Abstract
Purpose/Objectives
To test for patient characteristics associated with virtual versus office visits among radiation oncology patients.
Materials/Methods
Using the electronic health record, we extracted encounter data and corresponding patient information for the 6 months prior to and 6 months of COVID-enabled virtual visits (10/1/2019-3/22/2020 vs 3/23/2020-9/1/2020) at an NCI-Designated Cancer Center. Encounters during COVID were categorized as in-person or virtual visits. We compared patient demographic variables including race, age, sex, marital status, preferred language, insurance status as well as tumor type during the pre-COVID period as a baseline versus during the COVID period. Multivariable analyses (MVA) examined associations between these variables and virtual visit use.
Results
We analyzed 4,974 total encounters, (2,287 pre-COVID and 2,687 during COVID), for 3,960 unique patients. All (100%) pre-COVID encounters were in-person. During COVID, 21% of encounters were via virtual visits. There were no differences identified in pre- vs during COVID patient characteristics. However, we found significant differences in patient characteristics for in-person vs virtual encounters during COVID. On MVA, virtual visit use was less common among patients who were Black versus white (OR 0.75, 95%CI 0.57-0.99, p=0.044) and not married versus married (OR 0.76, 95%CI 0.59-0.98, p=0.037). Patients diagnosed with head and neck (OR 0.63, 95%CI 0.41-0.97, p=0.034), breast (OR 0.36, 95%CI 0.21-0.62, p=<0.001), GI/abdominal (OR 0.31, 95%CI 0.15-0.63, p=0.001) or hematologic malignancy (OR 0.20, 95%CI 0.04-0.95, p=0.043), were less likely to be scheduled for virtual visits relative to patients with genitourinary malignancy. No Spanish-speaking patients engaged in a virtual visit. We did not identify differences in the insurance status or sex of patients scheduled for virtual visits.
Conclusions
We found significant differences in virtual visit use by patient socio-demographic and clinical characteristics. Further investigation into implications of differential virtual visit use including social and structural determinants and subsequent clinical outcomes is indicated.
Key Words
Introduction
With the onset of the COVID-19 pandemic, telemedicine transformed into a mainstream modality for cancer healthcare delivery in order to maintain care access while enabling physical distancing.1,2 The regulatory landscape rapidly changed to facilitate this transformation, including provisions by the US Department of Health and Human Services to expand coverage for telehealth encounters to Medicare beneficiaries, with Medicaid and private insurance companies quickly following suit.3–5
The expansion of telemedicine has potential to improve access to oncology care. Initial survey results show high satisfaction with telemedicine visits among oncology patients and providers.2,6–8 However, digital access and perceptions of telemedicine vary among patient populations.9,10 Thus, despite offering an additional mechanism for access to care, the potential impact of expanded telemedicine on healthcare disparities is unknown.11 Early in the pandemic, hospital systems’ preliminary reports showed initial signs of differences in telemedicine utilization among racially, ethnically, and linguistically minoritized groups as well as older and rural populations.6,12–14 There is heightened national awareness of healthcare inequities, as evidenced by the Biden administration's establishment of the COVID-19 Health Equity Task Force and presidential call to action on reducing inequities in cancer care.15,16 With telemedicine accounting for a greater proportion of oncology care visits during the pandemic, differential patterns of engagement may have implications for cancer equity and warrant study.
There is significant interest among stakeholders to preserve telemedicine activity beyond the pandemic. Thus, if there are telemedicine access differences, there may be the potential for long-standing effects on the goal of achieving equitable care and perpetuation of the already well-described outcomes and access disparities in cancer care.11 Therefore, we aimed to evaluate patient-level factors associated with virtual visit use among patients with cancer seen in the radiation oncology clinic at an NCI-Designated Cancer Center during the COVID-19 pandemic.
Methods and Materials
Study cohort
Prior to the COVID-19 pandemic, rates of virtual visit encounters were near zero within the radiation oncology department. In March 2020, because of the COVID-19 pandemic, per the health system's institutional policy, elective procedures were postponed, and departments were instructed to offer virtual visits for all applicable encounters. In the Department of Radiation Oncology all encounters with the exception of simulation and on-treatment visits were eligible for virtual visits. Using the Electronic Health Record (EHR) we extracted encounter data and corresponding patient characteristics for 6 months pre-COVID (10/1/2019-3/22/2020) and the initial 6 months of the COVID pandemic (3/23/2020-9/1/2020). We defined the pre-COVID period and the COVID era according to the transition date in institutional policy.
Encounters were categorized as in-person or virtual (video or phone visit). Video visits were conducted using HIPAA (Health Insurance Portability and Accountability Act)-compliant video conferencing platforms within MyChart through Epic (Epic Systems Corporation, Verona, Wisconsin), WebEx (Cisco, San Jose, California) or Doximity (Doximity Inc, San Francisco, California). Per policy, at the time of appointment scheduling all virtual visit-eligible encounters were to be identified and patients were to be offered virtual visits in lieu of in-person visits. Administrative staff instructed patients to download the MyChart application to their smart device (phone, tablet, or computer) and guided them through setup instructions. Patients who did not have access to MyChart or were unable to setup necessary software were offered virtual visits through the secure WebEx platform; a tertiary option was via Doximity. If a patient was unable to participate in a virtual visit or refused, they were offered an in-person visit per standard workflow. This study was approved by the health system-affiliated Institutional Review Board.
Statistical analysis
Descriptive analysis was conducted on the characteristics of patient populations during the pre-COVID period to establish baseline demographics and compared to the COVID era. During the COVID era, univariable analysis compared characteristics of populations receiving in-person vs virtual visits. Patient demographics and disease characteristics examined included race (white, Black), age (mean, ≤70, >70), sex (male, female), marital status (married, unmarried), language preference (English, Spanish, Other), insurance status (commercial, Medicaid, Medicare, other, self-pay), encounter status (completed, not completed), MyChart status (activated, not activated), visit month (March-August) and diagnosis/disease site. Owing to the proportionately small number of patients (n=35) receiving virtual visits with race other than white or Black, these patients were excluded from univariate and multivariable analyses. For categorical variables, a χ2 test was used to evaluate differences between groups. For continuous factors, a Wilcoxon rank-sum test was used to compare means between populations. Age was evaluated as both a continuous and categorical variable. Multivariable generalized linear model analysis of patient characteristics corresponding to each encounter was performed to examine the predictive significance of each factor for utilization of virtual visits. A mixed effect model was used to account for multiple encounters corresponding to the same patient. A p-value of <0.05 was considered statistically significant. Statistical analysis was performed with R version 4.0.2.17
Results
Table 1 summarizes encounter characteristics relative to the onset of the COVID pandemic. Our analysis included 4,974 total encounters, (2,287 Pre-COVID and 2,687 during COVID), corresponding to a total of 3,960 patients. All Pre-COVID visits were in-person. During the COVID, 21% of visits were virtual, 15% via telephone and 6% via video-conference platforms.
Table 1Encounter characteristics during pre-COVID and COVID
| Pre-COVID | COVID | |||
|---|---|---|---|---|
| N | % | N | % | |
| Total # of patients | 1841 | 46 | 2119 | 54 |
| Total # of encounters | 2287 | 100 | 2687 | 100 |
| Total virtual visits | 0 | 0 | 571 | 21 |
| Phone Visit | 0 | 0 | 405 | 15 |
| Video Visit | 0 | 0 | 166 | 6 |
| Total In-person Visits | 2287 | 100 | 2116 | 79 |
| Consults | 804 | 35 | 446 | 17 |
| Follow up | 1483 | 65 | 1670 | 62 |
*Pre-COVID date range is 10/1/2019-3/22/2020. COVID date range is 3/23/2020-9/1/2020.
Table 2 shows the socio-demographic and clinical characteristics of patients with encounters pre-COVID and during the COVID. On univariable analysis, we did not find any difference in the race, age, sex, marital status, or language preference of patients evaluated by time period (pre vs. during COVID). Insurance status was associated with differential scheduling for virtual visits during COVID on univariable analysis. Virtual visit utilization peaked in April and May and then declined over the study period. The proportion of non-completed visits was higher during the COVID compared to pre-COVID (11.1% vs 6.4%, p<0.001) but lowest for virtual visit vs in person encounters (2.9% vs 13.4%, p<0.001).
Table 2Patient characteristics by time period and visit type within the COVID time period
| Pre-COVID | COVID | COVID era | ||||||||
|---|---|---|---|---|---|---|---|---|---|---|
| In-person | Virtual | |||||||||
| N | % | N | % | p-value | N | % | N | % | p-value | |
| Race | ||||||||||
| White | 1235 | 74.6 | 1394 | 73.2 | 1073 | 72.3 | 321 | 76.2 | ||
| Black | 421 | 25.4 | 511 | 26.8 | 0.362 | 411 | 27.7 | 100 | 23.8 | 0.121 |
| Age (Mean years) | 62.4 | - | 62.7 | - | 0.634 | 62.2 | - | 64.2 | - | 0.0012 |
| Age >70 | 552 | 30.0 | 620 | 29.3 | 0.643 | 466 | 28.0 | 154 | 33.8 | 0.0197 |
| Sex | ||||||||||
| Female | 839 | 45.6 | 935 | 44.1 | 743 | 44.7 | 192 | 42.1 | ||
| Male | 1002 | 54.5 | 1184 | 55.9 | 0.378 | 920 | 55.3 | 264 | 57.9 | 0.35 |
| Marital status | ||||||||||
| Married | 1040 | 56.5 | 1257 | 59.3 | 968 | 58.2 | 289 | 63.4 | ||
| Not married | 801 | 43.5 | 862 | 40.7 | 0.772 | 695 | 41.8 | 167 | 36.6 | 0.0528 |
| Language Preference | ||||||||||
| English | 1771 | 96.2 | 2037 | 96.1 | 1586 | 95.4 | 451 | 98.9 | ||
| Spanish | 54 | 2.9 | 59 | 2.8 | 59 | 3.6 | 0 | 0 | ||
| Other | 16 | 0.9 | 23 | 1.1 | 0.762 | 18 | 1.1 | 5 | 1.1 | <0.001 |
| Insurance status | ||||||||||
| Commercial | 894 | 48.6 | 1064 | 50.2 | 834 | 50.2 | 230 | 50.4 | ||
| Medicaid | 208 | 11.3 | 230 | 10.9 | 188 | 11.3 | 42 | 9.2 | ||
| Medicare | 439 | 23.9 | 470 | 22.2 | 349 | 21.0 | 121 | 26.5 | ||
| Other | 267 | 14.5 | 317 | 15.0 | 258 | 15.5 | 59 | 12.9 | ||
| Self-pay | 33 | 1.8 | 38 | 1.8 | 0.727 | 34 | 2.0 | 4 | 0.9 | 0.0298 |
| Encounter Status | ||||||||||
| Not-completed | 118 | 6.4 | 235 | 11.1 | <0.001 | 222 | 13.4 | 13 | 2.9 | <0.001 |
| MyChart | ||||||||||
| Activated | 1353 | 73.5 | 1690 | 79.8 | <0.001 | 1318 | 79.3 | 372 | 81.6 | 0.304 |
| Visit Month | ||||||||||
| March | 66 | 3.1 | 27 | 4.7 | ||||||
| April | 179 | 8.5 | 227 | 39.8 | ||||||
| May | 307 | 14.5 | 134 | 23.5 | ||||||
| June | 516 | 24.4 | 76 | 13.3 | ||||||
| July | 520 | 24.6 | 60 | 10.5 | ||||||
| August | 508 | 24.0 | 44 | 7.7 | <0.001 | |||||
| Tumor Type | ||||||||||
| CNS | 132 | 7.2 | 163 | 7.7 | 126 | 7.6 | 37 | 8.1 | ||
| Head and neck | 272 | 14.8 | 303 | 14.3 | 254 | 15.3 | 49 | 10.8 | ||
| Thoracic | 100 | 5.4 | 104 | 4.9 | 79 | 4.8 | 25 | 5.5 | ||
| Breast | 242 | 13.2 | 288 | 13.6 | 259 | 15.6 | 29 | 6.4 | ||
| GI/Abdominal | 138 | 7.5 | 130 | 6.1 | 118 | 7.1 | 12 | 2.6 | ||
| Gynecologic | 87 | 4.7 | 85 | 4.0 | 68 | 4.1 | 17 | 3.7 | ||
| Genitourinary | 394 | 21.4 | 421 | 19.9 | 326 | 19.6 | 95 | 20.8 | ||
| Bone/soft tissue | 70 | 3.8 | 41 | 1.9 | 30 | 1.8 | 11 | 2.4 | ||
| Hematologic | 33 | 1.8 | 31 | 1.5 | 29 | 1.7 | 2 | 0.4 | ||
| Unspecified/other | 373 | 20.3 | 553 | 26.1 | <0.001 | 374 | 22.5 | 179 | 39.3 | <0.001 |
Pre-COVID date range is 10/1/2019-3/22/2020. COVID date range is 3/23/2020-9/1/2020.
The individual socio-demographics and tumor types for patients participating in virtual vs in-person encounters during the COVID era are showing in Table 2. Compared to patients receiving in-person visits, no Spanish-speaking patients were scheduled for virtual visits (3.6% vs 0%, p<0.001). Patients presenting for virtual visits were older than those presenting in-person (mean age 64.2 vs 62.2, p=0.0012, age >70 33.8% vs 28.0%). We did not find any difference in the sex of patients using video visits.
On multivariable analysis (Table 3), patients who were Black (OR 0.75, 95%CI: 0.57-0.99, p=0.044), not married (OR 0.76, CI 0.59–0.98, p = 0.037) and those diagnosed with head and neck, breast, GI/abdominal or hematologic (vs genitourinary) malignancy were significantly associated with decreased odds of virtual visit utilization. Age, sex and insurance status were not associated with decreased odds of virtual visit utilization on multivariable analysis.
Table 3Multivariable analysis of patient visit characteristics and associations with virtual visit utilization (N=2687 encounters)
| Variable | Odds Ratio | 95% Confidence Interval | p-value |
|---|---|---|---|
| Race | |||
| White | REF | ||
| Black | 0.75 | 0.57 – 0.99 | 0.044 |
| Age | |||
| ≤70 | REF | ||
| >70 | 1.11 | 0.86 – 1.44 | 0.432 |
| Sex | |||
| Female | REF | ||
| Male | 0.80 | 0.60 – 1.07 | 0.126 |
| Marital status | |||
| Married | REF | ||
| Not married | 0.76 | 0.59 – 0.98 | 0.037 |
| Insurance status | |||
| Commercial | REF | ||
| Medicaid | 1.09 | 0.71 – 1.68 | 0.684 |
| Medicare | 1.28 | 0.94 – 1.74 | 0.118 |
| Other | 0.91 | 0.64 – 1.30 | 0.606 |
| Self-pay | 0.77 | 0.26 – 2.30 | 0.634 |
| Tumor Type | |||
| Genitourinary | REF | ||
| Head and neck | 0.63 | 0.41 – 0.97 | 0.034 |
| Thoracic | 1.23 | 0.72 – 2.10 | 0.456 |
| Breast | 0.36 | 0.21 – 0.62 | <0.001 |
| GI/Abdominal | 0.31 | 0.15 – 0.63 | 0.001 |
| Gynecologic | 0.68 | 0.33 – 1.41 | 0.296 |
| CNS | 1.02 | 0.61 – 1.70 | 0.950 |
| Bone/soft tissue | 1.43 | 0.61 – 3.35 | 0.411 |
| Hematologic | 0.20 | 0.04 – 0.95 | 0.043 |
| Unspecified/other | 2.15 | 1.52 – 3.03 | <0.001 |
*Odds ratio <1 indicates lower likelihood of virtual visit utilization
Discussion
In this study, which analyzed nearly 5,000 encounters, including over 2,500 COVID-enabled virtual visits over approximately 6 months, we found significant differences in virtual visit use based on patient demographics and tumor type in an NCI-Designated Cancer Center radiation oncology clinic. Specifically, on multivariable analysis we found patients who were Black, not married and those diagnosed with head and neck, breast, GI/abdominal, or hematologic malignancies had decreased odds of virtual visit use. No Spanish-speaking patients were scheduled for a virtual visit. Additionally, we found virtual visits declined over the study period, with a peak in April and declining through September 2020. Finally, we also found a lower non-completed visit proportion with virtual visits.
Our findings point to equity concerns in access to virtual visits. Understanding the root causes of the differential use of virtual visits is important as telemedicine is expected to remain part of the post-COVID care delivery landscape. Capitalizing on this paradigm shift should be done with equitable care as a goal.11 The impact on clinical outcomes from virtual versus in person visits in Oncology remain unknown. In the interim, identifying populations less likely to have a virtual visit, as this study does, is a foundational step towards ensuring equitable access to this service. More specifically, this study raises the hypothesis that additional systems-level attention to inclusivity regarding virtual visits may better ensure this patient-provider encounter type is an available option for all, regardless of individual socio-demographics or disease type.
COVID-19 has disproportionately impacted already marginalized populations via social determinants and structural racism.13,18,19 On MVA we found Black patients were less likely to be scheduled for virtual visits than white patients. We included the variable of race in our study based on the knowledge that race is a social construct and structural racism is a driver of many social inequities (e.g. residential segregation, inadequate access to internet and other utilities, poverty) that underlie differential access to health services.20,21 A limitation of our study is not having access to data on social risk factors (individual level) or social determinants (social/society level). Our results complement the findings of Shaverdian et al., who surveyed the visit satisfaction and preference of 351 radiation oncology patients seen during COVID at their institution. Interestingly, they report age, sex, race/ethnicity, consultation intent (i.e. palliative versus radical), disease site, and metastatic disease were not associated with a preference for an in-person versus telemedicine visit.2 It may be that their analyses were underpowered or that differences we found in rates of virtual visit participation between patient groups are driven less by patient preference and more by systemic factors such as those discussed below. To our knowledge this is one of the first reports looking at associations with virtual visit use in radiation oncology22; our findings are supported by reports from general oncology6,23 and non-oncology-specific investigations.10,12,13 Furthermore, in an analysis of RT courses during COVID, De et al., reported lower uptake among non-white patients and patients receiving shorter versus longer RT courses.22
We found zero virtual visit encounters for patients with a documented Spanish language preference. The institution had a robust in-person interpreter services program capable of providing services via phone or video, as evident in pre-pandemic patterns of care. Audio visits could also have been scheduled with a third-party telephone interpreter service that contracts with the institution (Pacific Interpreters). Similarly, in the primary care setting Nouri et al., found that after the implementation of telemedicine during COVID-19 the percent of patients with non-English language preference decreased by nearly half in a California-based practice of over 30,000 patients.12 These findings likely underscore the need to include interpreter services in any plan for virtual care.
We found patients who are not married to be less likely to be scheduled for virtual care visits, possibly reflecting the importance of social support in adoption of and access to new technologies. Marriage is not a definitive metric of social support, however, a Surveillance, Epidemiology and End Results program-based study of 1.2 million patients did show patients who are not married are at higher risk of presentation with metastatic cancer, undertreatment and death resulting from their cancer.24 An estimated 26.3% of Medicare beneficiaries lack access to a device with high speed internet or wireless data; this is true especially for beneficiaries who are widowed, older, Black, Hispanic, received Medicaid, were disabled or have up to a high school degree.10 Interestingly, in our study the proportion of non-completed visits to be lower for virtual visits versus in-person encounters, perhaps suggesting increased convenience leading to improved access.
We found insurance status, a well-known predictor of cancer care receipt and outcomes,25,26 was associated with virtual visit use in our cohort on univariate analysis but not on multivariable analysis, although the analysis may have been underpowered here with <2% of the cohort being self-pay. Several pandemic studies show a relationship between economic status indicators such as low household income, no insurance, insurance type (ex. Medicaid or Medicare vs commercial), income below the federal poverty line and decreased telemedicine engagement.6,12,14,23
In our study, the majority of virtual visits were conducted via phone (71%) vs video (29%). During the COVID-19 pandemic Centers for Medicare and Medicaid Services (CMS) has allowed audio-only communication, which was previously ineligible for reimbursement as part of telehealth programs. Early in the pandemic the CMS estimated 30% of telehealth visits were audio/phone only.27 Others have reported high rates of telephone visit use among Federally Qualified Health Centers suggesting audio-only visit rates may be higher among low-income patients facing geographic or resource barriers in accessing video visits.28 A cohort study of over 20,000 virtual care visits at the Princess Margaret Cancer Center showed similar predominance of phone visits.7 As a state public hospital, the served population includes patients in rural settings who may have insufficient internet facilities to support video visits. Additionally, provider preferences or the usability of current video visit technology may play a role in audio/phone versus video virtual care. Our results argue in support of permitting ongoing use of audio-only encounters as eliminating coverage may limit access to virtual visits.
Additionally, we found patients diagnosed with head and neck, breast, GI/abdominal or hematologic malignancy were less likely to use virtual visits relative to patients with genitourinary malignancy. These tumor types may lend themselves towards in-person visits given distinct clinical factors including the importance of physical exam (e.g. clinical breast exam) or in-person endoscopy (e.g. laryngoscopy). It is recognized that in moving forward beyond the pandemic virtual care should offer comparable safety and effectiveness to in-person visits; therefore, not all encounters are eligible for conversion to virtual visits.29–31 While the pandemic catapulted use of telemedicine with the subtext that virtual care is better than no care or risk of COVID exposure, challenges remain in identifying long-term whether clinical outcomes from in-person versus virtual visits are comparable and for precisely which indications, patient populations and treatment scenarios telemedicine is appropriate.30,32 Nonetheless, designing for equity in virtual visit access is critical.
While there is limited data on the clinical impact of virtual visits on outcomes in oncology, data from other specialties supports similar outcomes for these encounter modalities. In a review of randomized controlled trials of video teleconferencing interventions for respiratory, neurologic, pain, cardiovascular and orthopedic conditions as well as diabetes, virtual visit interventions resulted in similar clinical effectiveness, health care utilization and patient satisfaction.33 Thus, in settings where access to care is limited, such as lower-resource settings or low- and middle-income countries, virtual visits are potentially an important tool in reducing gaps in care. Though radiation oncology services must be delivered onsite, our data illustrates that various aspects of care coordination and patient evaluation can occur via virtual visits. However, our data also supports the hypothesis that there is a risk of perpetuating disparities without implementation of telemedicine through a thoughtful lens of health equity. Based on our preliminary findings, initiatives focused on systems-level attention to inclusivity such as intentional inclusion of interpreter services, offering virtual visit types compatible with a patient's access to technology (video and/or audio-only), in-person patient orientation to virtual visit tools/electronic patient portals, consensus guidelines on when virtual visits are appropriate to schedule (versus based on insurer or provider preference) may facilitate more equitable access to this encounter type.
This study has several limitations. Our study is retrospective and includes data from a single institution's radiation oncology service. Due to limitations in data availability, we did not include variables related to income, geography, transportation or internet access., Further research is needed to identify the social risk factors and social and structural determinants contributing to differences in virtual visit scheduling. Our study results reflect both patient and provider rationale for offers/acceptance of virtual visits, however, due to our study design we were unable to evaluate and report these separately. In our experience the institution's preferred virtual visit tools available at the beginning of the pandemic were difficult to access and use for patients and providers. For example, anecdotally, several video visits were converted to phone visits due to technical issues. This configuration is unlikely to reflect the long-term, optimized format for telemedicine.
In conclusion, there is significant interest among stakeholders including patients, clinicians, and policymakers to preserve telemedicine access beyond the pandemic; our study begins to offer insight into radiation oncology patients who are likely to have challenges accessing telemedicine without adequate support. We found significant differences in virtual visit use for Black patients, patients who were not married, and by tumor type. No patients with a documented Spanish language preference completed virtual visits. Our findings suggest that access to virtual care will not be equally distributed without intentional intervention. Further prospective and qualitative work is needed to understand and intervene upon the specific causes of disparate access in order to build equitable access.
Declaration of interests
The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
Conflict of Interest Disclosures
Dr. Royce reports employment at Flatiron Health, Inc., which is an independent subsidiary of the Roche Group and reports stock ownership in Roche and Agilix Health.
Funding
None
Acknowledgements
None
Data sharing
Research data are stored in an institutional repository and will be shared upon request to the corresponding author.
References
1. Abdel-Wahab M, Rosenblatt E, Prajogi B, Zubizarretta E, Mikhail M. Opportunities in Telemedicine, Lessons Learned After COVID-19 and the Way Into the Future. International Journal of Radiation Oncology*Biology*Physics. 2020;108(2):438-443. doi:10.1016/j.ijrobp.2020.07.006
2. Shaverdian N, Gillespie EF, Cha E, et al. Impact of Telemedicine on Patient Satisfaction and Perceptions of Care Quality in Radiation Oncology. J Natl Compr Canc Netw. Published online January 4, 2021:1-7. doi:10.6004/jnccn.2020.7687
3. Royce TJ, Sanoff HK, Rewari A. Telemedicine for Cancer Care in the Time of COVID-19. JAMA Oncology. Published online July 16, 2020. doi:10.1001/jamaoncol.2020.2684
4. Centers for Medicare & Medicaid Services. Final Policy, Payment, and Quality Provisions Changes to the Medicare Physician Fee Schedule for Calendar Year 2021.; 2020. Accessed June 21, 2021. https://www.cms.gov/newsroom/fact-sheets/final-policy-payment-and-quality-provisions-changes-medicare-physician-fee-schedule-calendar-year-1
5. Centers for Medicare & Medicaid Services. President Trump Expands Telehealth Benefits for Medicare Beneficiaries During COVID-19 Outbreak.; 2020. Accessed June 21, 2021. https://www.cms.gov/newsroom/press-releases/president-trump-expands-telehealth-benefits-medicare-beneficiaries-during-covid-19-outbreak
6. Darcourt JG, Aparicio K, Dorsey PM, et al. Analysis of the Implementation of Telehealth Visits for Care of Patients With Cancer in Houston During the COVID-19 Pandemic. JCO Oncology Practice. 2021;17(1):e36-e43. doi:10.1200/OP.20.00572
7. Berlin A, Lovas M, Truong T, et al. Implementation and Outcomes of Virtual Care Across a Tertiary Cancer Center During COVID-19. JAMA Oncol. Published online January 7, 2021. doi:10.1001/jamaoncol.2020.6982
8. Zhang H, Cha EE, Lynch K, et al. Radiation Oncologist Perceptions of Telemedicine from Consultation to Treatment Planning: A Mixed-Methods Study. Int J Radiat Oncol Biol Phys. 2020;108(2):421-429. doi:10.1016/j.ijrobp.2020.07.007
9. George S, Hamilton A, Baker RS. How Do Low-Income Urban African Americans and Latinos Feel about Telemedicine? A Diffusion of Innovation Analysis. International Journal of Telemedicine and Applications. 2012;2012:1-9. doi:10.1155/2012/715194
10. Roberts ET, Mehrotra A. Assessment of Disparities in Digital Access Among Medicare Beneficiaries and Implications for Telemedicine. JAMA Intern Med. 2020;180(10):1386. doi:10.1001/jamainternmed.2020.2666
11. Elston Lafata J, Smith AB, Wood WA, Fitzpatrick B, Royce TJ. Virtual Visits in Oncology: Enhancing Care Quality While Designing for Equity. JCO Oncol Pract. Published online February 4, 2021:OP2000645. doi:10.1200/OP.20.00645
12. Nouri, S., Khoong, E. C., Lyles, C. R., & Karliner, L. (2020). Addressing equity in telemedicine for chronic disease management during the Covid‐19 Pandemic. NEJM Catalyst. https://catalyst.nejm.org/doi/full/10.1056/CAT.20.0123.
13. Chunara R, Zhao Y, Chen J, et al. Telemedicine and healthcare disparities: a cohort study in a large healthcare system in New York City during COVID-19. Journal of the American Medical Informatics Association. 2021;28(1):33-41. doi:10.1093/jamia/ocaa217
14. Tam S, Wu VF, Williams AM, et al. Disparities in the Uptake of Telemedicine During the COVID-19 Surge in a Multidisciplinary Head and Neck Cancer Population by Patient Demographic Characteristics and Socioeconomic Status. JAMA Otolaryngol Head Neck Surg. Published online November 5, 2020. doi:10.1001/jamaoto.2020.3052
15. Ong M. The Bidens announce initiatives on health inequity and DARPA-like research for cancer. The Cancer Letter. 2021;47(8).
16. Presidential Actions. Executive Order on Ensuring an Equitable Pandemic Response and Recovery.; 2021. Accessed April 30, 2021. www.whitehouse.gov/briefing-room/presidential-actions/2021/01/21/executive-order-ensuring-an-equitable-pandemic-response-and-recovery/
17. R Core Team (2020). R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. URL https://www.R-project.org/.
18. Dorn A van, Cooney RE, Sabin ML. COVID-19 exacerbating inequalities in the US. The Lancet. 2020;395(10232):1243-1244. doi:10.1016/S0140-6736(20)30893-X
19. Egede LE, Walker RJ. Structural Racism, Social Risk Factors, and Covid-19 — A Dangerous Convergence for Black Americans. N Engl J Med. 2020;383(12):e77. doi:10.1056/NEJMp2023616
20. Bailey ZD, Krieger N, Agénor M, Graves J, Linos N, Bassett MT. Structural racism and health inequities in the USA: evidence and interventions. The Lancet. 2017;389(10077):1453-1463. doi:10.1016/S0140-6736(17)30569-X
21. Johnson TJ. Intersection of Bias, Structural Racism, and Social Determinants With Health Care Inequities. Pediatrics. 2020;146(2):e2020003657. doi:10.1542/peds.2020-003657
22. De B, Fu S, Chen YS, et al. Patient, physician, and policy factors underlying variation in use of telemedicine for radiation oncology cancer care. Cancer Med. 2022;11(10):2096-2105. doi:10.1002/cam4.4555
23. Katz AJ, Haynes K, Du S, Barron J, Kubik R, Chen RC. Evaluation of Telemedicine Use Among US Patients With Newly Diagnosed Cancer by Socioeconomic Status. JAMA Oncol. 2022;8(1):161. doi:10.1001/jamaoncol.2021.5784
24. Aizer AA, Chen MH, McCarthy EP, et al. Marital Status and Survival in Patients With Cancer. JCO. 2013;31(31):3869-3876. doi:10.1200/JCO.2013.49.6489
25. Ward E, Halpern M, Schrag N, et al. Association of Insurance with Cancer Care Utilization and Outcomes. CA: A Cancer Journal for Clinicians. 2008;58(1):9-31. doi:10.3322/CA.2007.0011
26. Walker GV, Grant SR, Guadagnolo BA, et al. Disparities in Stage at Diagnosis, Treatment, and Survival in Nonelderly Adult Patients With Cancer According to Insurance Status. JCO. 2014;32(28):3118-3125. doi:10.1200/JCO.2014.55.6258
27. Verma S. Early Impact Of CMS Expansion Of Medicare Telehealth During COVID-19. Health Affairs Blog.
28. Uscher-Pines L, Sousa J, Jones M, et al. Telehealth Use Among Safety-Net Organizations in California During the COVID-19 Pandemic. JAMA. 2021;325(11):1106. doi:10.1001/jama.2021.0282
29. Zulman DM, Verghese A. Virtual Care, Telemedicine Visits, and Real Connection in the Era of COVID-19: Unforeseen Opportunity in the Face of Adversity. JAMA. 2021;325(5):437. doi:10.1001/jama.2020.27304
30. Herzer KR, Pronovost PJ. Ensuring Quality in the Era of Virtual Care. JAMA. 2021;325(5):429. doi:10.1001/jama.2020.24955
31. American Medical Association: Telehealth Implementation Playbook, 2020.
32. Bashshur RL, Howell JD, Krupinski EA, Harms KM, Bashshur N, Doarn CR. The Empirical Foundations of Telemedicine Interventions in Primary Care. Telemedicine and e-Health. 2016;22(5):342-375. doi:10.1089/tmj.2016.0045
33. Albritton J, Ortiz A, Wines R, et al. Video Teleconferencing for Disease Prevention, Diagnosis, and Treatment: A Rapid Review. Ann Intern Med. 2022;175(2):256-266. doi:10.7326/M21-3511
Article info
Publication history
Published online: March 27, 2023
Accepted:
March 6,
2023
Received:
November 3,
2022
Identification
Copyright
Published by Elsevier Inc. on behalf of American Society for Radiation Oncology.
User license
Creative Commons Attribution – NonCommercial – NoDerivs (CC BY-NC-ND 4.0) | How you can reuse 
Elsevier's open access license policy
Creative Commons Attribution – NonCommercial – NoDerivs (CC BY-NC-ND 4.0)
Permitted
For non-commercial purposes:
- Read, print & download
- Redistribute or republish the final article
- Text & data mine
- Translate the article (private use only, not for distribution)
- Reuse portions or extracts from the article in other works
Not Permitted
- Sell or re-use for commercial purposes
- Distribute translations or adaptations of the article
Elsevier's open access license policy
