In-Person and Telehealth Ambulatory Contacts and Costs in a Large US Insured Cohort Before and During the COVID-19 Pandemic

Key Points Question How did ambulatory care patterns change after the initial COVID-19 surge in the US and what role did telehealth play? Findings In this cohort study of 36 568 010 US individuals, ambulatory contacts decreased by 18% between the 2019 and 2020 March to June periods, and telehealth use increased from 0.3% of contacts in 2019 to 23.6% of all contacts in 2020. Increased disease burden, COVID-19 prevalence, and greater social resources were associated with higher telehealth use. Meaning These findings suggest that the dramatic shift in the adoption of virtual care has many implications for health services provision both during and after the pandemic.


Introduction
The use of telehealth services during the COVID-19 pandemic has been unprecedented. During the initial March to April 2020 peak, case reports suggested that telehealth usage went from less than 1% of visits 1 to as much as 80% where COVID-19 prevalence was high. 2 Although telehealth services have been available in the US for decades, its adoption was still uncommon before the COVID-19 pandemic. 3,4 Barriers to use included limited insurance coverage and regulations regarding jurisdiction of licensure. Also, many practitioners and patients were not technically equipped to offer and use these digitally mediated services. 5 The COVID-19 pandemic has also placed unprecedented challenges on government, practitioners, and patients to mitigate the spread of this disease. Therefore, telehealth emerged as a way to deliver care at a social distance. In response, policy makers, payers, and practitioners eliminated almost all financial, regulatory, and technical barriers that hampered past telehealth expansion. [6][7][8] The shift to telehealth has impacted not only the more than 7.5% of Americans with confirmed COVID-19, 9 but also every person coming in contact with the health care system. Most policy, clinical, and electronic health experts believe that although coverage policies and practitioner and consumer telehealth adoption levels may change once the pandemic subsides, the adoption trajectory of these technologies has been forever changed. [10][11][12] This study undertakes an assessment of the shifts in telehealth and ambulatory visit use during the first 4 months of the COVID-19 pandemic (March to June 2020). We document overall ambulatory contacts and costs both before (2019) and after (2020) the pandemic's start within a very large cohort of more than 36 million working-age, insured US individuals. We assess the associations of key patient, community, and health system characteristics with service use. We also assess the association of telehealth use with a patient having a COVID-19-related diagnosis and/or residing in a high COVID-19 prevalence area.

Data Sources and Cohort Selection
The institutional review board of the Johns Hopkins Bloomberg School of Public Health reviewed and approved this cohort study as being exempt; thus, informed consent was not sought. This study conforms to the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) reporting guideline. 13 Data for this study came from the Blue Health Intelligence data repository. Blue Health Intelligence is an independent data and analytics company that is a licensee of the Blue Cross and Blue Shield Association. This database is sourced from a large number of health insurance plans. The study data set included complete claims files for commercial health plan members continuously enrolled from March 1, 2019, through June 30, 2020. Claims submitted through July 31, 2020, were included. eFigure 1 in the Supplement provides information on sample selection and subgroup identification. Distribution of the study population by US Census region is presented in eTable 1 in the Supplement.
Our analysis compared the 4-month period of March through June in 2019 and 2020 to account for seasonality. The study population was limited to persons covered through employer-based, Affordable Care Act, and other private health insurance plans, but not Medicare or Medicaid.
We developed an approach to identify persons treated for actual or suspected COVID-19. We designated a person to this COVID-19-related subgroup if they had any diagnosis code associated with COVID-19 disease, exposure, screening, or testing during the 2020 study period. eTable 2 in the Supplement presents identification criteria and study member counts for the COVID-19-related subgroup.

Definition of Ambulatory and Telehealth Encounters
Our unit of analysis was an ambulatory encounter with a telehealth-eligible service. Inpatient and emergency department services were excluded. An encounter was defined as a patient seeing a specific practitioner on a specific date. Telehealth-eligible services were identified using Current Procedural Terminology or Healthcare Common Procedure Codes that, on the basis of payer policy, were eligible for telehealth coverage (see eTable 3 and eTable 4 in the Supplement). 14 Telehealtheligible services were subdivided between those provided in person or via telehealth. Some telehealth-eligible codes can only be used for telehealth, whereas others may be provided either way.
Thus, we classified them as telehealth only when an appropriate modifier was present (see eTable 5 in the Supplement). In accordance with payment policy, we considered telehealth to include any service provided on a remote basis, whether via video, telephone, asynchronous or synchronous text or email transmission, or other similar technology. Details on designation of telehealth modality are provided in eTable 5 in the Supplement.
Some encounters include multiple claim lines (mean [SD], 1.13 [0.58] claim lines). To assign encounter-level attributes (eg, diagnosis, specialty), we selected a principal service for each unique encounter based on the claim line with the highest allowed charge. The categorization we used to assign a specialty to each practitioner is described in eTable 6 in the Supplement.

Study Member Characteristics and Definition of Measures
We captured information on the members' demographic characteristics, including age, sex, and zip code of residence from enrollment files. Using zip code, we mapped each study member to a US Census region, state, and county and to urban or rural categorizations. 15 We assigned a member's residence to 1 of 4 levels of social deprivation based on a national ranking of the Area Deprivation Index. 16 For person-oriented analyses, we classified a residence as a COVID-19 hot spot if their state had a prevalence greater than or equal to 1.5 times the national mean on May 1, 2020. 17 For encounter-level analyses, we assigned the national decile rank of prevalence for the patient's zip code during the week of service. 18 We documented each member's chronic condition count (0, 1, 2, or Ն3 conditions) according to all ambulatory care primary diagnoses noted during the 12 months of 2019. 19 We also categorized the study member's type of insurance as standard preferred provider organization, high-deductible plan (ie, a deductible >$1000), or health maintenance organization.
Finally, we classified each encounter as involving a new patient, if the enrollee had not visited the billing practitioner organization within the past 3 years and as a new condition if there had not been a visit for that primary diagnosis within the past 3 years. 20 All members of the cohort were continuously enrolled at least from March 2019, but for this continuity of care assessment, we reviewed data from 2016 forward for those members who were enrolled longer.

Service Costs
To assess health care costs, we analyzed allowed charges. This corresponded to the dollar amount allowed by plan fee schedules before any enrollee cost sharing. For comparability, 2019 charges were adjusted upward to reflect 2020 pricing using relevant components of the Consumer Price Index (1.4% to 3.7% depending on service type). 21 To account for services provided before the end of June 2020, but where claims were not submitted or closed as of July 31, 2020 (our data set creation date), completion factors were applied on the basis of 2018 patterns. These lag adjustments for 2020 equaled an upward adjustment of 12.8% for allowed charges and 5.1% for encounters.

Statistical Analysis
Log-binomial multivariable logistic regression was used to calculate relative risk ratios (RRRs) for member-specific and encounter-specific factors to assess their independent associations with the likelihood of telehealth vs in-person contacts in 2020. Significance was set at 2-sided P < .05. Only persons with 1 or more telehealth-eligible service were included in the analysis (15 437 217 individuals). These individuals had 46 453 007 in-scope encounters. For computational efficiency, in lieu of specifying a random effect structure, robust sandwich variance estimators were used to improve estimates for standard errors, to account for potential multiple encounters.
We measured the associations of member-level characteristics on whether the member experienced 1 or more telehealth service during the study period. For encounter-level analyses, the outcome was defined as a binary (yes or no) telehealth indicator for that encounter. For both types of analyses, we used categorical variables derived from the enrollment file and factors linked to a member's home zip code.
We used SAS statistical software version 9.4 (SAS Institute) and R statistical software version 4.0 (R Project for Statistical Computing) to conduct the analyses. 22 Data analysis was performed from June to October 2020.

Results
In contacts and the percentage delivered via telehealth are presented graphically week by week during 2020 in eFigure 2 and eFigure 3 in the Supplement.
On the basis of billing designations, video-supported telehealth visits represented 74.4% of the virtual visits (11 296 298 claims) ( Table 1). Given that 13.1% of telehealth claims (1 988 277 claims) did not specify a modality, it is possible that the majority of this missing data group also had video visits.   for the non-COVID-19 subgroup). eTable 7 in the Supplement compares the breakdown of age and chronic disease among the 2020 subgroups and indicates that persons with 1 or more telehealth visit were older and had greater preexisting disease burden than those with in-person visits only. For the entire (user and nonuser) study population, pharmacy costs increased slightly across the periods (from $126.32 in 2019 to $139.36 PMPM in 2020). Unlike medical costs, pharmacy costs were not appreciably higher among the COVID-19-related subgroup.

JAMA Network Open | Health Informatics
The Figure presents RRRs at either the encounter or member level as appropriate. These reflect the adjusted probability that in 2020 a user will be seen by telehealth vs in-person, given 4 key d Area Deprivation Index is a social deprivation measure based on a national population-weighted ranking. The higher the number, the more deprived the community (see text for additional details). 15 Note that for less than 1% of the sample it was not possible to assign an Area Deprivation Index Score. Weighted rankings are shown. In addition to the apparent influence of COVID-19 prevalence, these multivariable analyses showed a distinct pattern of RRRs across diagnoses, with eye, pregnancy, injury, and cancer encounters most likely to be in person. Encounters with behavioral diagnoses were far more likely to be virtual. Additionally, persons with greater chronic disease burden (in 2019) were considerably more likely to get care via telehealth during 2020. The Figure indicates that all else equal, the more rural the patient's home jurisdiction, the less likely they were to have at least 1 telehealth contact. a The full study sample included persons continuously enrolled from March 1, 2019, through June 30, 2020. These unadjusted rates are reported as number of telehealth eligible ambulatory contacts per enrollee that took place during each 4-month (March-June) study period. Percentages represent proportion of contacts that took place via telehealth. Note that all rows were calculated at encounter level and that the "all persons" columns include all enrollees whether or not they used any services. See text for further details. b Refers to 1 or more COVID-19-related diagnosis codes reported for each person. Refer to eTable 2 in the Supplement for more information on the methods used to identify persons with potential or actual COVID-19 diagnosis during 2020 study period. This includes people tested and/or treated and/or diagnosed with COVID-19.

Discussion
Using a very large, well-curated health insurance database, we identified a substantial decrease in the number of in-person ambulatory visits during the first 4 months of the COVID-19 pandemic's onset, but the use of telehealth services increased dramatically and thus filled some of the gap. Our analyses identified a series of patient, clinical, and geographical factors associated with higher uptake of telehealth, including chronic disease burden, behavioral diagnoses, being an existing patient with a previously diagnosed condition, living in the Northeast, living in urban or affluent neighborhoods, and having health maintenance organization coverage. We also documented the association between COVID-19 prevalence rates in a region and higher telehealth adoption. Among those receiving COVID-19-related care, visits and telehealth use rates were slightly higher, and their medical costs were substantially increased. We also documented that among the study membersboth those receiving COVID-19-related services and not-those with 1 or more telehealth contact during the March to June 2020 period had substantially higher per person costs, likely because they represented a more ill group of patients.

Limitations
Although this study has many strengths, its findings are subject to a series of limitations. This study documented patterns during the initial 4-month phase of the pandemic only. It will be important to continue assessing national telehealth use as virtual care becomes more established, as COVID-19 prevalence increases, and as coverage policies may change. It is likely that the patterns in our study group are not representative of the uninsured or those with Medicare and Medicaid insurance.
Although claims data arguably offer a high degree of precision regarding the identification of telehealth services, they may underestimate these services where coding is inadequate. For example, we believe that our 2019 results underestimate the actual number of telehealth services received by this cohort because of more limited coding and coverage at the time. More broadly, any claims-based research is sensitive to missing or inaccurate coding that corresponds to variations in policies or benefit structures across health plans. Our findings related to cost are limited by our focus on allowed insurance charges. We were only able to assess the association of patient factors linked to the data in the health plan's beneficiary file. For example, we had no racial/ethnic information on the members. Our cohort design included insured persons who were continuously enrolled for 16 months. Persons with uninterrupted coverage may differ from a cross-section of insured US individuals. For example, our inclusion criteria meant that persons dying during this time frame were excluded. Age-specific (pre-COVID-19) national death rates indicate that decedents would likely represent a very small percentage (>.005%) of those in our original database, 35 but especially when COVID-19-related deaths are considered, our results based only on nondecedents would underestimate total cost and utilization within the health plans, given the high end-of-life expenses.

Conclusions
The spring of 2020 represented the first time in US history that such a large proportion of Americans had wide access to telehealth services. By undertaking this study, we sought to gain an understanding of the patterns of virtual care during this initial phase of the COVID-19 era. In so doing, it was our intent to identify potential implications for the next phase of the pandemic and thereafter.
Our study identified a series of issues worthy of continued research. These include the potential impact of telehealth on protecting patients and practitioners from spread of the virus, an apparent social and geographical digital divide in telehealth access, and the need for a better understanding of cost, utilization, and outcome differences among those receiving and not receiving telehealth.
Although some of the associations we uncovered may be unique to the COVID-19 environment, arguably the insights we gained will be relevant to the future trajectory of telehealth no matter what direction it takes. This study can help guide future strategies and actions by policy makers, payers. and professional societies. Specifically, our findings can be used by these parties to assess and modify telehealth barriers and facilitators to maximize value to consumers served by the US health care system during this digital age.