Abstract
Background:
Telemedicine has expanded rapidly during the COVID-19 pandemic. Data on telemedicine utilization are lacking, and racial/ethnic disparities in utilization and satisfaction are unknown among breast cancer patients.
Methods:
This was a longitudinal study, with two surveys conducted in 2020 and 2021, among patients enrolled in the Chicago Multiethnic Epidemiologic Breast Cancer Cohort. Telemedicine utilization was modeled using mixed-effects logistic regression. Telemedicine satisfaction, assessed using a 5-point Likert scale, was modeled using mixed-effects proportional odds regression. Qualitative data on satisfaction were coded and analyzed using grounded theory.
Results:
Of 1,721 respondents, most (70.3%) were White, followed by 23.6% Black, 3.1% Asian, and 3.0% Hispanic. The median duration from breast cancer diagnosis to survey was 5.5 years (interquartile range: 2.7–9.4). In 2020, 59.2% reported telemedicine use; in 2021, 64.9% did, with a statistically significant increase (p < 0.001). Black patients had greater odds of telemedicine use than White patients (adjusted odds ratio [AOR] = 1.55, 95% confidence interval [CI]: 1.17–2.05). In 2020, 90.3% reported somewhat-to-extreme satisfaction; in 2021, 91.2% did, with a statistically significant, although clinically small, increase (p = 0.038). There were no racial/ethnic differences in telemedicine satisfaction between Black (AOR = 1.05, 95% CI: 0.81–1.35), Asian (AOR = 0.63, 95% CI: 0.34–1.16), or Hispanic (AOR = 0.63, 95% CI: 0.33–1.21) and White patients. Major themes emerged from the respondents that explained their levels of satisfaction were convenience, safety, specialty dependence, and technical issues.
Conclusions:
Telemedicine utilization and satisfaction were high among breast cancer patients over time and across races/ethnicities. Telemedicine could have great potential in reducing barriers to care and promoting health equity for breast cancer patients. However, patients' perceived challenges in accessing high-quality virtual care should be addressed.
Keywords: telemedicine, racial/ethnic disparities, utilization, satisfaction, breast cancer
Introduction
The Coronavirus disease 2019 (COVID-19) pandemic has disrupted health care delivery in the United States and worldwide. In response to the pandemic, U.S. cancer centers shifted their provision of certain care and services from in-person to telemedicine. Telemedicine utilization has increased in recent years, with widespread adoption by health organizations across the nation during the COVID-19 pandemic. Research has shown a dramatic increase in telemedicine utilization for primary care and mental health among rural Medicare populations or those privately insured.1,2 In 2019, the American Hospital Association reported that the implementation of telemedicine programs grew from 35.0% in 2010 to 76.0% in 2017.
Two studies illustrated that more than one in three American adults used telemedicine in the past 12 months,3 and telemedicine visits increased by 50.0% during the first quarter of 2020 compared with the same quarter in 2019.4 The 2020 policy changes in regulatory waivers and flexible reimbursement to Medicare providers released from the Centers for Medicare and Medicaid Services have facilitated the rapid expansion of telehealth.5–7 In 2021, the American Society of Clinical Oncology published comprehensive telehealth standards and practices for oncology.8
Literature has documented telemedicine use and satisfaction in oncology settings. A 2020 study of 311 cancer patients demonstrated that those who utilize telemedicine achieve similar clinical efficiency and safety outcomes as those with in-person visits.9 A pilot study suggested the feasibility of supportive care delivery through telemedicine during the pandemic.10 Prior research has also reported a 23.1% increase in overall satisfaction among cancer patients who used telemedicine in 2020,11 and that more than 90.0% of cancer patients are somewhat-to-very satisfied with their telemedicine experiences during the pandemic.12,13
Despite present research demonstration of the feasibility of virtual care delivery, the utilization and expansion of telemedicine, and the reported benefits and satisfaction in oncology overall during the COVID-19 pandemic, these findings may not depict breast cancer patients specifically because of the heterogeneity of cancer patient populations in the United States. Moreover, these studies are cross-sectional surveys conducted during the pandemic and are descriptive. No research currently has investigated the utilization of and satisfaction with telemedicine in patients with cancers (including breast cancer) over time. Therefore, longitudinal studies examining the trend, changes, and racial/ethnic disparities in telemedicine utilization and satisfaction among cancer patients are needed.
There is limited research on telemedicine use and satisfaction specifically in breast cancer patients. A retrospective analysis found that 46.8% of 77 breast cancer patients utilized telemedicine for follow-up care in 2020.14 A survey showed that more than a third of patients with breast or gynecologic cancer used telemedicine (n = 74), with 91.7% overall satisfaction.15 Another survey reported 84.6% of 123 breast cancer patients had a positive experience with telemedicine.16 A cross-sectional study found that 58.1% of 75 breast cancer patients had 1–2 telemedicine visits in the past, with a median satisfaction score of 5.5 based on a 7-point Likert scale.17
A study of teleconsultation conducted in 1,299 European breast cancer patients also found a high degree of overall satisfaction.18 Although with encouraging results, most of these studies were descriptive, small, and/or in predominantly White populations. Thus, the findings might not be generalizable to the breast cancer patient population in the United States. Furthermore, these previous studies have not examined differences in telemedicine utilization and satisfaction across racial/ethnic groups of breast cancer patients due to the lack of significant sample size, and therefore, it is unclear whether racial/ethnic disparities exist in this patient population. Given the increasing demand and use of telemedicine in the breast oncology setting, identifying racial/ethnic patients who are less likely to use and be satisfied with telemedicine may help cancer centers improve the delivery of virtual care and services for breast cancer patients.
To address these gaps in the literature, we sought to (1) examine telemedicine utilization and satisfaction since the pandemic; (2) quantitatively assess racial/ethnic disparities in telemedicine utilization and satisfaction over time; and (3) qualitatively evaluate patient satisfaction with telemedicine use, in a large, racially diverse, longitudinal cohort of breast cancer patients using a mixed-methods approach. The rapid growth of telemedicine implementation raises concerns about racial/ethnic disparities, making this study essential.
Methods
STUDY DESIGN AND POPULATION
This was a longitudinal study, with two surveys conducted in 2020 and in 2021, which collected data on telemedicine utilization and satisfaction among breast cancer patients enrolled in the Chicago Multiethnic Epidemiologic Breast Cancer Cohort (ChiMEC). Briefly, ChiMEC is a hospital-based study that has enrolled more than 4,000 patients who were diagnosed with breast cancer since 1993. Detailed information of ChiMEC has been previously described.19 From July to September 2020, 2,661 questionnaires were sent to ChiMEC participants. During the same period in 2021, a total of 2,788 questionnaires were sent. All patients provided their written informed consents to the study. This study was approved by the University of Chicago Institutional Review Board.
MEASURES
Telemedicine utilization, assessed by asking whether participants have utilized telemedicine (e.g., telephone call or Zoom meeting) with their providers during the COVID-19 pandemic, was per self-report as “yes/no.” Among participants who utilized telemedicine, satisfaction was assessed by asking how satisfied they were with their telemedicine use experiences, using a 5-point Likert scale (not at all satisfied, a little satisfied, somewhat satisfied, very satisfied, and extremely satisfied). In the 2020 survey, we also asked participants to share their experiences or recommendations for improvement regarding telemedicine use, which was further analyzed as qualitative data.
Demographic and clinical characteristics included age, highest level of education, marital status, type of health insurance, duration from breast cancer diagnosis to survey, Charlson comorbidity index (CCI),20 molecular subtype (hormone receptor [HR]-positive/human epidermal growth factor receptor 2 [HER2]-negative, HER2-positive, triple-negative breast cancer [TNBC]), stage group, receipt of chemotherapy, receipt of radiation therapy, and receipt of hormonal therapy. Stage group was classified according to the American Joint Committee on Cancer (AJCC)'s staging system.
DATA ANALYSIS
We calculated the means (standard deviations [SD]) for continuous variables, with distribution comparisons using ANOVA. We described categorical variables using frequencies (%) and compared groups using Pearson's chi-square or Fisher's exact tests. Mixed-effects binary logistic regression was used to model telemedicine utilization. Mixed-effects proportional odds regression was used to model telemedicine satisfaction. Because some patients completed both 2020 and 2021 surveys, the mixed-effects approach allowed us to account for clustering at the individual level and helped examine within-individual changes more accurately. Three multivariable regression models were built separately for utilization and satisfaction.
If a variable was significant at the alpha level of 0.05 and changed the parameter estimate of a significant covariate by >10%, then the variable was considered a potential confounder and remained in the adjusted model to account for its confounding effect. Because the level of education, type of health insurance, molecular subtype, and receipt of radiation therapy or hormonal therapy were not statistically significant in all multivariable models, they were excluded in the final models. Adjusted odds ratios (AOR) and 95% confidence intervals (CI) were calculated. The level of significance was set at two-sided p < 0.05. Statistical analyses were performed using Stata 17 (StataCorp, College Station, TX).
The qualitative data on telemedicine satisfaction were analyzed using grounded theory,21 which started by assigning codes to small portions of the data allowing for connections to be formed across a larger subset of the data. After analyzing these codes, categories were then created, which allowed for a theoretical understanding of the presented data. Two coders independently coded the data. Four themes that emerged from the codes were found to be consistent between the two coders, including “communication,” “wants physical exam,” “safety,” and “technical issues.” The remaining codes and themes were presented to the research team, and were determined to be “health care worker dependent,” “positive,” and “specialty dependent.”
Results
PATIENT CHARACTERISTICS
In 2020, 47.4% of 2,661 patients with breast cancer responded to the survey; in 2021, 46.6% of 2,788 patients did. Overall, a total of 1,721 breast cancer patients responded to the survey in 2020 and 2021. Of these, the mean age at survey was 60.7 (SD = 11.9) years; most (70.3%) were White, followed by 23.6% Black, 3.1% Asian, and 3.0% Hispanic; 72.4% had private insurance and 25.8% were on Medicaid or Medicare; 80.3% had stage I-III disease (Table 1).
Table 1.
Demographic and Clinical Characteristics of Breast Cancer Patients, Overall and by Race/Ethnicity
| CHARACTERISTIC | TOTAL (N = 1,721), N (%) | WHITE (N = 1,209), N (%) | BLACK (N = 405), N (%) | ASIAN (N = 53), N (%) | HISPANIC (N = 52), N (%) | p a |
|---|---|---|---|---|---|---|
| Age at cancer diagnosis, years, mean (SD) | 53.8 (11.8) | 53.9 (11.3) | 55.2 (12.8) | 48.2 (11.9) | 48.0 (11.6) | <0.001 |
| Age at survey, years | ||||||
| Mean (SD) | 60.7 (11.9) | 60.7 (11.5) | 62.3 (12.2) | 53.7 (12.3) | 53.3 (11.6) | <0.001 |
| <45 | 163 (9.5) | 107 (8.9) | 31 (7.7) | 11 (20.8) | 14 (26.9) | <0.001 |
| 4–65 | 884 (51.4) | 638 (52.8) | 185 (45.7) | 33 (62.3) | 28 (53.8) | |
| ≥65 | 674 (39.2) | 464 (38.4) | 189 (46.7) | 9 (17.0) | 10 (19.2) | |
| Hihest level of education | <0.001 | |||||
| High school/GED or less | 159 (10.3) | 96 (8.6) | 53 (15.7) | 0 (0.0) | 10 (21.7) | |
| Trade/technical school or Associate's degree | 368 (23.8) | 235 (21.2) | 114 (33.8) | 4 (7.7) | 15 (32.6) | |
| Bachelor's degree | 421 (27.2) | 320 (28.8) | 72 (21.4) | 21 (40.4) | 8 (17.4) | |
| Graduate or professional degree | 598 (38.7) | 459 (41.4) | 98 (29.1) | 27 (51.9) | 13 (28.3) | |
| Marital statusb | <0.001 | |||||
| Single or not married | 311 (18.8) | 133 (11.4) | 157 (40.4) | 11 (21.2) | 10 (20.8) | |
| Married | 1,155 (69.9) | 922 (79.3) | 162 (41.6) | 36 (69.2) | 35 (72.9) | |
| Separated, divorced, or widowed | 187 (11.3) | 108 (9.3) | 70 (18.0) | 5 (9.6) | 3 (6.2) | |
| Type of health insurancec | <0.001 | |||||
| Private | 1,184 (72.4) | 899 (78.2) | 202 (52.5) | 47 (90.4) | 36 (73.5) | |
| Medicaid | 81 (5.0) | 18 (1.6) | 58 (15.1) | 0 (0.0) | 5 (10.2) | |
| Medicare | 340 (20.8) | 211 (18.4) | 117 (30.4) | 5 (9.6) | 6 (12.2) | |
| Other | 31 (1.9) | 21 (1.8) | 8 (2.1) | 0 (0.0) | 2 (4.1) | |
| Duration from cancer diagnosis to survey, years, median (IQR) | 5.5 (2.7–9.4) | 5.7 (2.6–9.5) | 5.4 (2.8–9.5) | 4.5 (2.7–8.1) | 3.8 (1.3–6.9) | 0.025 |
| Duration from cancer diagnosis to survey, years | 0.031 | |||||
| <2 | 289 (17.4) | 205 (17.6) | 60 (15.3) | 9 (17.3) | 15 (30.6) | |
| 2–5 | 610 (36.7) | 407 (34.8) | 161 (41.0) | 21 (40.4) | 20 (40.8) | |
| ≥6 | 764 (45.9) | 556 (47.6) | 172 (43.8) | 22 (42.3) | 14 (28.6) | |
| Charlson comorbidity index | 0.002 | |||||
| 0 | 1,453 (87.4) | 1,038 (89.0) | 323 (82.2) | 45 (86.5) | 47 (94.0) | |
| ≥1 | 209 (12.6) | 128 (11.0) | 70 (17.8) | 7 (13.5) | 3 (6.0) | |
| AJCC stage group | 0.29 | |||||
| 0 | 305 (18.6) | 206 (17.9) | 80 (20.6) | 11 (21.6) | 8 (16.3) | |
| I | 727 (44.2) | 539 (46.7) | 147 (37.8) | 20 (39.2) | 21 (42.9) | |
| II | 437 (26.6) | 293 (25.4) | 115 (29.6) | 13 (25.5) | 15 (30.6) | |
| III | 156 (9.5) | 105 (9.1) | 40 (10.3) | 7 (13.7) | 4 (8.2) | |
| IV | 18 (1.1) | 10 (0.9) | 7 (1.8) | 0 (0.0) | 1 (2.0) | |
| Molecular subtype | <0.001 | |||||
| HR+/HER2− | 854 (66.9) | 633 (70.6) | 169 (56.1) | 24 (61.5) | 28 (70.0) | |
| HR+/HER2+ | 137 (10.7) | 94 (10.5) | 29 (9.6) | 9 (23.1) | 5 (12.5) | |
| HR−/HER2+ | 71 (5.6) | 42 (4.7) | 26 (8.6) | 2 (5.1) | 1 (2.5) | |
| TNBC | 215 (16.8) | 127 (14.2) | 77 (25.6) | 4 (10.3) | 6 (15.0) | |
| Having received chemotherapy | 0.14 | |||||
| No | 921 (55.9) | 668 (57.6) | 198 (51.0) | 27 (51.9) | 28 (57.1) | |
| Yes | 728 (44.1) | 491 (42.4) | 190 (49.0) | 25 (48.1) | 21 (42.9) | |
| Having received radiation therapy | 0.008 | |||||
| No | 636 (39.1) | 471 (41.3) | 129 (33.6) | 23 (44.2) | 12 (25.0) | |
| Yes | 990 (60.9) | 670 (58.7) | 255 (66.4) | 29 (55.8) | 36 (75.0) | |
| Having received hormonal therapy | 0.005 | |||||
| No | 517 (31.8) | 347 (30.4) | 146 (38.0) | 9 (17.3) | 14 (29.2) | |
| Yes | 1,109 (68.2) | 794 (69.6) | 238 (62.0) | 43 (82.7) | 34 (70.8) | |
p-Values were calculated using ANOVA for continuous data and Pearson's χ2 tests for categorical data.
Marital status was documented at the time of diagnosis.
Other included uninsured/self-pay, insurance not otherwise specified, TRICARE, and Military.
AJCC, American Joint Committee on Cancer; GED, general educational development; HER2, human epidermal growth factor receptor 2; HR, hormone receptors; IQR, interquartile range; SD, standard deviation; TNBC, triple-negative breast cancer.
Compared with other racial/ethnic groups, Black patients were older (62.3 [SD = 12.2] years) and were more likely to be single or not married (40.4%), be on Medicare (30.4%) or Medicaid (15.1%), and have a CCI of ≥1 (17.8%) (Table 1). Demographic and clinical characteristics were similar between patients who responded to the 2020 and 2021 surveys, suggesting that loss-to-follow-up in the longitudinal study was not related to these characteristics (Table 2).
Table 2.
Demographic and Clinical Characteristics of Breast Cancer Patients, by Survey Year
| CHARACTERISTIC | 2020 (N = 1,260), N (%) | 2021 (N = 1,299), N (%) |
|---|---|---|
| Age at cancer diagnosis, years, mean (SD) | 54.5 (11.8) | 53.5 (11.6) |
| Age at survey, years | ||
| Mean (SD) | 61.1 (11.9) | 61.2 (11.7) |
| <45 | 118 (9.4) | 110 (8.5) |
| 45–65 | 631 (50.1) | 659 (50.7) |
| ≥65 | 511 (40.6) | 530 (40.8) |
| Race/ethnicity | ||
| White | 907 (72.0) | 928 (71.4) |
| Black | 281 (22.3) | 285 (21.9) |
| Asian | 37 (2.9) | 43 (3.3) |
| Hispanic | 35 (2.8) | 43 (3.3) |
| Highest level of education | ||
| High school/GED or less | 106 (9.7) | 134 (10.4) |
| Trade/technical school or associate's degree | 260 (23.7) | 307 (23.9) |
| Bachelor's degree | 310 (28.3) | 351 (27.3) |
| Graduate or professional degree | 419 (38.3) | 495 (38.5) |
| Marital statusa | ||
| Single or not married | 229 (18.6) | 233 (18.6) |
| Married | 867 (70.5) | 889 (71.1) |
| Separated, divorced, or widowed | 134 (10.9) | 129 (10.3) |
| Type of health insuranceb | ||
| Private | 884 (71.5) | 916 (74.7) |
| Medicaid | 50 (4.0) | 60 (4.9) |
| Medicare | 280 (22.6) | 230 (18.8) |
| Other | 23 (1.9) | 20 (1.6) |
| Duration from cancer diagnosis to survey, years | ||
| <2 | 224 (17.8) | 104 (8.4) |
| 2–5 | 494 (39.2) | 473 (38.1) |
| ≥6 | 541 (43.0) | 666 (53.6) |
| Charlson comorbidity index | ||
| 0 | 1,093 (86.9) | 1,090 (87.7) |
| ≥1 | 165 (13.1) | 153 (12.3) |
| AJCC stage group | ||
| 0 | 235 (18.9) | 226 (18.4) |
| I | 543 (43.7) | 556 (45.2) |
| II | 333 (26.8) | 314 (25.5) |
| III | 119 (9.6) | 122 (9.9) |
| IV | 12 (1.0) | 13 (1.1) |
| Molecular subtype | ||
| HR+/HER2− | 644 (67.5) | 629 (65.9) |
| HR+/HER2+ | 97 (10.2) | 103 (10.8) |
| HR−/HER2+ | 54 (5.7) | 56 (5.9) |
| TNBC | 159 (16.7) | 166 (17.4) |
| Having received chemotherapy | ||
| No | 717 (57.5) | 682 (55.2) |
| Yes | 530 (42.5) | 553 (44.8) |
| Having received radiation therapy | ||
| No | 475 (38.6) | 472 (38.7) |
| Yes | 755 (61.4) | 747 (61.3) |
| Having received hormonal therapy | ||
| No | 390 (31.7) | 390 (32.0) |
| Yes | 840 (68.3) | 829 (68.0) |
Marital status was documented at the time of diagnosis.
Other included uninsured/self-pay, insurance not otherwise specified, TRICARE, and Military.
TELEMEDICINE UTILIZATION
In 2020, 59.2% of the patients reported having utilized telemedicine, while in 2021, 64.9% did. By race/ethnicity, in 2020, 71.7% of Black patients reported having utilized telemedicine compared to 67.7% of Asian, 55.2% of Hispanic, and 55.1% of White patients; in 2021, 72.7% of Hispanic patients reported having utilized telemedicine, 71.4% of Asian, 70.8% of Black, and 62.5% of White did (Table 3).
Table 3.
Telemedicine Utilization and Satisfaction Among Breast Cancer Patients, by Survey Year and Race/Ethnicity
| TOTAL, N (%) | WHITE, N (%) | BLACK, N (%) | ASIAN, N (%) | HISPANIC, N (%) | |
|---|---|---|---|---|---|
| Survey year | |||||
| Telemedicine utilizationa | |||||
| 2020 (N = 1,100) | |||||
| No | 449 (40.8) | 355 (45.0) | 69 (28.3) | 11 (32.4) | 14 (44.8) |
| Yes | 651 (59.2) | 434 (55.1) | 175 (71.7) | 23 (67.7) | 18 (55.2) |
| 2021 (N = 1,104) | |||||
| No | 387 (35.1) | 297 (37.5) | 71 (29.2) | 10 (28.6) | 9 (27.3) |
| Yes | 717 (64.9) | 495 (62.5) | 172 (70.8) | 25 (71.4) | 24 (72.7) |
| Satisfaction with telemedicineb | |||||
| 2020 (N = 649) | |||||
| Not at all satisfied | 19 (2.9) | 12 (2.8) | 7 (4.0) | 0 | 0 |
| A little satisfied | 44 (6.8) | 33 (7.6) | 6 (3.4) | 5 (21.7) | 0 |
| Somewhat satisfied | 188 (29.0) | 123 (28.5) | 54 (30.9) | 5 (21.7) | 6 (33.3) |
| Very satisfied | 276 (42.5) | 183 (42.4) | 69 (39.4) | 12 (52.2) | 11 (61.1) |
| Extremely satisfied | 122 (18.8) | 81 (18.8) | 39 (22.3) | 1 (4.4) | 1 (5.6) |
| 2021 (N = 715) | |||||
| Not at all satisfied | 15 (2.1) | 12 (2.4) | 3 (1.8) | 0 | 0 |
| A little satisfied | 48 (6.7) | 37 (7.5) | 9 (5.3) | 1 (4.0) | 1 (4.2) |
| Somewhat satisfied | 180 (25.2) | 113 (22.9) | 46 (26.9) | 9 (36.0) | 12 (50.0) |
| Very satisfied | 317 (44.3) | 222 (44.9) | 73 (42.7) | 13 (52.0) | 8 (33.3) |
| Extremely satisfied | 155 (21.7) | 110 (22.3) | 40 (23.4) | 2 (8.0) | 3 (12.5) |
Telemedicine utilization was assessed by asking whether or not participants have utilized telemedicine (telephone call or Zoom meeting) with their doctor(s).
Satisfaction with telemedicine was assessed by asking how satisfied participants were with their telemedicine experience among patients having telemedicine.
After adjusting for age group, duration from breast cancer diagnosis to survey, marital status, CCI, AJCC stage group, receipt of chemotherapy, and time effect (survey year), Black patients still had greater odds of having utilized telemedicine than White patients (AOR = 1.55, 95% CI: 1.17–2.05), while no difference in utilization was observed between Asian and White (AOR = 1.42, 95% CI: 0.77–2.60) or between Hispanic and White (AOR = 1.19, 95% CI: 0.63–2.25) patients.
In the same model (model 3), patients <45 years of age had significantly greater odds of telemedicine use than those 45–64 years of age (AOR = 1.46, 95% CI: 1.01–2.12). Compared with patients having ≥6 years from cancer diagnosis to survey, those having <2 years (AOR = 2.04, 95% CI: 1.48–2.82) or 2–5 years (AOR = 1.42, 95% CI: 1.14–1.80) had greater odds of telemedicine use. In addition, there was a significant increase in telemedicine utilization from 2020 to 2021 (Table 4).
Table 4.
Disparities in Telemedicine Utilization Among Breast Cancer Patients Over Time: Mixed-Effects Binary Logistic Regression
| CHARACTERISTIC | MODEL 1, AOR (95% CI) | MODEL 2, AOR (95% CI) | MODEL 3, AOR (95% CI) |
|---|---|---|---|
| Survey year | |||
| 2020 | 1.0 (reference) | 1.0 (reference) | 1.0 (reference) |
| 2021 | 1.35 (1.16–1.56)*** | 1.46 (1.24–1.72)*** | 1.45 (1.23–1.70)*** |
| Race/ethnicity | |||
| White | 1.0 (reference) | 1.0 (reference) | 1.0 (reference) |
| Black | 1.77 (1.37–2.30)*** | 1.54 (1.16–2.03)** | 1.55 (1.17–2.05)** |
| Asian | 1.49 (0.83–2.69) | 1.44 (0.79–2.62) | 1.42 (0.77–2.60) |
| Hispanic | 1.23 (0.67–2.24) | 1.23 (0.66–2.31) | 1.19 (0.63–2.25) |
| Age group, years | |||
| <45 | 1.45 (1.00–2.11) | 1.46 (1.01–2.12)* | |
| 45–64 | 1.0 (reference) | 1.0 (reference) | |
| ≥65 | 1.19 (0.95–1.49) | 1.17 (0.93–1.47) | |
| Duration from cancer diagnosis to survey, years | |||
| <2 | 2.08 (1.51–2.87)*** | 2.04 (1.48–2.82)*** | |
| 2–5 | 1.44 (1.15–1.80)** | 1.43 (1.14–1.80)** | |
| ≥6 | 1.0 (reference) | 1.0 (reference) | |
| Marital status | |||
| Married | 1.0 (reference) | 1.0 (reference) | |
| Single or not married | 1.32 (0.99–1.77) | 1.31 (0.98–1.76) | |
| Separated, divorced, or widowed | 1.26 (0.88–1.79) | 1.27 (0.89–1.81) | |
| Charlson comorbidity index | |||
| 0 | 1.0 (reference) | 1.0 (reference) | |
| ≥1 | 1.47 (1.06–2.05)* | 1.49 (1.07–2.07)* | |
| Having received chemotherapy | |||
| No | 1.0 (reference) | 1.0 (reference) | |
| Yes | 1.41 (1.12–1.76)** | 1.27 (0.94–1.70) | |
| AJCC stage group | |||
| 0 | 0.88 (0.65–1.21) | ||
| I | 1.0 (reference) | ||
| II | 1.06 (0.79–1.43) | ||
| III | 1.15 (0.75–1.75) | ||
| IV | 1.42 (0.49–4.09) | ||
p < 0.05; **p < 0.01; ***p < 0.0001.
AOR, adjusted odds ratio; CI, confidence interval.
TELEMEDICINE SATISFACTION
In 2020, 90.3% of the patients reported somewhat-to-extremely satisfied with their experiences of telemedicine use; in 2021, 91.2% did. By race/ethnicity, in 2020, all Hispanic patients reported somewhat-to-extreme satisfaction with telemedicine, followed by 92.6% of Black, 89.7% of White, and 78.3% of Asian; in 2021, 96.0% of Asian patients reported somewhat-to-extreme satisfaction with telemedicine compared to 95.8% of Hispanic, 93.0% of Black, and 90.1% of White patients did (Table 3).
After adjusting for age group, duration from breast cancer diagnosis to survey, CCI, AJCC stage group, and year of survey, we did not observe racial/ethnic differences in telemedicine satisfaction (Table 5). In the same model (model 3), patients ≥65 years of age had lower odds of telemedicine satisfaction than those 45–64 years of age (AOR = 0.72, 95% CI: 0.57–0.91). Patient satisfaction with telemedicine was significantly increased from 2020 to 2021. In addition, there was a higher level of telemedicine satisfaction among patients having <2 years from cancer diagnosis to survey (Table 5).
Table 5.
Racial/Ethnic Differences in Satisfaction with Telemedicine Use Experience Among Breast Cancer Patients Over Time: Mixed-Effects Proportional Odds Regression
| CHARACTERISTIC | MODEL 1, AOR (95% CI) | MODEL 2, AOR (95% CI) | MODEL 3, AOR (95% CI) |
|---|---|---|---|
| Survey year | |||
| 2020 | 1.0 (reference) | 1.0 (reference) | 1.0 (reference) |
| 2021 | 1.18 (1.00–1.39) | 1.20 (1.01–1.42)* | 1.20 (1.01–1.43)* |
| Race/ethnicity | |||
| White | 1.0 (reference) | 1.0 (reference) | 1.0 (reference) |
| Black | 1.03 (0.80–1.32) | 1.09 (0.84–1.40) | 1.05 (0.81–1.35) |
| Asian | 0.69 (0.38–1.24) | 0.67 (0.37–1.20) | 0.63 (0.34–1.16) |
| Hispanic | 0.75 (0.40–1.40) | 0.68 (0.36–1.27) | 0.63 (0.33–1.21) |
| Age group, years | |||
| <45 | 0.91 (0.64–1.32) | 0.89 (0.62–1.29) | |
| 45–64 | 1.0 (reference) | 1.0 (reference) | |
| ≥65 | 0.74 (0.59–0.93)* | 0.72 (0.57–0.91)** | |
| Duration from cancer diagnosis to survey, years | |||
| <2 | 1.0 (reference) | 1.0 (reference) | |
| 2–5 | 0.65 (0.48–0.87)** | 0.64 (0.48–0.87)** | |
| ≥6 | 0.76 (0.55–1.03) | 0.74 (0.54–1.03) | |
| Charlson comorbidity index | |||
| 0 | 1.0 (reference) | ||
| ≥1 | 1.11 (0.80–1.53) | ||
| AJCC stage group | |||
| 0 | 1.32 (0.96–1.82) | ||
| I | 1.0 (reference) | ||
| II | 1.23 (0.94–1.61) | ||
| III | 1.00 (0.68–1.47) | ||
| IV | 1.88 (0.70–5.02) | ||
p < 0.05; **p < 0.01.
ATTRIBUTES OF TELEMEDICINE SATISFACTION AND DISSATISFACTION
In the qualitative analysis of 256 patients who answered the free-text question, 6 major themes emerged from the patients, which explained their levels of satisfaction with telemedicine (Table 6). Overall, 7.0% reported satisfaction dependent on their prior relationships with providers and 7.8% reported satisfaction conditional on the nature of health care needs, while 12.5% and 8.6% reported dissatisfaction because of communication challenges and technical barriers, respectively. Moreover, 4.3% commented on the need for telemedicine because of the COVID-19 pandemic, and another 4.3% were neutral on telemedicine use (Supplementary Table S1).
Table 6.
Satisfaction or Dissatisfaction with Telemedicine Use Experience Reported by Breast Cancer Patients
| SATISFACTION STATUS | THEMES/REASONS | QUOTES FROM PATIENTS |
|---|---|---|
| Satisfied | 1. Patients are able to avoid issues with planning a visit due to logistical reasons. Telemedicine is convenient. | • “I think it is a really wonderful way to have communication with your doctor and get your point across, without worrying about all of the logistics related to an in-person visit.” • “Can be utilized more in future if personal or physical review not necessary. I have 5 hr drive to Chicago so having option of telemedicine would be appreciated.” |
| 2. Patients can reduce their anxiety about a medical issue by contacting providers without a break in their care due to COVID-19. | • “A very good idea especially for people who are anxious and wish to speak to the doctor.” • “It worked very well and I was appreciative that this was available instead of missing my appointment.” |
|
| 3. When telemedicine appointment is with a provider the patient is already familiar with. | • “I think Tele-med is great for returning patients. Not so sure without a relationship with a doctor it would be so great.” • “I am glad to see UChicago starting to provide telemedicine so I can use the service provided by the doctors I trust. I definitely won't use telemedicine with a random doctor for illness that needs doctors to diagnose.” |
|
| 4. Because telemedicine is the safest option during the pandemic. | • “I think it was important because of what was going on with COVID-19. I think it helped keep people safe and it worked fine. It worked well because the doctors were still able to figure out what patients needed, but people did not have to come in physically, so overall a good thing.” • “I see it only as a temporary thing for this big emergency. I am in good health and haven't needed care, but if I had major health problems I might feel differently.” |
|
| 5. Patients felt their questions and concerns were addressed through telemedicine. | • “I loved it, I could talk to drs about my concerns as I did in office, a wonderful experience.” • “Everything was fine, I was able to ask any questions I had and they gave me options about using the phone or live video chat, and I was very satisfied with the experience.” |
|
| Dissatisfied | 6. Impossible to have a physical examination by virtual visit. Patients feel reduction in quality of care. | • “I feel that telemedicine is difficult to be physically assessed appropriately. It was difficult to put into words symptoms and I felt the health care provider didn't really grasp what I was trying to communicate. Not a fan of telemedicine.” • “The telemedicine appointment was with an oncologist in Northwestern. I worry that the quality of care is not the same. Normally I'd get a manual exam by the doctor and spend more time talking in person than we did in the phone.” |
| 7. Poor communication in virtual health care setting. | • “Instructions were not clear. It needs to be clear that doctor will send link shortly before call, then link needs to work.” • “They need to tell the [patient] that they are changing their [appointment] I went to the hospital three times and they told me I couldn't see the doctor they said they called me but they did not call me so that was a waste of my time then I had to make appointments all over again while I was there at the dcam building.” |
|
| 8. For certain specialties, in-person visits are necessary. | • “For cancer survivors/patients it cannot substitute for them doing an in-person breast exam, ultrasound, mammogram or blood work.” • “I feel an in-person visit is needed on a scheduled basis so doctors can physically see and examine you. Also, me being diabetic, my feet are going numb and can't be seen by my doctor, my blood work needs to be done and vitals along with my hemoglobin checked which doesn't happen through telemedicine.” |
|
| 9. Poor follow-up post-telemedicine appointment. | • “I have had difficulty getting the follow-up appointments that were recommended during the telemedicine visits. It would be helpful if the clinic for the doctor doing the telemedicine visit followed up to make the recommended appointments just as would happen after an in person clinic visit.” • “I am cancer free as of April 27th and I am having a very difficult time getting in touch with the right people to change appointments and get surgery scheduled to have my ovaries and fallopian tubes removed in September. My oncologist's Nurse is no longer there and I have sent several emails that have gone unanswered. It's frustrating.” |
|
| 10. Glitches during visit as a barrier to proper care. | • “It is full of glitches and only works variably. Often we end up just speaking on the phone.” • “The doctor tried to call a couple of times, but because of the poor cell phone connection we were cut off. I tried to reschedule through the online MyChart, but was not successful. It was a routine visit, so I did not pursue further.” |
|
| 11. Not comfortable using technology. | • “I am not familiar telemedicine.” • “I don't know much about telemedicine. I hope to learn more with time.” |
Patients were satisfied with their experiences of telemedicine use because it removed logistical barriers to care access (Theme 1). Among patients who received specialty care, some opted to travel long distances to clinics that host renowned specialists. Patients commented on the convenience in such instances by saying, “[telemedicine] Can be utilized more in future if personal or physical review not necessary. I have 5 hour drive to Chicago so having option of telemedicine would be appreciated.”
Patients were conditionally satisfied if telemedicine appointments were with a provider they were familiar with (Theme 3). One patient cited, “I am glad to see UChicago starting to provide telemedicine so I can use the service provided by the doctors I trust. I definitely won't use telemedicine with a random doctor for illness that needs doctors to diagnose.” Another reason for conditional satisfaction with telemedicine use was dependent on the type of specialty care the patients were receiving (Theme 8). “For cancer survivors/patients it cannot substitute for them doing an in-person breast exam, ultrasound, mammogram or blood work.”
Patients were dissatisfied with their telemedicine experiences because physical examinations could not be offered as part of their visits. A small proportion of the patients were dissatisfied with telemedicine use because of the poor communication and follow-up care they received or technological glitches that occurred during their virtual appointments (Themes 6, 7, and 9). For instance, a patient commented, “I feel that telemedicine is difficult to be physically assess appropriately. It was difficult to put into words symptoms and I felt the health care provider didn't really grasp what I was trying to communicate. Not a fan of telemedicine.”
TELEMEDICINE SATISFACTION BY RACE/ETHNICITY
Black patients specifically mentioned that they felt their concerns were addressed. White patients stated how convenient telemedicine was and hoped the option would continue. Both Black and White patients mentioned positive experiences with providers they were familiar with. Very few patients reported satisfaction because they understood that telemedicine was implemented due to COVID-19 safety reasons, while White patients reported more fear of coming in for an office visit.
All racial/ethnic groups discussed missing physical examinations. Black patients were the only group who mentioned telemedicine feeling impersonal. White patients expressed their concerns about the security of Zoom. Hispanic patients felt anxious that the quality of care offered virtually was not the same as in-person. Black patients cited more frustration with appointments being canceled unbeknownst to them, whereas White patients were upset about the poor follow-up they received. Technical issues were also a problem that led to dissatisfaction across racial/ethnic groups. Several patients discussed annoyance with technological glitches and felt difficult to focus. Interestingly, only Black patients were to discuss wishing for more information on telemedicine access (Supplementary Table S2).
Discussion
To our knowledge, this is the first longitudinal study investigating telemedicine utilization and satisfaction over time and racial/ethnic disparities among breast cancer patients in the United States during the COVID-19 pandemic, using a mixed-methods approach. We found that telemedicine utilization and satisfaction increased over time, Black patients were more likely than White patients to use telemedicine, and there were high levels of satisfaction across all racial/ethnic groups. Our qualitative interview also identified major themes regarding satisfaction or dissatisfaction with telemedicine. These findings suggest that telemedicine could be a potential tool in tackling barriers related to care access and health disparities among breast cancer patients.
We found that 59.2% and 64.9% of breast cancer patients used telemedicine in 2020 and 2021, respectively. Compared to the estimated prevalence (ranging from 34.4% to 58.1%) of telemedicine utilization among breast cancer patients in previous research,14,15,17,18 our estimates are relatively higher. Although the percentage of utilization varies across current and prior studies in the general population, primary care patients, and breast cancer patients during the COVID-19 pandemic, there is a consistent upward trend, which is also observed across all racial/ethnic groups in our patient cohort. The differences among the studies are plausibly due to sampling and regional variabilities.
While our results are positive, additional longitudinal studies are needed to assess the long-term trend in telemedicine use and its benefits among breast cancer patients. Moreover, future research is needed to identify the types of care and services patients receive through telemedicine and evaluate whether telemedicine is as effective as in-person in delivering these care and services among breast cancer patients.
Furthermore, our analysis revealed that Black patients were more than 1.5 times as likely as their White counterparts to have utilized telemedicine over time, even after adjusting for key patient characteristics and time effects. Asian and Hispanic patients were also more likely to have utilized telemedicine than White patients, although not statistically significant, due to limited sample sizes in these two groups of patients. These findings are consistent with recent evidence in the general population or primary care patients,22,23 but not with a study in cancer patients.24
Campos-Castillo and Anthony observed a 1.4 times greater odds of telehealth utilization comparing Black with White adults in the United States.23 Reed et al. found that among patients in the primary care setting, Black patients were 1.3 and 1.6 times more likely than White patients to have used telephone and video visits, respectively. Contrarily, Qian et al. reported that among cancer (including breast) patients, Hispanics or Asians had a significantly lower likelihood of telemedicine use than Whites, while there was no significant difference between Black and White patients.24 The heterogeneous characteristics and unique needs of cancer patient populations with different cancer types might explain the inconsistencies of our results.
It is worth noting that younger patients were 1.5 times as likely as older patients to have used telemedicine, and that patients with at least one comorbidity were 1.5 times more likely than those without a comorbidity to have used telemedicine. Black patients in ChiMEC were older and were more likely than other racial/ethnic groups to be on Medicaid/Medicare and have a greater CCI. Our findings of racial/ethnic differences in characteristics might explain the higher likelihood of telemedicine use among minority patients, suggesting that telemedicine perhaps helped address certain unmet needs of care and services in racial/ethnic minorities, particularly in Black patients, during the COVID-19 pandemic. Therefore, future studies should assess patients' needs for telemedicine and whether these needs differ across racial/ethnic groups among breast cancer patients beyond the pandemic.
We found a remarkably high level of satisfaction with telemedicine use among breast cancer patients over time (>90% in both 2020 and 2021 surveys), which is congruent with the results from previous studies in cancer patients and survivors11–13,25,26 and specifically in breast cancer patients.15–18 Cadili et al. found that 84.6% of breast cancer patients had a positive experience with telemedicine and 78.9% disclosed that they would recommend telemedicine to a family member or friend.16 Zimmerman et al. reported a 91.7% overall telemedicine satisfaction in patients with breast or gynecologic cancers in 2020.15
A 2020 study documented a median satisfaction score of 5.5 (on a 7-point Likert scale) in breast cancer patients who utilized telemedicine.17 A European multi-site study demonstrated a mean satisfaction score of 73.3 (out of 100) among breast cancer patients with oncology consultations through telemedicine.18 However, these studies did not assess racial/ethnic differences in satisfaction among breast cancer patients who used telemedicine, making this study unique and valuable. We also observed a high level of satisfaction by race/ethnicity (>90.0%) in both years, with no significant difference in satisfaction between racial/ethnic minorities and their White counterparts. These findings have a salient implication that telemedicine is fairly acceptable in breast cancer patients, irrespective of race/ethnicity.
In the qualitative analysis, we identified six overarching themes that explain satisfaction with telemedicine use in our breast cancer patient cohort. Patients were satisfied because they perceived telemedicine to be convenient and safe, and that their questions and concerns were addressed. However, patients were dissatisfied because they needed a physical examination or specialty care during their visits, and other dissatisfied individuals complained of poor communication and technical issues. Black patients commonly elaborated on how telemedicine was able to address their concerns, but many commented about telemedicine feeling impersonal and their frustration with their appointments being canceled or changed unbeknownst to them. White patients tended to discuss the convenience of telemedicine, while some patients discussed the poor follow-up they received after an appointment and feeling uncomfortable with the security of Zoom. Despite these few differences, the overall level of satisfaction with telemedicine was similar across racial/ethnic groups.
This study has several limitations. First, our initial research interest focused on overall telemedicine utilization and satisfaction. Utilization may vary by the type of communication or technology implemented (e.g., telephone or video), which possibly influences patient experience and satisfaction. We did not collect and evaluate such data, and future research is warranted. Second, we did not ask whether participants had used telemedicine before the COVID-19 pandemic. Prior use likely has a positive impact on current use in patients who are satisfied, and therefore, it is worth exploring in future studies. Third, the prevalence of telemedicine use overall and across racial/ethnic groups may have been underestimated or overestimated. It may be underestimated, given a survey response rate of less than 50.0% (47.4% and 46.6% in 2020 and 2021, respectively). It may be overestimated because the data included only patients who were willing to participate in the survey and more likely to have used telemedicine. Nevertheless, our estimates are comparatively higher compared with previous findings, suggesting the unique and increased need for virtual care and services among breast cancer patients. Fourth, notwithstanding the adjustment of key factors in our regression models, there are other unmeasured confounders, such as additional socioeconomic status indicators, type of care and services, geographic location, patient and provider preference, and technological barriers, which should be taken into consideration in future study designs, data collection, and analyses. Finally, ChiMEC participants may not be representative of all breast cancer patients in the United States, which limits the generalizability of our findings.
Conclusions
In this large, multiethnic, longitudinal cohort of breast cancer patients, telemedicine utilization and satisfaction with telemedicine experience were high over time and across racial/ethnic groups. Furthermore, racial/ethnic minorities were more likely to have used telemedicine, and the levels of satisfaction were similar across these racial/ethnic groups over time. Major themes regarding satisfaction and dissatisfaction included convenience, safety, specialty dependence, and technical issues. Our findings suggest that telemedicine could have great potential in reducing barriers to cancer care and services and promoting health equity for breast cancer patients. To improve the quality of telemedicine and improve patients' satisfaction, it is imperative for breast cancer programs to address patients' perceived challenges in accessing virtual care.
Supplementary Material
Acknowledgments
We are grateful to the participants enrolled in the Chicago Multiethnic Epidemiologic Breast Cancer Cohort. We thank our research staff for their support and Michelle Liu for helping with qualitative data coding.
Authors' Contributions
J.Q.F.: Data analysis, creating and formatting statistical tables and figures, and writing the initial article. A.K.: qualitative data coding and analysis, creating qualitative tables, and writing part of the initial article. F.Z.: Conceptualization, design, survey development, research implementation, and data collection. R.N.: Conceptualization, design, survey development, and overall supervision. O.I.O.: Design and overall supervision. D.H.: Conceptualization, design, survey development, research implementation, and overall supervision. All authors contributed to interpretations of the findings, writing, review, and editing of the article, and approval of the final article and submission.
Disclosure Statement
J.Q.F., A.K., F.Z., and D.H. have no conflicts of interest to disclose. R.N. has disclosed advisory board involvement with and research funding from Arvinas, AstraZeneca, BeyondSpring, Celgene, FujiFilm, Genentech/Roche, Gilead, Infinity, iTeos, Merck, OBI Pharma, OncoSec, Pfizer, Relay Therapeutics, SeaGen, Sun Pharma, and Taiho. OIO has disclosed financial relationships with CancerIQ, 54gnene, HealthWell Solutions, Tempus; research funding from Ayala Pharmaceuticals, Cepheid, Color Genomics, Novartis, and Roche/Genentech.
Funding Information
This study was supported by the National Cancer Institute (P20CA233307), Susan G. Komen Breast Cancer Foundation (TREND21675016), and the Breast Cancer Research Foundation (BCRF-22-071).
Supplementary Material
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