key: cord-0989659-0y4ag4ng
authors: Weber, Ellerie; Miller, Sarah J; Astha, Varuna; Janevic, Teresa; Benn, Emma
title: Characteristics of Telehealth Users in NYC for COVID-related Care during the Coronavirus Pandemic
date: 2020-08-31
journal: J Am Med Inform Assoc
DOI: 10.1093/jamia/ocaa216
sha: 7ac3aa10fba463433e06208d5c55beea23bd5912
doc_id: 989659
cord_uid: 0y4ag4ng

The response to COVID-19 has involved an unprecedented expansion in telehealth. While older Americans and minority populations among others are known to be disadvantaged by the digital divide, few studies have examined disparities in telehealth specifically, and none during COVID-19. This study uses data from a large health system in NYC – the initial epicenter of the US crisis – to describe characteristics of patients seeking COVID-related care via telehealth, ER, or office encounters during the peak pandemic period. Demographic factors are significantly predictive of encounter type. Of any age group, patients 65+ had the lowest odds of using telehealth versus other modalities. By race and ethnicity, Black and Hispanic patients have lower odds of using telehealth versus either the ER or an office visit than either Whites or Asians – this remains true even after adjusting for age, comorbidities and preferred language. Additional research into sociodemographic heterogeneity in telehealth use is needed to prevent potentially further exacerbating health disparities overall.

The novel coronavirus of 2019 (COVID- 19) global pandemic has disrupted the way healthcare is delivered in the United States. Essentially overnight, as part of efforts to reduce the transmission of COVID-19, two of the major previous barriers to telehealth 1 use -poor financial reimbursement and low provider willingness 1 -were eliminated, thus massively speeding up adoption. On March 17, shortly after widespread stay-at-home orders were issued, the Center for Medicare and Medicaid Services (CMS) and commercial insurers made sweeping changes increasing telehealth coverage 2 , and providers began promoting it as a way to provide medical services -including for COVID-related symptoms -while maintaining social distance. One recent study found virtual health visits in NYC increased by more than 4,000 percent from early March to mid-April, most of which were for COVID-related care 3 .

Using a dataset from a single health system in NYC (the initial epicenter of the US crisis), this study describes differential characteristics of patients seeking care related to COVID-19 during the peak pandemic period whose first encounter with the health system occurred via telehealth, an ER visit or another modality. Documenting these differences is important given the sudden telehealth explosion and the nascent identification of racial disparities in COVID-19 prevalence and outcomes. While telehealth has many benefits, especially during a global pandemic, it may create and/or exacerbate health disparities 4 . Research prior to the pandemic found that older Americans, rural communities, vulnerable populations, racial and ethnic minorities, and those with lower socioeconomic status (SES) status are all groups disadvantaged by the digital divide [5] [6] [7] . However, apart from a few studies 8, 9 , the existing literature offers little analysis of the digital divide phenomenon applied to the subsector of telehealth. This gap is partially a result of minimal use of telehealth overall before COVID-19 -one report found among certain enrollees with employer-sponsored insurance, only 2.4% had utilized at least one telehealth service in 2018 10 . Our findings are relevant to policymakers and health leaders interested in understanding the extent of differential utilization of telehealth in the context of COVID- 19 . For context, it is important to understand the NYC environment during this time. The peak of the NYC pandemic (6,370 new daily cases) occurred on April 6. Throughout the first half of the peak period, from early-March until mid-April, COVID-19 tests were only available in hospital emergency rooms (ERs), or their specially designated alternatives (i.e., temporary assessment centers, tents, etcetera, set up in close physical proximity to ERs) , as well as several publicly run community-based health centers and drive-thru test sites. 11, 12 . Until late-April, physician offices and urgent care centers were operating in limited capacity, and did not have testing available. 13 However, health systems rapidly expanded video visits for online urgent care; patients seeking medical advice for COVID-related symptoms, but not necessarily an immediate test, could make a telehealth appointment. Moreover, tests were in short supply -essential workers, at-risk patients, and those showing moderate-to-severe symptoms were most likely to get tested. Patients going to the ER were likely to be sent home without being tested, depending on symptoms, medical history and hospital capacity and, given an extreme shortage in intensive care unit beds, only the sickest patients were admitted. 12, 14, 15 For this study, we are interested in describing the characteristics of patients who sought care or testing voluntarily for COVID-19 -either because of symptoms or potential exposure -via telehealth versus ER encounters versus office visits. As just described, these sites were close substitutes during the peak-pandemic period 12,14-16 , when even non-severe patients were using the ER to obtain COVID-19 testing, because tests were not widely available. In sensitivity analysis, we extend this comparison across more encounter types. We include only patients' first encounters with the system, because we want to understand initial contact points for care. We also exclude patients who came to the hospital not through the ER -that is, those who were tested incidental to non-COVID care, such as women delivering babies in the labor and delivery unit. See the online appendix for more details about classifying encounters. preferred language (English, Spanish and Other/Unspecified); and a comorbidity index (0-3+), defined as an unweighted sum across the 18 conditions in our data and censored at 3. See the online appendix for more details on the race/ethnicity categorization and comorbidity conditions.

Using multinomial logistic regression, we estimate the magnitude of the relationship between patient characteristics and the odds of having a first encounter via telehealth versus ER or office visit.

We use parameter estimates from the regressions to predict patients' probabilities of using different encounter types given their characteristics. In sensitivity analyses, we interact race/ethnicity with age and comorbidities and also broaden the number of encounter types.

Our data contain 76,845 encounters for 52,585 unique patients diagnosed with, tested for, or placed under investigation for COVID-19 between March 20 and May 18, 2020. As seen in Figure 1 , this time period encompasses the entirety of the NYC pandemic peak. Given data limitations, 4,261 patients erroneously appear to have multiple first encounters, so for those we select one randomly. Applying inclusion criteria -omitting 13,356 patients (25.4%) who have an encounter other than by ER, outpatient office, or telehealth -there are 39,229 first encounters. Omitted first encounters consisted mostly of lab work and "orders only"; see Appendix Table 1 for more details on encounters. We also conduct a sensitivity analysis where we do not exclude other types of encounters, and compare telehealth versus ER versus all other visits. Relationships between patient characteristics across ER versus telehealth remain similar, except the ORs between telehealth and other encounter types (e.g., besides ER) for patients of different race/ethnicities are attenuated, though still marginally significant.

The relative patterns, magnitudes and significance of other variables do not significantly change. See

Appendix Exhibits 6-8.

In this paper we find that during the peak pandemic period, use of telehealth versus ER or office visits in NYC for COVID-related care significantly differed by patient characteristics. Of any age group, patients 65+ had the lowest odds of using telehealth versus other modalities. Black and Hispanic patients had higher adjusted odds of using either ER or office visits versus telehealth than either Whites or Asians.

A number of factors may contribute to our findings, such as research (prior to COVID-19) that racial/ethnic differences in ER use versus alternative care settings for a variety of conditionsdifferences that persist after controlling for variables like SES, insurance status and usual-source-ofcare [19] [20] [21] . Also possible is that patients without a source of usual care (e.g., a primary or specialty care physician) would be more likely to go to the ER, and less likely to seek telehealth treatment through previously established care relationships. Furthermore, racial/ethnic disparities in patients' prevalence and/or severity of COVID-19 22 could be contributing -perhaps during the pandemic, Blacks and

Hispanics use the ER more because they are sicker. These are elements we could not control for given data limitations, but are exploring in further analysis.

However, the fact that we still find significant racial/ethnic disparities between outpatient office visits and telehealth indicate there may be other issues at play. Disparities in digital access, digital literacy, and telehealth awareness, as well as issues of cost and coverage, and mistrust of digital appointments where physical examinations, labs and vitals cannot be taken are all potential barriers to telehealth. Future research should explore these barriers in the context of the new telehealth explosion.

Moreover, our findings of racial differences in telehealth use should be interpreted within the context of persistent structural racism in the US. We include race as a predictor in our model because documenting racial/ethnic differences for public awareness is a necessary first step in reducing disparities, however we view race as a social construct. The formal and informal policies and interactions rooted in inequality, discrimination, oppression and exclusion, which underlie this construct and engender factors we hypothesize are contributing to our results, should also be addressed in future research. 23, 24 Limitations must be acknowledged. Our data is from a single large health system; patients could have had previous encounters elsewhere. For example, it is possible that a patient had a telehealth or in-person (office, clinic, and etcetera) visit outside the Mount Sinai system and were sufficiently sick that they were sent to the ER. In addition, insofar as the data is from one city in the early stage of the global pandemic, it is possible that our results are not generalizable to other regions, or could wane over time.

However, as other cities and countries navigate their own telehealth expansions, recognizing the potential for inequitable telehealth utilization may be helpful. Second, anecdotal reports suggest providers were not documenting all telehealth properly early-on. Last, we only have a limited set of patient and clinical characteristics, for instance, we do not have access to patients' zip codes or insurance status/type, although insurance coverage is generally high in NYC 25 , nor do we have reliable symptom/severity data.

The unprecedented speed with which providers are now adopting telehealth technology, the fact that telehealth insurance coverage changes are probably here to stay, and the number of patients who have now used it for the first time, means it is extremely likely that the percentage of patients choosing telehealth will remain high even after the pandemic ends. Policymakers and healthcare leaders must be careful to address sociodemographic heterogeneity in telehealth use to prevent further exacerbating disparities overall.

This research received no specific grant from any funding agency in the public, commercial or not-forprofit sectors.

The authors have no competing interests to declare.

All authors contributed sufficiently and meaningfully to the conception, design, data acquisition or analysis, data interpretation, drafting, editing, and revising the manuscript. All authors approved the final version for submission and agreed to be accountable for all aspects of the work.

This work was supported in part through the computational and data resources and staff expertise provided by Scientific Computing at the Icahn School of Medicine at Mount Sinai. 

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