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Online hospitals are part of an innovative model that allows China to explore telemedicine services based on national conditions with large populations, uneven distribution of medical resources, and lack of quality medical resources, especially among residents needing to be protected from COVID-19 infection.
In this study, we built a hypothesis model based on the Unified Theory of Acceptance and Use of Technology (UTAUT) in order to analyze the factors that may influence patients’ willingness to use mobile medical services. This research was designed to assist in the development of mobile medical services. Residents who do not live in urban areas and cannot access medical assistance would greatly benefit from this research, as they could immediately go to the online hospital when needed.
A cross-sectional study based at the West China Hospital, Sichuan University, was conducted in July 2020. A total of 407 respondents, 18 to 59 years old, in Western China were recruited by convenience sampling. We also conducted an empirical test for the hypothesis model and applied structural equation modeling to estimate the significance of path coefficients so that we could better understand the influencing factors.
Out of 407 respondents, 95 (23.3%) were aware of online hospitals, while 312 (76.7%) indicated that they have never heard of online hospitals before. Gender (
The goal of our research was to determine the factors that influence patients’ awareness and willingness to use online hospitals. Currently, the public’s awareness and usage of online hospitals is low. In fact, effort expectancy was the most important factor that influenced the use of online hospitals; being female and having a high education also played positive roles toward the use of mobile medical services.
With the advent of the
In the field of IT, users’ technology adoption behavior has always attracted much attention. There are many models and theories that can be used to study user behavior, such as the technology acceptance model, the rational behavior theory, the planned behavior theory, and the UTAUT. The UTAUT is a self-rational action theory based on the technology acceptance model by Venkatesh and Davis and others [
Due to the special attributes of telemedicine, there will be some risk factors in the use process; for example, the mobile medical platform uploads patient information to the information system, patients and doctors need to communicate in a virtual environment, etc. The way these risks are perceived by the patient will affect their willingness to use the mobile medical platform [
Construct definitions and model assumptions.
Model variable | Definition | Model assumptions | References |
Performance expectancy | Refers to the patients’ judgment of the benefit of mobile medical services | Performance expectancy has a positive impact on the patients’ willingness to use online hospitals | Venkatesh et al [ |
Effort expectancy | Refers to the patients’ judgment of the ease of acceptance and use of mobile medical services | Effort expectancy has a positive impact on the patients’ willingness to use online hospitals | Xue et al [ |
Social influence | Refers to the external influence that patients perceive when using the online hospital app | Social influence has a positive impact on the patients’ willingness to use online hospitals | Nguyen et al [ |
Perceived risk | Refers to the patients’ perception of possible adverse consequences through online hospital visits | Perceived risk has a negative impact on the patients’ willingness to use online hospitals | Kim and Park [ |
Facilitating conditions | Refers to whether the existing online hospital platform, technology, service-supporting measures, and personal equipment are sufficient to support mobile medical services | Facilitating conditions have a positive impact on the patients’ willingness to use online hospitals | Cilliers et al [ |
The average number of daily outpatients at the West China Hospital, Sichuan University, is about 16,000. Based on the fact that crowd gathering should be avoided during the COVID-19 pandemic, and considering the low awareness and utilization rates of online hospitals, the research team used convenience sampling to recruit survey subjects. Recruitment began on July 4, 2020, when questionnaires were distributed by specially trained investigators at the nurse stations of the West China Hospital, Sichuan University. The target group participants filled out the paper questionnaire on a voluntary basis or filled out the electronic questionnaire by scanning the code on their mobile phones. The questionnaires were answered anonymously and were collected on the spot. Inclusion criteria for participants included the following: (1) were between 18 and 59 years old; (2) were able to fill out the questionnaire independently, with clear consciousness and no obvious cognitive impairment; and (3) volunteered to participate in this study. Exclusion criteria included the following: (1) had a mental disorder and could not communicate normally and (2) refused to participate in this investigation.
This is a cross-sectional study investigating patients’ willingness to use an online hospital platform. The research setting was a large general hospital in Western China. Internet diagnosis and treatment services involved in this study are closely related to internet use. A previous survey about online hospital participation by the elderly administered by our team found that most elderly people were not proficient in using mobile phone apps and had some difficulties operating mobile phones; the children of most of these participants helped them operate mobile phones and use the apps Therefore, this survey was mainly aimed at a sample of young and middle-aged participants.
Using the questionnaire survey method, the researchers designed their own questionnaires under the guidance of experts based on the UTAUT model, combined with the characteristics of online hospital operations. To ensure the validity of the questionnaire content, all measurement items were modified based on relevant domestic and foreign documents; see
In this study, a total of 412 questionnaires were collected, of which 407 were valid, with an effective rate of 98.8%. The ratio of men to women in the survey was 1:1.19, including 319 young people out of 407 participants (78.4%) and 88 middle-aged people (21.6%). Among the survey respondents, 23.3% (95/407) knew about online hospitals, while 76.7% (312/407) said they did not know about, or had not heard of, online hospitals. A total of 95.3% (388/407) of the respondents expressed their support for, and had acceptable attitudes toward, the internet diagnosis and treatment model after being introduced to it by the researchers; see
Participants characteristics.
Characteristic | Value (N=407), n (%) | |
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Male | 186 (45.7) |
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Female | 221 (54.3) |
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18-44 | 319 (78.4) |
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45-59 | 88 (21.6) |
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Less than high school | 16 (3.9) |
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High school graduate | 98 (24.1) |
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Bachelor’s degree | 193 (47.4) |
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Master’s or doctoral degree | 100 (24.6) |
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Official | 131 (32.2) |
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Company employee | 137 (33.7) |
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Migrant worker | 10 (2.5) |
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Farmer | 7 (1.7) |
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Other | 122 (30.0) |
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0-2000 | 40 (9.8) |
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2001-5000 | 114 (28.0) |
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5001-8000 | 127 (31.2) |
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8001-10,000 | 68 (16.7) |
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>10,000 | 58 (14.3) |
This study used Cronbach α to measure the reliability of the questionnaire. The lowest value of Cronbach α for this questionnaire was .764 and the highest value was .900, indicating that the measurement scale had good reliability. In order to test the discriminant validity of variables in the model, we used SPSS Amos, version 24.0, to conduct confirmatory factor analysis on performance expectation, effort expectation, social impact, perceived risk, contributing factors, and willingness to use the platform. The indicators were as follows: adjusted goodness-of-fit index (AGFI)=0.858, goodness-of-fit index (GFI)=0.893, comparative fit index (CFI)=0.935, root mean square error of approximation (RMSEA)=0.063, composite reliability (CR)>0.7, and average variance extracted (AVE)>0.5. It can be seen that the goodness of fit of the model was good; that is, the variables had good discriminant validity (see
Results of the questionnaire reliability analysis.
Dimension and item number | Correlation matrix between items | Corrected item’s total correlation | Cronbach α if item deleted | Cronbach α of the dimension | ||||
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.784 | |
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PE.1 | 1 |
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0.538 | .895 | N/Aa |
PE.2 | 0.715 | 1 |
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0.647 | .893 | N/A | |
PE.3 | 0.413 | 0.448 | 1 |
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0.452 | .898 | N/A | |
PE.4 | 0.403 | 0.489 | 0.614 | 1 | 0.598 | .894 | N/A | |
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.764 | |
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EE.1 | 1 |
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0.690 | .891 | N/A |
EE.2 | 0.576 | 1 |
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0.673 | .892 | N/A | |
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.802 | |
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SI.1 | 1 |
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0.550 | .895 | N/A |
SI.2 | 0.629 | 1 |
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0.572 | .894 | N/A | |
SI.3 | 0.566 | 0.660 | 1 |
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0.502 | .896 | N/A | |
SI.4 | 0.415 | 0.429 | 0.382 | 1 | 0.699 | .891 | N/A | |
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.875 | |
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FC.1 | 1 |
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0.701 | .891 | N/A |
FC.2 | 0.638 | 1 |
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0.663 | .892 | N/A | |
FC.3 | 0.581 | 0.659 | 1 |
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0.651 | .893 | N/A | |
FC.4 | 0.630 | 0.586 | 0.727 | 1 | 0.675 | .892 | N/A | |
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.841 | |
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PR.1 | 1 |
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0.234 | .903 | N/A |
PR.2 | 0.600 | 1 |
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0.225 | .904 | N/A | |
PR.3 | 0.525 | 0.633 | 1 |
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0.227 | .904 | N/A | |
PR.4 | 0.473 | 0.558 | 0.646 | 1 | 0.140 | .909 | N/A | |
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.879 | |
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BI.1 | 1 |
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0.666 | .892 | N/A |
BI.2 | 0.662 | 1 |
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0.631 | .893 | N/A | |
BI.3 | 0.629 | 0.734 | 1 |
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0.625 | .893 | N/A | |
Total scale |
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.900 |
aN/A: not applicable; Cronbach α in this column was calculated for each dimension, not for each item.
Results of the questionnaire validity analysis.
Dimension and item number | Standardized factor load | Composite reliability | Average variance extracteda | |
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0.693 | |
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PE.1 | 0.684 | 0.899 | N/Ab |
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PE.2 | 0.832 |
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N/A |
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PE.3 | 0.933 |
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N/A |
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PE.4 | 0.862 |
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N/A |
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0.522 | |
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EE.1 | 0.625 | 0.710 | N/A |
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EE.2 | 0.695 |
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N/A |
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0.681 | |
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SI.1 | 0.868 | 0.864 | N/A |
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SI.2 | 0.845 |
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N/A |
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SI.3 | 0.758 |
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N/A |
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0.550 | |
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PR.1 | 0.867 | 0.826 | N/A |
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PR.2 | 0.804 |
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N/A |
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PR.3 | 0.541 |
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N/A |
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PR.4 | 0.714 |
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N/A |
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0.617 | |
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FC.1 | 0.780 | 0.828 | N/A |
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FC.2 | 0.695 |
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N/A |
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FC.3 | 0.872 |
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N/A |
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0.567 | |
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BI.1 | 0.568 | 0.793 | N/A |
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BI.2 | 0.829 |
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N/A |
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BI.3 | 0.831 |
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N/A |
aThe fitting indices were as follows: χ2/df=2.1, adjusted goodness-of-fit index (AGFI)=0.858, goodness-of-fit index (GFI)=0.893, comparative fit index (CFI)=0.935, and root mean square error of approximation (RMSEA)=0.063.
bN/A: not applicable; average variance extracted was calculated for each dimension, not for each item.
Structural equation modeling is a statistical method used to analyze the relationship between variables. According to the degree of consistency between the theoretical model and the actual data, the theoretical model is evaluated to achieve the goals of quantitative research on actual problems. This method overcomes the shortcomings of SPSS software’s widely used multiple regression analysis method. It not only explains the relationship between variables but also allows the existence of measurement error of the variables. It can realize the estimation of factor structure and relationship as well as the simultaneous estimation of the degree of model fitting. In this study, the structural equation model analysis software SPSS Amos, version 24.0, was used to draw the structural equation model analysis diagram (see
According to the estimation result of the model path coefficient and the analysis result of the adjustment effect, we finally determined the following: performance expectation, effort expectation, and contributing factors are the three main factors influencing patients’ use of online hospitals and they play a positive role.
Results of the structural equation model analysis. Note, e1 to e31 are residuals, which are not explained in the hypothesis equation. The number on each arrow indicates the factor load of the latent variable on the observed variable: the larger the factor load, the better. If the value is greater than .40 and less than 1, the contribution is qualified.
Verification of the path coefficients of the initial hypothesis model.
Hypothesis (H) | Path | Estimate | SE | Composite reliability | Results | |
H1 | PEa→BIb | 0.21 | 0.063 | 2.587 | .01 | Established |
H2 | EEc→BI | 0.58 | 0.076 | 6.290 | <.001 | Established |
H3 | SId→BI | 0.07 | 0.045 | 1.217 | .22 | Not established |
H4 | PRe→BI | 0.01 | 0.031 | 0.286 | .78 | Not established |
H5 | FCf→BI | 0.32 | 0.055 | 4.414 | <.001 | Established |
aPE: performance expectancy.
bBI: behavioral intention.
cEE: effort expectancy.
dSI: social influence.
ePR: perceived risk.
fFC: facilitating conditions.
Structural equation modeling and hypothesis tests were applied in this research. A
Variables in the equation.
Variable | B | SE | Wald value | Odds ratio (95% CI) | |
Gender | 1.045 | 0.528 | 3.918 | .048 | 2.844 (1.010-8.003) |
Age | –0.262 | 0.444 | 0.349 | .56 | 0.769 (0.323-1.836) |
Education | 0.782 | 0.383 | 4.162 | .04 | 2.187 (1.031-4.636) |
Income | –0.128 | 0.219 | 0.343 | .56 | 0.880 (0.573-1.351) |
A total of 407 young and middle-aged patients at a hospital in Western China were surveyed regarding their willingness to use an online hospital. This study analyzed the factors, and their relationships, that influenced patients’ use of online hospitals and provided some prior information and a theoretical basis upon which other researchers can carry out similar research. The results show that the public’s awareness and usage of online hospitals was low. According to the estimation results of the model path coefficient and the analysis of the adjustment effect, we determined the following: performance expectancy, effort expectancy, and facilitating conditions were the three main factors that affected patients’ willingness to use an online hospital. They all had a positive impact on behavioral intention. It can also be seen from the path coefficient results in
The current outbreak of COVID-19 is still spreading all over the world, which poses great threats to the safety and health of people around the world. Medical systems in all countries are facing great amounts of pressure and challenges. This study has several implications for both researchers and practitioners of mobile medicine and provides a reference for the response to the COVID-19 pandemic. Online hospitals constitute a new mode of telemedicine. Members of the public are limited by traditional modes of medical thinking and their awareness is relatively low. Most of them hold a conservative wait-and-see attitude. There will be a certain resistance to this in the early stages. Medical habits and thinking styles require a transformation process [
Since this study recruited a cross-sectional sample of young and middle-aged participants in Western China, it may not truly reflect the willingness of the entire Chinese population or groups in other regions to adopt mobile medical services. In addition, this study found that social influence and perceived risk have no significant impact on patients’ willingness to use online hospitals. This is inconsistent with the results of other studies on the willingness to adopt other mobile health technologies. Further research is needed to re-examine social influence and perceived risk. At the same time, mobile medical services are under continuous development in China, and the relevant research has not yet formed a relatively complete research framework.
Measurement items of research variables.
adjusted goodness-of-fit index
average variance extracted
comparative fit index
composite reliability
goodness-of-fit index
information technology
odds ratio
root mean square error of approximation
Unified Theory of Acceptance and Use of Technology
This research was supported by the 1.3.5 Project for Disciplines of Excellence of West China Hospital, Sichuan University (grant No. ZYJC18042).
None declared.