JMIR FORMATIVE RESEARCH

Guo et al

Original Paper

The Effect of Offline Medical Resource Distribution on Online
Physician-Patient Interaction: Empirical Study With Online andOffline Data
Shanshan Guo1, PhD; Yuanyuan Dang2, PhD; Doug Vogel3, PhD; Bofei She2, BA
1School of Business and Management, Shanghai International Studies University, Shanghai, China

2

School of Business Administration, South China University of Technology, Guangzhou, China

3

School of Management, Harbin Institute of Technology, Harbin, China

Corresponding Author:
Yuanyuan Dang, PhD
School of Business Administration, South China University of Technology
381 Wushan Road
Tianhe District
Guangzhou, 510641
China
Phone: 86 18845095850
Email: dyy_hit@163.com

Abstract
Background: The relationship between online health communities (OHCs) and offline medical care is unclear because bothprovide physician-patient interaction services and channels. Taking advantage of information and communication technology,
patients have been using OHCs widely. However, some physical medical resources (such as hospital beds and medical devices)cannot be replicated by information and communication technologies. Therefore, it is worth studying how offline medical resources
affect physician-patient interactions in OHCs and how OHCs help to solve resource scarcity and the uneven distribution of
traditional medical treatment.
Objective: This study aimed to support the notion that physician-patient consultations in OHCs are influenced by the objective
distribution of offline health care capital (accessibility and availability) and to provide suggestions for the allocation of medical
resources in practice through the judicious use of offline and online channels.
Methods: The empirical data in this study were collected from both online and offline channels. The offline data include 9 years(2006-2014) of medical resource statistics of 31 provincial administrative regions in mainland China. Moreover, data regarding
the geolocation-based physician-patient interaction network in the OHC were also collected. The online data come from one ofChina’s largest OHCs. We obtained 92,492 telephone consultation records of 6006 physicians using an automatic web crawler
program. Social network analysis was used to visualize the descriptive statistics of the offline geolocation-based physician-patientinteraction network in the OHC. A regression model with a squared variable was applied to analyze online and offline empirical
data to further test our hypothesis. Two types of robustness tests were used to increase the reliability of the test results of theinitial model.
Results: The results of our social network analysis show that there is a uniform geographic distribution of patients who use
OHCs, whereas the physician relies more on geographic advantage (eg, a higher medical resource capability). Moreover, theempirical results of the regression model support the notion that physician-patient telephone consultations are positively influenced
by physicians’ online contributions (βcontribution=.210; P<.001) and capital availability (βbed=.935; P=.07), and, interestingly,
spatial accessibility has an inverted U–shaped effect (βdistance=.199; P<.001 and βdistance2=–.00449; P=.008). The results indicatethat the use of OHCs, although constrained by offline medical resources, provides a channel for offline resources to flow fromareas with high availability to those with low availability.
Conclusions: This study explores the relationship between online and offline channels by investigating online physician-patientinteractions and offline medical resources. In particular, this study analyzes the impact of offline channels on online channels
and verifies the possibility of OHC capital use shifting from a high-availability area to a low-availability area. In addition, it
provides a theoretical and practical basis for understanding the interaction of online and offline channels of medical care.
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Guo et al(JMIR Form Res 2023;7:e43533) doi: 10.2196/43533
KEYWORDS
medical resources; online health community; physician-patient interaction; online and offline; social network analysis

Introduction
Background
Health care is one of the most critical elements of human life,
and as the population ages, and the prevalence of chronic
diseases increases, people’s health awareness increases.
Balancing the increasing demand and scarce medical resourcesin health care has become a global challenge. Online healthcommunities (OHCs), which use information andcommunication technology, are believed to be an essential tool
for addressing this challenge and key to achieving pleasing,ongoing physician-patient interactions [1]. With advances in
information and communication technology, the use of theinternet has decreased the cost of communication [2]. Someeven point out that the internet has eliminated distancelimitations [3] and created a “flat world” [4].
OHCs embody the nonregional nature of a network, enabling
the network to break the geographic bottleneck of traditional
health care. In addition, several scholars have demonstrated that
OHCs play significant roles in information dissemination,
emotional support, knowledge sharing, and so on [5-10].
Nevertheless, although the relationship between OHCs andoffline medical resources (eg, hospitals, testing equipment, andphysicians) has been growing slowly, it is easily depleted,difficult to replicate, and highly exclusive. Therefore, it isseldom considered. In fact, research on online and offline
channels shows that they are complementary and synergistic[11].
Ways to rationally allocate and improve the efficiency of offline
medical delivery with limited resources to provide maximum
health care and health management support to individuals area persistent topic of traditional health care statistical research[12]. Related research has focused on improving health careresource distribution in terms of spatial accessibility and capital
availability [13-15]. Spatial accessibility refers to the geographic
availability and accessibility of health care resources and isgenerally measured by using distance methods (eg, linear spatial
distance, network distance, and average elapsed time) [13]. As
part of spatial equity studies, capital availability refers to theaverage distribution of resources. It describes the amount ofhealth care capital available per capita, as measured by the
number of objective forms of health care resources (beds,
physicians, and nurses) per 1000 population. In traditional
studies, the rational allocation of offline medical resources, suchas the spatial (geographic) layout [13] of medical resources, isoften considered, whereas the complementary role of online
channels is usually ignored. Especially for accessibility(geographic distance), the allocation of offline resources showsa considerable impact compared with online interactions [16].
In recent years, the emergence of COVID-19 has provided alarge growth opportunity for OHCs. Several recent studies haveshown that for online medical services (including consultations),
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trust in the health care provider positively affects the use andeffectiveness of OHCs [17-19]. There is evidence that the
COVID-19 pandemic has led to an improvement in thephysician-patient relationship, including an increase in trust[19], thereby providing an impetus to use OHCs. Before the
COVID-19 pandemic, eHealth use was low, and only
approximately 10% of people communicated online withphysicians or had online medical appointments [20]. However,during the COVID-19 pandemic, the spatial accessibility oflimited medical resources became a challenge [21,22]. In several
countries, such as the United Kingdom and Uganda, there has
been an increase in the use of online health services, which areseen as a good method to mitigate reduced access to offline
health care resources [21]. Although recent evidence suggeststhat the geographic distribution of quality hospitals remains
unequal in China, OHCs have mitigated the inequity of medical
resources between rural and urban areas [22]. The use of OHCs
has been influenced by the spatial distribution and accessibilityof medical resources, and the COVID-19 pandemic has provided
an ideal opportunity to develop OHCs. However, previous
studies have not considered the specific impact of the
distribution of offline medical resources on the use of OHCs.
Therefore, it is necessary to explore the impact of the
distribution of offline medical resources on the social exchange
between physicians and patients in OHCs. This knowledge will
help to leverage the interaction between online and offline
channels to optimize the allocation of medical resources so that
medical resources are used most appropriately by health carestakeholders.
Hence, this study uses online (OHC) and offline (offline medical
resources) data to explore the impact of medical resource
distribution on online physician-patient interactions. This
research supports the notion that physician-patient consultations
in OHCs are influenced by the objective distribution of offline
health care resources, and this issue is studied in terms of both
spatial accessibility and capital availability. The results of ourstudy provide suggestions for how to allocate medical resourcesin practice through the judicious use of offline and online
channels.

The Impact of the Spatial Accessibility of Offline
Medical Resources on Online Physician-PatientInteractions
It is difficult and expensive to move offline medical resources
such as hospital buildings, equipment, beds, and long-tenured
physicians, as is the case with most tangible resources.
Therefore, the geographic distance between offline medical
resources and patients will directly influence the accessibility
and availability of these resources. Furthermore, the geographic
distribution of spatial accessibility not only affects the use ofresources in offline channels but also affects online channels;
for example, some empirical studies have pointed out that online
channels become more valuable when consumers are farther
away from offline markets [16]. Hence, online channels will
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play a more prominent role, specifically in terms of consumer
engagement, as offline spatial accessibility decreases (asdistance increases) [2]; for example, a patient in Shenyang, thecapital of China’s Liaoning province, who wants tocommunicate with a physician working at a tertiary hospital in
Beijing, almost 700 km away, may choose to have a telephone
consultation provided by the OHC because of geographic
distance, eliminating travel costs and saving commute time.
Nevertheless, prior studies indicate that although online channels
can compensate for the lack of offline spatial accessibility, local
familiarity [23,24] cannot be ignored. Molitor et al [25] studied
residents purchasing health care services (eg, cosmetic dentistry
and osteopathy) via the internet and showed that long distances
reduce consumers’purchase intentions. Meanwhile, other studies
have suggested that users’ online search behavior follows the
local familiarity effect, that is, users will limit themselves to ageographic search range, and the results beyond this range will
not be considered [26]. The local familiarity effect also applies
to access to health care resources. As health care is time
sensitive, patients are more likely to interact online with nearby
physicians in anticipation of a future face-to-face encounter.
Furthermore, patients likely have better insight into the medical
resources of their surrounding area, such as which hospital or
physician is better, including for a particular specialty. Given
the local familiarity effect, patients are more likely to have an
online physician-patient interaction with nearby physicians to
avoid social exchange involving low offline accessibility.
In summary, this study suggests that the probability of havingsocial exchange with physicians will increase when accessibility
increases within a certain area. For the area beyond, theprobability will decrease when accessibility decreases. On thebasis of the aforementioned discussion, we propose hypothesis
1: the frequency of online physician-patient interactions (via
telephone consultations) is influenced by the spatial accessibilityof offline medical resources, and the effect has an inverted U
shape.

The Effect of the Capital Availability of Offline
Medical Resources on Online Physician-PatientInteractions
In addition to spatial accessibility, the availability of offline
medical resources is another index of medical resource
distribution. In terms of macro resource allocation, spatial
accessibility refers to the distance to the nearest available
resource. Availability encompasses the ability of high-quality
resources to serve users in a geographic area, that is, per capita
occupancy. As mentioned earlier, medical resources arecharacterized by their exclusivity. Different customers cannot
use a single medical resource. Therefore, as measured by percapita occupancy, the exclusivity of medical resources is
considered when determining the availability of capital; forexample, suppose that there are 3 tertiary hospitals within anarea in region A, but there is only 1 tertiary hospital in the same
size area in region B. Compared with the residents in region B,
those in region A have a shorter average distance to reach atertiary care hospital; therefore, they have higher spatial
accessibility. However, region A, which has 3 million people,
is more densely populated than region B, which has only 0.5
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Guo et al
million people. As a result, the capital availability of region A
is 1 hospital per million population, whereas for region B, it is
2 hospitals per million population. Thus, the availability oftertiary hospitals in region B is better than that in region A. Fora patient, a lack of spatial accessibility means higher costs (eg,time and money) to reach the nearest hospital (or other healthcare resource) [16]. Insufficient capital availability implies thata patient will have to share health care resources (eg, hospitalbeds and physicians) with more patients, which may result in
longer wait times during the visit or increased costs because of
insufficient supply. These scenarios illustrate that spatial
accessibility and capital availability are 2 complementarymeasures for the rational distribution of medical resources andthat the absence of either decreases the likelihood of an effective
and reasonable allocation of medical resources.
The inequality in access to health care resources among people
in different regions was described by Goh et al [27] as “thehealth disadvantage of location.” The most typical example ofthis concept is the inherent difference in the availability of healthcare resources between urban and rural areas. The main sourcesof this variation are the social, economic, and environmental
differences between these settings [28]. This issue is difficult
to solve quickly. In China, tier 1 urban areas (eg, Beijing,
Shanghai, and Guangzhou) have the highest medical resource
availability [29]. Consequently, there have been population
movements to urban areas, especially for people with medical
needs [30]. People travel considerable distances from theirpermanent residences to cities with higher capital availability
to obtain superior medical resources.
The emergence of OHCs provides more opportunities to support
offline interactions between physicians and patients, such as
referrals (physicians can recommend offline face-to-facetreatment when OHCs cannot meet medical needs) and routine
telephone consultations to keep in touch as part of the offline
physician-patient relationship. Hence, for patients who seek
quality medical resources, especially in regions different fromtheir current areas of residence, the larger the difference incapital availability, the more inclined they will be to engage in
online interactions with a physician located in their destination
region. Therefore, based on the aforementioned discussion, we
propose hypothesis 2: the frequency of online physician-patientinteractions (by means of telephone consultations) is positively
correlated with the variation in offline medical resource
availability.

The Interaction of Spatial Accessibility and Capital
Availability of Offline Medical Resources
As spatial accessibility and capital availability are 2 independent
but complementary measures of the distribution of medical
resources, this study suggests that a complementary effect on
online physician-patient interactions exists between these 2
indicators. Specifically, the accessibility and availability ofoffline medical resources have a positive moderating effect on
the frequency of physician-patient interactions in OHCs.
There is a significant status difference between physicians andpatients in physician-patient interactions. Furthermore, thisrelationship is often life critical and entails responsibility,
obligation, and trust [31]. Physician-patient interactions follow
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social exchange theory and are understood in terms of “rewards
and costs” from an economic and utilitarian perspective [32,33].
During the social exchange, people are more likely to engage
in social exchange with lower costs and higher rewards [16].
When there is limited accessibility to offline medical resourcesthat are located outside the patient’s neighborhood, thephysician-patient interaction is less defined [34], and the patient
may pay more in terms of time or even financial costs whenoffline communication is needed.
For this reason, patients are more likely to forgo this onlinesocial exchange because of the trade-offs between the gains andlosses of the social exchange. However, suppose thathigher-quality health care resources are available to patients
when the target region of the physician has higher medical
resources. In that case, this availability increases the reward ofphysician-patient social exchange and consequently increases
the likelihood that the patient will choose to engage in an onlinesocial exchange with that physician. Similarly, when there ispoor availability of medical resources in the physician’s target
region, patients will choose not to have an online social
exchange with this physician because of long wait times andexcessive expenses. When the patient is physically closer to thephysician, this proximity reduces the patient’s costs caused bygeographic distance and compensates for poor capital
availability, thus increasing the possibility of a physician-patient
online social exchange.
In summary, this study hypothesizes that the accessibility andavailability of offline medical resources can compensate for
their respective deficits and complement each other to influence
online physician-patient interactions. Therefore, we propose

Guo et al
hypothesis 3: the frequency of online physician-patientinteractions (via telephone consultations) is positively correlatedwith the interaction between the spatial accessibility and capital
availability of offline medical resources.

Methods
Introduction of Online and Offline Data
The data in this study are divided into 2 parts: online data andoffline data. The offline data, which include data from 2006 to2014 from the China Health Statistical Yearbook 2014 as wellas the latitude and longitude coordinates of the 31 provincial
administrative regions in mainland China, describe the
distribution of offline medical resources in China. The online
data were collected from one of the largest OHCs in China by
an automatic web crawler program. These data include thepersonal information (eg, names, titles, hospitals, departments,
and contributions) of all the physicians who provided telephone
consultation services on the platform from its establishment
until September 2014, as well as the details of all telephone
consultations. Importantly, the OHC protects patient privacy
by encrypting the patient ID. However, patient locations and a
description of disease-specific symptoms are displayed on thesite with permission. This information enables physicians tobetter understand the patient’s history before the telephone
consultation to improve the interaction and give patients a senseof authenticity, thereby increasing the number of users. Figure
1 depicts the order information of a recent telephone
consultation. The detailed description of the patient’s symptoms
has been blurred for confidentiality reasons.

Figure 1. Example of a physician’s recent telephone consultation.

After obtaining the OHC data, we initially cleaned andpreprocessed them. Because of both privacy reasons and thedisplay mechanisms of the OHC, only telephone consultationsin the last month could be shown. After parsing the information
captured by the web crawler, we learned that 6006 physicians
had had telephone consultations with patients. This figure
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accounts for 22.4% (6006/26,813) of all physicians who hadprovided telephone consultation services. There were 92,492
telephone consultations, with an average of 15.4 (SD 75.86)
telephone consultations per physician in the last month.

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Next, the offline medical resource data were processed. This
study uses offline data sourced from China’s 2014 public healthstatistics in combination with the online data from the study
period of the last month. The offline data included statistics
from different provincial regions, such as the number of beds
in hospitals and health centers per 1000 population, general
hospital beds per 1000 population, and full-time physicians per1000 population. In addition, an adjacency matrix between
provinces was calculated using the central distance.

Ethical Considerations
As the online data set is composed of crawl data concerning
actual physician-patient telephone consultations, and these
consultations did not take place for research purposes, the data
set is considered a public domain data set and is thus exempt
from institutional review board approval as publicly availablesocial media data per Article 27 of the Data Security Law of
the People’s Republic of China. The automatic crawler program
followed the Robots Exclusion Protocol, which is considered
a common specification in the internet field. In addition, in
accordance with the Data Security Management Measures ofChina, we limited the number of concurrent programs to a single
source of web pages to avoid a heavy data burden. We also took
precautionary measures in handling the OHC data set becauseof its sensitive nature [7,8]. On the basis of the privacy policyof our target OHC platform, the patient ID is partially hidden(only the first character is retained, and asterisks replace therest). The home page example shows only the publicly available
information of the physician, and the patient’s user- generated
online content (such as the medical process description in thethank-you note) was automatically anonymized. After dealing
with the data set, we submitted the research project ethics review
application to the institutional review board of the Shanghai
International Studies University’s School of Business andManagement, which approved the study as exempt (review
number 2022BC024). Accordingly, the data collection of thisstudy complies with medical ethical norms and requirements.

Data Description Based on Social Network Analysis
To preliminarily explore whether the physician-patient online
interaction would be influenced by the offline channel, we usedsocial network analysis to visualize the descriptive statistics ofthe offline geolocation-based physician-patient interaction

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network in the Chinese OHC (Figure 2). In our network analysis,
each vertex represents a provincial region. On the basis of thelatitude and longitude of their relative locations, these vertices
roughly correspond to their actual geographic locations. As thedata available from the OHC are limited, the geographic location
information of patients is available only for telephone
consultations. Other functions and services (such as online
consultations or patient communities) do not provide patients’geographic locations. Therefore, this study uses the frequencyof physician-patient telephone consultations to represent thevertices’ online physician-patient interaction attributes. Thissocial network analysis is a directed graph based on offline
geographic locations; for example, what is meant when the
Beijing vertex points to the Heilongjiang vertex is very different
from what is meant when the Beijing vertex points to the
Heilongjiang vertex. The latter means that a physician in Beijing
has a social exchange with a patient in Heilongjiang, whereas
the former means that a patient in Beijing has a consultation
with a physician in Heilongjiang. The blue squares represent
physicians, specifically the in-degree of the provincial
administrative region. The red dots represent patients,
specifically the out-degree of the provincial administrative
region. The sizes of the blue squares and red dots represent thefrequency of interactions: the larger the square, the more
telephone consultations a physician in that provincial region
has had with a patient.
Figure 2 shows significant differences in the in-degree andout-degree of different provincial administrative regions. In
other words, the online physician-patient interactions in eachprovincial administrative region are not independent ofgeographic location and follow a completely random
distribution. This finding suggests that a new physician-patientinteraction network that is completely independent of offline
resource distribution cannot be formed.
To further understand the difference in the individual vertices
in both the network and overall distribution of online
physician-patient interactions, we conducted a statistical analysis
based on the relative (in-degree or out-degree) centrality andabsolute (in-degree or out-degree) centrality of different
provincial regions as well as the (in-degree or out-degree)
centralization of the overall network.

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Figure 2. The physician-patient interaction network in the Chinese online health community is based on an offline geographic location in mainland
China.

Degree centrality is measured by the total number of other actors
in the network who are directly linked to an actor of interest. It
represents the closeness of connections with others, and a higher
value indicates a more central position of the vertex in the
network. Degree centrality reflects an actor’s centrality in the
network, which implies the ability of its related vertex toinfluence the other vertices in the network. Formulas (1) and(2) [35] are as follows:CD,c(ni)=dc(ni)=Σjxij (1)CD,r(ni)=dr(ni)=Σjxij (2)where CD,c(ni) and CD,r(ni) represent the out-degree andin-degree of the nth vertex, respectively. If xij=0, it means that
there is no direct connection from vertex ni to vertex nj, whereas
if xij=1, it means that a direct connection exists between them.
Similarly, if xji=0, it means that there is no direct connection
from vertex nj to vertex ni, whereas if xji=1, it means that a directconnection exists between them.
Relative degree centrality is computed in another way using
formula (3) [35] to measure the relative centrality of a certain
vertex:C'D(ni)=CD(ni)/N–1 (3)where N represents the total number of the vertices in the
network, and CD(ni), specifically CD,c(ni) or CD,r(ni), is theabsolute degree centrality of vertex ni.
Table 1 shows that the top 4 provincial administrative regions
ranked by in-degree centrality are Beijing, Shanghai, Jiangsu,

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and Guangdong. In-degree centrality describes the local central
index of these 4 regions and implies that the physicians in theseregions enjoy a major advantage during web-based
physician-patient social exchange. In addition, in reference tothe distribution of offline health care capital, these regions are
considered to have high medical care standards in China, as
stated in the China Health Statistical Yearbook 2014.
Furthermore, these 4 regions hold the top positions in the
rankings in terms of the number of tertiary hospitals and general
hospitals. This statistical result supports the hypothesis that
objective offline medical resources influence the web-basedsocial exchange between physicians and patients. Network
centralization measures the degree of concentration of allvertices in the overall level of the network. The network is moreconcentrated when the value of network centralization is closer
to 1.
In centralization, the centrality of a central vertex is comparedwith that of an edge vertex. If a network is concentrated, thecentrality of the central vertex must be high, and the centralityof the edge vertex must be low. By contrast, if a network isdispersed, there is no obvious difference in centrality between
the central and edge vertices. Our result shows that theout-degree centrality is 2.885%, whereas the in-degree centrality
is 16.492%, meaning that patients seeking online consultations
show a better trend in terms of reasonable distribution becauseof more dispersed distribution than physicians who provide
telephone consultation services. As these physician consultants
construct a more centralized network, there is a uniform
geographic distribution of patients who use OHCs, whereas thephysician relies more on geographic advantage (eg, a higher
medical resource capability).

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Table 1. Statistical results of the social network analysis.
Province

Absolute out-degree centrality

Absolute in-degree centrality Relative out-degree centrality Relative in-degree centrality

Beijing

1292

22261

0.954

16.444

Shanghai

713

10343

0.527

7.64

Jiangsu

3652

1997

2.698

1.475

Guangdong

1647

1165

1.217

0.861

Tianjin

1350

922

0.997

0.681

Shandong

3426

905

2.531

0.669

Shaanxi

794

764

0.587

0.564

Henan

2087

558

1.542

0.412

Hubei

932

537

0.688

0.397

Hunan

963

476

0.711

0.352

Sichuan

772

294

0.57

0.217

Zhejiang

3783

293

2.794

0.216

Anhui

2099

290

1.55

0.214

Fujian

847

222

0.626

0.164

Liaoning

1695

199

1.252

0.147

Chongqing

357

177

0.264

0.131

Hebei

5010

125

3.701

0.092

Heilongjiang

1390

118

1.027

0.087

Shanxi

2506

67

1.851

0.049

Jiangxi

1032

64

0.762

0.047

Jilin

892

33

0.659

0.024

Yunnan

421

28

0.311

0.021

Guangxi

341

24

0.252

0.018

Gansu

684

22

0.505

0.016

Hainan

167

19

0.123

0.014

Inner Mongolia

1809

10

1.336

0.007

Guizhou

381

7

0.281

0.005

Xinjiang

434

4

0.321

0.003

Ningxia

259

1

0.191

0.001

Qinghai

163

0

0.12

0

Tibet

27

0

0.02

0

Construction of the Regression Model
This study verifies the related hypothesis that OHCs will be
influenced by offline medical resource distribution. The analysis
in this section uses a regression model to fit the empirical data.
The dependent variable in the model represents the frequency
of social exchange that happens via TeleConsultation in the
OHC between any 2 provinces; for example, among the 92,492
telephone consultation records between physicians and patients
crawled from the OHC, the provincial administrative region iwhere physicians are located can be matched using theinformation related to the hospital at which the physician works,
and the region j where patients are located can be extracted fromthe detailed patient information. Subsequently, the statistics arehttps://formative.jmir.org/2023/1/e43533

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measured by the teleconsultation frequency from region j toregion i.
On the basis of our hypothesis, the independent variables aremeasured by the spatial accessibility of objective offline medical
resources, the difference in their capital availability, and theinteraction of accessibility and availability. Spatial accessibility
refers to both the intensity and the possibility of interaction withmedical resources [13], in terms of the distance between regioni (the location of both the physician and the physician-patientsocial exchange) and region j (the patient location). Hypothesis
1 states that spatial accessibility has an inverted U–shaped effecton online physician-patient social exchange. Therefore, thesquared variable (distance2) is introduced to fit the model.
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Capital availability also refers to the service capability ofresources and is commonly measured by average resources per
capita. In traditional public health research, capital availability
is generally estimated by the number of beds in hospitals andhealth centers per 1000 population [13,29]. Therefore, this study
uses PerBedRate to estimate the difference in the availabilityof offline medical resources, specifically the ratio of the numberof beds in hospitals and health centers per 1000 population inprovincial region i to the number of beds in hospitals and healthcenters per 1000 population in provincial region j. Theinteraction of spatial accessibility and capital availability
distance_PerBedRate is measured by the product of distanceand PerBedRate.

to their contribution is displayed prominently on eachphysician’s home page and measures the physician’s web-based
contribution, which patients can view directly. Patients
understand the level of physicians’ web-based efforts bycomparing the numerical values assigned to their contributions;however, they only know the meaning that the numerical value
represents and do not clearly know the calculation method (suchas the weight of each dimension), that is, the contribution value
is the score obtained by physicians through their personal
choices and efforts [36,37]. Hence, we estimate the control
variable contribution by including a cumulative contribution
value of all the physicians in province i. A description of all thevariables in our model is listed in Table 2.

To narrow the focus to the functions of offline medical
resources, the effect of physicians’ web-based efforts on thedependent variable needs to be eliminated. In this study, aninternal index of the OHC, the contribution value, is used tomeasure the web-based performance of physicians. The
contribution value is automatically calculated by the softwaresystem of our target platform using an empirical algorithm thatis not publicly available. According to the brief introduction of
the contribution value on our target platform, it is calculated
based on the following 3 aspects: modifying personal
information on the home page, publishing health care papers,
and providing free consultations. The numerical value assigned

On the basis of these variables, we specify the regression modelwith the squared variable in formula (4):log(TeleConsultation)=β0+β1distance+β2distance2+
β 3 Pe r B e d R a t e + β 4 d i s t a n c e _ Pe r B e d R a t e +β5log(contribution)+ε (4)In this model, ε is a random error term. In addition, to eliminate
collinearity, we take the logarithm of the dependent variable(TeleConsultation) and control variable (contribution).
Moreover, we normalize the independent variable (PerBedRate).
Table 3 shows the descriptive statistics of the variables in themodel.

Table 2. Description of the variables in the regression model.
Type (variable)

Description

Dependent variable (TeleConsultation)

The frequency of web-based telephone consultations between 2 provincial
administrative regions

Independent variable: spatial accessibility
Distance

The spatial distance between 2 provinces

Distance2

The square of the spatial distance between 2 provinces

Independent variable: capital availability (PerBedRate)

The number of beds in hospitals and health care centers per 1000 population

Interaction variable (Distance_PerBedRate)

The interaction of accessibility and availability

Control variable (contribution)

The total contribution value of a physician in a certain provincial administrative region

Table 3. Descriptive statistics of the variables.
Variables

Sample size

Values, mean (SD; range)

Log(TeleConsultation)

635

2.43 (1.88; 0 to 9.70)

Distance635

972.05 (65.32; 0 to 658.63)

Distance2

635

1378219 (1662165; 0 to 9237601)

PerBedRate635

0.00 (0.211; –0.78 to 0. 77)

Distance_PerBedRate

635

200.08 (10.36; –507.21 to 507.21)

Log(contribution)

635

0.31 (4.40; 0 to 15.06)

Results

regression was used to fit the model. Model 1 contains only thecontrol variable, whereas models 2 and 3 continually add themain effects and interaction to the model.

To provide clearer insights into the explanatory power of eachvariable with respect to the dependent variable, stepwise
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Guo et al

Results of the Regression Models

telephone consultations in OHCs (βdistance=.199; P<.001 and

The results of the regression models are shown in Table 4.

βdistance2=–.00449; P=.008). This result supports hypothesis 1.
The frequency of web-based physician-patient interactions (via
telephone consultations) is influenced by the spatial accessibilityof offline medical resources, which shows an inverted U–shaped
effect. The capital availability result shows that the differencein the number of beds in hospitals and health care centers
between 2 regions positively influences physicians’ social
exchange (βbed=.935; P=.07). Therefore, hypothesis 2 is verified.
With the inclusion of the interaction, the explanatory power
improves considerably (R2=0.0523, F9,628=489.923). Theinteraction between spatial accessibility and capital availability
is confirmed, and the interaction coefficient is positive (P<.001).
Thus, model 3 validates hypothesis 3: the spatial accessibility
and availability of capital play complementary roles in
influencing web-based physician-patient social exchange. Insummary, all hypotheses are verified under a certain level of
explanatory power.

For models 1, 2, and 3, for a sample size of 635 each, the R2
values were 0.0632, 0.1405, and 0.1928, respectively, whereas
the F change values were 298.732, 636.662, and 489.923,
respectively.
Model 1 suggests that a physician’s online contribution
significantly affects the frequency of physician-patient telephone
consultations (βcontribution=.210; P<.001). However, this modelhas weak explanatory power because it measures only 6.32%(R2=0.0632, F9,627=298.732) of the interrelationship ofphysician-patient telephone consultations in the OHC. Excluding
the control variable, the addition of the main effect into themodel (model 2) leads to a significant increase in explanatory
power (R2=0.0773, F8,628=436.662). Spatial accessibility(distance) has an inverted U–shaped effect on physician-patient
Table 4. The results of the regression models.

aModel 1 (control variable)

Model 2 (adding the main effect)

Model 3 (adding the interaction)β (SE)a

P valueβ (SE)

P valueβ (SE)

P valueLog(contribution).210 (5.29)<.001.182 (5.24)<.001.180 (5.20)<.001

Distance

N/Ab

N/A

.199 (5.48)<.001.202 (5.68)<.001

Distance2

N/A

N/A

–.00449 (–3.25).008–.0046 (–3.50)<.001

PerBedRate

N/A

N/A

.935 (2.48).07

1.313 (3.46)<.001

Distance_PerBedRate

N/A

N/A

N/A

N/A

.00342 (1.17).06_Consb

N/A

N/A

3.599 (18.02)<.001

3.640 (18.26)<.001

Values in parentheses are the robust SEs.

b

Cons: constant term.

Robustness Checks
To increase the reliability of the test results of the initial model,
our study used 2 types of robustness tests.
First, prior research shows a significant difference in the levelof spontaneous consumption [38] and the efficiency of healthinput [29] between the northern and southern regions of China.
Therefore, this study fits the model by separating the sample
data by geographic location into the northern and southern
provincial administrative regions to eliminate the influence ofthe difference between the northern and southern regions. The
test results are shown in Table 5.
For check 1 (northern region), for a sample size of 330, the R2
value was 0.2011, whereas for check 2 (southern region), for asample size of 306, the R2 value was 0.6723.
In general, the number of beds in hospitals and health carecenters in the northern region has a nonsignificant (βbed=1.048;
P=.83) influence on online physician-patient social exchange.
All of the proposed hypotheses still hold, regardless of area.
This result indicates that the availability of offline medical
resources has no significant influence on decisions with regard
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to online physician-patient interactions. This finding may becaused by the generally low level of capital availability in north
China because patients are already less sensitive to capital
availability when seeing a physician. However, the validity ofthis speculation needs to be supported by additional empirical
data. Moreover, the explanatory power of the model issignificantly different between the northern and southern
regions, and the goodness of fit of the model for south Chinaisbetter
than
that
for
north
China
(R2south=0.6723>R2north=0.2011). It seems that most of thefrequency of online physician-patient telephone consultationsin the southern region can be explained by the distribution ofoffline medical resources, whereas in the northern region, there
are other influential factors.
We used replacement variables to conduct a second robustness
check. The number of general hospitals in public health research
was used to measure spatial accessibility [13]. Therefore, thisrobustness check was conducted by replacing the difference inthe number of beds in hospitals and health care centers per 1000
population with the difference in the number of hospitals(HospitalRate). Table 6 shows that most of the results are
consistent with the initial empirical analysis results after
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Guo et al

replacing the variable. However, the capital availability andspatial accessibility variables are not complementary under the
replacement variable. It is possible that when measuring theavailability of capital, the variable PerBedRate used in theoriginal model is more representative than the other indices,
resulting in measurement bias. Overall, the analysis results ofthis study pass the robustness tests and are credible (for a sample
size of 635, the R2 value was 0.1533).

Table 5. The robustness checks based on the classification of the samples.
Check 1 (northern region)a

Check 2 (southern region)β (SE)a

P valueβ (SE)

P valueLog(contribution).109 (2.70).004

1.050 (21.06)<.001

Distance.187 (3.75)<.001.213 (5.27)<.001

Distance2–.00389 (–2.18).07–.00597 (–3.67)<.001

PerBedRate

1.048 (1.59).83

0.886 (2.50).05

Distance_PerBedRate.00976 (3.23).003.00948 (3.13).006_Consb

3.736 (12.85)<.001–11.55 (–15.24)<.001

Values in parentheses are the robust SEs.

b

Cons: constant term.

Table 6. The robustness check based on a replacement variable.
Check 3 (replacement variable)β (SE)a

P valueLog(contribution).106 (3.66)<.001

Distance.192 (5.26)<.001–.00408 (–2.95).001

HospitalRate

1.027 (2.67).009

Distance_HospitalRate.00870 (0.25).47_Consb

2.615 (5.09)<.001

Distancea2

Values in parentheses are the robust SEs.

b

Cons: constant term.

Discussion
Principal Findings
This study explores the impact of the spatial accessibility andavailability of offline medical resources on the social exchange
between physicians and patients in OHCs based on online
physician-patient telephone consultation data and offline healthcare capital distribution data. The empirical test results support
the notion that physician-patient telephone consultations will
be positively influenced by physicians’ online contributions
and capital availability, and, interestingly, spatial accessibility
has an inverted U–shaped effect.
This study offers theoretical support for the impact of offline
channels on online channels. The results indicate that online
physician-patient social exchange in OHCs will be influenced
by the spatial distribution of offline medical resources. When
researching online telephone consultations, the geographic
distance from, and service capability of, offline resources should
be considered. Internet medical services are not an entirely new
channel that is independent of, and separate from, traditional
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medical health care. To a certain degree, OHCs still temporarily
serve as an auxiliary function of offline medical health care,and their use remains constrained by offline health care capital.
In addition, this study provides a relevant theoretical basis forresearch on the equity of medical resources related to online
channel support. Urban-rural health care inequity stems fromunequal access to medical resources [39]. The existence of
OHCs can reduce this inequity; for example, Goh et al [27]point out that, compared with rural users, urban users of OHCs
are more likely to provide social support, which is a helpful
mechanism for emotional support and medical knowledge toflow from urban areas to rural areas. Although offline medical
services constrain online consultation, they still provide achannel for medical resources to be distributed from places withhigh availability to those with low availability. This kind ofchannel effect shows a more significant effect on online
consultation as spatial accessibility and capital availability grow.
Therefore, OHCs offer a new research direction and theoreticalbasis for studies related to eliminating the geographic imbalance

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JMIR FORMATIVE RESEARCH
of medical resources caused by differences in economic
development and population distribution.

Limitations and Further Directions
Although this study has made significant contributions throughthis empirical research, it includes some limitations in terms of
the research scope and methods. First, the data in this study
come from China. Whether the results can be extended to otherregions may depend on the differentiated medical systems and
OHC development in these regions. Future cross-regional studies
will help to explore the generalization and extensibility of theresults of this study. Second, because of the privacy protection
settings of our target OHC, although we have inferred thegeographic location of the patients through IP calculation, we
still cannot accurately locate the online consultation interactionof patients. Therefore, detailed data obtained from different data
sources will help to make the research conclusions moreaccurate.

Implications for Practice
The relevant findings of this study have important implications
for the managements of OHCs and the government
policy–making process. First, the results show that online
consultations are not independent of offline treatment and thatthese online consultations are influenced by offline health care
capital distribution. Hence, the significant impact of theallocation of offline medical resources on online
physician-patient social exchange should be confronted by OHC
managements. Geolocation-based referrals can be used torecommend physicians who are farther away from the locationsof patients because through this measure, more appropriate
physicians can be distributed to patients who are searching forphysicians online. Moreover, other research findings forgovernment policy makers demonstrate that the emergence ofinternet health care (eg, OHCs) can compensate for geographic
distance–induced health care deficiencies. In fact, prior research

Guo et alhas demonstrated that OHCs reduce the rural-urban disparities
in the availability of institutional forms of health care capital(eg, emotional support) [27]. This study verifies that the use of
OHCs can compensate for the insufficient spatial accessibilityof offline medical resources and direct resources from areas
with high capital availability to those with low availability.
Indeed, this form of online medical care has reduced geographic
limitations and promoted the flow of medical resources acrossregions. Finally, the complementarity of spatial accessibility
and capital availability is verified under the premise that the
spatial accessibility of medical resources has an inverted
U–shaped impact on online physician-patient social exchange.
When the target area’s capital availability is increased, theinflection point of spatial availability will shift to the right, thus
expanding the available range for patients.

Conclusions
This study explores the relationship between online and offline
channels by investigating online physician-patient interactions
and offline medical resources. Offline capital distribution andonline physician-patient interactions are used to explore theimpact of medical resources on physician-patient social
exchange in OHCs. This study demonstrates that online
physician-patient interactions are influenced by spatial
accessibility and capital availability of offline medical resources.
This study creatively analyzes the influence of the offline
channel on the online channel and verifies the possibility that
capital can be transferred from high-availability regions to
low-availability regions by using OHCs. Moreover, it
complements the literature on the impact of offline medical
resources on online physician-patient social exchange and offers
theoretical support for OHC research and the distribution of
medical resources, as well as online and offline channels. The
related results are also used to make sensible recommendations
for practical applications to be used by OHC managements,
governments, and other stakeholder organizations.

Acknowledgments
This research was funded by the National Natural Science Foundation of China (72171152, 72101090, and 71801062), the Special
Fund Project for Scientific and Technological Innovation (Soft Science) of Guangdong province (2022A1515011620), Guangdong
Philosophy and Social Sciences (GD21YGL09), the China Postdoc Science Foundation (2021M701242), and the Innovative
Research Team of Shanghai International Studies University (2020114044).

Data Availability
The data analyzed in this study are publicly available. The offline data were obtained from the China Health Statistical Yearbook
2014; the online data can be obtained from Haodf (using a web crawler will reduce the time spent on data acquisition).

Conflicts of Interest
None declared.

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Abbreviations
OHC: online health community

Edited by A Mavragani; submitted 14.10.22; peer-reviewed by M Kapsetaki, J Liu; comments to author 11.11.22; revised version
received 20.11.22; accepted 23.11.22; published 10.01.23
Please cite as:
Guo S, Dang Y, Vogel D, She B
The Effect of Offline Medical Resource Distribution on Online Physician-Patient Interaction: Empirical Study With Online and Offline
Data
JMIR Form Res 2023;7:e43533
URL: https://formative.jmir.org/2023/1/e43533
doi: 10.2196/43533
PMID:©Shanshan Guo, Yuanyuan Dang, Doug Vogel, Bofei She. Originally published in JMIR Formative Research(https://formative.jmir.org), 10.01.2023. This is an open-access article distributed under the terms of the Creative Commons
Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction
in any medium, provided the original work, first published in JMIR Formative Research, is properly cited. The complete
bibliographic information, a link to the original publication on https://formative.jmir.org, as well as this copyright and license
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