KSE
Articles
- Page Path
- HOME > Epidemiol Health > Volume 44; 2022 > Article
-
COVID-19
Original Article
The general public’s perspectives on telemedicine during the COVID-19 pandemic in Korea: analysis of a nationwide survey -
EunKyo Kang1,2*
, Hyejin Lee3*, Ki Jeong Hong4, Jieun Yun5, Jin Yong Lee6,7,8, Yun-Chul Hong8,9 -
Epidemiol Health 2022;44:e2022020.
DOI: https://doi.org/10.4178/epih.e2022020
Published online: February 4, 2022
1National Cancer Control Institute, National Cancer Center, Goyang, Korea
2Department of Family Medicine, National Cancer Center, Goyang, Korea
3Department of Family Medicine, Seoul National University Bundang Hospital, Seongnam, Korea
4Department of Emergency Medicine, Seoul National University Hospital, Seoul, Korea
5Department of Pharmaceutical Engineering, Cheongju University, Cheongju, Korea
6ublic Healthcare Center, Seoul National University Hospital, Seoul, Korea
7HIRA Research Institute, Health Insurance Review and Assessment Service, Wonju, Korea
8Department of Preventive Medicine, Seoul National University College of Medicine, Seoul, Korea
9Institute of Environmental Medicine, Seoul National University Medical Research Center, Seoul, Korea
- Correspondence: Jin Yong Lee Public Healthcare Center, Seoul National University Hospital, 101 Daehak-ro, Jongno-gu, Seoul 03080, Korea E-mail: jylee2000@gmail.com
- Co-correspondence: Yun-Chul Hong Department of Preventive Medicine, Seoul National University College of Medicine, 103 Daehak-ro, Jongno-gu, Seoul 03080, Korea E-mail: ychong1@snu.ac.kr
- *Kang & Lee contributed equally to this work as joint first authors.
• Received: October 24, 2021 • Accepted: February 4, 2022
©2022, Korean Society of Epidemiology
This is an open-access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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.
- 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.
INTRODUCTION
- 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).
MATERIALS AND METHODS
- 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).
RESULTS
- 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.
DISCUSSION
SUPPLEMENTARY MATERIALS
Supplementary Material 1.
Supplementary Material 2
-
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.
NOTES
ACKNOWLEDGEMENTS
Table 1.Baseline characteristics of the study participants
Table 2.Awareness, approval, and intention to use telemedicine
Table 3.Approval rate according to the type of telemedicine
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.
- 1. Jeong E, Hagose M, Jung H, Ki M, Flahault A. Understanding South Korea’s response to the COVID-19 outbreak: a real-time analysis. Int J Environ Res Public Health 2020;17:9571.ArticlePubMedPMC
- 2. World Health Organization. WHO coronavirus (COVID-19) dashboard. [cited 2020 Dec 26]. Available from: https://covid19.who.int/.
- 3. Pollard CA, Morran MP, Nestor-Kalinoski AL. The COVID-19 pandemic: a global health crisis. Physiol Genomics 2020;52:549-557.ArticlePubMedPMC
- 4. Khairat S, Meng C, Xu Y, Edson B, Gianforcaro R. Interpreting COVID-19 and virtual care trends: cohort study. JMIR Public Health Surveill 2020;6:e18811.ArticlePubMedPMC
- 5. Bradford NK, Caffery LJ, Smith AC. Telehealth services in rural and remote Australia: a systematic review of models of care and factors influencing success and sustainability. Rural Remote Health 2016;16:3808.ArticlePubMed
- 6. Zhou L, Parmanto B. Reaching people with disabilities in underserved areas through digital interventions: systematic review. J Med Internet Res 2019;21:e12981.ArticlePubMedPMC
- 7. Hazenberg CE, aan de Stegge WB, Van Baal SG, Moll FL, Bus SA. Telehealth and telemedicine applications for the diabetic foot: a systematic review. Diabetes Metab Res Rev 2020;36:e3247.ArticlePubMed
- 8. Hong Y, Lee SH. Effectiveness of tele-monitoring by patient severity and intervention type in chronic obstructive pulmonary disease patients: a systematic review and meta-analysis. Int J Nurs Stud 2019;92:1-15.ArticlePubMed
- 9. Polinski JM, Barker T, Gagliano N, Sussman A, Brennan TA, Shrank WH. Patients’ satisfaction with and preference for telehealth visits. J Gen Intern Med 2016;31:269-275.ArticlePubMed
- 10. Katzow MW, Steinway C, Jan S. Telemedicine and health disparities during COVID-19. Pediatrics 2020;146:e20201586.ArticlePubMed
- 11. U.S. Department of Health & Human Services. Notification of enforcement discretion for telehealth remote communications during the COVID-19 nationwide public health emergency; 2020 [cited 2020 Dec 26]. Available from: https://www.hhs.gov/hipaa/for-professionals/special-topics/emergency-preparedness/notification-enforcement-discretion-telehealth/index.html.
- 12. Wosik J, Fudim M, Cameron B, Gellad ZF, Cho A, Phinney D, et al. Telehealth transformation: COVID-19 and the rise of virtual care. J Am Med Inform Assoc 2020;27:957-962.ArticlePubMedPMC
- 13. Hollander JE, Carr BG. Virtually perfect? Telemedicine for COVID-19. N Engl J Med 2020;382:1679-1681.ArticlePubMed
- 14. Lindström M. Marital status, social capital, material conditions and self-rated health: a population-based study. Health Policy 2009;93:172-179.ArticlePubMed
- 15. Wu S, Wang R, Zhao Y, Ma X, Wu M, Yan X, et al. The relationship between self-rated health and objective health status: a population-based study. BMC Public Health 2013;13:320.ArticlePubMedPMC
- 16. Monaghesh E, Hajizadeh A. The role of telehealth during COVID-19 outbreak: a systematic review based on current evidence. BMC Public Health 2020;20:1193.ArticlePubMedPMC
- 17. Elson EC, Oermann C, Duehlmeyer S, Bledsoe S. Use of telemedicine to provide clinical pharmacy services during the SARS-CoV-2 pandemic. Am J Health Syst Pharm 2020;77:1005-1006.ArticlePubMed
- 18. Hur J, Chang MC. Usefulness of an online preliminary questionnaire under the COVID-19 pandemic. J Med Syst 2020;44:116.ArticlePubMedPMC
- 19. Kim HS. Lessons from temporary telemedicine initiated owing to outbreak of COVID-19. Healthc Inform Res 2020;26:159-161.ArticlePubMedPMC
- 20. Li P, Liu X, Mason E, Hu G, Zhou Y, Li W, et al. How telemedicine integrated into China’s anti-COVID-19 strategies: case from a National Referral Center. BMJ Health Care Inform 2020;27:e100164.ArticlePubMed
- 21. Dubin JM, Wyant WA, Balaji NC, Ong WL, Kettache RH, Haffaf M, et al. Telemedicine usage among urologists during the COVID-19 pandemic: cross-sectional study. J Med Internet Res 2020;22:e21875.ArticlePubMedPMC
- 22. Clark PA, Capuzzi K, Harrison J. Telemedicine: medical, legal and ethical perspectives. Med Sci Monit 2010;16:RA261-RA272.PubMed
- 23. Horrell LN, Hayes S, Herbert LB, MacTurk K, Lawhon L, Valle CG, et al. Telemedicine use and health-related concerns of patients with chronic conditions during COVID-19: survey of members of online health communities. J Med Internet Res 2021;23:e23795.ArticlePubMedPMC
- 24. Almathami HK, Win KT, Vlahu-Gjorgievska E. Barriers and facilitators that influence telemedicine-based, real-time, online consultation at patients’ homes: systematic literature review. J Med Internet Res 2020;22:e16407.ArticlePubMedPMC
- 25. Powell RE, Henstenburg JM, Cooper G, Hollander JE, Rising KL. Patient perceptions of telehealth primary care video visits. Ann Fam Med 2017;15:225-229.ArticlePubMedPMC
- 26. Abdulhai S, Glenn IC, McNinch NL, Craner D, Chou E, Ponsky TA. Public perception of telemedicine and surgical telementoring in the pediatric population: results of a parental survey. J Laparoendosc Adv Surg Tech A 2018;28:215-217.ArticlePubMed
- 27. Zanaboni P, Scalvini S, Bernocchi P, Borghi G, Tridico C, Masella C. Teleconsultation service to improve healthcare in rural areas: acceptance, organizational impact and appropriateness. BMC Health Serv Res 2009;9:238.ArticlePubMedPMC
- 28. Waldura JF, Neff S, Dehlendorf C, Goldschmidt RH. Teleconsultation improves primary care clinicians’ confidence about caring for HIV. J Gen Intern Med 2013;28:793-800.ArticlePubMedPMC
- 29. Peine A, Paffenholz P, Martin L, Dohmen S, Marx G, Loosen SH. Telemedicine in Germany during the COVID-19 pandemic: multi-professional national survey. J Med Internet Res 2020;22:e19745.ArticlePubMedPMC
- 30. Loeb AE, Rao SS, Ficke JR, Morris CD, Riley LH 3rd, Levin AS. Departmental experience and lessons learned with accelerated introduction of telemedicine during the COVID-19 crisis. J Am Acad Orthop Surg 2020;28:e469-e476.ArticlePubMed
- 31. Huygens MW, Vermeulen J, Swinkels IC, Friele RD, van Schayck OC, de Witte LP. Expectations and needs of patients with a chronic disease toward self-management and eHealth for self-management purposes. BMC Health Serv Res 2016;16:232.ArticlePubMedPMC
REFERENCES
Figure & Data
References
Citations
Citations to this article as recorded by 
- A comparison of the characteristics of adult medicine patients seeking telemedicine consultations versus in-person consultations in a Philippine public hospital
Fatima Ericka S. Vista, Diana R. Tamondong-Lachica
Baylor University Medical Center Proceedings.2024; 37(1): 80. CrossRef - Selected Acute Safety Events Following the Use of Nirmatrelvir/Ritonavir or Molnupiravir for COVID‐19: A Nationwide Cohort Study in South Korea
Ju Hwan Kim, Ahhyung Choi, Gihwan Bae, Eun‐Jeong Joo, Min Joo Choi, Kyungmin Huh, Hyungmin Lee, Jungyeon Kim, Dong‐Hwi Kim, Min‐Gyu Yoo, Il Uk Jo, Poong Hoon Lee, Geun Woo Lee, Hee Sun Jung, Jaehun Jung, Ju‐Young Shin
Clinical Pharmacology & Therapeutics.2024;[Epub] CrossRef - Satisfaction Survey of Medical Staff for Telemedicine after the COVID-19 Pandemic: A Comparison Between the COVID-Pandemic and Post-COVID Pandemic Periods
Seung Eun Jung, Young-Mi Kwon, Soon-Yong Kwon, Young-Hoon Kim, Hyung-Youl Park
Telemedicine and e-Health.2024;[Epub] CrossRef - Factors Influencing the Behavioral Intentions and Use Behaviors of Telemedicine in Patients With Diabetes: Web-Based Survey Study
Huige Shao, Chaoyuan Liu, Li Tang, Bian Wang, Hebin Xie, Yiyu Zhang
JMIR Human Factors.2023; 10: e46624. CrossRef
PubReader
ePub Link
Cite
XML Download- Related articles
-
- Parental intention to vaccinate daughters with the human papillomavirus vaccine in Korea: a nationwide cross-sectional survey
- Changes in mental health service utilization before and during the COVID-19 pandemic: a nationwide database analysis in Korea
- Changes in mental health of Korean adolescents before and during the COVID-19 pandemic: a special report using the Korea Youth Risk Behavior Survey
The general public’s perspectives on telemedicine during the COVID-19 pandemic in Korea: analysis of a nationwide survey
The general public’s perspectives on telemedicine during the COVID-19 pandemic in Korea: analysis of a nationwide survey
| 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 |
| 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 |
| 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 | |
| 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 status |
||
| 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 conditions |
||
| 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 conditions |
||
| No | 1.00 (reference) | 1.00 (reference) |
| Yes | 1.65 (1.16, 2.35) | 1.94 (1.27, 2.96) |
| Experience with telemedicine |
||
| No | 1.00 (reference) | 1.00 (reference) |
| Yes | 1.16 (1.01, 1.32) | 4.28 (1.69, 10.82) |
Table 1. Baseline characteristics of the study participants
Values are presented as number (%).
Table 2. Awareness, approval, and intention to use telemedicine
Values are presented as number (%).
Table 3. Approval rate according to the type of telemedicine
Values are presented as number (%).
Table 4. Factors affecting approval and intention to use telemedicine1
Values are presented as adjusted odds ratio (95% confidence interval). Adjustment for sex, age, region, household income, educational status, private insurance, marital status, job status, and having a chronic illness. Adjustment for sex, age, region, household income, educational status, private insurance, marital status, job status, having a chronic illness, and experience with telemedicine. Adjustment for sex, age, region, household income, educational status, private insurance, marital status, job status, and change in health status. 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. 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.
