The impact of telehealth on medical expenses: An empirical research considering time and regional differences

The impact of telehealth on medical expenses: An empirical research considering time and regional differences | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article The impact of telehealth on medical expenses: An empirical research considering time and regional differences Qiaoqiao Sun, jianbin Zheng, Zilong Wang This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6201561/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Introduction The increase in medical expenses is a global challenge. Telehealth is considered an effective way to address this problem. Although previous research suggest that telehealth can reduce medical expenses, there is a lack of empirical research to support this conclusion. In particular, there is a lack of macro-level research evidence revealing the impact of telehealth on medical expenses. Our research aims to investigate the actual impact of telehealth on medical expenses at the macro level, considering time and regional differences. Material and Methods We took China as the subject of study, collected statistical data on medical expenses and telehealth from 31 provinces, and built an empirical model to reveal the actual impact of the use of telehealth on medical expenses. Results Our research findings indicate that the use of telehealth not only fails to alleviate medical expenses, but actually leads to an increase in medical expenses. By comparing coefficients, we found that the increase in medical expenses for urban residents is greater than that for rural residents. Furthermore, our research results also found significant differences in the impact of telehealth on outpatient and inpatient expenses, with the increase in expenses for inpatients being much higher than for outpatients. Conclusions As governments promote the development of telehealth, they need to strengthen the supervision of offline medical institutions. Moreover, the increase in medical expenses shows differences among urban and rural residents, and outpatients and inpatients. This suggests that governments need to stage medical service monitoring in practice, starting with urban residents or inpatients, and gradually extending to rural residents and outpatient groups. telehealth medical expenses urban residents rural residents outpatients inpatients Figures Figure 1 Figure 2 Figure 3 Figure 4 Introduction In recent years, reports released by the World Health Organization have pointed out that global medical expenses continue to rise. 1 According to statistical data released by the Centers for Medicare & Medicaid Services (CMS) in 2022, the medical expenses in the United States has exceeded 3 trillion dollars, accounting for 18% of GDP . Data released by the National Health Commission shows that China's medical expenses exceeded 8 trillion yuan, an increase of 12.2% compared to 2021. A report released by the American Heart Association (AHA) shows that among 2045 adult respondents, 49% of people do not dare to choose medical treatment for fear of not being able to afford high medical expenses . Reports released by the World Health Organization show that nearly 100 million people fall into extreme poverty each year due to disease treatment . The rapid growth of medical expenses has become a problem that urgently needs to be solved in the current medical and health field. 2 Telehealth is generally considered an effective way to solve the rise in medical expenses. 3 As a new channel for medical services, telehealth is deeply changing traditional offline medical activities. 4 On the one hand, it can better optimize the allocation of medical resources. 5 On the other hand, it provides more convenience for patients. 6 These factors will reduce the medical expenses of patients. 7 , 8 In the traditional offline medical environment, patients need to bear transfer costs and opportunity costs (e.g., education, work, leisure and entertainment, etc.) to get medical services. 9 , 10 Some research in pediatric surgery and chronic disease fields point out that telehealth has the characteristics of spanning time and space, and can reduce medical expenses by helping patients access medical resources at any time. 11 , 12 However, current research on reducing medical expenses through telehealth is mainly short-term research based on a micro perspective. In the long term and from a macro perspective, the addition of telehealth as a channel for medical service supply may lead to an increase in medical expenses. The relationship between telehealth and medical expenses is not straightforward and clear, and there is a certain contradiction in the specific theoretical analysis. According to the induced demand theory, introducing online medical services as a supplement to offline medical services, increasing the supply of medical services, not only can't reduce medical expenses, but may lead to a rise in the price of medical expenses. 13 , 14 This is mainly because, with the increase of service supply, doctors face greater competitive pressure, thus inducing patients to consume more unnecessary medical projects. 14 Due to the lack of judgment ability of patients for the necessity of medical projects, they often accept non-necessary medical projects proposed by doctors. Therefore, induced demand will lead to imbalance between demand and supply, and then increase patients' medical expenses. The actual impact of telehealth on medical expenses is not yet clear. There is currently a lack of empirical research to verify this issue, especially from a macro perspective. In order to fill this research gap, we choose to use the medical field in China as the research background, starting from a macro perspective, aiming to verify the actual impact of telehealth on medical expenses. The main objectives of our research are: (1) considering the heterogeneity of medical service groups, to investigate the impact of the use of telehealth on medical expenses of urban residents and rural residents; (2) considering the difference in types of medical services, to analyze the impact of telehealth on the medical expenses of outpatients and inpatients. Our research has made the following contributions to the field of medical research. First, our research starts from a macro perspective, and analyzes the actual impact of the development of telehealth on medical expenses from January 2018 to September 2022 through provincial data. This research considers the differences between regions and time factors, and can more accurately reflect the development of telehealth and the changes in medical expenses in different regions. In addition, this paper explores the changes in medical expenses for two types of residents in urban and rural areas and two types of patients in outpatient and inpatient under the use of telehealth. The conclusion proves that the impact of telehealth on medical expenses may vary due to different medical service groups and service types. We hope to further understand the impact mechanism of telehealth on medical expenses, and provide relevant references for improving medical services. Methods STUDY DESIGN Our research collected data from 31 provinces in China from January 2018 to September 2022 in the "China Health Statistical Yearbook", "China Statistical Yearbook", and Baidu Index. After collecting and integrating data, a total of 1767 monthly data were collected to evaluate whether China's use of telehealth can alleviate offline medical expenses. The dependent variable of our research is offline medical expenses, mainly measured using data from the China Health Statistics Yearbook. Offline medical expenses are mainly measured in the following eight variables: Urban residents' average medical expenses (UMEP), Rural residents' average medical expenses (RMEP), Outpatient patients' average total expenses (OPTE), Inpatient patients' average total expenses (IPTE), Outpatient patients' average pharmaceutical expenses (OPPD), Inpatient patients' average pharmaceutical expenses (IPPD), Outpatient patients' average examination expenses (OPEC), Inpatient patients' average examination expenses (IPEC). The independent variable of our research is telehealth, which we reflected using data from the Baidu index. As the most widely used keyword search and analysis tool in China, Baidu index platform can record the search volume of users in different regions for a specified keyword within a certain time frame. Previous studies have shown that Baidu index can reflect the actual demand in the real world. Therefore, we decided to use Baidu index to measure the use of telehealth in various regions. We have written a Python program to automatically collect daily data from the Baidu Search Index platform for three keywords related to telehealth, namely "telehealth", "telehealth" and "telehealth" in 31 provinces in China. In order to control the differences in the level of medical development between different regions, we also collected the total number of healthcare institutions, the number of tertiary hospitals, the number of health personnel, the number of practicing doctors per 1,000 people, and the number of beds in healthcare institutions in each province as control variables. In addition, we obtained the population and GDP data of each region from the China Statistical Yearbook to reduce the impact of differences in population and economic development levels on our research questions. VARIABLES AND DATA IN EMPIRICAL MODEL The variables used in our empirical research are summarized in Table 1 . we have selected the following variables as dependent variables for the empirical model: UMEP, RMEP, OPTE, IPTE, OPPD, IPPD, OPEC, IPEC. Medical expenses include medical insurance, commercial insurance, or other forms of healthcare expenses. The average total expenses for outpatient patients during their visits, including registration fees, consultation fees, laboratory fees, examination fees, treatment fees, etc. The average total expenses for inpatient patients during their hospital stays, including bed fees, treatment fees, surgery fees, medication fees, etc. Pharmaceutical expenses, including prescription drug expenses and non-prescription drug expenses. Examination expenses, including various imaging, laboratory, and other examination fees. In our research, we regard telehealth as independent variables and select Baidu index (Keywords: Telehealth; Telehealth; Telehealth) as their proxy variables. Considering that the differences in medical and economic development levels between different regions may affect the results of the model, we introduce some control variables, i.e., population, GDP, THI, TH, HP, LPP, HB. Table 1 Variables and descriptions Type of variable Variable Description Proxy Dependent variable UMEP Urban Residents' Average Medical Expenses Average amount of medical expenses paid by urban residents. This includes expenses related to medical insurance, commercial insurance, or other forms of healthcare expenses. (yuan) RMEP Rural Residents' Average Medical Expenses Average amount of medical expenses paid by rural residents. Similar to UMEP, it includes expenses related to medical insurance, commercial insurance, or other forms of healthcare expenses. (yuan) OPTE Outpatient Patients' Average Total Expenses Average total expenses incurred by outpatient patients during their visits. This includes fees for registration, consultation, laboratory tests, examinations, treatments, etc. (yuan) IPTE Inpatient Patients' Average Total Expenses Average total expenses incurred by inpatient patients during their hospital stay. This includes bed charges, treatment fees, surgical fees, medication expenses, etc. (yuan) OPPD Outpatient Patients' Average Pharmaceutical Expenses Average pharmaceutical expenses paid by outpatient patients per visit. This includes expenses for prescribed medications and over-the-counter drugs. (yuan) OPEC Outpatient Patients' Average Examination Expenses Average examination expenses paid by outpatient patients per visit. This includes expenses for various diagnostic examinations such as imaging studies, laboratory tests, etc. (yuan) IPPD Inpatient Patients' Average Pharmaceutical Expenses Average pharmaceutical expenses paid by inpatient patients per visit. Similar to OPPD, it includes expenses for prescribed medications and over-the-counter drugs. (yuan) IPEC Inpatient Patients' Average Examination Expenses Average examination expenses paid by inpatient patients per visit. Similar to OPEC, it includes expenses for various diagnostic examinations such as imaging studies, laboratory tests, etc. (yuan) Independent variable Telehealth The use of telehealth Baidu Index (Keyword: Telehealth; Telehealth; Telehealth) Control variable Population Population in different provinces in China Population GDP Gross domestic product in different provinces in China Gross domestic product THI Total Healthcare Institutions Total number of healthcare institutions in each region TH Tertiary Hospital Total number of tertiary hospitals in each region HP Health Personnel Total number of health personnel in each region (thousands) LPP Licensed Physicians Per 1000 Average number of practicing physicians per thousand people in each region HB Healthcare Bed Average number of beds in healthcare institutions in each region Period Taking each month as a period From January 2018 to September 2022, each month is a period, a total of 57 periods RESEARCH MODEL To further reveal the impact of telehealth on offline medical expenses, our empirical model adopts individual fixed effects and time trend effects. Our empirical model is as follows: $$\:{\varvec{E}\varvec{x}\varvec{p}\varvec{e}\varvec{n}\varvec{s}\varvec{e}\varvec{s}}_{\varvec{i}\varvec{t}\varvec{j}}={\varvec{\beta\:}}_{0}+{\varvec{\beta\:}}_{1}{\varvec{T}\varvec{e}\varvec{l}\varvec{e}\varvec{h}\varvec{e}\varvec{a}\varvec{l}\varvec{t}\varvec{h}}_{\varvec{i}\varvec{t}}+{\varvec{\beta\:}}_{2}{\varvec{P}\varvec{o}\varvec{p}\varvec{u}\varvec{l}\varvec{a}\varvec{t}\varvec{i}\varvec{o}\varvec{n}}_{\varvec{i}\varvec{t}}+{\varvec{\beta\:}}_{3}{\varvec{G}\varvec{D}\varvec{P}}_{\varvec{i}\varvec{t}}+{\varvec{\beta\:}}_{4}{\varvec{T}\varvec{H}\varvec{I}}_{\varvec{i}\varvec{t}}$$ \(\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:+{\varvec{\beta\:}}_{5}{\varvec{T}\varvec{H}}_{\varvec{i}\varvec{t}}+{\varvec{\beta\:}}_{6}{\varvec{H}\varvec{P}}_{\varvec{i}\varvec{t}}+{\varvec{L}\varvec{P}\varvec{P}}_{\varvec{i}\varvec{t}}+{\varvec{\beta\:}}_{8}{\varvec{H}\varvec{B}}_{\varvec{i}\varvec{t}}+{\varvec{\beta\:}}_{9}{\varvec{P}\varvec{e}\varvec{r}\varvec{i}\varvec{o}\varvec{d}}_{\varvec{t}}+{\varvec{\mu\:}}_{\varvec{i}}+{\varvec{\epsilon\:}}_{\varvec{i}\varvec{t}}\) ( 1 ) Where \(\:i\) = 1, 2, ..., N represents the province, \(\:t\) represents the period, and \(\:j\) represents eight variables related to medical expenses. In these empirical models, \(\:{\beta\:}_{0}\) to \(\:{\beta\:}_{9}\) are the parameters to be estimated. By using the fixed effect of provinces, we can control the impact of specific factors in each province on the research results, incorporating unobserved provincial-specific effects into the terms \(\:{\mu\:}_{i}\) and \(\:{\epsilon\:}_{it}\) . At the same time, we introduce the time variable as a trend effect, using Period to represent the time variable, to better understand whether the impact of telehealth use on offline medical expenses is affected by time factors. Telehealth is the independent variable in our study, representing the use of telehealth. In addition, Population, GDP, THI, TH, HP, LPP, and HB are control variables in this empirical model. Results DATA ANALYSIS According to the descriptive statistics of Table 2 , we found that the medical security expenses of urban residents was significantly higher than that of rural residents. At the same time, the correlation analysis of table m shows that the average medical expense per hospitalization is much higher than that of outpatients. According to the correlation analysis of Table 3 , no complete multicollinearity problem was found in the model. Table 2 Descriptive statistics Variable Obs Mean Std. Dev. Min Max UMEP 1,767 2,113.421 544.010 639.700 3,973.900 RMEP 1,767 1,314.704 376.949 147.500 2,246.900 OPTE 1,767 475.943 126.189 288.600 1,076.600 IPTE 1,767 15,001.770 5,370.879 8,447.000 36,952.500 OPPD 1,767 300.578 81.125 183.100 682.100 OPEC 1,767 58.310 10.425 26.600 86.400 IPPD 1,767 10,701.590 4,062.610 5,803.500 26,846.900 IPEC 1,767 1,008.489 249.096 595.800 1,740.200 TELEHEALTH 1,767 1,386.024 976.659 0.000 4,505.000 Population 1,767 4,542.745 2,994.986 354.000 12,684.000 GDP 1,767 8,234.913 6,745.685 344.220 36,197.970 THI 1,767 32,815.420 23,092.420 4,397.000 88,162.000 TH 1,767 95.221 59.896 12.000 299.000 HP 1,767 428,730.500 267,560.800 36,806.000 1,058,702.000 LPP 1,767 2.460 0.551 1.600 4.830 HB 1,767 290997.8 184062.9 16787 721329 Table 3 Correlations Var UMEP RMEP OPTE IPTE OPPD OPEC IPPD IPEC Telehealth UMEP 1 RMEP 0.794*** 1 OPTE 0.720*** 0.643*** 1 IPTE 0.680*** 0.617*** 0.885*** 1 OPPD 0.715*** 0.640*** 0.996*** 0.880*** 1 OPEC 0.421*** 0.510*** 0.501*** 0.245*** 0.504*** 1 IPPD 0.681*** 0.608*** 0.896*** 0.995*** 0.894*** 0.254*** 1 IPEC 0.670*** 0.630*** 0.743*** 0.812*** 0.734*** 0.398*** 0.824*** 1 Telehealth 0.283*** 0.429*** 0.482*** 0.498*** 0.487*** 0.181*** 0.482*** 0.379** 1 *** p < 0.001, ** p < 0.01, * p < 0.05 MODEL ESTIMATION We used Stata17 to test the empirical model and estimated the model using fixed effects and time trend effects to more accurately assess the impact of telehealth on offline medical expenses in various provinces and over time. All adjusted regression R-square and F-values showed reasonable and statistically significant results, as shown in Tables 4 to 7 . Considering that telehealth may have different impacts on medical expenses for urban and rural residents, we conducted regression analysis using UMEP and RMEP as the dependent variables of the model. In Model 1 and Model 2, we observed that the coefficient of Telehealth was significantly positive (Model 1: \(\:\beta\:\:\) = 0.089, \(\:t\:\) = 6.39, \(\:p\) < 0.01; Model 2: \(\:\beta\:\:\) = 0.057, \(\:t\:\) = 4.15, \(\:p\:\) < 0.01), indicating that the use of telehealth has a positive impact on medical expenses for both urban and rural residents. Specifically, the higher the frequency of telehealth use, the corresponding increase in offline medical expenses. Based on the regression results of models 1 and 2, we drew Fig. 1 , where we can intuitively observe that the slope of UMEP is significantly greater than that of RMEP, revealing that the use of telehealth has a greater impact on the medical expenses of urban residents than that of rural residents. In other words, increasing the frequency of telehealth use will increase medical expenses for urban residents more, while the increase for rural residents will be relatively small. Table 4 Estimations models of medical expenses for urban and rural Variables Fixed effects Model 1 Model 2 UMEP RMEP Baidu Index 0.089*** 0.057*** (6.34) (4.15) Population 0.330 0.733 (0.88) (1.58) GDP -0.009*** -0.007** (-3.33) (-2.22) THI -0.065*** -0.016 (-5.11) (-1.42) TH -4.549 1.257 (-2.01) (0.82) HP 0.003 -0.001 (1.61) (-0.5) LPP 352.662*** 274.497*** (3.05) (3.49) HB 0.004** 0.003*** (2.08) (3.04) Period 2.428 2.376 (1.83) (2.01) Provinces Yes Yes Constant N/A N/A R 2 0.556 0.593 F\Wald 26.9 56.93 ***p < 0.01, **p < 0.05, *p < 0.1 Our research not only validates the impact of telehealth on offline medical expenses in different groups, but also further explores the impact of telehealth on the expenses incurred by different service types. In models 3 and 4, we examined the influence of telehealth on the per capita total expenses of outpatients and inpatients, respectively. Table 5 presents the results, indicating that Telehealth has a statistically significant positive coefficient (model 3: \(\:\beta\:\:\) = 0.009, \(\:t\:\) = 4.91, \(\:p\:\) < 0.01; Model 4: \(\:\beta\:\:\) = 0.396, \(\:t\:\) = 5.06, \(\:p\:\) < 0.01), which means that the use of telehealth will increase the total per capita expense of outpatients and inpatients. At the same time, through the slope difference shown in Fig. 2 , we found that the impact of telehealth on the total per capita expense of outpatients is much smaller than that of inpatients. This conclusion further demonstrates the positive impact of telehealth on offline medical expenses, and finds that this impact mainly comes from inpatients, rather than outpatients. Table 5 Estimations models of medical expenses for outpatients and inpatients Variables Fixed effects Model 3 Model 4 OPTE IPTE Baidu Index 0.009*** 0.396*** (4.91) (5.06) Population 0.103 -3.577 (1.09) (-1.16) GDP 0.000 -0.028 (-0.48) (-1.26) THI 0.000 0.119 (0.12) (1.54) TH -0.197 -7.428 (-0.79) (-0.59) HP 0.000 0.018 (1.2) (1.64) LPP 127.049*** 2029.385*** (3.1) (3.12) HB 0.000 -0.020** (-1.24) (-2.2) Period 0.547 6.729 (1.6) (0.75) Provinces Yes Yes Constant N/A N/A R 2 0.694 0.403 F\Wald 43.55 19.38 ***p < 0.01, **p < 0.05, *p < 0.1 To understand the impact of telehealth on offline medical expenses, our research model uses the average expense of pharmaceutical per visit for outpatients and inpatients as the dependent variable. Table 6 displays the findings of Models 5 and 6, in which we evaluate the effect of Telehealth on OPME and IPME, respectively. The results reveal a statistically significant positive coefficient for Telehealth (model 5: \(\:\beta\:\:\) = 0.006, \(\:t\:\) = 4.75, \(\:p\:\) < 0.01; Model 6: \(\:\beta\:\:\) = 0.110, \(\:t\:\) = 2.34, \(\:p\:\) < 0.05). It can be seen that the use of telehealth will lead to an increase in the average pharmaceutical expenses per outpatient and inpatient visit. From the slope difference shown in Fig. 3 , it can be observed that the increase in pharmaceutical expenses for inpatients using telehealth is more significant than that for outpatients. It means that the use of telehealth may lead to more pharmaceutical use and related expenses for inpatient patients. Table 6 Estimations models of pharmaceutical expenses Variables Fixed effects Model 5 Model 6 OPPD IPPD Baidu Index 0.006*** 0.110** (4.75) (2.34) Population 0.065 -1.706 (1.15) (-0.79) GDP 0.000 -0.011 (-0.92) (-0.59) THI 0.000 0.093 (-0.08) (1.49) TH -0.121 -2.794 (-0.79) (-0.27) HP 0.000 0.015 (1.51) (1.83) LPP 75.980*** 1687.770*** (3.78) (3.94) HB 0.000 -0.020** (-1.43) (-2.3) Period 0.508** 18.350** (2.57) (2.69) Provinces Yes Yes Constant N/A N/A R 2 0.742 0.621 F\Wald 50.57 30.81 ***p < 0.01, **p < 0.05, *p < 0.1 We examined the impact of telehealth on the average examination expense per outpatient and inpatient in models 7 and 8, respectively. The results are shown in Table 7 . The statistical results show that the coefficient of Telehealth is positive (model 7: \(\:\beta\:\:\) = 0.001, \(\:t\:\) = 3.04, \(\:p\:\) < 0.0; Model 8: \(\:\beta\:\:\) = 0.009, \(\:t\:\) = 2.16, \(\:p\:\) < 0.01), which indicates that the use of telehealth has a positive impact on the examination expenses of outpatients and inpatients. Further observation of Fig. 4 shows that the slope of inpatients is significantly greater than that of outpatients, which means that the use of telehealth has a more significant impact on the average examination expense per inpatient. In other words, compared to outpatients, hospitalized patients are more likely to experience an increase in the average expense per examination when using telehealth. Table 7 Estimations models of examination Variables Fixed effects Model 7 Model 8 OPEC IPEC Baidu Index 0.001*** 0.009** (3.04) (2.16) Population 0.014 0.172 (0.72) (0.59) GDP 0.000 -0.005 (-0.52) (-1.93) THI 0.000 0.007 (0.41) (0.74) TH 0.003 -0.276 (0.06) (-0.23) HP 0.000 0.001 (1.17) (0.8) LPP 17.355** 294.652 (2.2) (1.85) HB 0.000** -0.001 (-2.48) (-1.54) Period 0.102** 2.893** (2.13) (2.56) Provinces Yes Yes Constant N/A N/A R 2 0.709 0.745 F\Wald 23.03 38.75 ***p < 0.01, **p < 0.05, *p < 0.1 Discussion SUMMARY OF MAIN FINDINGS Our research primarily investigates the actual impact of telehealth on the medical expenses of two types of residents (i.e., Rural and urban) and two types of patients (i.e., outpatients and inpatients). The main research results are as follows: Firstly, telehealth has a positive impact on the medical expenses of urban and rural residents. This means that the more telehealth is used, the more medical expenses will increase for both urban and rural residents. This phenomenon can be explained by two important reasons. First, the development of telehealth intensifies competition among medical service providers, possibly undermining the patient-centered medical service concept and jeopardizing patient interests. 15 , 16 According to the induced demand theory, doctors facing greater competition may induce patients to accept more unnecessary medical items in pursuit of financial benefits. Due to the information asymmetry between doctors and patients, patients may blindly accept all medical services out of trust for the doctor, thereby increasing medical expenses. 1 Second, the accessibility of telehealth is improved, making it more convenient and quicker for patients to obtain medical services. This leads to an increase in medical expenses due to increased frequency of patient visits. In the context of undeveloped telehealth, traditional offline medical services face problems such as long queue times, lack of communication time with patients due to an overload of patients, and difficulty for patients to find suitable doctors due to information asymmetry. 17 The development of telehealth can alleviate these problems to some extent, encouraging patients to seek medical services proactively. 18 Furthermore, the results of Fig. 1 clearly show that the increase in medical expenses for urban residents is greater than that for rural residents. This may be because urban residents have better purchasing power and medical resources than rural residents, 19 making them more receptive to more medical services, and hence their medical expenses increase more significantly. Secondly, telehealth has a positive impact on medical expenses for both outpatients and inpatients. Under normal circumstances, outpatients have less severe symptoms, while inpatients are severely ill. 20 This means that the more telehealth is used, the total medical expenses of both mildly and severely ill patients will increase. The increase in medical expenses may be due to two reasons. First, telehealth makes medical services more convenient, which may lead to an increase in the frequency of visits by mildly ill patients, thereby increasing medical expenses. When diseases occur, patients often need to weigh the time cost and opportunity cost (e.g., loss of wages) of offline visits against the risks of not seeking medical attention. 21 For mildly ill patients, the risk of not seeking medical attention is relatively small due to their less severe symptoms. Therefore, some patients may choose not to seek medical attention. However, with the prevalence of telehealth, the cost of seeking medical attention is reduced, and more patients may choose to seek medical attention, leading to increased medical expenses. Second, telehealth allows patients to access high-quality medical resources from all over the country, providing more and better treatment opportunities for severely ill patients 22 , but also increasing their medical expenses. In addition, cross-regional medical services often come with additional costs such as transportation and accommodation, which also increase patients' medical expenses. It is worth noting that the results of Fig. 2 found that the impact of telehealth on the medical expenses of inpatients is much greater than that of outpatients. In other words, the majority of the increase in medical expenses comes from inpatients. This may be because severely ill patients have more complex medical service needs, and doctors are more likely to induce inpatients to undertake unnecessary medical procedures, thereby increasing the medical expenses of inpatients. Thirdly, telehealth has a positive impact on the pharmaceutical expenses of both outpatients and inpatients. This means that the more telehealth is used, the more the pharmaceutical expenses of outpatients and inpatients will increase. The increase in pharmaceutical expenses may be due to two reasons. First, the increase in service supply caused by telehealth may induce doctors to prescribe more unnecessary or high-priced pharmaceuticals for patients in order to increase their income. Second, with the development of telehealth, doctors face greater competition and may prescribe pharmaceuticals in larger doses to enhance their competitiveness and ensure patient efficacy. According to the results of Fig. 3 , the impact of telehealth on the pharmaceutical expenses of inpatients is much greater than that of outpatients. This may be because inpatients are mostly severely ill patients who usually need a larger variety and dosage of pharmaceuticals. In this situation, patients are more likely to accept the doctor's pharmaceutical recommendations, leading to unnecessary pharmaceutical expenses. Fourthly, telehealth has a positive impact on the examination expenses of both outpatients and inpatients. This means that the more telehealth is used, the more the examination expenses of outpatients and inpatients will be. The increase in examination expenses may be due to two reasons. First, doctors on telehealth platforms cannot directly observe the actual situation of patients. In order to get a more accurate diagnosis, doctors will suggest that patients undergo more examinations offline, which will increase the patient's expenses. Second, doctors, in order to increase income, are likely to recommend more unnecessary examinations for patients. According to the results of Fig. 4 , the impact of telehealth on the expenses of inpatients is much greater than that of outpatients. This may be because inpatients are mostly severely ill patients who usually need a larger variety and a higher frequency of examinations. In this situation, patients are more likely to accept the doctor's examination recommendations, leading to unnecessary examination expenses. IMPLICATION Our research provides some insights for the government, medical institutions, and healthcare service providers. Firstly, for the government, supervision of medical institutions should be strengthened. The government can ensure that patients are not induced to accept unnecessary examinations or treatments for economic benefits during the treatment process by establishing stricter regulatory mechanisms and strengthening law enforcement. Also, while promoting the development of telehealth, the government needs to manage and monitor the expenses of offline hospitals to prevent unreasonable pricing behavior of medical services by medical institutions. Our research found that the impact of telehealth on rural and urban residents, outpatients, and inpatients varies. Urban residents, who have more purchasing power and resources, face a more significant increase in medical expenses due to telehealth. Among outpatients and inpatients, the impact of telehealth on inpatients is far greater. Based on this, policy implementation can be staged, first piloted among urban residents or inpatients, and then gradually promoted to rural residents and outpatients. Secondly, for medical and health institutions, an effective internal review and supervision mechanism should be established. Additionally, medical institutions should strengthen professional ethics education for doctors. Our research shows that the use of telehealth could lead to an increase in medical expenses. According to the induced demand theory, we can reasonably speculate that due to patients' lack of medical expertise, most patients are willing to delegate decision-making power to doctors, thereby being induced to undertake unnecessary medical service items (Bickerdyke). Internal supervision mechanisms of medical institutions can supervise and evaluate medical behaviors, ensuring that medical services are reasonable, standardized, and effective, and prevent doctors from inducing patients to undertake unnecessary medical items. This series of measures can restrain the occurrence of doctors' induced behaviors in the external environment. Furthermore, medical institutions also need to take on the responsibility of strengthening doctors' professional ethics. This can be achieved through regular ethical training and lectures on medical ethics by medical institutions. Further research found that the increase in medical expenses mainly occurs among urban residents and inpatients, meaning that urban residents and inpatients are more likely to induce demand. Therefore, medical and health institutions need to strictly supervise the behavior of inpatient doctors, and focus on checking the charging situation of patients with high consumption prices, to avoid the misuse of medical resources and ensure that medical services are used on patients who need care. Declarations Authors’ Contribution Hualong Yang: Conceptualization, data collection, funding acquisition, writing original draft. Zhiying Cheng: Empirical model, software, writing original draft. Yuechen Ou: Conceptualization, visualization, writing original draft. Dan Li: Funding acquisition, supervision, validation, writing review and editing. Acknowledgements The authors highly appreciate the Editors and anonymous reviewers for their insightful comments and suggestions. Disclosure statement All authors concur with the content of this paper, and agree to submit it to Public Health. There are no conflicts of interest exist. Funding Information This study was partially funded by the National Natural Science Foundation of China Grants (72001049; 71901073). References Seyedin H, Afshari M, Isfahani P, Hasanzadeh E, Radinmanesh M, Bahador RC. The main factors of supplier-induced demand in health care: A qualitative study. J Educ Health Promot 2021; 10 (1):10-49. https://doi.org/10.4103/jehp.jehp_68_20 Zhang G, Zhang X, Bilal M, Dou W, Xu X, Rodrigues JJPC. Identifying fraud in medical insurance based on blockchain and deep learning. Future Gener Comp Sy 2022; 130:140-154. https://doi.org/10.1016/j.future.2021.12.006 Cappitelli A, Wenzinger E, Langa OC, Nuzzi L, Ganor O, Rogers-Vizena CR et al. Cost and satisfaction implications of using telehealth for plagiocephaly. Prs-glob Open 2022; 10 (6):e4392. https://doi.org/10.1097/GOX.0000000000004392 De Guzman KR, Snoswell CL, Smith AC. The impact of telehealth policy changes on general practitioner consultation activity in Australia: a time-series analysis. Aust Health Rev 2022; 46 (5):605-612. https://doi.org/10.1071/AH22058 Zhang MD, Zhang CH, Shi QL, Zeng SZ, Balezentis T. Operationalizing the telemedicine platforms through the social network knowledge: An MCDM model based on the CIPFOHW operator. Technol Forecast Soc 2022; 174: e121303. https://doi.org/10.1016/j.techfore.2021.121303 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 (3):269-275. https://doi.org/10.1007/s11606-015-3489-x Andino JJ, Castaneda PR, Shah PK, Ellimoottil C. The impact of video visits on measures of clinical efficiency and reimbursement. Urol Pract 2021; 8 (1):53-57. https://doi.org/10.1016/j.juro.2018.02.1636 van Steenbergen G, van Veghel D, van Lieshout D, Sperwer M, ter Woorst J, Dekker L. Effects of video-based patient education and consultation on unplanned health care utilization and early recovery after coronary artery bypass surgery (IMPROVED): Randomized controlled trial. J Med Internet Res 2022; 24 (8): e37728. https://doi.org/10.2196/37728 Jungbauer Jr WN, Gudipudi R, Brennan E, Melvin CL, Pecha PP. The cost impact of telehealth interventions in pediatric surgical specialties: A systematic review. J Pediatr Surg 2023; 58 (8):1527-1533. https://doi.org/10.1016/j.jpedsurg.2022.10.008 Metzger GA, Cooper J, Lutz C, Jatana KR, Nishimura L, Deans KJ et al. The value of telemedicine for the pediatric surgery patient in the time of COVID-19 and beyond. J Pediatr Surg 2021; 56 (8):1305-1311. https://doi.org/10.1016/j.jpedsurg.2021.02.018 Liu W, Saxon DR, McNair B, Sanagorski R, Rasouli N. Endocrinology telehealth consultation improved glycemic control similar to face-to-face visits in veterans. J Diabetes Sci Techn 2016; 10 (5):1079-1086. https://doi.org/10.1177/1932296816648343 Wood CL, Clements SA, McFann K, Slover R, Thomas JF, Wadwa RP. Use of telemedicine to improve adherence to american diabetes association standards in pediatric type 1 diabetes. Diabetes Technol The 2016; 18 (1):7-14. https://doi.org/10.1089/dia.2015.0123 Dzampe AK, Takahashi S. Competition and physician-induced demand in a healthcare market with regulated price: evidence from Ghana. Int J Health Econ Ma 2022; 22 (3):295-313. https://doi.org/10.1007/s10754-021-09320-7 Sekimoto M, Ii M. Supplier-induced demand for chronic disease care in Japan: Multilevel analysis of the association between physician density and physician-patient encounter Frequency. Value Health 2015; 6:103-110. https://doi.org/10.1016/j.vhri.2015.03.010 Dzampe AK, Takahashi S. Competition and physician-induced demand in a healthcare market with regulated price: evidence from Ghana. International Journal of Health Economics and Management 2022; 22 (3):295-313. 10.1007/s10754-021-09320-7 Yu J, Qiu Y, He Z. Is universal and uniform health insurance better for China? Evidence from the perspective of supply-induced demand. Diabetes Technol The 2020; 15 (1):56-71. https://doi.org/10.1017/S1744133118000385 Agarwal R, Singh BK. An analytical study of queues in medical sector. Opsearch 2018; 55 (2):268-287. https://doi.org/10.1007/s12597-017-0324-7 Jiang SH. The Relationship between face-to-face and online patient-provider communication: Examining the moderating roles of patient trust and patient satisfaction. Health Commun 2020; 35 (3):341-349. https://doi.org/10.1080/10410236.2018.1563030 Qiu YK, Lu W, Guo JK, Sun CZ, Liu XY. Examining the urban and rural healthcare progress in big cities of China: Analysis of monitoring data in dalian from 2008 to 2017. Int J Env Res Pub He 2020; 17 (4):1148. https://doi.org/10.3390/ijerph17041148 Jansky M, Lindena G, Nauck F. Well-being of patients receiving specialized palliative care at home or in hospital. Schmerz 2012; 26 (1):46-53. https://doi.org/10.1007/s00482-011-1119-z Gali K, Joshi S, Hueneke S, Katzenbach A, Radecki L, Calabrese T et al. Barriers, access and management of paediatric epilepsy with telehealth. J Telemed Telecare 2022; 28 (3):213-223. https://doi.org/10.1177/1357633X20969531 Cardinale AM. The Opportunity for Telehealth to Support Neurological Health Care. Telemed e-health 2018; 24 (12):969-978. https://doi.org/10.1089/tmj.2017.0290 Footnotes Federal Register: 2022 Federal Register Index: Centers for Medicare & Medicaid Services Heart Disease and Stroke Statistics—2022 Update: A Report From the American Heart Association | Circulation (ahajournals.org) World health statistics 2023: monitoring health for the SDGs, sustainable development goals (who.int) Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-6201561","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":427661060,"identity":"e38de7ae-07f5-45e9-80ff-fec400dd802e","order_by":0,"name":"Qiaoqiao Sun","email":"","orcid":"","institution":"Guangdong University of Technology","correspondingAuthor":false,"prefix":"","firstName":"Qiaoqiao","middleName":"","lastName":"Sun","suffix":""},{"id":427661062,"identity":"74356436-0a54-4ea3-99bd-99be51f1019f","order_by":1,"name":"jianbin Zheng","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA8ElEQVRIiWNgGAWjYDACCRDBAyQlGBsYPpCshXEG8VqgDGYeYnTIz24+9vCLjIW8uXRz22ObX4flddsPMD6u+IVbC+OcY+nGMjwShjvnHGw3zu07bLjtTAKz4dk+3FqYJXLMpCV4JBg33Ehsk87tuZ1gdoOBTbKxB7cWNon8byAt9mAtlsRo4ZHIYZP8wCORCNbC8AOqpeEHbi0SEmlm0kCNyRvuHGyT7G34D/RLYrNhYwNuLfIzkp9J/uyps91wu/2ZxI8/afJmxw8ffNjwB7cWcBDwwlzO2AYmG6AM3IDxB9zlfzAYo2AUjIJRMAoYABvtU+8d1ZzzAAAAAElFTkSuQmCC","orcid":"","institution":"Guangzhou Women and Children's Medical Center","correspondingAuthor":true,"prefix":"","firstName":"jianbin","middleName":"","lastName":"Zheng","suffix":""},{"id":427661065,"identity":"e1c334c2-5c2b-4a9b-b3c1-b997fe955b8b","order_by":2,"name":"Zilong Wang","email":"","orcid":"","institution":"Guangdong University of Technology","correspondingAuthor":false,"prefix":"","firstName":"Zilong","middleName":"","lastName":"Wang","suffix":""}],"badges":[],"createdAt":"2025-03-11 09:08:35","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6201561/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6201561/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":78418479,"identity":"116a67b5-1d29-4556-a79f-aea1ae9a00e7","added_by":"auto","created_at":"2025-03-13 05:12:00","extension":"jpeg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":439686,"visible":true,"origin":"","legend":"\u003cp\u003eEmpirical results of medical expenses for urban and rural\u003c/p\u003e","description":"","filename":"floatimage1.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-6201561/v1/8832625c52ad792eb6407feb.jpeg"},{"id":78418474,"identity":"536af0ec-3225-4d1b-a6b4-772fb9bfd39d","added_by":"auto","created_at":"2025-03-13 05:12:00","extension":"jpeg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":365376,"visible":true,"origin":"","legend":"\u003cp\u003eEmpirical results of medical expenses for outpatients and inpatients\u003c/p\u003e","description":"","filename":"floatimage2.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-6201561/v1/9c3f2cac7e2e52d3fd7c6d47.jpeg"},{"id":78419415,"identity":"c1d69340-349a-48e6-a05c-018972bb8c9a","added_by":"auto","created_at":"2025-03-13 05:28:00","extension":"jpeg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":343926,"visible":true,"origin":"","legend":"\u003cp\u003eEmpirical results of pharmaceutical expenses\u003c/p\u003e","description":"","filename":"floatimage3.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-6201561/v1/fb8342fd4795da00f77d9877.jpeg"},{"id":78419416,"identity":"623896d1-0847-41b2-aef1-f588f3bf5cd9","added_by":"auto","created_at":"2025-03-13 05:28:00","extension":"jpeg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":363000,"visible":true,"origin":"","legend":"\u003cp\u003eEmpirical results of examination\u003c/p\u003e","description":"","filename":"floatimage4.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-6201561/v1/b67a55d3e248718bb1f8354a.jpeg"},{"id":78693857,"identity":"657b9fe1-bbc7-4149-9abd-4e2862c10774","added_by":"auto","created_at":"2025-03-17 16:46:49","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2360878,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6201561/v1/2c97f790-da1c-45f6-b878-6a43212d4552.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"The impact of telehealth on medical expenses: An empirical research considering time and regional differences","fulltext":[{"header":"Introduction","content":"\u003cp\u003eIn recent years, reports released by the World Health Organization have pointed out that global medical expenses continue to rise.\u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u003c/sup\u003e According to statistical data released by the Centers for Medicare \u0026amp; Medicaid Services (CMS) in 2022, the medical expenses in the United States has exceeded 3 trillion dollars, accounting for 18% of GDP\u003ca class=\"FNLink\" href=\"#Fn1\" id=\"#FNLinkFn1\"\u003e\u003c/a\u003e. Data released by the National Health Commission shows that China's medical expenses exceeded 8 trillion yuan, an increase of 12.2% compared to 2021. A report released by the American Heart Association (AHA) shows that among 2045 adult respondents, 49% of people do not dare to choose medical treatment for fear of not being able to afford high medical expenses\u003ca class=\"FNLink\" href=\"#Fn2\" id=\"#FNLinkFn2\"\u003e\u003c/a\u003e. Reports released by the World Health Organization show that nearly 100\u0026nbsp;million people fall into extreme poverty each year due to disease treatment\u003ca class=\"FNLink\" href=\"#Fn3\" id=\"#FNLinkFn3\"\u003e\u003c/a\u003e. The rapid growth of medical expenses has become a problem that urgently needs to be solved in the current medical and health field.\u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e \u003cp\u003eTelehealth is generally considered an effective way to solve the rise in medical expenses.\u003csup\u003e\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u003c/sup\u003e As a new channel for medical services, telehealth is deeply changing traditional offline medical activities.\u003csup\u003e\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u003c/sup\u003e On the one hand, it can better optimize the allocation of medical resources.\u003csup\u003e\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u003c/sup\u003e On the other hand, it provides more convenience for patients.\u003csup\u003e\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u003c/sup\u003e These factors will reduce the medical expenses of patients.\u003csup\u003e\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e,\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u003c/sup\u003e In the traditional offline medical environment, patients need to bear transfer costs and opportunity costs (e.g., education, work, leisure and entertainment, etc.) to get medical services.\u003csup\u003e\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e,\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u003c/sup\u003e Some research in pediatric surgery and chronic disease fields point out that telehealth has the characteristics of spanning time and space, and can reduce medical expenses by helping patients access medical resources at any time.\u003csup\u003e\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e,\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u003c/sup\u003e However, current research on reducing medical expenses through telehealth is mainly short-term research based on a micro perspective. In the long term and from a macro perspective, the addition of telehealth as a channel for medical service supply may lead to an increase in medical expenses. The relationship between telehealth and medical expenses is not straightforward and clear, and there is a certain contradiction in the specific theoretical analysis. According to the induced demand theory, introducing online medical services as a supplement to offline medical services, increasing the supply of medical services, not only can't reduce medical expenses, but may lead to a rise in the price of medical expenses.\u003csup\u003e\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e,\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u003c/sup\u003e This is mainly because, with the increase of service supply, doctors face greater competitive pressure, thus inducing patients to consume more unnecessary medical projects.\u003csup\u003e\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u003c/sup\u003e Due to the lack of judgment ability of patients for the necessity of medical projects, they often accept non-necessary medical projects proposed by doctors. Therefore, induced demand will lead to imbalance between demand and supply, and then increase patients' medical expenses.\u003c/p\u003e \u003cp\u003eThe actual impact of telehealth on medical expenses is not yet clear. There is currently a lack of empirical research to verify this issue, especially from a macro perspective. In order to fill this research gap, we choose to use the medical field in China as the research background, starting from a macro perspective, aiming to verify the actual impact of telehealth on medical expenses. The main objectives of our research are: (1) considering the heterogeneity of medical service groups, to investigate the impact of the use of telehealth on medical expenses of urban residents and rural residents; (2) considering the difference in types of medical services, to analyze the impact of telehealth on the medical expenses of outpatients and inpatients. Our research has made the following contributions to the field of medical research. First, our research starts from a macro perspective, and analyzes the actual impact of the development of telehealth on medical expenses from January 2018 to September 2022 through provincial data. This research considers the differences between regions and time factors, and can more accurately reflect the development of telehealth and the changes in medical expenses in different regions. In addition, this paper explores the changes in medical expenses for two types of residents in urban and rural areas and two types of patients in outpatient and inpatient under the use of telehealth. The conclusion proves that the impact of telehealth on medical expenses may vary due to different medical service groups and service types. We hope to further understand the impact mechanism of telehealth on medical expenses, and provide relevant references for improving medical services.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eSTUDY DESIGN\u003c/h2\u003e \u003cp\u003eOur research collected data from 31 provinces in China from January 2018 to September 2022 in the \"China Health Statistical Yearbook\", \"China Statistical Yearbook\", and Baidu Index. After collecting and integrating data, a total of 1767 monthly data were collected to evaluate whether China's use of telehealth can alleviate offline medical expenses.\u003c/p\u003e \u003cp\u003eThe dependent variable of our research is offline medical expenses, mainly measured using data from the China Health Statistics Yearbook. Offline medical expenses are mainly measured in the following eight variables: Urban residents' average medical expenses (UMEP), Rural residents' average medical expenses (RMEP), Outpatient patients' average total expenses (OPTE), Inpatient patients' average total expenses (IPTE), Outpatient patients' average pharmaceutical expenses (OPPD), Inpatient patients' average pharmaceutical expenses (IPPD), Outpatient patients' average examination expenses (OPEC), Inpatient patients' average examination expenses (IPEC).\u003c/p\u003e \u003cp\u003eThe independent variable of our research is telehealth, which we reflected using data from the Baidu index. As the most widely used keyword search and analysis tool in China, Baidu index platform can record the search volume of users in different regions for a specified keyword within a certain time frame. Previous studies have shown that Baidu index can reflect the actual demand in the real world. Therefore, we decided to use Baidu index to measure the use of telehealth in various regions. We have written a Python program to automatically collect daily data from the Baidu Search Index platform for three keywords related to telehealth, namely \"telehealth\", \"telehealth\" and \"telehealth\" in 31 provinces in China.\u003c/p\u003e \u003cp\u003eIn order to control the differences in the level of medical development between different regions, we also collected the total number of healthcare institutions, the number of tertiary hospitals, the number of health personnel, the number of practicing doctors per 1,000 people, and the number of beds in healthcare institutions in each province as control variables. In addition, we obtained the population and GDP data of each region from the China Statistical Yearbook to reduce the impact of differences in population and economic development levels on our research questions.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eVARIABLES AND DATA IN EMPIRICAL MODEL\u003c/h3\u003e\n\u003cp\u003eThe variables used in our empirical research are summarized in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. we have selected the following variables as dependent variables for the empirical model: UMEP, RMEP, OPTE, IPTE, OPPD, IPPD, OPEC, IPEC. Medical expenses include medical insurance, commercial insurance, or other forms of healthcare expenses. The average total expenses for outpatient patients during their visits, including registration fees, consultation fees, laboratory fees, examination fees, treatment fees, etc. The average total expenses for inpatient patients during their hospital stays, including bed fees, treatment fees, surgery fees, medication fees, etc. Pharmaceutical expenses, including prescription drug expenses and non-prescription drug expenses. Examination expenses, including various imaging, laboratory, and other examination fees. In our research, we regard telehealth as independent variables and select Baidu index (Keywords: Telehealth; Telehealth; Telehealth) as their proxy variables. Considering that the differences in medical and economic development levels between different regions may affect the results of the model, we introduce some control variables, i.e., population, GDP, THI, TH, HP, LPP, HB.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eVariables and descriptions\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eType of variable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eDescription\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eProxy\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"7\" rowspan=\"8\"\u003e \u003cp\u003eDependent variable\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eUMEP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eUrban Residents' Average Medical Expenses\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAverage amount of medical expenses paid by urban residents. This includes expenses related to medical insurance, commercial insurance, or other forms of healthcare expenses. (yuan)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRMEP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eRural Residents' Average Medical Expenses\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAverage amount of medical expenses paid by rural residents. Similar to UMEP, it includes expenses related to medical insurance, commercial insurance, or other forms of healthcare expenses. (yuan)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOPTE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eOutpatient Patients' Average Total Expenses\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAverage total expenses incurred by outpatient patients during their visits. This includes fees for registration, consultation, laboratory tests, examinations, treatments, etc. (yuan)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eIPTE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eInpatient Patients' Average Total Expenses\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAverage total expenses incurred by inpatient patients during their hospital stay. This includes bed charges, treatment fees, surgical fees, medication expenses, etc. (yuan)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOPPD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eOutpatient Patients' Average Pharmaceutical Expenses\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAverage pharmaceutical expenses paid by outpatient patients per visit. This includes expenses for prescribed medications and over-the-counter drugs. (yuan)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOPEC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eOutpatient Patients' Average Examination Expenses\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAverage examination expenses paid by outpatient patients per visit. This includes expenses for various diagnostic examinations such as imaging studies, laboratory tests, etc. (yuan)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eIPPD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eInpatient Patients' Average Pharmaceutical Expenses\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAverage pharmaceutical expenses paid by inpatient patients per visit. Similar to OPPD, it includes expenses for prescribed medications and over-the-counter drugs. (yuan)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eIPEC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eInpatient Patients' Average Examination Expenses\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAverage examination expenses paid by inpatient patients per visit. Similar to OPEC, it includes expenses for various diagnostic examinations such as imaging studies, laboratory tests, etc. (yuan)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIndependent variable\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTelehealth\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eThe use of telehealth\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eBaidu Index (Keyword: Telehealth; Telehealth; Telehealth)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"7\" rowspan=\"8\"\u003e \u003cp\u003eControl variable\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePopulation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePopulation in different provinces in China\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ePopulation\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGDP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eGross domestic product in different provinces in China\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eGross domestic product\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTHI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eTotal Healthcare Institutions\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eTotal number of healthcare institutions in each region\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTH\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eTertiary Hospital\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eTotal number of tertiary hospitals in each region\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eHealth Personnel\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eTotal number of health personnel in each region (thousands)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLPP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eLicensed Physicians Per 1000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAverage number of practicing physicians per thousand people in each region\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHB\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eHealthcare Bed\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAverage number of beds in healthcare institutions in each region\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePeriod\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eTaking each month as a period\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eFrom January 2018 to September 2022, each month is a period, a total of 57 periods\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e\n\u003ch3\u003eRESEARCH MODEL\u003c/h3\u003e\n\u003cp\u003eTo further reveal the impact of telehealth on offline medical expenses, our empirical model adopts individual fixed effects and time trend effects. Our empirical model is as follows:\u003cdiv id=\"Equa\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equa\" name=\"EquationSource\"\u003e\n$$\\:{\\varvec{E}\\varvec{x}\\varvec{p}\\varvec{e}\\varvec{n}\\varvec{s}\\varvec{e}\\varvec{s}}_{\\varvec{i}\\varvec{t}\\varvec{j}}={\\varvec{\\beta\\:}}_{0}+{\\varvec{\\beta\\:}}_{1}{\\varvec{T}\\varvec{e}\\varvec{l}\\varvec{e}\\varvec{h}\\varvec{e}\\varvec{a}\\varvec{l}\\varvec{t}\\varvec{h}}_{\\varvec{i}\\varvec{t}}+{\\varvec{\\beta\\:}}_{2}{\\varvec{P}\\varvec{o}\\varvec{p}\\varvec{u}\\varvec{l}\\varvec{a}\\varvec{t}\\varvec{i}\\varvec{o}\\varvec{n}}_{\\varvec{i}\\varvec{t}}+{\\varvec{\\beta\\:}}_{3}{\\varvec{G}\\varvec{D}\\varvec{P}}_{\\varvec{i}\\varvec{t}}+{\\varvec{\\beta\\:}}_{4}{\\varvec{T}\\varvec{H}\\varvec{I}}_{\\varvec{i}\\varvec{t}}$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003e \u003cspan class=\"InlineEquation\"\u003e \u003cspan class=\"mathinline\"\u003e\\(\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:+{\\varvec{\\beta\\:}}_{5}{\\varvec{T}\\varvec{H}}_{\\varvec{i}\\varvec{t}}+{\\varvec{\\beta\\:}}_{6}{\\varvec{H}\\varvec{P}}_{\\varvec{i}\\varvec{t}}+{\\varvec{L}\\varvec{P}\\varvec{P}}_{\\varvec{i}\\varvec{t}}+{\\varvec{\\beta\\:}}_{8}{\\varvec{H}\\varvec{B}}_{\\varvec{i}\\varvec{t}}+{\\varvec{\\beta\\:}}_{9}{\\varvec{P}\\varvec{e}\\varvec{r}\\varvec{i}\\varvec{o}\\varvec{d}}_{\\varvec{t}}+{\\varvec{\\mu\\:}}_{\\varvec{i}}+{\\varvec{\\epsilon\\:}}_{\\varvec{i}\\varvec{t}}\\)\u003c/span\u003e \u003c/span\u003e \u003cb\u003e( 1 )\u003c/b\u003e \u003c/p\u003e \u003cp\u003eWhere \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:i\\)\u003c/span\u003e\u003c/span\u003e = 1, 2, ..., N represents the province, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:t\\)\u003c/span\u003e\u003c/span\u003e represents the period, and \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:j\\)\u003c/span\u003e\u003c/span\u003e represents eight variables related to medical expenses. In these empirical models, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\beta\\:}_{0}\\)\u003c/span\u003e\u003c/span\u003e to \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\beta\\:}_{9}\\)\u003c/span\u003e\u003c/span\u003e are the parameters to be estimated. By using the fixed effect of provinces, we can control the impact of specific factors in each province on the research results, incorporating unobserved provincial-specific effects into the terms \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\mu\\:}_{i}\\)\u003c/span\u003e\u003c/span\u003e and \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\epsilon\\:}_{it}\\)\u003c/span\u003e\u003c/span\u003e. At the same time, we introduce the time variable as a trend effect, using Period to represent the time variable, to better understand whether the impact of telehealth use on offline medical expenses is affected by time factors. Telehealth is the independent variable in our study, representing the use of telehealth. In addition, Population, GDP, THI, TH, HP, LPP, and HB are control variables in this empirical model.\u003c/p\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003eDATA ANALYSIS\u003c/h2\u003e \u003cp\u003eAccording to the descriptive statistics of Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, we found that the medical security expenses of urban residents was significantly higher than that of rural residents. At the same time, the correlation analysis of table m shows that the average medical expense per hospitalization is much higher than that of outpatients. According to the correlation analysis of Table \u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e, no complete multicollinearity problem was found in the model.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eDescriptive statistics\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eObs\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMean\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eStd. Dev.\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eMin\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eMax\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUMEP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1,767\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2,113.421\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e544.010\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e639.700\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3,973.900\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRMEP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1,767\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1,314.704\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e376.949\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e147.500\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2,246.900\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOPTE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1,767\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e475.943\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e126.189\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e288.600\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1,076.600\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIPTE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1,767\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e15,001.770\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e5,370.879\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e8,447.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e36,952.500\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOPPD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1,767\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e300.578\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e81.125\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e183.100\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e682.100\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOPEC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1,767\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e58.310\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e10.425\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e26.600\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e86.400\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIPPD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1,767\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e10,701.590\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4,062.610\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e5,803.500\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e26,846.900\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIPEC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1,767\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1,008.489\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e249.096\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e595.800\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1,740.200\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTELEHEALTH\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1,767\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1,386.024\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e976.659\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e4,505.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePopulation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1,767\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e4,542.745\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2,994.986\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e354.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e12,684.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGDP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1,767\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e8,234.913\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e6,745.685\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e344.220\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e36,197.970\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTHI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1,767\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e32,815.420\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e23,092.420\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4,397.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e88,162.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTH\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1,767\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e95.221\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e59.896\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e12.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e299.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1,767\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e428,730.500\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e267,560.800\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e36,806.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1,058,702.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLPP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1,767\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2.460\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.551\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.600\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e4.830\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHB\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1,767\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e290997.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e184062.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e16787\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e721329\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e 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class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVar\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eUMEP\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eRMEP\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eOPTE\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eIPTE\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eOPPD\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eOPEC\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eIPPD\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003eIPEC\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c10\"\u003e \u003cp\u003eTelehealth\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUMEP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRMEP\u003c/p\u003e \u003c/td\u003e 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\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOPEC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.421***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.510***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.501***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.245***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.504***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIPPD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.681***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.608***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.896***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.995***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.894***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.254***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIPEC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.670***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.630***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.743***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.812***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.734***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.398***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.824***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTelehealth\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.283***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.429***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.482***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.498***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.487***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.181***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.482***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.379**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"10\"\u003e***\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001, **\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01, *\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eMODEL ESTIMATION\u003c/h2\u003e \u003cp\u003eWe used Stata17 to test the empirical model and estimated the model using fixed effects and time trend effects to more accurately assess the impact of telehealth on offline medical expenses in various provinces and over time. All adjusted regression R-square and F-values showed reasonable and statistically significant results, as shown in Tables\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e to \u003cspan refid=\"Tab7\" class=\"InternalRef\"\u003e7\u003c/span\u003e.\u003c/p\u003e \u003cp\u003eConsidering that telehealth may have different impacts on medical expenses for urban and rural residents, we conducted regression analysis using UMEP and RMEP as the dependent variables of the model. In Model 1 and Model 2, we observed that the coefficient of Telehealth was significantly positive (Model 1: \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\beta\\:\\:\\)\u003c/span\u003e\u003c/span\u003e= 0.089, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:t\\:\\)\u003c/span\u003e\u003c/span\u003e= 6.39, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:p\\)\u003c/span\u003e\u003c/span\u003e \u0026lt; 0.01; Model 2: \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\beta\\:\\:\\)\u003c/span\u003e\u003c/span\u003e= 0.057, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:t\\:\\)\u003c/span\u003e\u003c/span\u003e= 4.15, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:p\\:\\)\u003c/span\u003e\u003c/span\u003e\u0026lt; 0.01), indicating that the use of telehealth has a positive impact on medical expenses for both urban and rural residents. Specifically, the higher the frequency of telehealth use, the corresponding increase in offline medical expenses. Based on the regression results of models 1 and 2, we drew Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, where we can intuitively observe that the slope of UMEP is significantly greater than that of RMEP, revealing that the use of telehealth has a greater impact on the medical expenses of urban residents than that of rural residents. In other words, increasing the frequency of telehealth use will increase medical expenses for urban residents more, while the increase for rural residents will be relatively small.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eEstimations models of medical expenses for urban and rural\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eVariables\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eFixed effects\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eModel 1\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eModel 2\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eUMEP\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eRMEP\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eBaidu Index\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.089***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.057***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(6.34)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(4.15)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003ePopulation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.330\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.733\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(0.88)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(1.58)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eGDP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.009***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.007**\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(-3.33)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(-2.22)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eTHI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.065***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.016\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(-5.11)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(-1.42)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eTH\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-4.549\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.257\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(-2.01)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.82)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eHP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.003\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(1.61)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(-0.5)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eLPP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e352.662***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e274.497***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(3.05)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(3.49)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eHB\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.004**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.003***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(2.08)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(3.04)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePeriod\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.428\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.376\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(1.83)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(2.01)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eProvinces\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eConstant\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eN/A\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eN/A\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eR\u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.556\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.593\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eF\\Wald\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e26.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e56.93\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"3\"\u003e***p\u0026thinsp;\u0026lt;\u0026thinsp;0.01, **p\u0026thinsp;\u0026lt;\u0026thinsp;0.05, *p\u0026thinsp;\u0026lt;\u0026thinsp;0.1\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eOur research not only validates the impact of telehealth on offline medical expenses in different groups, but also further explores the impact of telehealth on the expenses incurred by different service types. In models 3 and 4, we examined the influence of telehealth on the per capita total expenses of outpatients and inpatients, respectively. Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e presents the results, indicating that Telehealth has a statistically significant positive coefficient (model 3: \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\beta\\:\\:\\)\u003c/span\u003e\u003c/span\u003e= 0.009, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:t\\:\\)\u003c/span\u003e\u003c/span\u003e= 4.91, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:p\\:\\)\u003c/span\u003e\u003c/span\u003e\u0026lt; 0.01; Model 4: \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\beta\\:\\:\\)\u003c/span\u003e\u003c/span\u003e= 0.396, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:t\\:\\)\u003c/span\u003e\u003c/span\u003e= 5.06, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:p\\:\\)\u003c/span\u003e\u003c/span\u003e\u0026lt; 0.01), which means that the use of telehealth will increase the total per capita expense of outpatients and inpatients. At the same time, through the slope difference shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, we found that the impact of telehealth on the total per capita expense of outpatients is much smaller than that of inpatients. This conclusion further demonstrates the positive impact of telehealth on offline medical expenses, and finds that this impact mainly comes from inpatients, rather than outpatients.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eEstimations models of medical expenses for outpatients and inpatients\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eVariables\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eFixed effects\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eModel 3\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eModel 4\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOPTE\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eIPTE\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eBaidu Index\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.009***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.396***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(4.91)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(5.06)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003ePopulation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.103\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-3.577\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(1.09)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(-1.16)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eGDP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.028\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(-0.48)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(-1.26)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eTHI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.119\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(0.12)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(1.54)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eTH\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.197\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-7.428\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(-0.79)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(-0.59)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eHP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.018\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(1.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(1.64)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eLPP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e127.049***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2029.385***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(3.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(3.12)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eHB\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.020**\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(-1.24)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(-2.2)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePeriod\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.547\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6.729\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(1.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.75)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eProvinces\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eConstant\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eN/A\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eN/A\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eR\u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.694\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.403\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eF\\Wald\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e43.55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e19.38\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"3\"\u003e***p\u0026thinsp;\u0026lt;\u0026thinsp;0.01, **p\u0026thinsp;\u0026lt;\u0026thinsp;0.05, *p\u0026thinsp;\u0026lt;\u0026thinsp;0.1\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eTo understand the impact of telehealth on offline medical expenses, our research model uses the average expense of pharmaceutical per visit for outpatients and inpatients as the dependent variable. Table\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\u003c/span\u003e displays the findings of Models 5 and 6, in which we evaluate the effect of Telehealth on OPME and IPME, respectively. The results reveal a statistically significant positive coefficient for Telehealth (model 5: \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\beta\\:\\:\\)\u003c/span\u003e\u003c/span\u003e= 0.006, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:t\\:\\)\u003c/span\u003e\u003c/span\u003e= 4.75, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:p\\:\\)\u003c/span\u003e\u003c/span\u003e\u0026lt; 0.01; Model 6: \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\beta\\:\\:\\)\u003c/span\u003e\u003c/span\u003e= 0.110, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:t\\:\\)\u003c/span\u003e\u003c/span\u003e= 2.34, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:p\\:\\)\u003c/span\u003e\u003c/span\u003e\u0026lt; 0.05). It can be seen that the use of telehealth will lead to an increase in the average pharmaceutical expenses per outpatient and inpatient visit. From the slope difference shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e, it can be observed that the increase in pharmaceutical expenses for inpatients using telehealth is more significant than that for outpatients. It means that the use of telehealth may lead to more pharmaceutical use and related expenses for inpatient patients.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab6\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 6\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eEstimations models of pharmaceutical expenses\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eVariables\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eFixed effects\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eModel 5\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eModel 6\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOPPD\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eIPPD\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eBaidu Index\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.006***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.110**\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(4.75)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(2.34)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003ePopulation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.065\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-1.706\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(1.15)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(-0.79)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eGDP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.011\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(-0.92)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(-0.59)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eTHI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.093\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(-0.08)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(1.49)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eTH\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.121\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-2.794\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(-0.79)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(-0.27)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eHP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.015\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(1.51)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(1.83)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eLPP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e75.980***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1687.770***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(3.78)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(3.94)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eHB\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.020**\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(-1.43)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(-2.3)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePeriod\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.508**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e18.350**\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(2.57)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(2.69)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eProvinces\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eConstant\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eN/A\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eN/A\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eR\u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.742\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.621\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eF\\Wald\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e50.57\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e30.81\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"3\"\u003e***p\u0026thinsp;\u0026lt;\u0026thinsp;0.01, **p\u0026thinsp;\u0026lt;\u0026thinsp;0.05, *p\u0026thinsp;\u0026lt;\u0026thinsp;0.1\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eWe examined the impact of telehealth on the average examination expense per outpatient and inpatient in models 7 and 8, respectively. The results are shown in Table\u0026nbsp;\u003cspan refid=\"Tab7\" class=\"InternalRef\"\u003e7\u003c/span\u003e. The statistical results show that the coefficient of Telehealth is positive (model 7: \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\beta\\:\\:\\)\u003c/span\u003e\u003c/span\u003e= 0.001, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:t\\:\\)\u003c/span\u003e\u003c/span\u003e= 3.04, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:p\\:\\)\u003c/span\u003e\u003c/span\u003e\u0026lt; 0.0; Model 8: \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\beta\\:\\:\\)\u003c/span\u003e\u003c/span\u003e= 0.009, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:t\\:\\)\u003c/span\u003e\u003c/span\u003e= 2.16, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:p\\:\\)\u003c/span\u003e\u003c/span\u003e\u0026lt; 0.01), which indicates that the use of telehealth has a positive impact on the examination expenses of outpatients and inpatients. Further observation of Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e shows that the slope of inpatients is significantly greater than that of outpatients, which means that the use of telehealth has a more significant impact on the average examination expense per inpatient. In other words, compared to outpatients, hospitalized patients are more likely to experience an increase in the average expense per examination when using telehealth.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab7\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 7\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eEstimations models of examination\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eVariables\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eFixed effects\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eModel 7\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eModel 8\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOPEC\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eIPEC\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eBaidu Index\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.001***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.009**\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(3.04)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(2.16)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003ePopulation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.014\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.172\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(0.72)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.59)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eGDP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.005\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(-0.52)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(-1.93)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eTHI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.007\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(0.41)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.74)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eTH\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.003\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.276\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(0.06)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(-0.23)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eHP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(1.17)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.8)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eLPP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e17.355**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e294.652\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(2.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(1.85)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eHB\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.000**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(-2.48)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(-1.54)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePeriod\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.102**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.893**\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(2.13)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(2.56)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eProvinces\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eConstant\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eN/A\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eN/A\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eR\u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.709\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.745\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eF\\Wald\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e23.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e38.75\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"3\"\u003e***p\u0026thinsp;\u0026lt;\u0026thinsp;0.01, **p\u0026thinsp;\u0026lt;\u0026thinsp;0.05, *p\u0026thinsp;\u0026lt;\u0026thinsp;0.1\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003eSUMMARY OF MAIN FINDINGS\u003c/h2\u003e \u003cp\u003eOur research primarily investigates the actual impact of telehealth on the medical expenses of two types of residents (i.e., Rural and urban) and two types of patients (i.e., outpatients and inpatients). The main research results are as follows:\u003c/p\u003e \u003cp\u003eFirstly, telehealth has a positive impact on the medical expenses of urban and rural residents. This means that the more telehealth is used, the more medical expenses will increase for both urban and rural residents. This phenomenon can be explained by two important reasons. First, the development of telehealth intensifies competition among medical service providers, possibly undermining the patient-centered medical service concept and jeopardizing patient interests.\u003csup\u003e\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e,\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u003c/sup\u003e According to the induced demand theory, doctors facing greater competition may induce patients to accept more unnecessary medical items in pursuit of financial benefits. Due to the information asymmetry between doctors and patients, patients may blindly accept all medical services out of trust for the doctor, thereby increasing medical expenses.\u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u003c/sup\u003e Second, the accessibility of telehealth is improved, making it more convenient and quicker for patients to obtain medical services. This leads to an increase in medical expenses due to increased frequency of patient visits. In the context of undeveloped telehealth, traditional offline medical services face problems such as long queue times, lack of communication time with patients due to an overload of patients, and difficulty for patients to find suitable doctors due to information asymmetry.\u003csup\u003e\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u003c/sup\u003e The development of telehealth can alleviate these problems to some extent, encouraging patients to seek medical services proactively.\u003csup\u003e\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u003c/sup\u003e Furthermore, the results of Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e clearly show that the increase in medical expenses for urban residents is greater than that for rural residents. This may be because urban residents have better purchasing power and medical resources than rural residents,\u003csup\u003e\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u003c/sup\u003e making them more receptive to more medical services, and hence their medical expenses increase more significantly.\u003c/p\u003e \u003cp\u003eSecondly, telehealth has a positive impact on medical expenses for both outpatients and inpatients. Under normal circumstances, outpatients have less severe symptoms, while inpatients are severely ill.\u003csup\u003e\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u003c/sup\u003e This means that the more telehealth is used, the total medical expenses of both mildly and severely ill patients will increase. The increase in medical expenses may be due to two reasons. First, telehealth makes medical services more convenient, which may lead to an increase in the frequency of visits by mildly ill patients, thereby increasing medical expenses. When diseases occur, patients often need to weigh the time cost and opportunity cost (e.g., loss of wages) of offline visits against the risks of not seeking medical attention.\u003csup\u003e\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u003c/sup\u003e For mildly ill patients, the risk of not seeking medical attention is relatively small due to their less severe symptoms. Therefore, some patients may choose not to seek medical attention. However, with the prevalence of telehealth, the cost of seeking medical attention is reduced, and more patients may choose to seek medical attention, leading to increased medical expenses. Second, telehealth allows patients to access high-quality medical resources from all over the country, providing more and better treatment opportunities for severely ill patients \u003csup\u003e\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u003c/sup\u003e, but also increasing their medical expenses. In addition, cross-regional medical services often come with additional costs such as transportation and accommodation, which also increase patients' medical expenses. It is worth noting that the results of Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e found that the impact of telehealth on the medical expenses of inpatients is much greater than that of outpatients. In other words, the majority of the increase in medical expenses comes from inpatients. This may be because severely ill patients have more complex medical service needs, and doctors are more likely to induce inpatients to undertake unnecessary medical procedures, thereby increasing the medical expenses of inpatients.\u003c/p\u003e \u003cp\u003eThirdly, telehealth has a positive impact on the pharmaceutical expenses of both outpatients and inpatients. This means that the more telehealth is used, the more the pharmaceutical expenses of outpatients and inpatients will increase. The increase in pharmaceutical expenses may be due to two reasons. First, the increase in service supply caused by telehealth may induce doctors to prescribe more unnecessary or high-priced pharmaceuticals for patients in order to increase their income. Second, with the development of telehealth, doctors face greater competition and may prescribe pharmaceuticals in larger doses to enhance their competitiveness and ensure patient efficacy. According to the results of Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e, the impact of telehealth on the pharmaceutical expenses of inpatients is much greater than that of outpatients. This may be because inpatients are mostly severely ill patients who usually need a larger variety and dosage of pharmaceuticals. In this situation, patients are more likely to accept the doctor's pharmaceutical recommendations, leading to unnecessary pharmaceutical expenses.\u003c/p\u003e \u003cp\u003eFourthly, telehealth has a positive impact on the examination expenses of both outpatients and inpatients. This means that the more telehealth is used, the more the examination expenses of outpatients and inpatients will be. The increase in examination expenses may be due to two reasons. First, doctors on telehealth platforms cannot directly observe the actual situation of patients. In order to get a more accurate diagnosis, doctors will suggest that patients undergo more examinations offline, which will increase the patient's expenses. Second, doctors, in order to increase income, are likely to recommend more unnecessary examinations for patients. According to the results of Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e, the impact of telehealth on the expenses of inpatients is much greater than that of outpatients. This may be because inpatients are mostly severely ill patients who usually need a larger variety and a higher frequency of examinations. In this situation, patients are more likely to accept the doctor's examination recommendations, leading to unnecessary examination expenses.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eIMPLICATION\u003c/h2\u003e \u003cp\u003eOur research provides some insights for the government, medical institutions, and healthcare service providers.\u003c/p\u003e \u003cp\u003eFirstly, for the government, supervision of medical institutions should be strengthened. The government can ensure that patients are not induced to accept unnecessary examinations or treatments for economic benefits during the treatment process by establishing stricter regulatory mechanisms and strengthening law enforcement. Also, while promoting the development of telehealth, the government needs to manage and monitor the expenses of offline hospitals to prevent unreasonable pricing behavior of medical services by medical institutions. Our research found that the impact of telehealth on rural and urban residents, outpatients, and inpatients varies. Urban residents, who have more purchasing power and resources, face a more significant increase in medical expenses due to telehealth. Among outpatients and inpatients, the impact of telehealth on inpatients is far greater. Based on this, policy implementation can be staged, first piloted among urban residents or inpatients, and then gradually promoted to rural residents and outpatients.\u003c/p\u003e \u003cp\u003eSecondly, for medical and health institutions, an effective internal review and supervision mechanism should be established. Additionally, medical institutions should strengthen professional ethics education for doctors. Our research shows that the use of telehealth could lead to an increase in medical expenses. According to the induced demand theory, we can reasonably speculate that due to patients' lack of medical expertise, most patients are willing to delegate decision-making power to doctors, thereby being induced to undertake unnecessary medical service items (Bickerdyke). Internal supervision mechanisms of medical institutions can supervise and evaluate medical behaviors, ensuring that medical services are reasonable, standardized, and effective, and prevent doctors from inducing patients to undertake unnecessary medical items. This series of measures can restrain the occurrence of doctors' induced behaviors in the external environment. Furthermore, medical institutions also need to take on the responsibility of strengthening doctors' professional ethics. This can be achieved through regular ethical training and lectures on medical ethics by medical institutions. Further research found that the increase in medical expenses mainly occurs among urban residents and inpatients, meaning that urban residents and inpatients are more likely to induce demand. Therefore, medical and health institutions need to strictly supervise the behavior of inpatient doctors, and focus on checking the charging situation of patients with high consumption prices, to avoid the misuse of medical resources and ensure that medical services are used on patients who need care.\u003c/p\u003e \u003c/div\u003e "},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAuthors\u0026rsquo; Contribution\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eHualong Yang: Conceptualization, data collection, funding acquisition, writing original draft. Zhiying Cheng: Empirical model, software, writing original draft. Yuechen Ou: Conceptualization, visualization, writing original draft. Dan Li: Funding acquisition, supervision, validation, writing review and editing.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors highly appreciate the Editors and anonymous reviewers for their insightful comments and suggestions.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDisclosure statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll authors concur with the content of this paper, and agree to submit it to Public Health. There are no conflicts of interest exist.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding Information\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was partially funded by the National Natural Science Foundation of China Grants (72001049; 71901073).\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eSeyedin H, Afshari M, Isfahani P, Hasanzadeh E, Radinmanesh M, Bahador RC. 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Endocrinology telehealth consultation improved glycemic control similar to face-to-face visits in veterans. J Diabetes Sci Techn 2016; 10 (5):1079-1086. https://doi.org/10.1177/1932296816648343\u003c/li\u003e\n\u003cli\u003eWood CL, Clements SA, McFann K, Slover R, Thomas JF, Wadwa RP. Use of telemedicine to improve adherence to american diabetes association standards in pediatric type 1 diabetes. Diabetes Technol The 2016; 18 (1):7-14. https://doi.org/10.1089/dia.2015.0123\u003c/li\u003e\n\u003cli\u003eDzampe AK, Takahashi S. Competition and physician-induced demand in a healthcare market with regulated price: evidence from Ghana. Int J Health Econ Ma 2022; 22 (3):295-313. https://doi.org/10.1007/s10754-021-09320-7\u003c/li\u003e\n\u003cli\u003eSekimoto M, Ii M. Supplier-induced demand for chronic disease care in Japan: Multilevel analysis of the association between physician density and physician-patient encounter Frequency. 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Health Commun 2020; 35 (3):341-349. https://doi.org/10.1080/10410236.2018.1563030\u003c/li\u003e\n\u003cli\u003eQiu YK, Lu W, Guo JK, Sun CZ, Liu XY. Examining the urban and rural healthcare progress in big cities of China: Analysis of monitoring data in dalian from 2008 to 2017. Int J Env Res Pub He 2020; 17 (4):1148. https://doi.org/10.3390/ijerph17041148\u003c/li\u003e\n\u003cli\u003eJansky M, Lindena G, Nauck F. Well-being of patients receiving specialized palliative care at home or in hospital. Schmerz 2012; 26 (1):46-53. https://doi.org/10.1007/s00482-011-1119-z\u003c/li\u003e\n\u003cli\u003eGali K, Joshi S, Hueneke S, Katzenbach A, Radecki L, Calabrese T et al. Barriers, access and management of paediatric epilepsy with telehealth. J Telemed Telecare 2022; 28 (3):213-223. https://doi.org/10.1177/1357633X20969531\u003c/li\u003e\n\u003cli\u003eCardinale AM. The Opportunity for Telehealth to Support Neurological Health Care. Telemed e-health 2018; 24 (12):969-978. https://doi.org/10.1089/tmj.2017.0290\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Footnotes","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003e Federal Register: 2022 Federal Register Index: Centers for Medicare \u0026amp; Medicaid Services\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003e Heart Disease and Stroke Statistics\u0026mdash;2022 Update: A Report From the American Heart Association | Circulation (ahajournals.org)\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003e World health statistics 2023: monitoring health for the SDGs, sustainable development goals (who.int)\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"telehealth, medical expenses, urban residents, rural residents, outpatients, inpatients","lastPublishedDoi":"10.21203/rs.3.rs-6201561/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6201561/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eIntroduction\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe increase in medical expenses is a global challenge. Telehealth is considered an effective way to address this problem. Although previous research suggest that telehealth can reduce medical expenses, there is a lack of empirical research to support this conclusion. In particular, there is a lack of macro-level research evidence revealing the impact of telehealth on medical expenses. Our research aims to investigate the actual impact of telehealth on medical expenses at the macro level, considering time and regional differences.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMaterial and Methods\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe took China as the subject of study, collected statistical data on medical expenses and telehealth from 31 provinces, and built an empirical model to reveal the actual impact of the use of telehealth on medical expenses.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eOur research findings indicate that the use of telehealth not only fails to alleviate medical expenses, but actually leads to an increase in medical expenses. By comparing coefficients, we found that the increase in medical expenses for urban residents is greater than that for rural residents. Furthermore, our research results also found significant differences in the impact of telehealth on outpatient and inpatient expenses, with the increase in expenses for inpatients being much higher than for outpatients.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAs governments promote the development of telehealth, they need to strengthen the supervision of offline medical institutions. Moreover, the increase in medical expenses shows differences among urban and rural residents, and outpatients and inpatients. This suggests that governments need to stage medical service monitoring in practice, starting with urban residents or inpatients, and gradually extending to rural residents and outpatient groups.\u003c/p\u003e","manuscriptTitle":"The impact of telehealth on medical expenses: An empirical research considering time and regional differences","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-03-13 05:11:55","doi":"10.21203/rs.3.rs-6201561/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"c105f546-d55a-4cb6-9aa6-b8eeaa104131","owner":[],"postedDate":"March 13th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-03-17T16:38:40+00:00","versionOfRecord":[],"versionCreatedAt":"2025-03-13 05:11:55","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6201561","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6201561","identity":"rs-6201561","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}