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Epidemiol Health > Volume 44; 2022 > Article
Kang, Lee, Hong, Yun, Lee, and Hong: The general public’s perspectives on telemedicine during the COVID-19 pandemic in Korea: analysis of a nationwide survey

Abstract

OBJECTIVES

We investigated the awareness, experience, approval, intention to use, and the desired type of telemedicine among Korean general public.

METHODS

From November to December 2020, we conducted an online self-reported survey on awareness, experience, approval, and intent to use telemedicine services among Korean residents aged 20 years or older. A total of 2,097 participants completed the survey.

RESULTS

Of the 2,097 participants, 1,558 (74.3%) were aware of, 1,198 (57.1%) approved of, and 1,474 (70.3%) had the intention to use telemedicine. Participants from regions other than the Seoul metropolitan area and Daegu–Gyeongbuk Province (adjusted odds ratio [aOR], 1.29; 95% confidence interval [CI], 1.02 to 1.63), households with a monthly household income of US$6,000 or more (aOR, 1.44; 95% CI, 1.01 to 2.08), participants who had a college/university or associate’s degree (aOR, 1.35. 95% CI, 1.04 to 1.75) or a master’s degree or above (aOR, 1.73; 95% CI, 1.20 to 2.50), and housewives (aOR, 1.30; 95% CI, 1.03 to 1.64) had higher odds of approval. Elderly participants, those with a chronic disease (aOR, 1.26; 95% CI, 1.04 to 1.54), those who had experienced delays of healthcare services (aOR, 1.94; 95% CI, 1.27 to 2.96), and those who had experience with telemedicine (aOR, 4.28; 95% CI, 1.69 to 10.82) were more likely to intend to use telemedicine services. Regarding types of telemedicine, teleconsultation between doctors showed the highest approval rate (73.1%).

CONCLUSIONS

In the context of the coronavirus disease 2019 pandemic, more than 70% of participants had already used or intended to use telemedicine at some point. Groups with a substantial need for telemedicine were more in favor of telemedicine.

INTRODUCTION

Following the World Health Organization’s pandemic declaration in March 2020, the coronavirus disease 2019 (COVID-19) outbreak has resulted in an unprecedented global health crisis [1-3]. Social distancing became the global norm in order to prevent the spread of this novel disease. In addition, as a suboptimal substitute for in-person care, telemedicine has been recommended for patients with both chronic and acute diseases to prevent the transmission of the virus [4]. In Korea, telemedicine first emerged in Daegu and Gyeongsangbuk Province, which were most strongly affected by COVID-19 in the early stages of the outbreak.
Globally, telemedicine services have emerged as an alternative to in-person care that does not interfere with the continuity or quality of care. Before the pandemic, telemedicine improved access to care for those in underserved areas who experienced difficulty accessing healthcare [5,6]. Previous studies have reported that telemedicine positively affected the prevention, evaluation, management, and monitoring of disease [7] and reduced healthcare costs due to a decrease in emergency room visits and hospitalizations [8]. Patients who received telemedicine services reported satisfaction with the overall services, most specifically in regards to communication with healthcare professionals, cost-effectiveness, and time savings [9]. Healthcare professionals have also reported that telemedicine is advantageous for communicating with patients, as it is cost-effective and time-effective. However, despite these benefits for expanding healthcare access, there are concerns about health inequities among vulnerable populations who are in a lower socioeconomic status and less likely to have access to the necessary technology and knowledge of how to use it [10,11].
It has been argued that the telemedicine system, which was efficiently built during the COVID-19 pandemic, should be actively utilized even after COVID-19 [12,13]. Telemedicine, from the doctor’s perspective, has desirable outcomes, such as efficiency and cost-effectiveness of care. However, there has been limited research from patients’ and the general population’s perspectives towards telemedicine; thus, demands for telemedicine, attitudes towards it, and the desired type of telemedicine are not fully understood. In Korea, changes in perspectives towards telemedicine may occur, since telemedicine has been temporarily allowed during the pandemic. The aim of this study is to investigate the awareness, experience, approval, and intention to use telemedicine, as well as the perceived reasonable cost range and the desired type of telemedicine, in a nationwide sample.

MATERIALS AND METHODS

Data collection and recruitment

We performed a cross-sectional study using survey data (n=2,097) from November 10 to December 4, 2020. The survey candidates were selected using stratified sampling by age, sex, and geographical region. Those who were informed of the purpose of the study and consented to participate were enrolled in the study.

Measurements

The online survey consisted of a set of questions on basic demographics, self-reported changes in health status during the COVID-19 pandemic, chronic disease management, awareness of telemedicine, and attitudes toward telemedicine. In the survey, telemedicine was defined as “the remote consultation and prescription to patients over a wired/wireless phone and video without a direct visit to healthcare providers when doing so is deemed to be safe according to the physician’s judgment.” The demographic variables included age, sex, area of residence, household income, education level, supplementary private insurance, marital status, and occupation. Participants were categorized into 5 age groups (20-29, 30-39, 40-49, 50-59, and 60 and older), and 3 groups in terms of the area of residence (Seoul metropolitan area, Daegu-Gyeongbuk Province, and others), which were categorized according to the magnitude of the COVID-19 outbreak. Monthly household income (US$) was divided into 4 groups (≤ 2,000, 2,000-3,999, 4,000-5,999, ≥ 6,000), and education level was divided into 3 groups (high school graduate and under, college/university graduate or associate’s degree, and master’s degree or above). Marital status was categorized into 3 groups (single, married, and divorced or widowed), and occupations were divided into 4 groups (office workers, manual workers, self-employed, and housewife/student/unemployed). Self-reported health status before and after the COVID-19 pandemic was measured on a 5-point Likert scale, and then divided into 3 groups (unchanged, improved, or worsened) [14,15]. The questions regarding chronic disease management asked whether participants currently had a chronic disease (yes/no); then, those who did were further asked whether they have experienced delays in healthcare services after the COVID-19 outbreak, for either chronic disease management (“Have you ever experienced delays in consultation or had problems while you needed a prescription refill or consultation resulting from an uncontrolled chronic disease?”) or any other conditions (“Have you ever experienced delays or disruptions in healthcare services, such as consultations, tests, and treatment?”).
Lastly, participants were asked about their awareness, experience, approval, and intent to use telemedicine services. They were asked to identify a reasonable range of cost as well as other factors important for telemedicine use. Regarding awareness, participants were asked to indicate whether they had heard of telemedicine. Those who indicated that they were aware of telemedicine were further asked whether they had experience with telemedicine. Then, a yes-or-no question inquired about participants’ feeling of approval of telemedicine after the COVID-19 pandemic. Participants were also asked to indicate whether they had a very low, low, high, or very high degree of intention to use telemedicine services. Additionally, participants were asked to choose their level of approval for each of the 6 types of telemedicine, which included (1) tele-consultations between doctors, (2) telemedicine between a doctor and a patient, (3) telemedicine between a doctor and a patient’s caregiver, (4) telemedicine for diagnosis or consultation (e.g. telepathology, teleradiology), (5) remote care in a ward or intensive care unit (e.g., a tele-intensive care unit), and (6) telemedicine in which a doctor continuously monitors a patient’s condition. Regarding the reasonable cost of telemedicine, participants were asked an open-ended question and answered with a specific amount. Lastly, regarding the factors to be considered for telemedicine, participants were asked, among a choice of 5, to select the factor with the highest priority. The choices included (1) the possibility of connecting to face-to-face treatment, if necessary, (2) availability whenever the patient needs, (3) management tailored to each patient’s situation and characteristics, (4) accessibility for use independent of disease type, and (5) availability without economic burden.

Statistical analysis

We performed descriptive statistics and the chi-square test on demographics, awareness, experience, approval, intention to use, approval rate by type of telemedicine, and factors to be considered for telemedicine. We then performed multivariate logistic regression on factors associated with approval and intention to use telemedicine and the t-test or analysis of variance on the average reasonable copay amounts for telemedicine. Statistical significance was defined as a two-tailed p-value < 0.05. All statistical analyses were performed using Stata version 23 (StataCorp., College Station, TX, USA).

Ethics statement

This study protocol was approved by the Seoul National University Hospital Institutional Review Board (IRB approval No. E-2011-102-1173).

RESULTS

Baseline characteristics of participants and people who had experience with telemedicine

Table 1 shows the basic demographics and awareness of telemedicine among the study participants. The study participants were evenly distributed by sex and across all age groups. Of the 2,097 participants, 401 (19.1%) lived in the Seoul metropolitan area, 196 (9.4%) lived in Daegu-Gyeongbuk Province, and 1,500 (71.5%) lived in other areas. The lowest household income group (below US$2,000) was the least frequent, with 192 (9.2%) participants. The majority of participants were college/university graduates or had associate’s degrees (n=1,498, 71.4%), and 1,718 (81.9%) held supplementary private health insurance. Married participants (n=1,251, 59.7%) and office workers comprised majorities (n=1,110, 52.9%). There were 1,081 (51.6%) participants with more than one pre-existing chronic condition. The majority of participants responded that their health had not changed (70.5%) after the COVID-19 pandemic. Furthermore, 108 (10.0%) participants reported delays in healthcare services for chronic diseases, while 159 (7.6%) reported delays for conditions other than chronic diseases. When we examined the characteristics of participants receiving telemedicine services among those who were aware of telemedicine (n=1,558), those who lived in other areas were less likely to have received telemedicine services than those who lived in the Seoul metropolitan area (3.3 vs. 7.9%, p=0.001). Participants with supplementary health insurance were more likely to receive telemedicine than those without (4.8 vs. 1.6%, p=0.03), while participants with pre-existing chronic disease were more likely to receive telemedicine than those without (6.3 vs. 1.8%, p<0.001). There were no statistically significant associations between receiving telemedicine services and household income, education level, marital status, and occupation (Table 1).

Awareness, approval, and intention to use telemedicine

Of the 2,097 participants, 1,558 (74.3%) responded that they were aware of telemedicine. Older participants were more likely to be aware of telemedicine. All age groups showed a higher awareness rate than those 20-29 years old (p≤ 0.05). Participants with higher monthly household income, corresponding to US$4,000-5,999 (75.6 vs. 64.6%, p=0.003) and ≥ US$6,000 (78.8 vs. 64.6%, p<0.001), participants who had supplementary health insurance (75.7 vs. 67.8%, p=0.001), participants who were married (78.2 vs. 67.6%, p<0.001), and participants with underlying chronic diseases (78.9 vs. 69.4%, p<0.001) were more likely to be aware of telemedicine than their counterparts. Participants who had experienced delays in healthcare services for reasons other than chronic diseases were more likely to be aware of telemedicine than those who had not (85.5 vs. 73.4%, p=0.001).
With regard to approval, 1,198 (57.1%) of the total study population approved of telemedicine services. Females had a lower rate of approval than males (53.4 vs. 60.8%, p=0.001). All age groups showed a higher approval rate than those 20-29 years old (p≤0.020). Participants with a monthly income of more than US$6,000 (60.3 vs. 50.0%, p=0.010), participants with a master’s degree or above (63.8 vs. 54.0%, p=0.020), married participants (61.7 vs. 49.3%, p<0.001), self-employed participants (65.8 vs. 55.7%, p=0.009) had higher rates of approval of telemedicine. Participants with chronic diseases (60.8 vs. 53.3%, p=0.001), and participants who experienced delays in healthcare for conditions other than chronic diseases (66.0 vs. 56.4%, p=0.020) were more likely to approve telemedicine than their counterparts.
Among all participants, 1,474 (70.3%) intended to use telemedicine. Compared to those in their 20s, all other age groups were significantly more likely to use telemedicine (p≤ 0.03). Those without supplementary health insurance were less likely to use telemedicine treatment (62.5 vs. 72.0%, p<0.001). Married participants, (74.3 vs. 63.7%, p<0.001), those with chronic diseases (66.6 vs. 73.7%, p<0.001). Additionally, those who experienced delays in healthcare for a chronic disease after the COVID-19 outbreak (72.6 vs. 84.3%, p=0.010) or for any other conditions (69.4 vs. 818%, p=0.001), as well as those who had previously received telemedicine (72.6 vs. 92.5%, p=0.001) had more intent to use telemedicine (Table 2).

Rate of approval according to the type of telemedicine

Among the types of telemedicine, teleconsultation between doctors (73.1%) had the highest rate of approval, followed by telemedicine for diagnosis or consultation, and telemedicine in which a doctor continuously monitors a patient’s condition. Telemedicine between a doctor and a patient’s caregiver (62.0%) had the lowest rate of approval. Each type of telemedicine showed differences with respect to socio-demographic factors, including age, household income, supplementary private insurance, marital status, and pre-existing chronic disease (Table 3).

Factors associated with Intention to use telemedicine

When we analyzed the factors associated with approval of telemedicine, women had lower odds than men (adjusted odds ratio [aOR], 0.73; 95% confidence interval [CI], 0.61 to 0.88). Participants in their 60s had the highest odds (aOR, 2.59; 95% CI, 1.74 to 3.86). The aOR was higher in other regions (aOR, 1.29; 95% CI, 1.02 to 1.63), households with a household income of US$6,000 or more (aOR, 1.44; 95% CI, 1.01 to 2.08), participants with a college/university or associate’s degree (aOR, 1.35. 95% CI 1.04 to 1.75) or a master’s degree or above (aOR, 1.73; 95% CI, 1.20 to 2.50), and housewives (aOR, 1.30; 95% CI, 1.03 to 1.64). Those who experienced delays in treatment due to circumstances other than chronic diseases (aOR, 1.65; 95% CI, 1.16 to 2.35) and those who had experience with telemedicine (aOR, 1.16; 95% CI, 1.01 to 1.32) were also more likely to approve of telemedicine.
As for the factors associated with intent to use telemedicine, participants with older age had a higher aOR, while those without supplementary health insurance had a lower (aOR, 0.65; 95% CI, 0.51 to 0.84). Participants with a chronic disease (aOR, 1.26; 95% CI, 1.04 to 1.54), those who had experience delays of healthcare services (aOR, 1.94; 95% CI, 1.27 to 2.96), and those who had experience with telemedicine (aOR, 4.28; 95% CI, 1.69 to 10.82) were more likely to intend to use telemedicine services (Table 4).

The appropriate amount of copay for telemedicine and factors considered to be important in telemedicine

The average reasonable copay amount for telemedicine was US$29.54. In particular, participants with chronic disease and those with improved health status after the COVID-19 pandemic were willing to pay higher rates for telemedicine (US$32.61, p=0.010 and US$49.49, p<0.001, respectively). Furthermore, participants who had experienced delays in healthcare services for both chronic diseases (US$75.03, p<0.001), and other conditions (US$51.01, p<0.001) were willing to pay more for telemedicine than other groups (Supplementary Material 1).
When participants were asked about factors considered to be important for telemedicine, the most frequently chosen was that management is tailored to each patient’s situation and characteristics (24.9%), followed by the possibility of connecting to face-to-face treatment if necessary (23.6%). Socio-demographic factors, including sex, monthly household income, education level, supplementary private insurance, and occupation, were associated with the prioritization of factors considered to be important for telemedicine (Supplementary Material 2).

DISCUSSION

Telemedicine has contributed to the ability to continue delivering healthcare services under emergency circumstances in order to prevent the collapse of the health system [16]. The use of telemedicine to contain the spread of COVID-19 has become a global phenomenon [17,18]. Correspondingly, the Korean government has temporarily allowed teleconsultations starting on March 2, 2020 [19]. In this study, we investigated public opinion on telemedicine through a representative sample of the Korean population. We found that more than half of the total study population agreed with the implementation of telemedicine. Although telemedicine has been useful during emergency circumstances such as COVID-19 [20], several concerns remain that it is difficult to conduct direct consultations and complete lab tests. The main reported barriers to the implementation of telemedicine are insufficient understanding and access among users [21]. Furthermore, the sustainability of telemedicine in Korea is still controversial due to legal and ethical issues, as well as safety and responsibility among healthcare providers [22]. We explored these questions through the perspective of the public, as healthcare consumers, towards telemedicine, which will affect whether telemedicine will be implemented in the long term.
In this study, more than 70% of participants intended to use telemedicine, and this intention was associated with older age. Considering that the usage rate of telemedicine was higher among the younger age group [23], our study result suggests that older people have a high awareness of telemedicine and intention to use it, but there are barriers to actual use. Having additional private health insurance and a pre-existing chronic disease also showed a positive association with intention to use telemedicine. These findings aligned with previous studies that people with higher household income and chronic diseases were more likely to receive telemedicine services during the COVID-19 pandemic [23]. Similar to previous findings that people who received guidance on how to use the telemedicine platform and who had previously experienced telemedicine were more likely to approve of telemedicine, we found that those who previously used telemedicine were more in favor of the long-term use of telemedicine. In addition, participants who experienced delays in healthcare due to the COVID-19 outbreak were more likely to approve of telemedicine, which may reflect the need for remote care [24]. The main advantage felt by patients who have actually experienced telemedicine was convenience, and non-delayed care delivery and the benefits of receiving care in their own home were important factors for patients [25].
In regard to types of telemedicine services, teleconsultations between doctors had the highest approval rate, followed by teleradiology or telepathology. These results were different from those of previous studies, according to which patients agreed most with routine doctor visits, followed by post-surgery visits, expert consultations, and surgical remote mentoring in previous studies [26]. Teleconsultation between doctors was occasionally used in practice prior to the pandemic because it promoted access to healthcare in rural areas and increased the capacity of primary healthcare physicians [27,28], and a report found that medical staff working at private hospitals experienced fewer restrictions or barriers to telemedicine than medical staff working at university hospitals [29]. Thus, there was already a certain level of social acceptance for teleconsultation prior to its widespread use during the pandemic. Telemedicine between a doctor and a patient’s caregiver had the lowest approval rate, which may reflect the anxiety of the public regarding non-face-to-face care through a patient’s caregiver without direct patient contact.
In this study, the factor considered to be most important among study participants was management tailored to each patient’s situation and characteristics. According to Loeb et al. [30], selecting appropriate patients for telemedicine should be included in the task checklist for telemedicine launch. Likewise, it is remarkably important not only that appropriate patients should be selected for telemedicine services, but that the type of telemedicine should be tailored to each individual [31]. Previous studies that dealt with the advantages and disadvantages of telemedicine found that the main advantage was a reduction in travel and associated costs. Next, tailored care should be considered important, considering that physical examinations are limited in telemedicine. The possibility of connecting to face-to-face treatment if necessary is also an important factor related to previously reported limitations. The factor rated important by the fewest participants was accessibility for use independent of disease type, reflecting that patient-specific characteristics were considered more significant than disease-specific characteristics in telemedicine.
There are several limitations of this study. First, since the study population was limited to those speaking Korean and residing in Korea, our results may have limited generalizability to other populations. Second, although the study participants were recruited by stratifying the Korean population by age, sex, and region, selection bias may have occurred because they were given the option to participate in this study.
In conclusion, the COVID-19 pandemic has marked a turning point for not only healthcare providers, but patients and society. When we analyzed the survey results, it was found that the majority of the public was in favor of the use of telemedicine even after COVID-19. Interestingly, the approval of telemedicine had a positive correlation with age, indicating that technology use may not be a barrier to using telemedicine. Individuals with healthcare needs, such as those with chronic diseases and experiences of delays in healthcare services due to COVID-19, had a higher approval rate of telemedicine. Additionally, the financial status of patients (e.g., having supplementary health insurance) may potentially affect the approval of telemedicine. The general population considered individually tailored management to be important. Aspects for ensuring safety in care should be also considered while building infrastructure for telemedicine services after the COVID-19 pandemic. Telemedicine has demonstrated advantages in delivering timely care while minimizing exposure to COVID-19 and protecting healthcare providers and patients amid the COVID-19 pandemic, and it may be widely utilized after the pandemic.

SUPPLEMENTARY MATERIALS

Supplementary materials are available at https://www.e-epih.org/.

Supplementary Material 1.

Responses on the appropriate amount of copays for telemedicine
epih-44-e2022020-suppl1.docx

Supplementary Material 2

Factors considered to be important in telemedicine
epih-44-e2022020-suppl2.docx

NOTES

CONFLICT OF INTEREST
The authors have no conflicts of interest to declare for this study.
FUNDING
None.
AUTHOR CONTRIBUTIONS
Conceptualization: Kang E, Lee H, Lee JY, Hong YC. Data curation: Kang E, Lee JY. Formal analysis: Kang E. Funding acquisition: None. Methodology: Kang E, Lee H, Lee JY, Hong YC. Project administration: Kang E, Hong KJ. Visualization: Yun J, Lee H. Writing–original draft: Kang E, Lee H, Yun J, Lee JY. Writing–review & editing: Kang E, Lee H, Hong KJ, Yun J, Lee JY, Hong YC.

ACKNOWLEDGEMENTS

We would like to express our gratitude to survey participants. In addition, we appreciate the effort made by the field workforce to conduct the survey and for the support and advice provided by the related academic societies and expert advisory groups.

Table 1.
Baseline characteristics of the study participants
Characteristics Total Experienced p-value
Total 2,097 (100) 67 (4.3)
Sex
 Male 1,058 (50.5) 37 (4.7)
 Female 1,039 (49.6) 30 (3.9) 0.450
Age (yr)
 20-29 377 (18.0) 10 (4.4)
 30-39 411 (19.6) 14 (5.0) 0.720
 40-49 485 (23.1) 18 (5.0) 0.710
 50-59 479 (22.8) 13 (3.2) 0.470
 ≥60 345 (16.5) 12 (4.2) 0.920
Region
 Seoul metropolitan area 401 (19.1) 23 (7.9)
 Daegu–Gyeongbuk Province 196 (9.4) 7 (4.8) 0.240
 Others 1,500 (71.5) 37 (3.3) 0.001
Household income (US$)
 <2,000 192 (9.2) 4 (3.2)
 2,000-3,999 684 (32.8) 26 (5.3) 0.340
 4,000-5,999 610 (29.2) 16 (3.5) 0.890
 ≥6,000 600 (28.8) 21 (4.4) 0.550
Educational status
 High school graduate and under 359 (17.1) 7 (2.7)
 College/university graduate 1,498 (71.4) 51 (4.6) 0.190
 Master's degree or above 240 (11.4) 9 (5.0) 0.220
Private insurance
 Yes 1,718 (81.9) 63 (4.8)
 No 379 (18.1) 4 (1.6) 0.030
Marital status
 Single 755 (36.0) 17 (3.3)
 Married 1,251 (59.7) 48 (4.9) 0.160
 Widowed/divorced 91 (4.3) 2 (2.9) 0.830
Job
 Office worker 1,110 (52.9) 40 (4.8)
 Manual worker 212 (10.1) 8 (5.4) 0.760
 Own business 193 (9.2) 7 (4.5) 0.860
 Housewife/Student/Unemployed 582 (27.8) 12 (2.9) 0.110
Having a chronic illness
 No 1,016 (48.5) 13 (1.8)
 Yes 1,081 (51.6) 54 (6.3) <0.001
Subjective change in health status
 No change 1,478 (70.5) 32 (3.0)
 Improved 199 (9.5) 13 (8.3) 0.001
 Worsened 420 (20.0) 22 (6.9) 0.002
Delayed treatment for chronic conditions
 No 973 (90.0) 22 (2.9)
 Yes 108 (10.0) 32 (34.4) <0.001
Delayed elective treatment and treatment for non-chronic conditions
 No 1,938 (92.4) 33 (2.3)
 Yes 159 (7.6) 34 (25.0) <0.001

Values are presented as number (%).

Table 2.
Awareness, approval, and intention to use telemedicine
Variables Awareness p-value Approval p-value Intention to use p-value
Total 1,558 (74.3) 1,198 (57.1) 1,474 (70.3)
Sex
 Male 790 (74.7) 643 (60.8) 757 (71.6)
 Female 768 (73.9) 0.690 555 (53.4) 0.001 717 (69.0) 0.200
Age (yr)
 20-29 230 (61.0) 169 (44.8) 226 (60.0)
 30-39 278 (67.6) 0.050 219 (53.3) 0.020 278 (67.6) 0.030
 40-49 359 (74.0) <0.001 263 (54.2) 0.006 342 (70.5) 0.001
 50-59 403 (84.1) <0.001 312 (65.1) <0.001 348 (72.7) <0.001
 ≥60 288 (83.5) <0.001 235 (68.1) <0.001 280 (81.2) <0.001
Region
 Seoul metropolitan area 291 (72.6) 214 (53.4) 279 (69.6)
 Daegu-Gyeongbuk Province 145 (74.0) 0.720 109 (55.6) 0.610 137 (69.9) 0.940
 Others 1,122 (74.8) 0.360 875 (58.3) 0.070 1,058 (70.5) 0.710
Household income (US$)
 <2,000 124 (64.6) 96 (50.0) 126 (65.6)
 2,000-3,999 491 (71.8) 0.060 386 (56.4) 0.110 483 (70.6) 0.190
 4,000-5,999 461 (75.6) 0.003 352 (57.7) 0.060 428 (70.2) 0.240
 ≥6,000 473 (78.8) <0.001 362 (60.3) 0.010 432 (72.0) 0.090
Educational status
 High school graduate and under 257 (71.6) 194 (54.0) 249 (69.4)
 College/university graduate 1,121 (74.8) 0.210 851 (56.8) 0.340 1,052 (70.2) 0.750
 Master’s degree or above 180 (75.0) 0.360 153 (63.8) 0.020 173 (72.1) 0.470
Private insurance
 Yes 1,301 (75.7) 998 (58.1) 1,237 (72.0)
 No 257 (67.8) 0.001 200 (52.8) 0.060 237 (62.5) <0.001
Marital status
 Single 510 (67.6) 372 (49.3) 481 (63.7)
 Married 978 (78.2) <0.001 772 (61.7) <0.001 929 (74.3) <0.001
 Widowed/divorced 70 (76.9) 0.070 54 (59.3) 0.070 64 (70.3) 0.210
Job
 Office worker 835 (75.2) 618 (55.7) 781 (70.4)
 Manual worker 149 (70.3) 0.130 125 (59.0) 0.380 158 (74.5) 0.220
 Own business 157 (81.4) 0.070 127 (65.8) 0.009 144 (74.6) 0.230
 Housewife/Student/Unemployed 417 (71.7) 0.110 328 (56.4) 0.790 391 (67.2) 0.180
Having a chronic illness
 No 705 (69.4) 541 (53.3) 677 (66.6)
 Yes 853 (78.9) <0.001 657 (60.8) 0.001 797 (73.7) <0.001
Subjective change in health status
 No change 1,084 (73.3) 843 (57.0) 1,042 (70.5)
 Improved 156 (78.4) 0.130 117 (58.8) 0.640 144 (72.4) 0.590
 Worsened 318 (75.7) 0.330 238 (56.7) 0.890 288 (68.6) 0.470
Delayed treatment for chronic conditions
 No 760 (78.1) 592 (60.8) 706 (72.6)
 Yes 93 (86.1) 0.060 65 (60.2) 0.890 91 (84.3) 0.010
Delayed elective treatment and treatment for non-chronic conditions
 No 1,422 (73.4) 1,093 (56.4) 1344 (69.4)
 Yes 136 (85.5) 0.001 105 (66.0) 0.020 130 (81.8) 0.001
Experience with telemedicine
 No - 965 (64.7) 1,083 (72.6)
 Yes - 47 (70.2) 0.360 62 (92.5) 0.001

Values are presented as number (%).

Table 3.
Approval rate according to the type of telemedicine
Variables Teleconsultations between doctors p-value Telemedicine between a doctor and a patient p-value Telemedicine between a doctor and a patient's caregiver p-value Telemedicine for diagnosis or consultation p-value Remote care in a ward or ICU p-value Telemedicine in which the doctor continuously monitors the patient’s condition p-value
Total 1,533 (73.1) 1,386 (66.1) 1,301 (62.0) 1,497 (71.4) 1,359 (64.8) 1,476 (70.4)
Sex
Male 763 (72.1) 711 (67.2) 643 (60.8) 759 (71.7) 687 (64.9) 740 (69.9)
Female 770 (74.1) 0.300 675 (65.0) 0.280 658 (63.3) 0.230 738 (71.0) 0.720 672 (64.7) 0.900 736 (70.8) 0.650
Age (yr)
20-29 241 (63.9) 214 (56.8) 201 (53.3) 224 (59.4) 206 (54.6) 233 (61.8)
30-39 275 (66.9) 0.380 240 (58.4) 0.640 238 (57.9) 0.200 253 (61.6) 0.540 239 (58.2) 0.320 258 (62.8) 0.780
40-49 365 (75.3) <0.001 314 (64.7) 0.020 298 (61.4) 0.020 364 (75.1) <0.001 319 (65.8) 0.001 341 (70.3) 0.009
50-59 381 (79.5) <0.001 352 (73.5) <0.001 325 (67.9) <0.001 375 (78.3) <0.001 337 (70.4) <0.001 374 (78.1) <0.001
≥60 271 (78.6) <0.001 266 (77.1) <0.001 239 (69.3) <0.001 281 (81.5) <0.001 258 (74.8) <0.001 270 (78.3) <0.001
Region
Seoul metropolitan area 310 (77.3) 269 (67.1) 259 (64.6) 302 (75.3) 256 (63.8) 298 (74.3)
Daegu–Gyeongbuk Province 138 (70.4) 0.070 129 (65.8) 0.760 116 (59.2) 0.200 133 (67.9) 0.060 129 (65.8) 0.640 139 (70.9) 0.380
Others 1,085 (72.3) 0.050 988 (65.9) 0.650 926 (61.7) 0.300 1,062 (70.8) 0.080 974 (64.9) 0.680 1,039 (69.3) 0.050
Household income (US$)
<2,000 135 (70.3) 111 (57.8) 100 (52.1) 124 (64.6) 120 (62.5) 126 (65.6)
2,000-3,999 472 (69.0) 0.730 424 (62.0) 0.300 386 (56.4) 0.280 471 (68.9) 0.260 419 (61.3) 0.750 466 (68.1) 0.510
4,000-5,999 466 (76.4) 0.090 427 (70.0) 0.002 402 (65.9) 0.001 450 (73.8) 0.010 396 (64.9) 0.540 434 (71.2) 0.150
≥6,000 453 (75.5) 0.150 419 (69.8) 0.002 407 (67.8) <0.001 445 (74.2) 0.010 417 (69.5) 0.070 444 (74.0) 0.030
Educational status
High school graduate and under 251 (69.9) 226 (63.0) 224 (62.4) 232 (64.6) 232 (64.6) 241 (67.1)
College/university graduate 1,109 (74.0) 0.110 998 (66.6) 0.190 930 (62.1) 0.910 1,093 (73.0) 0.002 961 (64.2) 0.870 1,061 (70.8) 0.170
Master’s degree or above 173 (72.1) 0.570 162 (67.5) 0.250 147 (61.3) 0.780 172 (71.7) 0.070 166 (69.2) 0.250 174 (72.5) 0.160
Private insurance
Yes 1,276 (74.3) 1,159 (67.5) 1,098 (63.9) 1,246 (72.5) 1,131 (65.8) 1,217 (70.8)
No 257 (67.8) 0.010 227 (59.9) 0.005 203 (53.6) <0.001 251 (66.2) 0.010 228 (60.2) 0.040 259 (68.3) 0.340
Marital status
Single 504 (66.8) 438 (58.0) 408 (54.0) 481 (63.7) 439 (58.2) 477 (63.2)
Married 958 (76.6) <0.001 887 (70.9) <0.001 834 (66.7) <0.001 946 (75.6) <0.001 859 (68.7) <0.001 935 (74.7) <0.001
Widowed/divorced 71 (78.0) 0.030 61 (67.0) 0.100 59 (64.8) 0.050 70 (76.9) 0.010 61 (67.0) 0.110 64 (70.3) 0.180
Job
Office worker 803 (72.3) 736 (66.3) 689 (62.1) 789 (71.1) 700 (63.1) 785 (70.7)
Manual worker 154 (72.6) 0.930 134 (63.2) 0.380 141 (66.5) 0.220 153 (72.2) 0.750 148 (69.8) 0.060 141 (66.5) 0.220
Own business 147 (76.2) 0.270 131 (67.9) 0.670 111 (57.5) 0.230 147 (76.2) 0.150 130 (67.4) 0.250 136 (70.5) 0.940
Housewife/Student/Unemployed 429 (73.7) 0.550 385 (66.2) 0.950 360 (61.9) 0.930 408 (70.1) 0.670 381 (65.5) 0.330 414 (71.1) 0.860
Having a chronic illness
No 707 (69.6) 632 (62.2) 606 (59.7) 706 (69.5) 631 (62.1) 691 (68.0)
Yes 826 (76.4) <0.001 754 (69.8) <0.001 695 (64.3) 0.030 791 (73.2) 0.060 728 (67.4) 0.010 785 (72.6) 0.020
Subjective change in health status
No change 1,067 (72.2) 973 (65.8) 903 (61.1) 1,058 (71.6) 945 (63.9) 1,049 (71.0)
Improved 142 (71.4) 0.810 121 (60.8) 0.160 130 (65.3) 0.250 134 (67.3) 0.220 132 (66.3) 0.510 130 (65.3) 0.100
Worsened 324 (77.1) 0.040 292 (69.5) 0.160 268 (63.8) 0.310 305 (72.6) 0.680 282 (67.1) 0.230 297 (70.7) 0.920
Delayed treatment for chronic conditions
No 1,170 (78.5) 1,059 (71.0) 992 (66.5) 1,136 (76.2) 1,008 (67.6) 1,124 (75.4)
Yes 54 (80.6) 0.680 47 (70.2) 0.880 40 (59.7) 0.250 52 (77.6) 0.790 46 (68.7) 0.860 45 (67.2) 0.130
Delayed elective treatment and treatment for non-chronic conditions
No 748 (76.9) 682 (70.1) 630 (64.8) 718 (73.8) 654 (67.2) 714 (73.4)
Yes 78 (72.2) 0.280 72 (66.7) 0.460 65 (60.2) 0.350 73 (67.6) 0.170 74 (68.5) 0.780 71 (65.7) 0.090
Experience with telemedicine
No 1,409 (72.7) 1,268 (65.4) 1,192 (61.5) 1,371 (70.7) 1,254 (64.7) 1,363 (70.3)
Yes 124 (78.0) 0.150 118 (74.2) 0.030 109 (68.6) 0.080 126 (79.3) 0.020 105 (66.0) 0.740 113 (71.1) 0.840

Values are presented as number (%).

Table 4.
Factors affecting approval and intention to use telemedicine1
Variables Approval of telemedicine Intention to use telemedicine
Sex
 Male 1.00 (reference) 1.00 (reference)
 Female 0.73 (0.61, 0.88) 0.93 (0.76, 1.13)
Age (yr)
 20-29 1.00 (reference) 1.00 (reference)
 30-39 1.46 (1.07, 1.99) 1.29 (0.93, 1.77)
 40-49 1.44 (1.03, 2.00) 1.42 (1.00, 2.01)
 50-59 2.28 (1.60, 3.24) 1.63 (1.13, 2.36)
 ≥60 2.59 (1.74, 3.86) 2.68 (1.73, 4.14)
Region
 Seoul metropolitan area 1.00 (reference) 1.00 (reference)
 Daegu–Gyeongbuk Province 1.12 (0.78, 1.59) 1.01 (0.69, 1.47)
 Others 1.29 (1.02, 1.63) 1.05 (0.82, 1.35)
Household income (US$)
 <2,000 1.00 (reference) 1.00 (reference)
 2,000-3,999 1.21 (0.86, 1.71) 1.07 (0.74, 1.54)
 4,000-5,999 1.26 (0.88, 1.82) 1.00 (0.68, 1.46)
 ≥6,000 1.44 (1.01, 2.08) 1.08 (0.73, 1.59)
Educational status
 High school graduate and under 1.00 (reference) 1.00 (reference)
 College/university graduate 1.35 (1.04, 1.75) 1.24 (0.94, 1.64)
 Master's degree or above 1.73 (1.20, 2.50) 1.26 (0.85, 1.86)
Private insurance
 Yes 1.00 (reference) 1.00 (reference)
 No 0.80 (0.62, 1.02) 0.65 (0.51, 0.84)
Marital status
 Single 1.00 (reference) 1.00 (reference)
 Married 1.04 (0.81, 1.35) 1.08 (0.82, 1.42)
 Widowed/divorced 1.03 (0.63, 1.69) 0.86 (0.51, 1.47)
Job
 Office worker 1.00 (reference) 1.00 (reference)
 Manual worker 1.20 (0.87, 1.67) 1.24 (0.86, 1.78)
 Own business 1.33 (0.95, 1.87) 1.11 (0.77, 1.61)
 Housewife/Student/Unemployed 1.30 (1.03, 1.64) 0.97 (0.76, 1.25)
Having a chronic illness
 No 1.00 (reference) 1.00 (reference)
 Yes 1.20 (1.00, 1.44) 1.26 (1.04, 1.54)
Subjective change in health status2
 No change 1.00 (reference) 1.00 (reference)
 Improved 1.21 (0.88, 1.66) 1.17 (0.83, 1.65)
 Worsened 1.06 (0.84, 1.33) 0.95 (0.75, 1.22)
Delayed treatment for chronic conditions3
 No 1.00 (reference) 1.00 (reference)
 Yes 1.14 (0.74, 1.74) 2.04 (1.17, 3.54)
Delayed elective treatment and treatment for non-chronic conditions4
 No 1.00 (reference) 1.00 (reference)
 Yes 1.65 (1.16, 2.35) 1.94 (1.27, 2.96)
Experience with telemedicine5
 No 1.00 (reference) 1.00 (reference)
 Yes 1.16 (1.01, 1.32) 4.28 (1.69, 10.82)

Values are presented as adjusted odds ratio (95% confidence interval).

1 Adjustment for sex, age, region, household income, educational status, private insurance, marital status, job status, and having a chronic illness.

2 Adjustment for sex, age, region, household income, educational status, private insurance, marital status, job status, having a chronic illness, and experience with telemedicine.

3 Adjustment for sex, age, region, household income, educational status, private insurance, marital status, job status, and change in health status.

4 Adjustment for sex, age, region, household income, educational status, private insurance, marital status, job status, having a chronic illness, change in health status, and experience with telemedicine.

5 Adjustment for sex, age, region, household income, educational status, private insurance, marital status, job status, having a chronic illness, change in health status, and experience with telemedicine.

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