Frontiers in Public Health 01 frontiersin.org
Who can help me? Understanding
the antecedent and consequence
of medical information seeking
behavior in the era of bigdata
JiweiSun
1,2,3,4,5,6,7,8†
, ShujieZhang
9,10†
, MinHou
1,2,3,4,5,6,7
,
QianSun
10
, FenglinCao
8
, ZhonghaoZhang
10
, GuiyaoTang
10
,
XingyuanWang
10
, LingGeng
11
*, LinlinCui
1,2,3,4,5,6,7
and
Zi-JiangChen
1,2,3,4,5,6,7,12,13
1
Center for Reproductive Medicine, Cheeloo College of Medicine, Shandong University, Jinan, China,
2
Research Unit of Gametogenesis and Health of ART-Ospring, Chinese Academy of Medical Sciences,
Jinan, China,
3
Key Laboratory for Reproductive Endocrinology, Ministry of Education, Shandong
University, Jinan, Shandong, China,
4
Shandong Key Laboratory of Reproductive Medicine, Jinan, China,
5
Shandong Provincial Clinical Medicine Research Center for Reproductive Health, Jinan, Shandong,
China,
6
Shandong Technology Innovation Center for Reproductive Health, Jinan, China,
7
National
Research Center for Assisted Reproductive Technology and Reproductive Genetics, Jinan, China,
8
School of Nursing and Rehabilitation, Cheeloo College of Medicine, Shandong University, Jinan,
Shandong, China,
9
Business School, Shandong Normal University, Jinan, Shandong, China,
10
School of
Management, Shandong University, Jinan, Shandong, China,
11
Shandong Provincial Hospital Aliated to
Shandong First Medical University, Jinan, China,
12
Shanghai Key Laboratory for Assisted Reproduction
and Reproductive Genetics, Shanghai, China,
13
Center for Reproductive Medicine, Renji Hospital,
School of Medicine, Shanghai Jiao Tong University, Shanghai, China
Introduction: The advent of bigdata era fundamentally transformed the nature
of medical information seeking and the traditional binary medical relationship.
Weaving stress coping theory and information processing theory, we developed
an integrative perspective on information seeking behavior and explored the
antecedent and consequence of such behavior.
Methods: Data were collected from 573 women suering from infertility who
was seeking assisted reproductive technology treatment in China. We used AMOS
22.0 and the PROCESS macro in SPSS 25.0 software to test our model.
Results: Our findings demonstrated that patients’ satisfaction with information
received from the physicians negatively predicted their behavior involvement
in information seeking, such behavior positively related to their perceived
information overload, and the latter negatively related to patient-physician
relationship quality. Further findings showed that medical information seeking
behavior and perceived information overload would serially mediate the impacts
of satisfaction with information received from physicians on patient-physician
relationship quality.
Discussion: This study extends knowledge of information seeking behavior by
proposing an integrative model and expands the application of stress coping theory
and information processing theory. Additionally, it provides valuable implications
for patients, physicians and public health information service providers.
KEYWORDS
medical information seeking, satisfaction with information, information overload,
patient-physician relationship, stress coping theory, information processing theory
OPEN ACCESS
EDITED BY
Yanwu Xu,
Baidu, China
REVIEWED BY
Margaret S. Zimmerman,
Florida State University, UnitedStates
Hanyi Yu,
South China University of Technology, China
*CORRESPONDENCE
Ling Geng
†
These authors have contributed equally to this
work and share first authorship
RECEIVED 23 March 2023
ACCEPTED 28 August 2023
PUBLISHED 18 September 2023
CITATION
Sun J, Zhang S, Hou M, Sun Q, Cao F, Zhang Z,
Tang G, Wang X, Geng L, Cui L and Chen Z-J
(2023) Who can help me? Understanding the
antecedent and consequence of medical
information seeking behavior in the era of
bigdata.
Front. Public Health 11:1192405.
doi: 10.3389/fpubh.2023.1192405
COPYRIGHT
© 2023 Sun, Zhang, Hou, Sun, Cao, Zhang,
Tang, Wang, Geng, Cui and Chen. This is an
open-access article distributed under the terms
of the Creative Commons Attribution License
(CC BY). The use, distribution or reproduction
in other forums is permitted, provided the
original author(s) and the copyright owner(s)
are credited and that the original publication in
this journal is cited, in accordance with
accepted academic practice. No use,
distribution or reproduction is permitted which
does not comply with these terms.
TYPE Original Research
PUBLISHED 18 September 2023
DOI 10.3389/fpubh.2023.1192405
Sun et al. 10.3389/fpubh.2023.1192405
Frontiers in Public Health 02 frontiersin.org
1. Introduction
With the rapid development and growing prevalence of internet
based information technology, the internet is becoming the primary
tool for patients in search of medical information (1–3). According to
Pew Internet Project’s research, over 80% of internet users in the
UnitedStates search for health information online (4). Along with the
increase in online health information search, the volume of medical
related information on the internet is surging (1). Medical information
has ooded the internet and penetrated daily life through computers,
mobile phones, and other media (2, 5). e internet provides new
ways of transmitting medical information in a convenient manner (6),
but also brings potential risks that cannot beignored (7–9).
In the context of health care, when patients receive a diagnosis
from their physicians, the process of coping with uncertainty would
betriggered (10). e typical response of patients is to search for a
frame of reference that enables them to assess the severity of their
condition (10). For example, they may ask their physicians “Am Iin
danger? Will Ibeokay? How bad is this?” When patients suspect that
the physician does not provide accurate answers to their questions or
is holding something back from them, and they are dissatised with
the information provided by their physicians, they may experience the
accumulation of uncertainty (10). To reduce the perceived stress
caused by uncertainty, seeking information from other sources can
beused to enhance coping by helping individuals understand the
health threat and the its associated challenges (11), determine available
resources to manage the stressors, and thus increase predictability and
feelings of control (12–14).
However, exposure to excessive amounts of medical information
may lead to information overload (1, 15, 16), where an individual’s
eciency in using available information is hampered by its
overwhelming quantity (17). According to the information processing
theory, the cognitive resources for an individual to select, store and
retrieve information are limited (15, 18). Receiving a high variety of
information requires patients to identify the most useful parts related
to their symptoms, diagnosis, treatment and so on (1). Unfortunately,
it is dicult for patients with limited medical knowledge to lter the
important information and separate it from noise (15, 19). When new
information continuously arrives and competes for limited processing
resources, patients may experience strain in their capacity to process
information (17, 18). Just as Jiang and Beaudoin claimed, information
overload can result from people’s continued eorts in searching for
information (16).
Information overload can cause various adverse eects on
patients’ cognition, emotions, and attitudes (1, 5, 20). Eppler and
Mengis reported that patients experiencing information overload tend
to feel stressed and confused, and ignore further information input
(18). In addition, the research conducted by Swar etal. has shown that
perceived information overload is positively associated with negative
aect, depressive symptoms, and feelings of anxiety and anger (1). e
change in patients’ psychological state and emotion may aect their
interaction with physicians, and subsequently inuence the quality of
their relationship. In line with this perspective, a study examined the
eects of information overload on patients’ behavioral intentions and
suggested that perceived information overload had a direct negative
impact on patients’ compliance in treatment (5).
Echoing this trend, weproposed an integrated theoretical model
that encompasses the antecedent (i.e., satisfaction with information
received from physicians) and consequences (i.e., medical
information overload and patient-physician relationship quality) of
medical information seeking. In terms of research subjects, wefocus
on women suering from infertility. In recent years, information on
assisted reproduction has grown rapidly. e explosion of such
information, however, has greatly disturbed the normal diagnostic
and treatment procedures for women suering from infertility due to
their diculty in distinguishing the quality of relevant information,
which may lead to conict and confusion. erefore, we target
women suering from infertility as the research subjects of this study
and explore the antecedents and outcomes of their information
seeking behavior. Specically, wepropose that patients’ satisfaction
with the information received from physicians is negatively related to
their involvement in medical information seeking, which may result
in perceived information overload. Consequently, patients’ perception
pf information overload may undermine the quality of their
relationship with physicians. Additionally, we assume that
information seeking and information overload play a sequential
mediating role in the relationship between patients’ satisfaction of
information received from physicians and quality of their relationship
with their physicians. In summary, Figure 1 presents our
conceptual model.
2. Methods
2.1. Participants and procedure
We contacted a hospital for reproductive medicine in China
to collect data. This hospital is one of the largest reproductive
hospitals in eastern China. The patients at this hospital have
diverse demographic backgrounds, including various age groups
and different socioeconomic statuses, which enables us to obtain
reliable and generalizable research results. Prior to conducting
our field survey, wecontacted the managers in charge of this
hospital and clearly communicated that our project was intended
solely for research purposes and strictly confidential. After
gaining approval for the study, weprinted questionnaires and
sent them to doctors in charge of the assisted reproductive
technology (ART) unit who helped us in distributing these
questionnaires to all voluntary participants.
e sample consisted of women who arrived for their initial visit
at the ART units in this hospital. Women were included in the study
if they were in the rst phase of fertility consultation and could
understand and complete questionnaires in Chinese. Weplaced no
restrictions on age, education, or socioeconomic status. e doctors
in charge of the unit approached the participants and introduced the
broad topic of the study as well as its requirements for participation.
Subsequently the doctors asked if they would like to participate in the
study. To encourage participation, they assured participants that (1)
their participation will be voluntary, (2) surveys will be kept
condential, (3) they have right to retrieve and/or withdraw their
information from the study at any time, (4) their response will beused
for academic research purposes only, and (5) their participation was
in no way related to the medical treatment they would receive at the
clinic. With the written informed consent of the participants, the
women received the questionnaire in a sealed envelope. Subsequently,
the authors contacted the women to verify that the questionnaire had
Sun et al. 10.3389/fpubh.2023.1192405
Frontiers in Public Health 03 frontiersin.org
been completed and submitted, and to respond to any distress it may
have aroused. Aer completed the questionnaires, the participant
received a letter of thanks from us for his/her contribution to the study.
During the data collection, 602 women met the inclusion criteria
(i.e., they were in the initial phase of ART and were capable of
completing the Chinese instruments). Twenty-nine women declined
to participate. e nal sample consisted of 573 women aged 22–47
(M = 33.86, SD = 4.91), Table1 shows the demographic characteristics
of the 573 participants.
2.2. Instruments
We translated the measures from English to Chinese following
Brislin’s (21) translation-back translation procedure. All ratings were
made via a 5-point Likert scale ranging from 1 (strongly disagree) to
5 (strongly agree) unless otherwise indicated. To evaluate the internal
consistency reliability of the scales, we calculated Cronbach’s
alpha coecient.
2.2.1. Satisfaction with information received from
physician
A 3-item scale, adapted from Matthews etal. (22), was used to
assess the woman’s subjective satisfaction with information provided
by their physicians (e.g., “I amsatised with the information Ireceived
from my primary physician(s) about my diagnosis”). Since the original
scale developed by Matthews etal. was used to assess cancer patients’
satisfaction with the medical information received from their
physicians (22), wemodied some of these statements to suit our
research context of assisted reproductive technology. Cronbach’s alpha
value for this scale in this study was 0.90. e mean score was
computed by averaging the responses to all three items, with higher
scores indicating greater satisfaction with the information provided
by physicians.
2.2.2. Behavior involvement in medical
information seeking
Behavior involvement in medical information seeking was rated
by the 9-item adapted from Krantz etal. (23). Example items include
“I tend to learn how to cure some of my own illness without contacting
a physician.” Cronbach’s alpha for this scale in this study was 0.68. e
mean scores were computed for each participant by averaging the
responses to all items, with higher scores indicating greater level of
involvement in information seeking behavior.
2.2.3. Perceived information overload
We measured participant’s perceived information overload
using a 13-item scale adapted from Jensen etal. (24). Sample items
are “ere are so many dierent recommendations about assisted
FIGURE1
Integrate model of medical information seeking. In our model, wepropose that: (1) patients’ satisfaction with the information received from physicians
is negatively related to their involvement in medical information seeking. (2) Behavior involvement in medical information seeking is positively related
to perceived information overload. (3) Perceived information overload is negatively related to patient-physician relationship quality. (4) Behavior
involvement in medical Information seeking and perceived information overload play a sequential mediating role in the relationship between patients’
satisfaction of information received from physicians and patient-physician relationship quality.
TABLE1 Demographic characteristics of the participants (n = 573).
Variables N %
Age
<30 145 25.31
30 ~ 34 256 44.68
35 ~ 39 118 20.59
≥40
54 9.42
Education
9th grade and below 159 27.74
High school 118 20.59
Junior college 123 21.47
Bachelor’s degree 134 23.39
Master’s degree and above 34 5.93
Did not respond 5 0.87
Monthly income (Yuan)
<1,500 79 13.79
1,500 ~ 3,000 149 26.00
3,000 ~ 5,000 197 34.38
5,000 ~ 8,000 61 10.65
8,000 ~ 10,000 24 4.19
>10,000 12 2.09
Did not respond 51 8.90
Sun et al. 10.3389/fpubh.2023.1192405
Frontiers in Public Health 04 frontiersin.org
reproductive technology, it’s hard to know which ones to follow”
and “It has gotten to the point where Ido not even care to hear new
information about assisted reproductive technology.” Cronbach’s
alpha for this scale in this study was 0.92. e mean scores were
computed for each participant by averaging the responses to all
items, with higher scores indicating greater perception of
information overload.
2.2.4. Patient-physician relationship quality
We used a 5-item scale, which was adapted from Ganz etal. (25),
to measure the women’s perceived relationship quality with her
provider (e.g., “It’s dicult to discussing new symptoms with my
doctors”). Cronbach’s alpha for this scale in this study was 0.89. e
mean total score was computed by averaging the responses to all
items, with a higher score indicating the lower quality of patient-
provider relationship.
2.2.5. Demographic questionnaire
A demographic questionnaire was used to obtain information
regarding personal characteristics, including age, education and
monthly income. Detailed information was present in Table1.
2.3. Data analysis
IBM SPSS 25.0 and AMOS 22.0 soware was used for statistical
analysis. First, prior to the main analysis, we conducted several
preliminary analyses. Specically, weused SPSS 25.0 to address the
issue of missing values due to incomplete questionnaires since it may
cause biased sampling. Analysis across the core variables revealed
relatively low rates of missing values, ranging from as low as none to
a high of 5.2 percent. Little’s test for Missing Completely at Random
(MCAR) revealed that missing items were completely at random
(χ
2
= 1689.118, df = 1,652, p = 0.257), and that no missing values were
related to a specic variable or a specic respondent (26).
In the second stage, common method bias was tested followed the
suggestion of Podsako etal. (27). Besides, descriptive analyses were
conducted to calculate the means, standard deviations of core variables
and Pearson correlation was used to examine the associations between
variables. Composite reliabilities and average variance extracted were
also calculated in this stage. Next, a series of conrmatory factor
analysis was conducted in AMOS 22.0 to assess the measurement
model. Finally, weadopted the PROCESS macro in SPSS 25.0 soware
with bootstrapping techniques developed by Preacher and Hayes (28)
to test our hypotheses. In light of the literature, background
characteristics (age, education, income) were entered as control
variable in the model (2, 29).
3. Results
3.1. Tests of common method bias
We used Harman’s single factor procedure to address the issue
about common method bias raised by the measures weused. e logic
underlying this approach is that if method variance is largely
responsible for the covariation among the measures, a factor analysis
should yield a single (method) factor (27). erefore, principal
component analysis without rotation was conducted. e statistical
results show that there are 5 factors whose eigenvalues are greater than
1, and the rst factor accounts for 29.27% of the total variance, which
is far lower than the critical value of 40%. ese suggest that common
method bias did not cause a serious threat to interpreting our ndings.
3.2. Descriptive statistics
Table 2 shows the mean scores and standard deviations of
variables. e results of Pearson correlations are also presented in
Table2. e results reveal that women’s satisfaction with information
has signicant negative relationships with behavior involvement in
information seeking (r = −0.28, p < 0.001). is indicts that patients
with lower satisfaction have more information search behavior.
Besides, there is a positive correlation between behavior involvement
in information seeking and perceived information overload (r = 0.14,
p < 0.01). us, higher behavior involvement in information seeking
is associated with higher perceived information overload. In addition,
perceived information overload (r = −0.51, p < 0.001) is negatively
associate with perceived patient-provider relationship quality. It
implies that information overload may undermine patient-
provider relationship.
3.3. Psychometric properties
Table 3 shows the assessment of composite reliabilities and
convergent validity. Composite reliabilities (CR) in the proposed
model are above the 0.7 threshold indicating a high reliability of items
used for each construct. Convergent validity is assessed by evaluating
the average variance extracted (AVE) from the measures. e AVE is
above the threshold value of 0.5, meeting the criteria of convergent
validity. Discriminant validity is assessed by examining the square
root of AVE as recommended by Fornell and Bookstein (30). As
shown in Table2, the square root of AVE of each construct is greater
than the correlations between itself and all other constructs. Moreover,
all the constructs are found to have a stronger correlation with their
TABLE2 Mean, standard deviation, and correlation between variables.
Variables M SD 1 2 3
1. Satisfaction with information 4.13 0.67 0.71
2. Behavior involvement in information seeking 2.19 0.46
−0.28***
0.71
3. Perceived information overload 2.81 0.78
−0.24*** 0.14**
0.72
4. Patient-physician relationship quality 3.71 1.06
0.44*** −0.26*** −0.51***
0.82
e diagonal elements (in bold) represent the square root of AVE.
Sun et al. 10.3389/fpubh.2023.1192405
Frontiers in Public Health 05 frontiersin.org
own measures than to those of others. is also shows the proper
assessment of discriminant validity.
Next, a series of conrmatory factor analyses (CFAs) was
conducted. Weused AMOS 22.0 to conduct the CFAs by contrasting
the four-factor CFA model against alternatives to evaluate the
distinctiveness of the key variables. As can beseen in Table4, the four-
factor model (including all factors we hypothesis) ts the data
considerably better than any of the alternatives (χ
2
(371) = 1131.116,
p < 0.001; Comparative Fit Index (CFI) = 0.910, Tucker-Lewis Index
(TLI) = 0.905, Root Mean Square Error of Approximation
(RMSEA) = 0.060, Akaike information criterion (AIC) = 1259.116,
Bayesian Information Criterion (BIC) = 1537.572).
3.4. Tests of hypothetical model
In the next stage, the PROCESS macro in SPSS 25.0 software
was used to test our hypothesis model. The results in Table5
reveal that, patients’ satisfaction of information received from
physicians has a negative and significantly effect on behavior
involvement in medical information seeking (β = −0.19 s.e. = 0.03,
p < 0.001, 95% CI = [−0.25, −0.14]). In addition, patients’ behavior
involvement in medical information seeking has a significantly
positive effect on perceived medical information overload
(β = 0.14, s.e. = 0.07, p < 0.05, 95% CI = [0.01, 0.29]). Besides, the
result shows that the relationship between perceived medical
information overload and patient-physician relationship quality
is negative and significant (β = −0.27, s.e. = 0.08, p < 0.001, 95%
CI = [−0.42, −0.11]), indicating that the more a woman perceives
information overload, the worse quality of the relationship with
her physicians she experienced.
To further verify the indirect or mediated eect of information
seeking and information overload, weuse the 95% bias-corrected
bootstrapped condence intervals (CI) provided by Preacher and
Hayes (28). Bootstrapping is a ‘nonparametric’ way of computing a
sampling distribution, which has been recommended as a more
powerful method of testing conditional indirect eect (28). As the
results of bootstrapping showed, the direct eect of satisfaction of
information on patient-physician relationship quality is signicant
(β = 0.49, s.e. = 0.06, 95% CI = [0.38, 0.60]), and the indirect eect of
satisfaction of information on patient-physician relationship quality
through information seeking and information overload (β = 0.02,
s.e. = 0.03, 95% CI = [0.01, 0.03]) is also signicant.
4. Discussion
With the expanding availability of medical and health
information, more and more patients tend to search for and
acquire relevant information from multi-source by themselves (2,
3). Since topics related to medical information seeking are
emerging but underestimated, this study focuses on the patients’
involvement in seeking medical information and examines the
relationship between patients’ satisfaction with information
received from physicians, information seeking behavior, perceived
information overload, and the quality of their relationship with
their physicians.
Specically, we explore the relationship between perceived
information satisfaction and medical information seeking behavior by
drawing on stress coping theory. In addition, based on information
processing theory, the relationships between medical information
seeking behavior, information overload and patient-practitioner
relationship quality are investigated. Next, we examine the serial
mediating eect of information seeking and information overload on
the relationship between satisfaction with information and patient-
practitioner relationship quality.
e results of our study show that the patients’ mistrust of their
practitioners may lead to information seeking behavior. With the
increasing amount of varied information encountered, patients may
experience information overload. As patients typically do not possess
deep prior knowledge of the symptoms, diagnosis, treatment or
TABLE3 Psychometric properties of the measurement model.
Variables CR AVE
Satisfaction with information 0.88 0.67
Behavior involvement in
information seeking
0.90 0.50
Perceived information
overload
0.93 0.51
Patient-physician relationship
quality
0.84 0.52
CR, composite reliability; AVE, average variance extracted.
TABLE4 Results of confirmatory factor analysis for variables studied.
Model χ
2
df CFI TLI RMSEA AIC BIC
Four-factor model 1131.116 371 0.910 0.905 0.060 1259.116 1537.572
ree-factor
model-1
1852.891 374 0.814 0.798 0.083 1974.891
2240.295
ree-factor
model-2
2204.759 403 0.777 0.759 0.088 2328.759
2598.514
ree-factor
model-3
2352.906 402 0.759 0.739 0.092 2478.906
2753.012
One-factor model 4233.147 405 0.526 0.491 0.129 4353.147 4614.201
ree-factor model-1: satisfaction with information and behavior involvement in information seeking combined; ree-factor model-2: behavior involvement in information seeking and
perceived information overload combined; ree-factor model-3: perceived information overload and patient-physician relationship quality combined. CFI, comparative t index; TLI, Tucker-
Lewis index; RMSEA, root-mean-square error of approximation; SRMR, standard root mean-square residual; AIC, Akaike information criterion; BIC, Bayesian information criterion.
Sun et al. 10.3389/fpubh.2023.1192405
Frontiers in Public Health 06 frontiersin.org
administration of their health conditions, it is dicult for them to
lter and separate useful information from large volume of noise (1,
2, 15, 16). us, information overload may damage the relationship
between patients and practitioners, which further lead to conicts in
medication choices and other treatment issue.
4.1. Theoretical contributions
Medical information seeking shows how people assessing the
medical information needs, partnering with other medical
information resources and acting on information transmitted to them
from various information carriers (31). Few of studies have
investigated the role of the interact between patients and physicians
might play in predicting the information seeking behavior, and little
is known about the dark-side eects of information seeking on the
patients themselves and the relationship with their physicians (2, 5).
Our study is one of the few studies to explore the medical information
seeking behavior of a Chinese sample. By theoretically constructing
and empirically testing a synthetic model that integrates the factors
that inuence information seeking behavior and the potential dark-
side eect of such behavior. Our research contributes to the literature
of medical information seeking.
First, our research explores the important role of information
satisfaction plays in predicting information seeking behavior.
Although past research has indicated there are many predictors of
information seeking, most of them focus on the individual
characteristics (e.g., socioeconomic status, information seeking
preferences and experiences) (32, 33), little is known about the
inuence of information lacking on the seeking behavior of patients.
By involving stress coping theory, our study demonstrate that patients’
satisfaction of information received from their physicians impacts
their behavior involvement in medical information seeking.
ese ndings expand the application of stress coping theory and
indicate that medical information seeking can beused as a coping
strategy for patients who lacking necessary information to fulls their
needs for control the stressful situation (2, 10, 11). According to stress
coping theory, when encounter health problems, individual choose
the next coping strategy according to the information he/she already
has (10, 11). For patients who dissatised with information provided
by their physicians (such as have unanswered questions about their
illness and treatment), their feeling of stress caused by uncertainty
increase, which compel them to seek additional information through
information channels other than physicians.
Second, our study responds to the call of in-depth research on the
outcomes of information seeking (16, 34, 35) and also expands the
application of information processing theory into a new eld. e
ndings demonstrate that the information seeking behavior can cause
perceived information overload and further damage the relationship
between patients and physicians. In addition, the results of our study
reveal that the satisfaction of information can aect patient-physician
relationship quality via the mechanism of behavior involvement in
information seeking and perceived information overload.
Consistent with information process theory, these results suggest
that with the limitation of cognitive resources, information seeking
can lead to the threat of information overload. With the deepening of
information seeking, the information volume and heterogeneity
increase and information relevance decrease (36), which bring heavy
burden to individual’s cognitive resources and increase the possibility
of information overload (1, 15, 16). Excessive and diverse information
may interfere with the process of information ltering, selecting and
processing (3), which trigger changes in patients’ cognition, emotion
and attitude (3, 5, 20) and impact the patient-physician
relationship quality.
4.2. Practical implications
By considering the complexity and cognitive aspects of
information seeking, this study provides important implications for
public health promotion, patient empowerment, and quality of health
communication. It also bring a good opportunity for health
information professionals to contribute more to this interdisciplinary
discourse. First of all, our study describes the causes of patients’
anxiety about excessive information and implies a solution to the
physician-patient communication. It is reasonable to assume that the
informational support provided by their physicians might help
patients to cope with and prevent information overload. Studies on
TABLE5 Results of hypothetical model.
Information seeking Information overload patient-physician
relationship quality
Age
−0.00 0.01* −0.00
Education
−0.01 −0.03 0.10***
Monthly income
−0.01 −0.02 −0.02
Satisfaction with information
−0.19*** −0.26*** 0.48***
Information seeking
0.14** −0.33***
Information overload
−0.27***
Eect SE 95% CI
Direct eect of SI on PPRQ 0.49 0.06 [0.38, 0.60]
Indirect eect of SI on PPRQ through 0.02 0.03 [0.01, 0.03]
SI, satisfaction with information; PPRQ, patient-physician relationship quality. Bootstrap samples = 5,000; 95% CI: 95% condence intervals. ***p < 0.001, **p < 0.01, *p < 0.05. SE, standard
error.
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the use of and preferences for information sources among health
information seekers show that there is a discrepancy between the
sources patients reported to have used (that is, the Internet) and the
sources they preferred to use (that is, health care providers) (19). In
line with this, our research suggests that it is better for professionals
to provide more health related information to public and undertake
the responsibility of patient education.
Secondly, it is suggesting for patients to monitor their
information seeking behavior and thus protect themselves from
drowning in information. As researches indicated, confusion might
increase as the number of sources increase and particular sources
may not be in a good position to make any type of relevance
judgments, nor is it guaranteed that they can evaluate the quality of
the information accurately (1, 15, 16). So, wesuggest that patients
can take the “usefulness of information” as the main criterion when
they receive massive information, and break the habit of labelling a
large amount of information as “probably useful.” It is also
important for patients to improve their information literacy, which
includes the ability to discover, evaluate, use and exchange
information (35). In turn, this underscores the importance of
literacy approaches in health communication and education
campaigns (37).
irdly, this empirical research on medical information overload
brings us closer to improving information campaigns and services to
help individuals with dierent literacy levels meet their specic health
information needs. During the information exchange between the
proxies and the patient, some information might not becommunicated
accurately or completely, which may result lead to confusion (16, 34).
erefore, it is suggested that information service providers to update
and strengthen the function of information purication/classication,
make statistical analysis of the terms in the web searched by users, and
classify the search result according to the subject matter, so as to save
users’ energy in viewing a large number of websites.
In sum, due to the expanding availability of medical and health
information, it is dicult to curb the patients’ tendency to seek
medical information. e informational support provided by the
physicians could help patients to cope with and prevent information
overload. It was suggested that medical professionals should undertake
the responsibility of patient education and provide more health related
information to the public. Furthermore, information service providers
should update and strengthen the function of information
purication/classication, so as to save users’ energy from the
web noises.
4.3. Limitations and future research
ere are several limitations of this study which may provide
inspirations for future research. First, although we provide an
integrated model of medical information seeking, the mechanisms
underlying the relationships among variables need further
examination. For example, future research can focus on how
information overload aects patient-physician relationship and
explore the mediation role of cognitive resource depletion, negative
emotion etc., and the moderation role of social support, self-regulation
and other alternative factors. In addition, this study has examined the
eects of information seeking behavior on information overload and
patient-physician relationship, further examination of other outcomes
(such as clinical compliance and self-treatment) of medical
information seeking may help to draw a more comprehensive picture
of medical information seeking.
We recognized that information seeking is a complex and context
based construct, and we acknowledge that relying solely on self-
reported data clearly limits our ability to make any clear cut
generalizations from our ndings. is as an important opportunity
to explore and identify the essence part of medical information
seeking behavior for future studies. For example, by collecting real
time data on the web, future studied can analysis the dierences of
user information seeking behavior under various search situations.
For health information professionals, this research brings
important questions to the fore. What are the characteristics of people
who suer from medical information overload? How to protect
patients from information overload? What patients do to cope with
overload? e answers to these questions have important implications
on how we should deliver health information and assess future
information services. erefore, future research can use experimental
method or long-term surveys to explore the above issues and propose
relevant interventions.
Data availability statement
e raw data supporting the conclusions of this article will
bemade available by the authors, without undue reservation.
Author contributions
SZ, JS, and GT: conceptualization. SZ, JS, and LG: data curation.
SZ and QS: formal analysis. LC: funding acquisition. JS, MH, ZZ, LG,
and LC: investigation. SZ, JS, QS, FC, GT, and XW: methodology. MH:
project administration. Z-JC: resources. GT, LC, and Z-JC:
supervision. SZ and JS: writing – original dra. QS, FC, and XW:
writing – review and editing. All authors contributed to the article and
approved the submitted version.
Funding
is study was supported by the National Key R&D Program of
China (2022YFC2702905), CAMS Innovation Fund for Medical
Sciences (2021-I2M-5-001), Taishan Scholars Program for Young
Experts of Shandong Province (tsqn201909195), National Natural
Science Foundation of China (32200897, 72072101), and Shandong
Provincial Natural Science Foundation (ZR2021QC147). e funders
had no role in study design, data collection and analysis, decision to
publish, or preparation of the manuscript.
Conflict of interest
e authors declare that the research was conducted in the
absence of any commercial or nancial relationships that could
beconstrued as a potential conict of interest.
Sun et al. 10.3389/fpubh.2023.1192405
Frontiers in Public Health 08 frontiersin.org
Publisher’s note
All claims expressed in this article are solely those of the
authors and do not necessarily represent those of their affiliated
organizations, or those of the publisher, the editors and the
reviewers. Any product that may be evaluated in this article, or
claim that may be made by its manufacturer, is not guaranteed or
endorsed by the publisher.
References
1. Swar B, Hameed T, Reychav I. Information overload, psychological ill-being, and
behavioral intention to continue online healthcare information search. Comput Human
Behav. (2017) 70:416–25. doi: 10.1016/j.chb.2016.12.068
2. Khaleel I, Wimmer BC, Peterson GM, Zaidi STR, Roehrer E, Cummings E, et al.
Health information overload among health consumers: a scoping review. Patient Educ
Couns. (2020) 103:15–32. doi: 10.1016/j.pec.2019.08.008
3. Marriott JV, Piotr S, Tarek ET, Yakoub K, Peter B, Arri C. Infertility information on
the world wide web: a cross-sectional survey of quality of infertility information on the
internet in the UK. Hum Reprod. (2008) 23:1520–5. doi: 10.1093/humrep/den072
4. Fox S, Duggan M. Health Online 2013. Washington, DC: In Pew Internet &
American Life Project (2013).
5. Liu CF, Kuo KM. Does information overload prevent chronic patients from reading
self-management educational materials? Int J Med Inform. (2016) 89:1–8. doi: 10.1016/j.
ijmedinf.2016.01.012
6. Zhang Y. Dynamic power relations in online medical consultation in China:
disrupting traditional roles through discursive positioning. Chin J Commun. (2021)
14:369–85. doi: 10.1080/17544750.2021.1891556
7. Silberg WM, Lundberg GD, Musacchio RA. Assessing, controlling, and assuring
the quality of medical information on the internet: Caveant lector et viewor—let the
reader and viewer beware. J Amer Med Assoc. (1997) 277:1244–5. doi: 10.1001/
jama.1997.03540390074039
8. Bickmore TW, Trinh H, Olafsson S, O'Leary TK, Asadi RNM, Cruz R. Patient and
consumer safety risks when using conversational assistants for medical information: an
observational study of Siri, Alexa, and Google assistant. J Med Internet Res. (2018)
20:e11510. doi: 10.2196/11510
9. Winker MA, Flanagin A, Chi-Lum B, White J. Musa cc Hio, R.a. guidelines for
medical and health information sites on the internet: principles governing AMA web
sites. J Amer Med Assoc. (2000) 283:1600–6. doi: 10.1001/jama.283.12.1600
10. Folkman S. Stress, Coping, and Hope. USA: Springer (2013).
11. Chae J. Who avoids cancer information? Examining a psychological process
leading to cancer information avoidance. J Health Commun. (2016) 21:837–44. doi:
10.1080/10810730.2016.1177144
12. Płaciszewski K, Wierzba W, Ostrowski J, Pinkas J, Jankowski M. Use of the internet
for health purposes—a National web-Based Cross-Sectional Survey among adults in
Poland. Int J Environ Res Public Health. (2022) 19:16315. doi: 10.3390/ijerph192316315
13. Jung M, Ramanadhan S, Viswanath K. Eect of information seeking and avoidance
behavior on self-rated health status among cancer survivors. Patient Educ Couns. (2013)
92:100–6. doi: 10.1016/j.pec.2013.02.008
14. Karim S, Choukas-Bradley S, Radovic A, Roberts SR, Maheux AJ, Escobar-Viera
CG. Support over social media among socially isolated sexual and gender minority
youth in rural U.S. during the COVID-19 pandemic: opportunities for intervention
research. Int J Environ Res Public Health. (2022) 19:15611. doi: 10.3390/ijerph192315611
15. NollRG, Krier JE. Some implications of cognitive psychology for risk regulation.
J. Legal Stud. (1990) 19:747–79. doi: 10.1086/467870
16. Jiang S, Beaudoin CE. Health literacy and the internet: an exploratory study on the
2013 HINTS survey. Comput Human Behav. (2016) 58:240–8. doi: 10.1016/j.chb.2016.01.007
17. Bawden D, Holtham C, Courtney N. Perspectives on information overload. Aslib
Proc. (1999) 51:249–55. doi: 10.1108/EUM0000000006984
18. Simon HA. Information-processing theory of mind. Am. Psychol, (1995)50:507–8.
doi: 10.1037/0003-066X.50.7.507
19. Hasenpusch C, Faßhauer H, Minow A, Kannengießer L, Hrudey I, Walter S, et al.
Digital health information provided by public health stakeholders on colorectal cancer
screening: a systematic evaluation. Int J Environ Res Public Health. (2022) 19:15624. doi:
10.3390/ijerph192315624
20. Eppler M, Mengis J. e concept of information overload: a review of literature
from organization science, accounting, marketing, MIS, and related disciplines. Inf Soc.
(2004) 20:325–44. doi: 10.1080/01972240490507974
21. Brislin RW. Translation and content analysis of oral and written materials In: HC
Triandis and JW Berry, editors. Handbook of Cross-Cultural Psychology, vol. 2. Boston:
Allyn & Bacon (1980). 389–444.
22. Matthews AK, Sellergren SA, Manfredi C, Williams M. Factors inuencing
medical information seeking among African American cancer patients. J Health
Commun. (2002) 7:205–19. doi: 10.1080/10810730290088094
23. Krantz DS, Baum A, Wideman MV. Assessment of preferences for self-treatment and
information in health care. J Pers Soc Psychol. (1980) 39:977–90. doi: 10.1037/0022-3514.39.5.977
24. Jensen JD, Carcioppolo N, King AJ, Scherr CL, Jones CL, Niederdeppe J. e
cancer information overload (CIO) scale: establishing predictive and discriminant
validity. Patient Educ Couns. (2014) 94:90–6. doi: 10.1016/j.pec.2013.09.016
25. Ganz PA, Schag C, Lee J, Sim MS. e CARES: a generic measure of quality of life
for patients with cancer. Qual Life Res. (1992) 1:19–29. doi: 10.1007/BF00435432
26. Little RJA. A test of missing completely at random for multivariate data with
missing values. J AmStat Assoc. (1988) 83:1198–202.
27. Podsako PM, MacKenzie SB, Podsako NP. Sources of method bias in social
science research and recommendations on how to control it. Annu Rev Psychol. (2012)
63:539–69. doi: 10.1146/annurev-psych-120710-100452
28. Preacher KJ, Hayes AF. Asymptotic and resampling strategies for assessing and
comparing indirect eects in multiple mediator models. Behav Res Methods. (2008)
40:879–91. doi: 10.3758/BRM.40.3.879
29. Diaz JA, Grith RA, Ng JJ, Reinert SE, Friedmann PD, Moulton AW. Patients’ use
of the internet for medical information. J Gen Intern Med. (2002) 17:180–5. doi:
10.1046/j.1525-1497.2002.10603.x
30. Fornell C, Bookstein FL. Two structural equation models: LISREL and PLS applied
to consumer exit-voice theory. J Marketing Res. (1982) 19:440–52. doi: 10.1177/
002224378201900406
31. Johnson JD, Case DO, Andrews JE, Allard SL, Kelly K. Intention to seek
information on cancer genetics. Inf Res. (2005) 10:323–9. doi:
10.1080/10810730590950048
32. Nguyen GT, Bellamy SL. Cancer information seeking preferences and experiences:
disparities between Asian Americans and whites in the health information National
Trends Survey (HINTS). J Health Commun. (2006) 11:173–80. doi: 10.1080/
10810730600639620
33. Rutten LJF, Augustson E, Wanke K. Factors associated with patients’ perceptions
of health care providers’ communication behavior. J Health Commun. (2006) 11:135–46.
doi: 10.1080/10810730600639596
34. Longo DR, Schubert SL, Wright BA, LeMaster J, Williams CD, Clore JN. Health
information seeking, receipt, and use in diabetes self-management. Ann Fam Med.
(2010) 8:334–40. doi: 10.1370/afm.1115
35. Kim K, Lustria MLA, Burke D. Predictors of cancer information overload: ndings
from a national survey. Inf Res. (2007) 12:1–29.
36. Macdonald J, Bath P, Booth A. Information overload and information poverty:
challenges for healthcare services managers? J Doc. (2011) 67:238–63. doi:
10.1108/00220411111109458
37. Ji Q, Ha L, Sypher U. e role of news media use and demographic characteristics
in the possibility of information overload prediction. Int J Commun. (2014) 8:699–714.
