightly higher for rural beneficiaries, however, both groups experienced large reductions in outpatient E&M service use during the first 2 waves of peak COVID-19 infections in Louisiana. Differences in outpatient E&M telemedicine use between rural and urban Louisiana Medicaid beneficiaries were negligible before the pandemic’s onset (0.4 claims per 1000 beneficiaries, 95% CI: 0.3–0.5). Rural beneficiaries were somewhat more likely to adopt outpatient E&M telemedicine services during the pandemic and continued to experience slightly higher use rates by December 2020 (4.9 additional claims per 1000 beneficiaries, 95% CI: 4.0–5.8). Our findings of greater reliance on telemedicine during the COVID-19 pandemic for individuals living in a rural setting contrast with findings from other large-scale studies and should be the subject of further investigation.11,23,31

Limitations

Our analysis has several limitations. Identification in our ITS models relies on trend breaks in the outcome measures, which could be driven by confounders unrelated to the pandemic.50 However, we believe bias from this type of confounding to be minimal as the sharp decreases in service use we observed corresponded to the timing of physical distancing and other mitigation orders in Louisiana and the 2 peak waves of COVID-19 infections in April and July 2020. We also analyzed data from a single state, and while results from Louisiana are of particular interest for reasons previously noted, we cannot be sure that our findings would hold for Medicaid beneficiaries in other states. This is especially true of our findings regarding outpatient E&M service use for Hispanic beneficiaries who make up a relatively small share of beneficiaries in Louisiana Medicaid. We lack visibility into the mechanisms driving differences in telemedicine uptake for the Louisiana Medicaid population. As we noted above, documented disparities in access to telemedicine services across race, ethnicity, and urban/rural geographies exist among low-income populations. We also observed such disparities in telemedicine use by race and ethnicity in Louisiana Medicaid before the COVID-19 pandemic. However, despite these disparities, gaps in outpatient E&M service use between non-Hispanic White and non-Hispanic Black beneficiaries had narrowed by December 2020. Finally, examining changes in health outcomes associated with pandemic-related changes in service utilization was beyond the scope of this analysis.

CONCLUSION

The COVID-19 pandemic had a significant impact on the use of both in-person and telemedicine outpatient E&M services among Louisiana Medicaid beneficiaries. Telemedicine use, uncommon before COVID-19, increased dramatically during the pandemic for all subgroups that we studied, though the extent to which telemedicine alleviated reductions in in-person outpatient E&M service use varied. Non-Hispanic Black and non-Hispanic White Medicaid beneficiaries in Louisiana exhibited higher rates of telemedicine adoption during the pandemic and had higher rates of sustained use by December 2020 than Hispanic beneficiaries. Those living in rural counties experienced increased telemedicine use for E&M services compared with those in urban counties, though differences between these 2 groups were minimal. Our findings imply that continued access to telemedicine could help to bridge gaps in care and promote health equity, though attention should be focused on take-up among those of Hispanic ethnicity. From a policy perspective, recent expansions in telemedicine coverage could build on new 21st Century Cures Act investments in health information technology that aim to support expanded access to telemedicine. Future research should assess whether the increase in telemedicine use among underserved populations has reduced access barriers in the long-term and whether COVID-related disruptions in service use have impacted disparities in health outcomes.

REFERENCES 1. Patel SY, Mehrotra A, Huskamp HA, et al. Trends in outpatient care delivery and telemedicine during the COVID-19 pandemic in the US. JAMA Intern Med. 2021;181:388–391. 2. Mehrotra A, Huskamp HA, Souza J, et al. Rapid growth in mental health telemedicine use among rural Medicare beneficiaries, wide variation across states. Health Aff. 2017;36:909–917. 3. Barnett ML, Ray KN, Souza J, et al. Trends in telemedicine use in a large commercially insured population, 2005-2017. JAMA. 2018;320:2147–2149. 4. Yu J, Mink PJ, Huckfeldt PJ, et al. Population-level estimates of telemedicine service provision using an all-payer claims database. Health Aff. 2018;37:1931–1939. 5. Park J, Erikson C, Han X, et al. Are state telehealth policies associated with the use of telehealth services among underserved populations? Health Aff. 2018;37:2060–2068. 6. Creedon TB, Schrader KE, O’Brien PL, et al. Rural-nonrural differences in telemedicine use for mental and substance use disorders among Medicaid beneficiaries. Psychiatr Serv. 2020;71:756–764. 7. Yoon H, Jang Y, Vaughan PW, et al. Older adults’ internet use for health information: digital divide by race/ethnicity and socioeconomic status. J Appl Gerontol. 2020;39:105–110. 8. Ortega G, Rodriguez JA, Maurer LR, et al. Telemedicine, COVID-19, and disparities: policy implications. Health Policy Technol. 2020;9:368–371. 9. Nouri S, Khoong EC, Lyles CR, et al. Addressing equity in telemedicine for chronic disease management during the Covid-19 pandemic. NEJM Catalyst Innov Care Deliv. 2020;1. 10. 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:e2031640–e2031640. 11. Poeran J, Cho LD, Wilson L, et al. Pre-existing disparities and potential implications for the rapid expansion of telemedicine in response to the coronavirus disease 2019 pandemic. Med Care. 2021;59:694–698. 12. Chang JE, Lai AY, Gupta A, 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. 13. Samuels-Kalow M, Jaffe T, Zachrison K. Digital disparities: designing telemedicine systems with a health equity aim. Emerg Med J. 2021;38:474–476. 14. Pierce RP, Stevermer JJ. Disparities in use of telehealth at the onset of the COVID-19 public health emergency. J Telemed Telecare. 2020:1357633X20963893. 15. 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;27:1816–1818. 16. Ryan CL, Lewis JM. Computer and internet use in the United States: 2015. US Department of Commerce, Economics and Statistics Administration, US Census Bureau; 2017. 17. Drake C, Zhang Y, Chaiyachati KH, et al. The limitations of poor broadband internet access for telemedicine use in rural America: an observational study. Ann Intern Med. 2019;171:382–384. 18. Wilcock AD, Rose S, Busch AB, et al. Association between broadband internet availability and telemedicine use. JAMA Intern Med. 2019;179:1580–1582. 19. Kim Y, Gordon A, Rowerdink K, et al. The impact of the COVID-19 pandemic on health care utilization among insured individuals with common chronic conditions. Med Care. 2022;60:673. 20. Nieman CL, Oh ES. Connecting with older adults via telemedicine. Am Coll Physicians. 2020;173:831–832. 21. Medicare Payment Advisory Commission (MEDPAC). Report to the Congress: Medicare Payment Policy, 2021. Available at: https://www.medpac.gov/wp-content/uploads/import_data/scrape_files/docs/default-source/reports/mar21_medpac_report_to_the_congress_sec.pdf. Accessed August 29, 2022. 22. Harju A, Neufeld J. Telehealth utilization during the COVID-19 pandemic: a preliminary selective review. Telemed Rep. 2022;3:38–47. 23. Patel SY, Mehrotra A, Huskamp HA, et al. Variation in telemedicine use and outpatient care during the COVID-19 pandemic in the United States. Health Aff. 2021;40:349–358. 24. Patel SY, Rose S, Barnett ML, et al. Community factors associated with telemedicine use during the COVID-19 pandemic. JAMA Netw Open. 2021;4:e2110330–e2110330. 25. Gmunder KN, Ruiz JW, Franceschi D, et al. Demographics associated with US healthcare disparities are exacerbated by the telemedicine surge during the COVID-19 pandemic. J Telemed Telecare. 2021:1357633X211025939. [Online ahead of print]. 26. Cortez C, Mansour O, Qato DM, et al. Changes in short-term, long-term, and preventive care delivery in US office-based and telemedicine visits during the COVID-19 pandemic. JAMA Health Forum. 2021;2:e211529. 27. Alexander GC, Tajanlangit M, Heyward J, et al. Use and content of primary care office-based vs telemedicine care visits during the COVID-19 pandemic in the US. JAMA Netw Open. 2020;3:e2021476. 28. Baum A, Kaboli PJ, Schwartz MD. Reduced in-person and increased telehealth outpatient visits during the COVID-19 pandemic. Ann Intern Med. 2021;174:129–131. 29. Darrat I, Tam S, Boulis M, et al. Socioeconomic disparities in patient use of telehealth during the coronavirus disease 2019 surge. JAMA Otolaryngol. 2021;147:287–295. 30. Roghani A, Panahi S. Does telemedicine reduce health disparities? Longitudinal evidence during the COVID-19 pandemic in the US. medRxiv. 2021. 31. Cantor JH, McBain RK, Pera MF, et al. Who is (and is not) receiving telemedicine care during the covid-19 pandemic. Am J Prev Med. 2021;61:434–438. 32. Federal Reserve Bank of St. Louis. Real Median Household Income by State, Annual. Available at: https://fred.stlouisfed.org/release/tables?eid=259515&rid=249. Accessed August 29, 2022. 34. Kaiser Family Foundation. The Louisiana Health Care Landscape. 2016. Available at: https://www.kff.org/health-reform/fact-sheet/the-louisiana-health-care-landscape/. Accessed August 29, 2022. 35. Louisiana Office of the Governor Briefing. COVID-19 Louisiana Response. 2022. Available at: https://gov.louisiana.gov/assets/docs/covid/govCV19Brief-2.pdf. Accessed August 29, 2022. 36. Louisiana Department of Health. Medicaid Professional Services Provider Manual. 2022. 37. Louisiana Department of Health. Information Bulletin 20-5: COVID-19 Provider Update. 2020. Available at: https://ldh.la.gov/assets/docs/BayouHealth/Informational_Bulletins/2020/IB20-5.pdf. Accessed August 29, 2022. 38. Kaiser Family Foundation. Opportunities and Barriers for Telemedicine in the US During the COVID-19 Emergency and Beyond. 2020. Available at: https://www.kff.org/womens-health-policy/issue-brief/opportunities-and-barriers-for-telemedicine-in-the-u-s-during-the-covid-19-emergency-and-beyond/. Accessed August 29, 2022. 39. Walker DM, Hefner JL, Fareed N, et al. Exploring the digital divide: age and race disparities in use of an inpatient portal. Telemed e-Health. 2020;26:603–613. 40. Woodall T, Ramage M, LaBruyere JT, et al. Telemedicine services during COVID‐19: considerations for medically underserved populations. J Rural Health. 2021;37:231–234. 41. Sachs JW, Graven P, Gold JA, et al. Disparities in telephone and video telehealth engagement during the COVID-19 pandemic. JAMIA Open. 2021;4:1–5. 42. Ryskina KL, Shultz K, Zhou Y, et al. Older adults’ access to primary care: gender, racial, and ethnic disparities in telemedicine. J Am Geriatr Soc. 2021;69:2732–2740. 43. Roberts ET, Mehrotra A. Assessment of disparities in digital access among Medicare beneficiaries and implications for telemedicine. JAMA Intern Med. 2020;180:1386–1389. 44. Quinton JK, Ong MK, Vangala S, et al. The Association of Broadband Internet Access and Telemedicine Utilization in rural Western Tennessee: an observational study. BMC Health Serv Res. 2021;21:1–6. 45. National Center for Health Statistics. NCHS Urban-Rural Classification Scheme for Counties. 2013. Available at: https://www.cdc.gov/nchs/data_access/urban_rural.htm. Accessed August 29, 2022. 46. Blundell AR, Kroshinsky D, Hawryluk EB, et al. Disparities in telemedicine access for Spanish‐speaking patients during the COVID‐19 crisis. Pediatr Dermatol. 2021;38:947–949. 47. Xiong G, Greene NE, Lightsey HM IV, et al. Telemedicine use in orthopaedic surgery varies by race, ethnicity, primary language, and insurance status. Clin Orthop Relat Res. 2021;479:1417–1425. 48. Qian AS, Schiaffino MK, Nalawade V, et al. Disparities in telemedicine during COVID‐19. Cancer Med. 2022;11:1192–1201. 49. Khatana SAM, Yang L, Eberly LA, et al. Predictors of telemedicine use during the COVID-19 pandemic in the United States—an analysis of a national electronic medical record database. PLoS One. 2022;17:e0269535. 50. Bernal JL, Cummins S, Gasparrini A. Interrupted time series regression for the evaluation of public health interventions: a tutorial. Int J Epidemiol. 2017;46:348–355.