Exploring the Impact of Doctors' Gratuitous Treatment Behavior on

Their Online Consultations during the Covid-19 Epidemic

Jiarui Li

†

, Xiao Li

and Shanshan Guo

‡

School of Business and Management, Shanghai International Studies University, Shanghai China

Keywords:

Online Health Community, Online Gratuitous Treatment, The COVID-19, Online Consultation.

Abstract: In 2019, the COVID-19 (Corona Virus Disease 2019) erupts. The outbreak of the epidemic has almost cut off

the offline consultation channels across the region, while the Online Health Community (OHC) has relieved

the urgent needs of patients seeking high-quality medical resources. To explore the influence of prosocial

behaviors such as online gratuitous treatment by doctors on consultation volume during the COVID-19

epidemic, this study builds a model based on the prosocial theory and the Stressor-Strain-Outcome theory and

applies Python data crawler and empirical analysis to the normative research method. The results indicate that

the gratuitous treatment provided by doctors had a positive impact on the online consultation of patients, and

the COVID-19 severity also had a moderating effect on this relationship. The results can not only provide

theoretical innovation for online medical research but also provide new theoretical and practical perspectives

for the continuous improvement of the user experience of OHCs.

1 INTRODUCTION

1.1 Background and Motivation

The COVID-19 broke out as a major threat to the

health of the world. Countries involved in the

outbreak introduced quarantine policies. However,

the isolation measures adopted also cut off the normal

access of many patients with non-epidemic diseases

(Cheng-Wei, Xiu-Fen, & Zhi-Fang 2020). For some

patients who are physically ill but cannot go to offline

hospitals, online health communities (OHCs) can

relieve the medical pressure brought by offline

consultation through online treatment, reduce the risk

of cross-infection, and provide people with timely,

professional, and effective health guidance.

After the outbreak of the epidemic, the visits of

OHCs kept rising. When there are major public

disasters or health emergencies occur, gratuitous

treatment plays a unique role in providing social

assistance and spreading altruistic public welfare

culture. As a kind of public welfare behavior under

the theory of prosocial behavior, gratuitous medical

consultation can save costs for patients, provide

health guidance and prescription reference, and also

play a positive role in the effective control of the

COVID-19. Exploring how doctors' gratuitous

treatment behavior in OHCs affects patients'

consultation since the outbreak of the COVID-19 can

not only help doctors get more attention and revenue

from online consultation services but also help OHCs

acquire and retain more high-quality doctor resources

to achieve sustainable development.

2 LITERATURE REVIEW

As the importance of e-health becomes more

obvious, a lot of studies related to OHC are emerging,

and the research field is expanding (Alam, Wang, &

Uddin 2020). The OHC is changing the way patients

see a doctor and providing them with more access to

medical knowledge. In addition, OHC can provide

more health guidance for patients, including

information guidance and emotional assistance

(Kucukyazici, Verter, & Mayo 2011). Different from

the traditional offline medical treatment mode, OHC

can help allocate medical resources reasonably, free

from time and space constraints, and open interaction

process, which greatly increases the type of medical

procedures and the reference value for others.

Li, J., Li, X. and Guo, S.

Exploring the Impact of Doctors’ Gratuitous Treatment Behavior on Their Online Consultations during the Covid-19 Epidemic.

DOI: 10.5220/0011367400003444

In Proceedings of the 2nd Conference on Artiﬁcial Intelligence and Healthcare (CAIH 2021), pages 275-282

ISBN: 978-989-758-594-4

275

2.1 Online Consultation

Previous studies have focused on the factors

influencing users' attention and selection of doctors

in OHC. With the emergence of more and more

online public welfare behaviors, scholars have

gradually made incisive studies on doctors’

gratuitous treatment under prosocial behavior and its

impact on consultation and attention. Qing Liu et al.

(Liu, Hong, Shen, & Juan 2019) collected doctors'

data and conducted an empirical analysis to explore

how online gratuitous treatment affects patients'

choice, however, due to time limitations, the impact

of the COVID-19 on them was not mentioned. The

research of Abidin, Z. et al. aims to explore the

factors influencing information sharing in OHCs

from the perspective of social support, commitment-

trust theory, and trust transfer theory.

2.2 Gratuitous Treatment

Xing Chen et al. (Chen, Zhang, Zeng, & Hu 2017)

studied the potential benefits and spillover effects of

contributors providing public welfare services on

their own from the perspective of the motivation of

prosocial behaviors. Research related to gratuitous

treatment also paid more attention to what factors

bring spillover effects of gratuitous treatment.

Johnston et al. (Johnston, Worrell, Gangi, & Wasko

2013) explored that information utility and social

support are two beneficial utilities obtained by

participants in OHCs, which gives enlightenment to

the study. In addition, Zhang Xing et al. (Zhang, Liu,

Deng, & Chen 2017) investigated the external

reputation, internal self-efficacy, altruism, and

compassion motivation of doctors and users to build

models and further research, which expanded the

understanding of the motivations that may affect the

free sharing of knowledge in the context of OHC.

3 MODEL CONSTRUCTION AND

RESEARCH HYPOTHESES

3.1 Theory

3.1.1 Prosocial Theory and Cost-benefit

Model of Helping

Prosocial behavior mostly refers to the benevolent

behavior consistent with altruism. Wispé was the first

to conceptualize prosocial behavior (Wispé 2010).

With further development, Marjorie believes that

prosocial behavior can not only bring benefits to

others but also provide ways for individuals to

integrate into social situations, thus promoting the

improvement of their relationships. In addition,

Penner discussed from the social level that prosocial

behavior represents positive social value, is the

embodiment of social service and responsibility, and

is a vital part of building a harmonious society

(Penner, Dovidio, Piliavin, & Schroeder 2005).

Doctors and other public service providers carry out

gratuitous treatment either out of compassion or out

of potential benefits and spillover effects. Based on

social exchange Theory, Dovidio (Dovidio, Piliavin,

Schroeder, & Penner 2017) proposed to combine

helping behavior with cost-benefit theory to explain

individual helping motivation and behavior.

According to this model, when people are in the

situation of helping others, they will analyze and

measure the possible benefits and costs of helping

others to maximize their interests.

3.1.2 SSO Model

The "stressor-strain-outcome" (SSO) model was first

proposed by Koeske in 1993, and has been widely

recognized by the Academy of Psychology (Koeske

& Koeske 1993). The model consists of three parts:

all kinds of events are visualized as stressors,

stressors bring about strain, and finally, people bring

feedback with psychological or physical outcomes. If

a patient has a cold, cough, fever, respiratory

problems and other physical problems during this

period, the public will feel extremely anxious and

worry, and they are eager to seek guidance on health

problems. But at the same time, isolation measures

also block offline access to medical treatment, and

people tend to choose professional doctors in online

health communities for health assessment and self-

diagnosis of diseases. In this study, major public

health emergencies such as the COVID-19 can be

regarded as stressors, psychological pressure brought

by it and users' anxiety can be regarded as stress, and

the treatment choices made by patients can be

regarded as the outcomes.

3.2 Research Hypothesis

3.2.1 Influence of Doctors' Gratuitous

Treatment on Their Consultations

According to prosocial theory and the cost-benefit

theory of helping others, when doctors show altruistic

behavior, especially when medical treatment is

urgently needed in a major public crisis event, their

CAIH 2021 - Conference on Artiﬁcial Intelligence and Healthcare

276

image leaves a good impression in the public mind,

which helps personal impression management and

reputation gain. For example, patients are more likely

to choose gratuitous treatments providers than

indifferent ones, because people are more likely to

communicate and engage with altruists than self-

interested ones.

Starting from prosocial behavior and cost-benefit

theory, this study proposes the hypothesis that

physicians who conduct charity consultation will

receive more attention and consultation. In this

report, the number of total physicians’ gratuitous

graphic consultations is used as an indicator of

volunteer services, based on the theory that existing

literature (Cheng-Yu 2016) uses physician service

visits as a quantitative indicator to indicate the degree

of physician participation, and the following

hypothesis is proposed:

H1: The number of a doctor’s gratuitous

treatments positively affects the number of online

consultations by the users of that doctor.

3.2.2 Influence of the COVID-19 Epidemic

Severity on User Consultation

In the case of physiological problems and non-urgent

diseases that can be solved by remote diagnosis,

patients who have made online voluntary

consultations have reported that it is difficult to go to

offline hospitals because of the epidemic(Liu, Xi-

Xiang, & Lei 2007), and they prefer online medical

treatment. As the epidemic continues, people's

irrational emotions will be stimulated to a deeper

degree, while patients of all types will want to receive

a lot of health guidance through online consultations.

As a result, people are more likely to choose online

counseling and consultation in the pandemic. This

research proposes the following hypothesis:

H2: The severity of the COVID-19 pandemic

positively affects the number of online consultations

of a doctor.

3.2.3 Moderating Effect of the COVID-19

Epidemic Severity

Based on the OHC, doctors can protect themselves

and make a great contribution to the fight against the

pandemic by guiding patients with the advantages of

convenient and rapid Internet remote communication

and their professional skills. When the pandemic is

severe, the impact of gratuitous treatment on online

consultation services will increase, and conversely,

when the pandemic is effectively contained, the

impact of gratuitous treatment on online consultation

services will moderate. This is because when the

pandemic is severe, various OHCs provide a platform

for gratuitous treatment services to a wide range of

users for reasons such as platform reputation

enhancement and public service, which will prompt

doctors to perform more online gratuitous treatment

services, while under the SSO theoretical model,

patients will be upset and thirsty for guidance on

health issues during this period due to the fear of

getting contaminated. It can be said that the epidemic

has had an extremely broad impact on the public's

way of accessing and choosing medical care.

Therefore, this study proposes the following

hypothesis.

H3: The severity of the COVID-19 pandemic

positively moderates the effect of doctors' gratuitous

treatments on their online consultations.

3.3 Model Construction

3.3.1 Model

This report investigates the impact of physician-

provided volunteer services on the volume of user

online consultations in OHCs during the COVID-19

epidemic.

Firstly, to investigate the effect of doctors'

voluntary consultation behavior on users' online

consultation volume. Secondly, investigate the effect

of the severity of the epidemic on the number of

online consultations by users. Lastly, investigate

whether the severity of the COVID-19 epidemic

affects the online consultation volume of users in

OHCs.

Based on the research hypotheses constructed

above, the conceptual model is proposed as below:

Figure 1: Model of the effect of physicians' online

gratuitous treatment on their user consultations during the

COVID-19.

3.3.2 Independent Variable and Dependent

Variable

In this study, the number of doctors' daily voluntary

graphic consultation is used as the independent

Exploring the Impact of Doctors’ Gratuitous Treatment Behavior on Their Online Consultations during the Covid-19 Epidemic

277

variable measure of gratuitous treatment behavior;

the number of new confirmed COVID-19 cases per

day is used as the independent variable measure of

the severity of the COVID-19 pandemic, and the

number of doctors' gratuitous treatment services in

haodf.com is used as the dependent variable measure

of users' online consultation volume.

Dependent variable: the number of online

consultations.

Independent variables: severity of the COVID-

19 pandemic and physician gratuitous treatment

behavior.

3.3.3 Control Variable

The online consultation volume is influenced by

many factors. In addition to the influence of the

gratuitous treatment behavior under the severity of

the COVID-19 pandemic, the doctor's online

recommendation degree, online service satisfaction

and service quality evaluation, doctor’s consultation

price, the doctor's title, and the level of the hospital

where he/she is located all can influence the user's

choice. In this research, we need to consider these

control variables to reduce the error and establish a

fairer and more objective model. The following

variables are selected as the control variables:

The doctor's online recommendation degree: The

feedback mechanism of list recommendation in

OHCs can convey useful information to users and

help patients to make their choices.

The doctor’s consultation price: As patients do

not have comprehensive information about the true

quality of the service before they consult online

(Baker 2015), they pay more attention to the doctor's

popularity, reputation, etc., and use the price of the

doctor's service as one of the criteria for their choice

(Baker 2015). Since the asymmetry of information

also brings uncertainty about the quality of the

service (Akerlof 1978), the price is also an indicator

for patients to measure and choose a doctor.

The doctors’ response positivity: Doctors'

motivation for online gratuitous treatments varies

widely due to their wishes, such as medical ethics,

work intensity, compensation patterns, and family

situations. Information asymmetry can cause adverse

selection and moral hazard (Akerlof 1978), while if

doctors actively participate in consultation responses

to mitigate patients' perceived risk, the positivity of

doctors' responses can also be a measure of whether

to choose that doctor's services.

The doctor’s title: In this study, the doctor's title

is set as 1 for resident; 2 for attending physician; 3 for

the associate chief physician; and 4 for chief

physician according to the title.

Hospital level: According to the Grading

Management Standards for General Hospitals (Trial

Draft), hospitals are divided into 3 grades, and each

grade is further divided into 3 levels, A, B, and C.

Due to the different medical levels, allocation of

resources and hospital reputation, patients may prefer

to choose doctors in high-grade hospitals, such as

doctors in 3A hospitals, which indicates that hospital

level may also be a factor influencing users'

consultation. Due to the doctors' hospitals in our

target OHC are mostly tertiary hospitals and above,

so we set 3A hospitals as 1 and non-3A hospitals as

3.3.4 Measurement Model

In this study, the ordinary least squares (OLS) model

is used to test the hypothesis. This method is widely

used in many disciplines of data processing such as

error estimation, uncertainty, system identification

and forecasting and prediction.

This section develops a multiple regression model

based on the conceptual model described above.



𝑃𝑢𝑟𝑐ℎ𝑎𝑠𝑒𝑠



− 𝑃𝑢𝑟𝑐ℎ𝑎𝑠𝑒𝑠





(1)

is the dependent variable, indicating the number of

daily paid online consultations by doctors in six

months.



𝑃𝑢𝑟𝑐ℎ𝑎𝑠𝑒𝑠



− 𝑃𝑢𝑟𝑐ℎ𝑎𝑠𝑒𝑠





(2)

And

ln𝐶𝑜𝑣𝑖𝑑19



(3)

are explanatory variables, denoting the number of

physician visits per day and the number of new

confirmed diagnoses per day of the new coronary

pneumonia outbreak, respectively.

The control variables indicate the

recommendation hotness, online consultation price,

telephone consultation price, doctor's response

positivity, doctor's title, and hospital level. In

addition, 𝛽



denotes the intercept in the multiple

linear regression model, 𝛽



to 𝛽



denote the

regression coefficients of each variable and

interaction term, and 𝜀



denotes the error term.



𝑃𝑢𝑟𝑐ℎ𝑎𝑠𝑒𝑠



− 𝑃𝑢𝑟𝑐ℎ𝑎𝑠𝑒𝑠





=𝛽



𝛽



ln𝐹𝑟𝑒𝑒 𝑆𝑒𝑟𝑣𝑖𝑐𝑒𝑠



+𝛽



ln𝐶𝑜𝑣𝑖𝑑19



𝛽



ln𝐹𝑟𝑒𝑒 𝑆𝑒𝑟𝑣𝑖𝑐𝑒𝑠



×ln𝐶𝑜𝑣𝑖𝑑19



𝛽



𝑅𝑒𝑐𝑜𝑚𝑚𝑒𝑛𝑑𝑎𝑡𝑖𝑜𝑛



+𝛽



𝑂𝑛𝑙𝑖𝑛𝑒 𝑃𝑟𝑖𝑐𝑒



𝛽



𝑇𝑒𝑙𝑒𝑝ℎ𝑜𝑛𝑒 𝑃𝑟𝑖𝑐𝑒



𝛽



𝑅𝑒𝑠𝑝𝑜𝑛𝑠𝑒 𝑃𝑜𝑠𝑖𝑡𝑖𝑣𝑖𝑡𝑦



𝛽



𝐻𝑜𝑠𝑝𝑖𝑡𝑎𝑙 𝑡𝑖𝑡𝑙𝑒



+𝛽



𝐻𝑜𝑠𝑝𝑖𝑡𝑎𝑙 𝑙𝑒𝑣𝑒𝑙



+𝜀



(4)

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4 METHODOLOGY

4.1 Data Collection

The sample data for this article were collected from

Haodf.com. It has become one of the fastest, largest

and most competitive websites in the domestic OHC

market. Its services and design and construction are

increasingly perfect, and established the most mature

and effective set of online appointment, consultation,

registration, referral services and other functional

mechanisms in China. In addition, Haodf.com with

its detailed hospital, doctor reference information,

timely high-quality consultation information

transmission, reasonable and effective service

guarantee, good service feedback mechanism in the

current online medical market enjoys a high-quality

and efficient reputation. Up to July 2021, it has

collected 785,264 doctors from 9,683 regular

hospitals and 240,515 doctors from public hospitals.

It can be seen that the research on OHC based on it is

sufficient and persuasive.

This study uses Python for data crawling and

statistics. The data sources of this study are mainly

the online consultation data and gratuitous treatment

data of pneumonia doctors with high reputation from

January 22, 2020, to July 23, 2020, with a total of

6,202 items. The COVID-19 data are from the daily

surveillance data provided by the National Health

Commission and Netease News.

4.2 Data Analysis

By matching each variable according to the date and

the doctor’s name, delete the missing samples of the

online recommendation degree in the past two years,

and the recommendation outliers with a degree of 0

or negative and incomplete doctor data with too much

missing information are deleted. The main variables

are tailed at the one-hundredth level, and the

logarithmic value is taken.

Table 1: Descriptive Statistics.

In the 6,202 valid data after cleaning, the average

value of a doctor's online recommendation in the

range of 3.3 to 5 is 3.752, and the average difference

is 0.374, indicating that overall doctors'

recommendation is relatively ordinary and average.

The average level of doctors' professional titles is

very high, reaching 3.450 within the division range

of 1 to 4, indicating that the doctors who conduct

online consultations in Haodf.com are mostly chief

physicians and associate chief physicians with

intermediate and senior professional titles. The

average level of the doctor’s hospital is 0.971, which

shows that doctors on Haodf.com generally work in

the top three hospitals. It reflects that the OHC

provides a platform for high-level doctors from major

hospitals to serve, consult and guide their patients'

health and develop themselves.

4.3 Empirical Analysis

We use Stata as a tool to perform OLS regression

statistical analysis. The empirical model results are

shown in Table 2 and Table 3. This work adopts

stepwise regression.

Model 1 is the original model without regulating

variables and other control variables. Model 2 adds

gratuitous treatment* epidemic situation as the

regulating variable for analysis, and model 3 adds

other control variables for analysis. Model 1 shows

that two variables, the number of online gratuitous

treatments by doctors, and the severity of the new

crown epidemic have a significant positive impact on

the amount of online paid consultations by users.

Among them, the number of online gratuitous

treatments by doctors significantly positively affects

the amount of online paid consultations (β=0.178***,

p<0.01), and H1 has been verified; the severity of the

new crown epidemic has a significant positive impact

on the amount of online paid consultations (β=

0.146***, p<0.01), H2 is verified. Model 2 adds an

interaction term to consider the moderating effect of

the severity of the new crown epidemic on doctors'

gratuitous treatment behavior. According to Table 3,

the severity of the new crown epidemic significantly

positively

regulates the impact of gratuitous

Exploring the Impact of Doctors’ Gratuitous Treatment Behavior on Their Online Consultations during the Covid-19 Epidemic

279

Table 2: Correlation coefficient matrix.

* means p<0.01

Table 3. Regression model results.

(1) (2) (3)

Free Services 0.178*** 0.176*** 0.075***

(37.12) (36.88) (14.95)

Covid19 0.146*** 0.144*** 0.043***

(44.07) (41.60) (3.95)

Covid19* Free Services 0.005*** 0.030***

(4.92) (14.18)

Title -0.002**

(-0.52)

Hospital level 0.019**

(1.15)

Recommendation 0.002**

(0.25)

Response positivity 0.004**

(1.96)

Online price 0.002

(0.36)

Telephone price -0.005

(-0.68)

_cons 4.026*** 4.033*** 4.333***

(626.62) (605.96) (97.02)

N 6202 6202 6202

r2_a 0.827 0.827 0.842

* means p<0.10, ** means p<0.05, *** means p<0.01, the heteroscedasticity robust standard error is in parentheses

treatments on the amount of online paid consultations

(β=0.005***, p<0.01), and H3 has been verified.

Compared with Model 1, Model 3 adds more control

variables to improve the explanatory ability of the

model, including the doctor's position, whether the

hospital is a top three, online recommendation,

doctor's response enthusiasm, graphic consultation

price, telephone consultation price. The results show

that among the selected adjustment variables, the

doctor’s title, whether the hospital is a top three, the

degree of online recommendation, and the doctor’s

response enthusiasm all significantly affect the

amount of online paid consultations, and the effect of

the price of the consultation on the amount of online

paid consultations Not significant. The fit has risen

slightly, which also verifies the hypothesis.

5 CONCLUSIONS

5.1 Discussions

Based on the prosocial theory and SSO theory, this

study constructs a model of the influence of doctors'

gratuitous treatment behavior on their consultation

volume under the COVID-19 epidemic. It enriches

the empirical research on the influence of prosocial

behaviors (public welfare services) on the

consultation of service providers under public health

emergencies, and at the same time proves the

significant strengthening effect brought by the

pressure of the epidemic. Supplements-related

research on gratuitous treatment and public service in

the OHC.

CAIH 2021 - Conference on Artiﬁcial Intelligence and Healthcare

280

The results showed that the number of doctors

participating in gratuitous treatments is positively

affecting the number of doctors’ online paid

consultations. If doctors provide effective and

reliable gratuitous treatment services, people will be

impressed by them and will be more willing to follow

up online consultations. At the same time, the more

doctors are consulted, people tend to think that the

services of such doctors are efficient and quality,

which can alleviate the potential risks of patients, so

they can choose to provide health guidance and

improve doctors' benefits. Therefore, if doctors want

to increase the income of paid consultation services,

they can provide gratuitous treatment services in their

spare time, such as free guidance and public

consultations for patients in backward areas or

patients with a specific disease.

5.2 Implications and Limitations

5.2.1 Suggestions for Doctors

In the face of a major public emergency crisis

represented by the COVID-19 epidemic, doctors in

the OHC, as service providers, actively participate in

gratuitous treatments, establish a public image, and

make contributions to society within their

capabilities, which can attract more patients. The

results of this study show that before patients choose

counseling services, patients will take into account

factors such as online recommendation, response

enthusiasm, job title, service price and other factors,

they will also refer to doctors' enthusiasm for

participating in public welfare consultations. The

reason is that the more doctors participate in

gratuitous treatments, the more they are willing to

give, get close to society, and be kind to others, and

they have the ability and enthusiasm to provide

consultation services.

5.2.2 Suggestions for the OHC

The OHC can improve the incentive mechanism and

incentive policies, and call for the promotion of more

free clinic activities that serve the society. The results

of the study indicate that doctors’ participation in

public welfare consultations is beneficial for patients

to choose online paid consultation services; The OHC

managers should encourage doctors to participate

more in gratuitous treatment services. At the same

time, incentive mechanisms and incentive policies

need to be designed and improved to encourage

word-of-mouth doctors to participate more. The free

clinic service of the company fulfills social

responsibility and sets an example; at the same time,

it also encourages doctors with lower reputations or

standards to improve their abilities and provide better

and more popular consultation services. This will not

only help maintain the retention of high-quality

doctors but also enable the website to enjoy a public

good image, improve reputation and word of mouth

and enhance its competitiveness.

5.2.3 Limitations

Due to the design of the Haodf.com, the service

quality evaluation for a single consultation cannot be

obtained, and it can be supplemented and researched

according to other OHCs in the future. In addition,

the cross-sectional data is used in this article for

discussion. In the future, panel data can be used to

track changes in online consultations to obtain more

accurate results.

5.3 Conclusions

In the context of the OHC, this study explores the

influence of prosocial behaviors such as online

doctors' online gratuitous treatments on the number

of consultations during the COVID-19 epidemic. The

results confirmed that the provision of gratuitous

treatment services by doctors has a positive impact

on patient online consultation. At the same time,

doctors are affected by the COVID-19 epidemic and

have carried out more online public service

behaviors. This behavior also has an impact on the

increase in online consultations. The results can not

only provide theoretical innovations for research in

the online medical field but also provide practical

guidance for public welfare behavior and OHC

development.

ACKNOWLEDGMENTS

This study was partially funded by the National

Natural Science Foundation of China (71801062,

72171152) and China Postdoctoral Science

Foundation (2018M640301, 2019T120278).

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