Keywords Social media - ethics - research design - privacy - patient selection - bibliometrics
1 Introduction
For this 29th edition of the Yearbook of Medical Informatics, the special topic of “Ethics and
Health Informatics” comes at the time when individuals are increasingly exposed to
data sharing practices and growing numbers of researchers and healthcare professionals
reuse encoded medical data and social media data to add real-life context to clinical
research purposes.
The idea of utilizing social media in clinical research is not recent. From a research
perspective, social media is often perceived as a tool to facilitate patient recruitment,
monitor patient involvement, and improve patient retention in clinical trials [1 ]
[2 ]. The ubiquitous use of online social platforms has also led researchers to envision
new ways in which social media can promote better health outcomes. There is growing
interest in building a “social mediome” using data derived from social media platforms
that reveal individual- and population-level health information to gain greater insight
into patient health habits to facilitate treatment [3 ].
The special topic of the 2020 Yearbook of Medical Informatics is timely since social
media data collected by devices and web applications are increasing exponentially.
Yet the ethical concerns to use such data for research purposes warrant further consideration,
as the rigorous ethical principles applied in clinical research are often not transferred
into the online and social media world [4 ].
But those massive amounts and varying types of data are attractive not only to traditional
researchers at academic institutions, but also to non-traditional researchers, such
as commercial entities and citizen or community scientists. Devices, web applications,
and social online platforms collect a myriad of data in varying details, where such
data is made available to third parties for research and other purposes, many of which
may be unregulated, and often without users’ awareness or permission. The health-related
nature of data collected in such a way may affect user perception of and expectation
for privacy. Users may expect that their data are protected because data are health-related.
This misconception could be compounded if a health care provider recommends or encourages
patients to use a device to log and track symptoms or health behaviours. In any event,
patients may reveal personal or sensitive health-related information without realizing
that such data are not protected from unwanted use or access, such as targeted advertising.
2 Methodology
Search Strategy
We used PubMed to conduct our search, capturing papers published in the year 2019
on consumer-facing technologies and ethical concerns. The search strategy was based
on the PICO framework (P-Population/Problem, I–Intervention, C-Comparison, O-Outcome),
where ‘Problem’ refers to the various digital environments consumers and patients
participate in (e.g ., social media, online health communities); “Intervention” is considered as the topic
“ethics” (e.g ., philosophy, integrity, honour, respect, right); “Outcome” outlines the result of
ethical effects in health (e.g ., benefit, risk, issue, policy, guidelines). A ‘comparison’ intervention was not
included as it is not relevant in this review. We started from a core query adopted
in previous work. Step by step, we refined the query to include keywords related to
digital/social media (42 keywords), ethics (23 keywords), and results (5 keywords).
MeSH terms and the syntax “[All Fields]” were used wherever possible to ensure our
search strategy was comprehensive. The final search query is listed below:
((2019[DP] NOT “Epub ahead of print”) NOT Bibliography[pt] NOT Comment[pt] NOT Editorial[pt]
NOT Letter[pt] NOT News[pt] NOT Case Reports[pt] NOT Published Erratum[pt] NOT Historical
Article[pt] NOT Legal Case[pt] NOT legislation[pt] NOT (“review”[pt] OR “review literature
as topic”[MeSH Terms] OR “literature review”[All Fields]))
AND (“ehealth”[All Fields] OR “e-health”[All Fields] OR “tele-health”[All Fields]
OR “mhealth”[All Fields] OR “telemedicine”[All Fields] OR “electronic patient-physician
communication”[All Fields] OR “electronic medical record”[All Fields] “electronic
health record”[All Fields] OR “personal health record”[All Fields] OR “electronic
patient record”[All Fields] OR “online”[All Fields] OR “electronic”[All Fields] OR
“information technology”[All Fields] OR “communication technology”[All Fields] OR
“mobile”[All Fields] OR “online systems”[All Fields] OR “internet”[All Fields] OR
“web”[All Fields] OR “website”[All Fields] OR “patient portal”[All Fields] OR “cell
phone”[All Fields])
AND (“social media”[All Fields] OR “facebook”[All Fields] OR “twitter”[All Fields]
OR “youtube”[All Fields] OR “instagram”[All Fields] OR “social network site”[All Fields]
OR “social web”[All Fields] OR “online social network”[All Fields] OR “social environment”[All
Fields] OR “social process”[All Fields] OR “social competition”[All Fields] OR “social
norm”[All Fields] OR “social feedback”[All Fields] OR “social influence”[All Fields]
OR “social comparison”[All Fields] OR “social network”[All Fields] OR “discussion
group”[All Fields] OR “support group”[All Fields] OR “social support”[All Fields]
OR “community network”[All Fields] OR “online community”[All Fields])
AND (“access”[All Fields] OR “sharing”[All Fields] OR “share”[All Fields] OR “privacy”[All
Fields] OR “protection”[All Fields] OR “informed consent”[All Fields] OR “ethics”[All
Fields] OR “bioethics”[All Fields] OR “ethical”[All Fields] OR “moral”[All Fields]
OR “principle”[All Fields] OR “value”[All Fields] OR “integrity”[All Fields] OR “honour”[All
Fields] OR “right”[All Fields] OR “responsibility”[All Fields] OR “honesty”[All Fields]
OR “conscience”[All Fields] OR “fairness”[All Fields] OR “virtue”[All Fields] OR “philosophy”[All
Fields] OR “choice”[All Fields] OR “respect”[All Fields])
AND (“benefit”[All Fields] OR “risk”[All Fields] OR “issue”[All Fields] OR “policy”[All
Fields] OR “guideline”[All Fields] )
Bibliometrics Analysis
To understand the state of the literature, we applied various bibliometrics tools
onto the original set of articles returned from the search query. The “Bibliometrix”
package from R [5 ] was used on the retrieved articles to report frequency of the 50 most popular words
found in all abstracts. We also counted the occurrences of keywords. We analysed keywords
and words in titles to uncovers links between concepts through word co-occurrences.
Conceptual structure is used to reveal topics covered by scholars and identify what
are the most important words. We also study co-word network and draw clusters of words
(keywords and titles words). We plot a thematic map to analyse these clusters according
to the quadrant in which they are placed: (1) upper-right quadrant: motor-themes;
(2) lower-right quadrant: basic themes; (3) lower-left quadrant: emerging or disappearing
themes; and (4) upper-left quadrant: very specialized or niche themes.
3 Results
State of the Literature
The search query allowed to selected 368 articles. A descriptive analysis of these
articles was conducted, analysing the frequency of keywords, and the frequency of
words in titles and abstracts. We used 1,109 distinct keywords, 1,714 distinct words
in titles, and 8,147 in abstracts.
[Table 1 ] shows keywords occurrences. As a MeSH qualifier, “ethics” was associated with the
following MeSH major terms:
Automation/ethics
Automobiles/ethics/legislation & jurisprudence
Confidentiality/ethics/standards
Ergonomy/ethics
Mental health/ethics/standards
Mobile applications/ethics/standards
Physician-patient relations/ethics
Risk management/ethics/legislation & jurisprudence
Telemedicine/ethics/standards
Table 1
Frequency of keywords (reported from most frequent to least frequent)
Words
Frequency
Words
Frequency
humans
206
decision making
7
female
134
intention
7
male
115
mental health
7
adult
96
reproducibility of results
7
middle aged
64
self efficacy
7
adolescent
53
choice behavior
6
surveys and questionnaires
52
cohort studies
6
young adult
52
focus groups
6
social media
41
infant
6
aged
37
interpersonal relations
6
social support
36
motivation
6
internet
24
quality of life
6
qualitative research
24
social media/trends
6
cross-sectional studies
22
socioeconomic factors
6
risk factors
18
attitude to health
5
child
16
awareness
5
health knowledge attitudes practice
16
child preschool
5
pregnancy
14
Europe
5
social networking
14
health behavior
5
aged 80 and over
12
health promotion/methods
5
communication
12
Homosexuality male/psychology/statistics & numerical data
5
United States
11
incidence
5
Australia
9
information dissemination
5
research design
9
online social networking
5
risk assessment
9
pilot projects
5
risk-taking
9
program evaluation
5
attitude of health personnel
8
retrospective studies
5
prevalence
8
schools
5
prospective studies
8
smartphone
5
social media/standards
8
social environment
5
social media/statistics & numerical data
8
treatment outcome
5
time factors
8
United Kingdom
5
[Table 2 ] reports the frequency of words used in titles in comparison with abstracts. The
words “ethics” or “ethical” were retrieved only 33 and 32 times respectively, with
the same frequency reported in titles and abstracts. [Table 3 ] shows the frequency of words used in the query related to ethics.
Table 2
Top 10 words and their frequency in titles and abstracts
Words
Frequency in titles
Frequency in abstracts
Ranked according to frequency in titles (1 = most frequent)
Ranked according to frequency in abstracts (1 = most frequent)
social
88
755
1
1
health
76
611
2
2
study
68
409
3
3
media
47
357
4
5
online
35
299
5
8
analysis
29
206
6
13
support
27
381
7
4
risk
25
331
8
7
intervention
23
177
9
16
patients
22
288
10
9
Table 3
Frequency of words found in abstracts
Words
Frequency in abstracts
Ranked according to frequency in titles (1 = most frequent)
privacy
60
125
ethics
33
307
ethical
32
324
informed
23
468
protection
21
512
respect
13
843
moral
10
1103
responsibility
10
1103
principle
7
1526
rights
6
1699
bioethics
2
3384
Regarding the conceptual structure of the set of the 368 retrieved articles, [Figure 1 ] shows the co-occurrences of titles words based on the three most frequent words:
social (pink network), health (blue network), and study (green network). Different
variations of these three words are:
Social: media, analysis, survey
Health: online, support, risk, patients, mental, care, cancer
Study: protocol, qualitative, intervention, trial, randomized, factors, young
Fig. 1 Co-occurrence network of words used in titles
Regarding thematic maps of keywords and of titles words ([Figures 2 ] and [3 ]), we identified the most representative clusters according to centrality and density
in each quadrant and compare them accordingly ([Table 4 ]).
Fig. 2 Thematic map of clusters of words used as keywords
Fig. 3 Thematic map of clusters words found in titles
Table 4
Comparative analysis of thematic maps regarding the locations of clusters
Map location
Keywords map
Titles word map
Upper-right quadrant: motor-themes
aged, risk factors, aged and over, risk assessment, prospective studies
study, risk, qualitative, young, adults
Lower-right quadrant: basic themes
humans, female, male, adult, middle aged
health, online, support, patients, care
Lower-left quadrant: emerging or disappearing themes
qualitative research, Australia, decision making, attitude to health
adolescent, depression, urban, response
Upper-left quadrant: very specialized or niche themes
cohort studies
perceptions, sexual, preferences, finding
Best Paper Selection
The 368 retrieved articles were then manually reviewed by section editors, which resulted
in 15 articles considered for best paper selection. Elements that were considered
in the screening decision include: 1) level of relevance regarding the 2020 Yearbook
topic “Ethics in Health Informatics”; 2) whether the study was focused only on patients
and consumers; 3) nature of the issues addressed; and 4) level of innovative approach.
The selected 15 articles were then presented to a panel of international experts for
full paper review and scoring according to the IMIA Yearbook best paper selection
process. Only one paper was selected to be the best paper after discussions at a consensus
meeting (see [Table 5 ]). The description of the study and the main results are described in the Appendix.
Table 5
Best paper selection of articles for the IMIA Yearbook of Medical Informatics 2020
in the section ‘Consumer Health Informatics and Education’. The articles are listed
in alphabetical order of the first author's surname.
Section
Consumer Health Informatics and Education
▪ Reuter K, Zhu Y, Angyan P, Le N, Merchant AA, Zimmer M. Public concern about monitoring
twitter users and their conversations to recruit for clinical trials: survey study.
J Med Internet Res 2019 Oct 30;21(10):e15455.
The top five studies [6 ]
[7 ]
[8 ]
[9 ]
[10 ] as ranked by the review of external reviewers reported the following ethical concerns
when using social media at different phases in a research study.
Four studies examined public concern [6 ]
[7 ]
[8 ]
[9 ] and one study provides information on professional concern [10 ].
Two studies analysed the ethical concerns related to recruitment [6 ]
[8 ]. First study was selected as best paper [6 ]. The objective was to ascertain people’s attitudes and level of concern about the
use of social media monitoring on Twitter for targeted clinical trial recruitment.
The expressed attitudes were highly contextual, depending on factors such as the type
of disease or health topic and the entity or person who monitored users on Twitter.
In the second paper [8 ], authors tried to determine whether willingness to participate and willingness to
share social media data are associated with the type of research team and online recruitment
platform. Participants were significantly less likely to participate in federally
sponsored or pharmaceutical company research than university-led studies. Authors
suggested that researchers may see reduced online research participation and data
sharing, particularly for research conducted outside academia.
One study reported professional opinions on participant engagement [10 ]. It concluded that engaging participants via social media introduces unique methodological
and ethical issues, requiring researchers and ethics committee members to be familiar
with the technology and aware of its risks and limitations. Participation in training
and access to resources may improve researchers’ and ethics committee members’ familiarity
with social media platforms and ethical conduct.
One study examined patient perspectives on the risks and benefits of linking existing
data sources for research [7 ]. It concluded that developing methods to link databases to minimise the exposure
of unique identifiers may improve patient comfort levels with linking data for research
purposes.
One study explored the ethics of using social media for detecting and monitoring adverse
events in research studies [9 ]. It concluded that there is a wide disparity in attitudes towards research using
social media data. Adverse effects are viewed as personal and, therefore, more likely
to attract ethical consideration.
4 Conclusions
Ethics and health are two professional and research domains intimately connected.
Health decision-making process as well as clinical research emphasise the importance
of informed consent and ethical principles. Beyond the legislative arsenal framing
clinical research and data protection in many countries, the expression of a patient’s
informed consent is certainly not the only key to create a sustainable trust relationship
between the “donor” of data and the data “processor”. Findings suggest that most users
do not think that monitoring Twitter for clinical trial recruitment constitutes inappropriate
surveillance or a violation of privacy. However, further research is needed to identify
whether and how views on ethical concerns differed between social media platforms
and across populations.
Appendix: Content Summaries of Selected Best Paper for the IMIA Yearbook 2020
Reuter K, Zhu Y, Angyan P, Le N, Merchant AA, Zimmer M
Public concern about monitoring twitter users and their conversations to recruit for
clinical trials: survey study
J Med Internet Res 2019 Oct 30;21(10):e15455
This study used two online surveys to examine public attitudes of using Twitter to
recruit clinical trial participants. While nearly half the survey respondents agreed
that social media monitoring constitutes a form of eavesdropping, the authors concluded
that most social media users do not think monitoring Twitter for clinical trial recruitment
constitutes inappropriate surveillance or a violation of privacy. The authors also
remind researchers to remain mindful that some participants might find social media
monitoring problematic when associated with certain conditions or health topics, and
that further research is required to isolate factors that influence the level of concern
among social media users across platforms and populations.
