Integration of Telemedicine Consultation Into a Tertiary Radiation Oncology Department: Predictors of Use, Treatment Yield, and Effects on Patient Population

PURPOSE The COVID-19 pandemic led to rapid expansion of telemedicine. The implications of telemedicine have not been rigorously studied in radiation oncology, a procedural specialty. This study aimed to evaluate the characteristics of in-person patients (IPPs) and virtual patients (VPs) who presented to a large cancer center before and during the pandemic and to understand variables affecting likelihood of receiving radiotherapy (yield) at our institution. METHODS A total of 17,915 patients presenting for new consultation between 2019 and 2021 were included, stratified by prepandemic and pandemic periods starting March 24, 2020. Telemedicine visits included video and telephone calls. Area deprivation indices (ADIs) were also compared. RESULTS The overall population was 56% male and 93% White with mean age of 63 years. During the pandemic, VPs accounted for 21% of visits, were on average younger than their in-person (IP) counterparts (63.3 years IP v 62.4 VP), and lived further away from clinic (215 miles IP v 402 VP). Among treated VPs, living closer to clinic was associated with higher yield (odds ratio [OR], 0.95; P < .001). This was also seen among IPPs who received treatment (OR, 0.96; P < .001); however, the average distance from clinic was significantly lower for IPPs than VPs (205 miles IP v 349 VP). Specialized radiotherapy (proton and brachytherapy) was used more in VPs. IPPs had higher ADI than VPs. Among VPs, those treated had higher ADI (P < .001). CONCLUSION Patient characteristics and yield were significantly different between IPPs and VPs. Telemedicine increased reach to patients further away from clinic, including from rural or health care–deprived areas, allowing access to specialized radiation oncology care. Telemedicine has the potential to increase the reach of other technical and procedural specialties.


INTRODUCTION
Telehealth, telemedicine, and related terms are defined by the Centers for Medicare and Medicaid Services as services that a physician or practitioner provides via two-way, interactive technology that substitutes for an in-person (IP) visit. 1 The use of telemedicine rapidly expanded during the COVID-19 pandemic as many health care facilities were forced to halt IP appointments.Telemedicine became integral to ensuring timely, continued care for patients with nonelective medical needs such as cancer.
Before the pandemic, only 15%-20% of physicians reported experience with telemedicine. 2As the pandemic accelerated the need for remote medical care, there was a 154% increase in the use of telemedicine in the United States in the first quarter of 2020 when compared with the first quarter of 2019. 3This was driven by the need for health care during social isolation and supported by policy changes, including the declaration of a public health emergency (PHE) in January 2020.The PHE waived previous telemedicine regulations and allowed for easier implementation and access. 4ince then, there has been a sustained growth in the use of telemedicine. 5t al found radiation oncology to be the fourth lowest utilizer of telemedicine among 45 specialties between 2020 and 2021.6 Comparing a population of radiation oncology patients presenting IP before the COVID-19 pandemic with those presenting virtually during the pandemic, one study found telemedicine users to be younger, female, and with higher performance status.7 Another study evaluating disparities in telemedicine use in a large cancer care network found race, ethnicity, income, and type of insurance most influential to telemedicine utilization.8 Given the limited understanding of telemedicine in the radiation oncology setting, we aimed to not only further characterize patient utilization of telemedicine in a large radiation oncology center in the United States, but also to understand if the use of telemedicine affected access to radiation oncology care and the likelihood of receiving radiation at our center.

Selection and Description of Patients
We conducted a retrospective, observational study within a large tertiary radiation oncology center in the midwestern United States.All new patient consultations between January 2019 and December 2021 were identified.Before March 2020, all consultations were performed IP.Telemedicine consultations, which started in March 24, 2020, included video conferencing technology or telephone calls.The term telemedicine is used interchangeably with virtual patients (VPs) in this study.A total of 17,915 patients were included.This study was approved by the institutional review board.Demographic, medical, and treatment variables were extracted from the electronic medical record.Medicare Advantage plans were coded as private insurance, while primary Medicare/Medicaid plans were coded as public insurance.If a patient had multiple consultations in the study time range, only their first consultation was included.Miles from clinic, also referred to as distance traveled to clinic or patient distance from clinic, was the distance between a patient's primary address and the radiation oncology center.The clinic and treatment center were at the same location.Those with a primary US address were labeled as US residents.Radiation treatment modalities included photon, proton, brachytherapy, Gamma Knife, and other.The other treatment modalities included intraoperative radiation therapy, orthovoltage, and electron radiotherapy.Treatment yield was defined by whether a patient received radiation treatment at our center by December 31, 2021.
The area deprivation index (ADI), as developed by the Health Resources and Services Administration and Kind et al, 9 was used to assess the national health care deprivation percentiles of patients on the basis of zip code. 10,11A higher ADI number indicates higher health care deprivation.To better understand the population of patients served by our clinic, we used a map of county populations by state provided by the 2020 US Census. 12

Statistical Analysis
A mean (standard deviation) and number (proportion) are reported for continuous and categorical variables, respectively.
Statistical analyses included comparing demographic and treatment variables by type of visit (IP v virtual), yield, and prepandemic versus during the pandemic (starting March 24, 2020).Sex, race, ethnicity, US residency, insurance type (private v public), visit type, consultation year, primary diagnosis, categorized and mean miles from clinic, and treatment modality were assessed for association with yield.A chi square test was used to compare discrete unordered variables, a Wilcoxon rank-sum test (comparison between to be significantly different between two groups, our racial distribution was so homogeneous that we did not believe inclusion into our modeling would yield clinically significant results.We also believed many of our variables to be correlated with each other (sex and primary diagnosis, US residency status and miles from clinic, race and ethnicity within our population) and therefore only one of these variables were included.
The alpha level was set at P < .05for statistical significance.SAS version 9.4 was used for analysis.
During the pandemic, VP accounted for 21% of visits, non-US resident patient consultations decreased, the use of private insurance increased, and patients' average miles from clinic decreased.Treatment yield was not affected by the pandemic.

Patient Characteristics During the Pandemic (March 2020-December 2021)
Table 2 shows differences between IPPs and VPs during the pandemic only.On univariate analysis, VPs were more likely to be male, younger in age, present from further distances, and were 1.5 times less likely to receive treatment at our institution (yield: 48% VPs v 72% IPPs

IPPs
A total of 8,211 patients were seen IP during the pandemic, of whom 5,883 (72%) underwent treatment at our institution (Table 3).On univariate analysis, those who received treatment were younger and lived closer to the clinic.We did not appreciate sex or insurance differences on univariate analysis.Patients who self-reported as White or multiracial were the most likely to undergo treatment.By disease category, those with benign, pediatric, and CNS diagnoses were most likely to undergo treatment.Multivariable logistic modeling found male sex and fewer miles from clinic to be associated with higher yield in IPPs, but age was no longer a significant factor.

VPs
There were 2,143 new VP consultations, of whom 1,026 (48%) underwent treatment at our institution (Table 4).In contrast to the IP population, likelihood of treatment was not affected by sex.On univariate analysis, younger patients and those presenting from closer distances were more likely to undergo treatment; however, these distances were still significantly further away than their IP counterparts (205 miles IP v 349 miles VP).Lymphoma, pediatric, and benign diagnoses had the highest yields, albeit significantly lower than their IP counterparts.Upon multivariable logistic modeling, miles from clinic was the only factor contributing to the likelihood of VPs undergoing treatment at our institution, with patients living closer to clinic being more likely to receive treatment at the facility.
Compared with IPPs, VPs were 1.9 times more likely to receive proton radiation (23% IP v 44% VP) and had slightly higher use of brachytherapy (4% IP v 6% VP).Proton use for lymphoma and CNS VPs were 2.5 and 2.2 times higher than in IPPs, respectively.GU malignancies accounted for the largest absolute number of VP treatments (188 VPs treated).

Census and ADI
Appendix Figure A1 shows a map from the US Census Bureau of counties by persons per square mile, with circles representing incremental 100-mile radii away from the clinic.Apart from one large metropolitan area at the edge of the 100-mile radius, most surrounding counties can be classified as rural, with <500 persons per square mile. 13As seen in Table 1, over 60% of all patients presented from within 200 miles of the clinic.
Appendix Figure A2 shows the national ADI for Minnesota and its surrounding states, with red representing the most disadvantaged group.Those presenting IP had significantly higher ADI than those presenting virtually (Table 5).In both IPPs and VPs, those who received treatment had significantly higher ADIs (Appendix Tables A1 and A2).In fact, those who presented IP and underwent treatment had the highest ADI.
However, the difference in ADI by yield was most significant in VPs (46 v 43).

DISCUSSION
The COVID-19 pandemic elevated telemedicine to the forefront of medical practice.Despite rapid adoption, the impact of telemedicine on access to cancer care, especially to procedural specialties such as radiation oncology, remains unclear.In our study of 17,915 new patient consultations seen from 2019 to 2021 in a tertiary radiation oncology clinic, we aimed to understand the impact of rapid telemedicine implementation on patient demographics and treatment yield and telemedicine's potential to increase access to radiotherapy.Although VPs were on average younger and lived farther than their IP counterparts, only distance from clinic affected the likelihood of treatment for VPs, with patients who elected to undergo treatment living on average closer than those who did not.Similarly, IPPs were more likely to undergo treatment if they were men and living closer to the clinic.Although treatment yield was higher in the VPs living closer, they remained statistically further from the clinic compared with IPPs.Compared with VPs who did not undergo treatment, VPs undergoing treatment had a higher ADI.These findings suggest that telemedicine was effective in increasing access to specialized radiotherapy for patients living further away, especially those with higher ADI and potentially less access to specialized treatment near their home.
5][16][17] One study found that almost 41% of patients with cancer experienced decreases in total visits and up to 52% reported decreased IP visits. 15Another study found that 20% of patients with cancer reported a delay in their cancer care. 18In comparison, our department experienced just a 5% reduction in new patient volume in 2020, possibly related to swift adoption of telemedicine by the institution and patients. 19As our institution adapted to the pandemic, new patient consultations in 2021 returned to prepandemic numbers and 22% of visits remained virtual.Our VPs were on average younger and were more likely to be male.In 2021, the CDC found that 37% of adults used telemedicine within the preceding year with higher use reported by females, those older than 65 years, and Non-Hispanic American Indian or Alaskan Natives. 20By contrast, a report by the Department of Health and Human Services found the highest telemedicine utilizers to be young adults and White patients, with the least utilization by people older than 65 years, and Black, Asian, and Latino populations. 21Like our study, a medical oncology outpatient cancer with 29% virtual visits in 2020 reported higher utilization among non-Hispanic White patients. 8The differences in the use of telemedicine in the general population and those presenting for radiotherapy may be at least partially attributable to the older demographic of patients with cancer (who may be less privy to using telemedicine), 22 the increase in men presenting for GU diagnoses, as well as our large catchment of nonmetropolitan patients.Our data showed that VPs presented more frequently for GU diagnoses and less often for palliative consultations.Similarly, a small study reviewing patient satisfaction with telemedicine in radiotherapy reported more use of telemedicine in younger patients and among those presenting for GU and less for palliative diagnoses. 7Although this may indicate a preference for palliative care closer to home, it may also indicate a need for improved telemedicine infrastructure for patients with palliative needs.Although telemedicine may hold promise for increasing access to care for disadvantaged groups, intentional effort may be needed to best reach these groups.
Understanding treatment yield for IPPs and VPs was a primary and novel objective of this study.Our VPs had significantly lower yield than IPPs.VPs with GU diagnoses had the largest reduction in yield compared with IPPs, likely attributed to the decreased acuity and favorable natural history of most prostate cancers that allow for the opportunity to seek multiple medical opinions. 23VPs who underwent treatment lived closer than those who did not; treated VPs still lived significantly farther away than treated IPPs.This shows telemedicine's ability to reach patients who may have faced challenges accessing care or those seeking a second opinion.
With lower ADI in VPs, it could be suggested that intrapandemic telemedicine reached those considered to be less health care-deprived.However, this may be explained by the unexpected increase in demand for telemedicine, which limited direct efforts to reach the medically underprivileged.With increased ADIs among VPs undergoing treatment, we show potential to expand oncology care access to patients who may not have radiotherapy near their home, including those who live in rural and/or health care-deprived areas.Most of our catchment area is considered rural and the majority of those living more than 100 miles away have high ADIs.As telemedicine becomes a more integral part of daily practice, improved identification of underserved populations, those needing telemedicine infrastructure, and those who would benefit from specialty radiotherapy is warranted.
A unique aspect of our institution compared with other regional radiation oncology centers is the availability of specialty radiotherapy services such as proton radiotherapy, brachytherapy, and access to a large comprehensive cancer center with clinical trial availability.VPs who underwent treatment used proton radiotherapy 1.9 times more than IPPs.This difference could be explained by significant increases in proton therapy utilization for those with CNS, head/neck/skin, sarcoma, GU, and lymphoma diagnoses, all sites in which proton therapy indications are merited and/or rising.This suggests that the availability of a unique treatment modality may have prompted a greater conversion from consultation to treatment or that consultation was performed for review of indications for proton therapy.This highlights the potential of telemedicine, with improved Abbreviations: GU, genitourinary; NA, not available; SD, standard deviation.
a Treatment modality of those who did not receive treatment at our institution was not known and therefore removed from statistical analysis but included in the percentages shown.
infrastructure, to improve access to specialty radiation oncology care.
This study has several limitations.Our center is a large tertiary cancer center with subspecialized care and treatment modalities, which may not reflect the general radiation oncology practice.Also, our patient population is generally homogenous in race.Therefore, it may not discern telemedicine and care access challenges faced by racial or ethnic minorities.Although our expanded virtual reach leaned in favor of insured, more privileged patients, we were able to treat patients further from clinic with a higher ADI and increase the access to specialized radiotherapy techniques using telemedicine services.Expansion of telemedicine to underprivileged populations is warranted, and a broader study of telemedicine in radiation oncology would benefit from increased racial, gender, and geographic diversity.In determining treatment yield, we only assessed patients who underwent radiotherapy at our center.Some patients may not have been recommended radiotherapy and the proportion may have differed between IP and VPs.Also, patients may have undergone consultation within the study window but initiated radiotherapy outside of the December 2021 study cutoff date.As a result of these factors, the actual treatment yield may have been higher than reported.
Our study did not investigate the patient attitudes toward telemedicine, their ability to use telemedicine interfaces, and how attitudes may have changed due to the pandemic.Although studies show general patient satisfaction and equivalent care outcomes with telemedicine, [24][25][26][27] assessment of patient satisfaction with virtual technologies is merited for continued improvement in quality.Similarly, the effect of telemedicine on cancer care and outcomes is worthy of continued investigation.5][16][17] Development of tools to reach patients with limited access and/or specialty care needs is warranted.
Telemedicine in radiation oncology is a developing resource with potential to enhance cancer care.Since the novelty and alarm of the COVID-19 pandemic have subsided, telemedicine has transformed from a necessity to a powerful tool that can enhance access to medical care.Telemedicine can help increase the reach of subspecialty care within radiation oncology, a technical and procedural field, and as a result increase access to other technical medical specialties.Further research is needed to improve telemedicine outreach and infrastructure.

FIG A1 .
FIG A1.Persons per square mile by county per state, US Census 2020.Each circle represents an additional 100-mile radius from Rochester, MN, up to 500 miles.

TABLE 1 .
Demographics and Medical Characteristics Stratified by Prepandemic and Pandemic Periods (N 5 17,915)

TABLE 2 .
Demographic and Medical Characteristics by Visit Type During the Pandemic (n 5 10,354)

TABLE 2 .
Demographic and Medical Characteristics by Visit Type During the Pandemic (n 5 10,354) (continued)Treatment modality of those who did not receive treatment at our institution was not known and therefore removed from statistical analysis but included in the percentages shown. a

TABLE 3 .
In-Person Patient Characteristics During the Pandemic by Yield (n 5 8,211)

TABLE 3 .
In-Person Patient Characteristics During the Pandemic by Yield (n 5 8,211) (continued)Treatment modality of those who did not receive treatment at our institution was not known and therefore removed from statistical analysis but included in the percentages shown. a

TABLE 4 .
Virtual Patient Characteristics During the Pandemic by Yield (n 5 2,143)

TABLE A1 .
ADI for IP Visits by Yield

TABLE A2 .
ADI for Virtual Visits by Yield