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About the publisher.

At JMIR Publications w e envision a world where people are empowered by health research and technology to make effective, informed decisions, take control of their health and well-being, and live happier and healthier lives.

Mission Statement

Through leading-edge thinking, community involvement, and continuous innovation, we help leaders in the health technology space to collaborate and disseminate their ideas and research results. We connect vetted, quality research outputs in novel, effective, and timely ways with those who need it.

JMIR Publications is a rapidly growing, leading open access publisher. The company was built on the success of JMIR (Journal of Medical Internet Research), which started in 1998 as a pioneering, small independent open access project hosted at a university, which subsequently grew into the most influential journal in medical informatics (ranked in Q1 by Impact Factor by Thomson Reuters as well as Scimago) and e-health services research. Due to the growth in influence and submissions, and to make the operations more sustainable and professional, the journal was incorporated as a company in 2011. Shortly after incorporation, several sister journals were launched. Currently, JMIR Publications Inc. publishes more than 3,500 articles annually in more than 30 journals. See the JMIR Publications site for more details.

About the Journal

Indexing and impact factor.

JMIR is indexed in more than 18 bibliographic databases and abstracting services, including MEDLINE [Index Medicus], PubMed , PMC , Directory of Open Access Journals (DOAJ Seal), CINAHL, Information Science Abstracts, INSPEC (Institution of Electrical Engineers), Communication Abstracts, The Informed Librarian Online, LISA (Library and Information Science Abstracts), EMBASE, Scopus , Science Citation Index Expanded , PsycINFO, CABI, LISTA (Library / Information Sciences & Technology Abstracts), ASSIA (Applied Social Sciences Index and Abstracts) database, CSA Social Services Abstracts database, EBSCO, and others.

In 2023, JMIR received a Journal Impact Factor™ of 7.4 (5-Year Journal Impact Factor™: 7.6) according to the latest release of the Journal Citation Reports™ from Clarivate, 2023.

JMIR continues to be a

* Q1 journal in the categories of ‘Medical Informatics’ (ranked 5/31 in SCIE) and * Q1 in ‘Health Care Sciences and Services’ (ranked 3/105 in SCIE) (Source: Journal Citation Reports™ from Clarivate, 2023) * Q1 in 'Medical Informatics" by Citescore (Scopus data) (ranked 93th percentile, 8/123) * Q1 by Scimago Journal Rank (Health Informatics) * Q1, ranked #1 on Google Scholar 'Medical Informatics'

Vol. 25 (2023)

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Digital Health Reviews

Effects of Mobile Mindfulness Meditation on the Mental Health of University Students: Systematic Review and Meta-analysis

Bin Chen , Ting Yang , Lei Xiao , Changxia Xu , Chunqin Zhu

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Accuracy of Augmented Reality–Assisted Navigation in Dental Implant Surgery: Systematic Review and Meta-analysis

Hang-Nga Mai , Van Viet Dam , Du-Hyeong Lee

Cost-effectiveness of Internet Interventions Compared With Treatment as Usual for People With Mental Disorders: Systematic Review and Meta-analysis of Randomized Controlled Trials

Pieter J Rohrbach , Alexandra E Dingemans , Catharine Evers , Eric F Van Furth , Philip Spinhoven , Jiska J Aardoom , Irene Lähde , Fleur C Clemens , M Elske Van den Akker-Van Marle

Analyzing the Effect of Telemedicine on Domains of Quality Through Facilitators and Barriers to Adoption: Systematic Review

Clemens Scott Kruse , Annamaria Molina-Nava , Yajur Kapoor , Courtney Anerobi , Harshita Maddukuri

Digital Health Interventions for Adult Patients With Cancer Evaluated in Randomized Controlled Trials: Scoping Review

Kyunghwa Lee , Sanghee Kim , Soo Hyun Kim , Sung-Hee Yoo , Ji Hyun Sung , Eui Geum Oh , Nawon Kim , Jiyeon Lee

The Use of Digital Health Interventions for Cardiometabolic Diseases Among South Asian and Black Minority Ethnic Groups: Realist Review

Aumeya Goswami , Lydia Poole , Zareen Thorlu-Bangura , Nushrat Khan , Wasim Hanif , Kamlesh Khunti , Paramjit Gill , Madiha Sajid , Ann Blandford , Fiona Stevenson , Amitava Banerjee , Mel Ramasawmy

Stakeholder Perspectives of Clinical Artificial Intelligence Implementation: Systematic Review of Qualitative Evidence

Henry David Jeffry Hogg , Mohaimen Al-Zubaidy , Technology Enhanced Macular Services Study Reference Group , James Talks , Alastair K Denniston , Christopher J Kelly , Johann Malawana , Chrysanthi Papoutsi , Marion Dawn Teare , Pearse A Keane , Fiona R Beyer , Gregory Maniatopoulos

Digital Medicine Society (DiMe)

Advancing Digital Health Innovation in Oncology: Priorities for High-Value Digital Transformation in Cancer Care

Smit Patel , Jennifer C Goldsack , Grace Cordovano , Andrea Downing , Karen K Fields , Cindy Geoghegan , Upinder Grewal , Jorge Nieva , Nikunj Patel , Dana E Rollison , Archana Sah , Maya Said , Isabel Van De Keere , Amanda Way , Dana L Wolff-Hughes , William A Wood , Edmondo J Robinson

E-Health Policy and Health Systems Innovation

The Assessment of Medical Device Software Supporting Health Care Services for Chronic Patients in a Tertiary Hospital: Overarching Study

Erik Baltaxe , Hsin Wen Hsieh , Josep Roca , Isaac Cano

Artificial Intelligence

Machine Learning–Based Prediction of Acute Kidney Injury Following Pediatric Cardiac Surgery: Model Development and Validation Study

Xiao-Qin Luo , Yi-Xin Kang , Shao-Bin Duan , Ping Yan , Guo-Bao Song , Ning-Ya Zhang , Shi-Kun Yang , Jing-Xin Li , Hui Zhang

Human Factors and Usability Case Studies

Factors Affecting the Successful Implementation of a Digital Intervention for Health Financing in a Low-Resource Setting at Scale: Semistructured Interview Study With Health Care Workers and Management Staff

Leon Schuetze , Siddharth Srivastava , Abdallah Mtiba Missenye , Elizeus Josephat Rwezaula , Manfred Stoermer , Manuela De Allegri

Infodemiology and Infoveillance

Addictive Potential of e-Cigarettes as Reported in e-Cigarette Online Forums: Netnographic Analysis of Subjective Experiences

Daria Szafran , Tatiana Görig , Sabine Vollstädt-Klein , Nadja Grundinger , Ute Mons , Valerie Lohner , Sven Schneider , Marike Andreas

Robots in Healthcare

Enhancing Nurse–Robot Engagement: Two-Wave Survey Study

Gen-Yih Liao , Tzu-Ling Huang , May-Kuen Wong , Yea-Ing Lotus Shyu , Lun-Hui Ho , Chi Wang , T C E Cheng , Ching-I Teng

Peer-to-Peer Support and Online Communities

The Effects of Patient Health Information Seeking in Online Health Communities on Patient Compliance in China: Social Perspective

Discretionary Corrigenda

Correction: Content and Dynamics of Websites Shared Over Vaccine-Related Tweets in COVID-19 Conversations: Computational Analysis

Iain Cruickshank , Tamar Ginossar , Jason Sulskis , Elena Zheleva , Tanya Berger-Wolf

Medicine 2.0: Social Media, Open, Participatory, Collaborative Medicine

Social Media Use Among Members of the Assessment of Spondyloarthritis International Society: Results of a Web-Based Survey

Yu Heng Kwan , Jie Kie Phang , Ting Hui Woon , Jean W Liew , Maureen Dubreuil , Fabian Proft , Sofia Ramiro , Anna Molto , Victoria Navarro-Compán , Manouk de Hooge , Bhowmik Meghnathi , Nelly Ziade , Sizheng Steven Zhao , Maria Llop , Xenofon Baraliakos , Warren Fong

Telehealth and Telemonitoring

Mailed Letter Versus Phone Call to Increase Diabetic-Related Retinopathy Screening Engagement by Patients in a Team-Based Primary Care Practice: Prospective, Single-Masked, Randomized Trial

Vess Stamenova , Megan Nguyen , Nike Onabajo , Rebecca Merritt , Olivera Sutakovic , Kathryn Mossman , Ivy Wong , Lori Ives-Baine , R Sacha Bhatia , Michael H Brent , Onil Bhattacharyya

E-Health / Health Services Research and New Models of Care

How the Behavior Change Content of a Nationally Implemented Digital Diabetes Prevention Program Is Understood and Used by Participants: Qualitative Study of Fidelity of Receipt and Enactment

Lisa M Miles , Rhiannon E Hawkes , David P French

Characteristics and Health Care Use of Patients Attending Virtual Walk-in Clinics in Ontario, Canada: Cross-sectional Analysis

Lauren Lapointe-Shaw , Christine Salahub , Cherryl Bird , R Sacha Bhatia , Laura Desveaux , Richard H Glazier , Lindsay Hedden , Noah M Ivers , Danielle Martin , Yingbo Na , Sheryl Spithoff , Mina Tadrous , Tara Kiran

e-Mental Health and Cyberpsychology

Internet-Based Self-Assessment for Symptoms of Internet Use Disorder—Impact of Gender, Social Aspects, and Symptom Severity: German Cross-sectional Study

Jan Dieris-Hirche , Laura Bottel , Stephan Herpertz , Nina Timmesfeld , Bert Theodor te Wildt , Klaus Wölfling , Peter Henningsen , Anja Neumann , Rainer Beckers , Magdalena Pape

JMIR Infodemiology

Focusing on determinants and distribution of health information and misinformation on the internet, and its effect on public and individual health..

Tim Ken Mackey, MAS, PhD, University of California San Diego, USA

JMIR Infodemiology (JI, ISSN 2564-1891, Editor-in-Chief: Tim Ken Mackey) launched in 2021, is a PubMed Central /PubMed, MEDLINE , Scopus , DOAJ , Web of Science , EBSCO/EBSCO Essentials, and CABI-indexed, peer-reviewed journal, focusing on infodemiology, the study of determinants and the distribution of health information and misinformation on the internet, and its effect on public and individual health. The new scientific discipline of " Infodemiology ," first introduced in 2002, has been gaining momentum due to the COVID-19 infodemic, with the  WHO  recognizing it as an important pillar to manage public health emergencies.  JMIR Publications is proud to have been spearheading the advancement of this new scientific discipline for more than a decade . We are now accelerating the development of this new interdisciplinary discipline with the first and only journal devoted to this rapidly evolving field, by bringing together thought leaders in research, data science, and policy. Areas of interest include information monitoring (infoveillance, including social listening); ehealth literacy and science literacy; knowledge refinement and quality improvement processes and policies; and the influence of political and commercial interests on effective knowledge translation. 

Recent Articles

Attitudes toward the human papillomavirus (HPV) vaccine and accuracy of information shared about this topic in web-based settings vary widely. As real-time, global exposure to web-based discourse about HPV immunization shapes the attitudes of people toward vaccination, the spread of misinformation and misrepresentation of scientific knowledge contribute to vaccine hesitancy.

Abortion (also known as termination of pregnancy) is an essential element of women’s reproductive health care. Feedback from women who underwent medical termination of pregnancy about their experience is crucial to help practitioners identify women’s needs and develop necessary tools to improve the abortion care process. However, the collection of this feedback is quite challenging. Social media offer anonymity for women who share their abortion experience.

Health misinformation on social media can negatively affect knowledge, attitudes, and behaviors, undermining clinical care and public health efforts. Therefore, it is vital to better understand the public’s experience with health misinformation on social media.

Lupus erythematosus (LE) is an autoimmune condition that is associated with significant detriments to quality of life and daily functioning. TikTok, a popular social networking platform for sharing short videos, provides a unique opportunity to understand experiences with LE within a nonclinical sample, a population that is understudied in LE research. This is the first qualitative study that explores LE experiences using the TikTok platform.

Despite being a pandemic, the impact of the spread of COVID-19 extends beyond public health, influencing areas such as the economy, education, work style, and social relationships. Research studies that document public opinions and estimate the long-term potential impact after the pandemic can be of value to the field.

Self-harm and suicide are major public health concerns worldwide, with attention focused on the web environment as a helpful or harmful influence. Longitudinal research on self-harm and suicide–related internet use is limited, highlighting a paucity of evidence on long-term patterns and effects of engaging with such content.

Social media posts by clinicians are not bound by the same rules as peer-reviewed publications, raising ethical concerns that have not been extensively characterized or quantified.

The COVID-19 pandemic triggered unprecedented global vaccination efforts, with social media being a popular tool for vaccine promotion.

Despite challenges related to the data quality, representativeness, and accuracy of artificial intelligence–driven tools, commercially available social listening platforms have many of the attributes needed to be used for digital public health surveillance of human papillomavirus vaccination misinformation in the online ecosystem.

Throughout the COVID-19 pandemic, social media has served as a channel of communication, a venue for entertainment, and a mechanism for information dissemination.

The COVID-19 pandemic prompted global behavioral restrictions, impacting public mental health. Sentiment analysis, a tool for assessing individual and public emotions from text data, gained importance amid the pandemic. This study focuses on Japan’s early public health interventions during COVID-19, utilizing sentiment analysis in infodemiology to gauge public sentiment on social media regarding these interventions.

Health agencies have been widely adopting social media to disseminate important information, educate the public on emerging health issues, and understand public opinions. The Centers for Disease Control and Prevention (CDC) widely used social media platforms during the COVID-19 pandemic to communicate with the public and mitigate the disease in the United States. It is crucial to understand the relationships between the CDC’s social media communications and the actual epidemic metrics to improve public health agencies’ communication strategies during health emergencies.

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JMIR Publications helps scientists to disseminate innovations, ideas, protocols, and research results to the widest possible audience. This includes not only other researchers, but also patients/consumers and other knowledge users.

We do so in a timely manner, adding value to the quality of the work and adhering to the highest ethical and quality standards.

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The JMIR Publications team comprises a talented group of individuals in their respective fields. They have come together to work toward the JMIR mission and vision.

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Explore the latest research in the field of digital health, including innovations in health care technologies, patient and caregiver education, participatory medicine, biomedical engineering and medical informatics.

Journal of Medical Internet Research

The leading peer-reviewed journal for digital medicine and health and health care in the internet age. June 2023 - Journal Impact Factor: 7.4. Q1 journal in “Medical Informatics” and “Health Care Science & Services” categories.(Source: Journal Citation Reports™ 2023 from Clarivate™)

JMIR Public Health and Surveillance

A multidisciplinary journal that focuses on the intersection of public health and technology, public health informatics, mass media campaigns, surveillance, participatory epidemiology, and innovation in public health practice and research. June 2023 - Journal Impact Factor: 8.5. Q1 journal in “Public, Environmental & Occupational Health” category (Source: Journal Citation Reports™ 2023 from Clarivate™)

JMIR Mental Health

A journal focused on Internet interventions, technologies, and digital innovations for mental health and behavior change. Official journal of the Society for Digital Psychiatry. June 2023 - Journal Impact Factor: 5.2 (Source: Journal Citation Reports™ 2023 from Clarivate™)

JMIR mHealth and uHealth

Focused on health and biomedical applications in mobile and tablet computing, pervasive and ubiquitous computing, wearable computing and domotics. June 2023 - Journal Impact Factor: 5.0. Q1 journal in “Health Care Science & Services” category. (Source: Journal Citation Reports™ 2023 from Clarivate™)

Digital health technologies, apps, and informatics for patient education, medicine and nursing, preventative interventions, and clinical care / home care for elderly populations. June 2023 - Journal Impact Factor: 4.9 (Source: Journal Citation Reports™ 2023 from Clarivate™)

JMIR Serious Games

A multidisciplinary journal on gaming and gamification including simulation and immersive virtual reality for health education/promotion, teaching, medicine, rehabilitation, and social change. June 2023 - Journal Impact Factor: 4.0 (Source: Journal Citation Reports™ 2023 from Clarivate™)

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Prevalence of Health Misinformation on Social Media: Systematic Review

Victor suarez-lledo.

1 Department of Biomedicine, Biotechnology and Public Health, University of Cadiz, Cadiz, Spain

2 Computational Social Science DataLab, University Research Institute on Social Sciences, University of Cadiz, Jerez de la Frontera, Cadiz, Spain

Javier Alvarez-Galvez

Associated data.

Search terms and results from the search query.

Data extraction sheet.

Summary of quality scores.

Summary table with objectives and conclusions about misinformation prevalence in social media.

Although at present there is broad agreement among researchers, health professionals, and policy makers on the need to control and combat health misinformation, the magnitude of this problem is still unknown. Consequently, it is fundamental to discover both the most prevalent health topics and the social media platforms from which these topics are initially framed and subsequently disseminated.

This systematic review aimed to identify the main health misinformation topics and their prevalence on different social media platforms, focusing on methodological quality and the diverse solutions that are being implemented to address this public health concern.

We searched PubMed, MEDLINE, Scopus, and Web of Science for articles published in English before March 2019, with a focus on the study of health misinformation in social media. We defined health misinformation as a health-related claim that is based on anecdotal evidence, false, or misleading owing to the lack of existing scientific knowledge. We included (1) articles that focused on health misinformation in social media, including those in which the authors discussed the consequences or purposes of health misinformation and (2) studies that described empirical findings regarding the measurement of health misinformation on these platforms.

A total of 69 studies were identified as eligible, and they covered a wide range of health topics and social media platforms. The topics were articulated around the following six principal categories: vaccines (32%), drugs or smoking (22%), noncommunicable diseases (19%), pandemics (10%), eating disorders (9%), and medical treatments (7%). Studies were mainly based on the following five methodological approaches: social network analysis (28%), evaluating content (26%), evaluating quality (24%), content/text analysis (16%), and sentiment analysis (6%). Health misinformation was most prevalent in studies related to smoking products and drugs such as opioids and marijuana. Posts with misinformation reached 87% in some studies. Health misinformation about vaccines was also very common (43%), with the human papilloma virus vaccine being the most affected. Health misinformation related to diets or pro–eating disorder arguments were moderate in comparison to the aforementioned topics (36%). Studies focused on diseases (ie, noncommunicable diseases and pandemics) also reported moderate misinformation rates (40%), especially in the case of cancer. Finally, the lowest levels of health misinformation were related to medical treatments (30%).

Conclusions

The prevalence of health misinformation was the highest on Twitter and on issues related to smoking products and drugs. However, misinformation on major public health issues, such as vaccines and diseases, was also high. Our study offers a comprehensive characterization of the dominant health misinformation topics and a comprehensive description of their prevalence on different social media platforms, which can guide future studies and help in the development of evidence-based digital policy action plans.

Introduction

Over the last two decades, internet users have been increasingly using social media to seek and share health information [ 1 ]. These social platforms have gained wider participation among health information consumers from all social groups regardless of gender or age [ 2 ]. Health professionals and organizations are also using this medium to disseminate health-related knowledge on healthy habits and medical information for disease prevention, as it represents an unprecedented opportunity to increase health literacy, self-efficacy, and treatment adherence among populations [ 3 - 9 ]. However, these public tools have also opened the door to unprecedented social and health risks [ 10 , 11 ]. Although these platforms have demonstrated usefulness for health promotion [ 7 , 12 ], recent studies have suggested that false or misleading health information may spread more easily than scientific knowledge through social media [ 13 , 14 ]. Therefore, it is necessary to understand how health misinformation spreads and how it can affect decision-making and health behaviors [ 15 ].

Although the term “health misinformation” is increasingly present in our societies, its definition is becoming increasingly elusive owing to the inherent dynamism of the social media ecosystem and the broad range of health topics [ 16 ]. Using a broad term that can include the wide variety of definitions in scientific literature, we here define health misinformation as a health-related claim that is based on anecdotal evidence, false, or misleading owing to the lack of existing scientific knowledge [ 1 ]. This general definition would consider, on the one hand, information that is false but not created with the intention of causing harm (ie, misinformation) and, on the other, information that is false or based on reality but deliberately created to harm a particular person, social group, institution, or country (ie, disinformation and malinformation).

The fundamental role of health misinformation on social media has been recently highlighted by the COVID-19 pandemic, as well as the need for quality and veracity of health messages in order to manage the present public health crisis and the subsequent infodemic. In fact, at present, the propagation of health misinformation through social media has become a major public health concern [ 17 ]. The lack of control over health information on social media is used as evidence for the current demand to regulate the quality and public availability of online information [ 18 ]. In fact, although today there is broad agreement among health professionals and policy makers on the need to control health misinformation, there is still little evidence about the effects that the dissemination of false or misleading health messages through social media could have on public health in the near future. Although recent studies are exploring innovative ways to effectively combat health misinformation online [ 19 - 22 ], additional research is needed to characterize and capture this complex social phenomenon [ 23 ].

More specifically, four knowledge gaps have been detected from the field of public health [ 1 ]. First, we have to identify the dominant health misinformation trends and specifically assess their prevalence on different social platforms. Second, we need to understand the interactive mechanisms and factors that make it possible to progressively spread health misinformation through social media (eg, vaccination myths, miracle diets, alternative treatments based on anecdotal evidence, and misleading advertisements on health products). Factors, such as the sources of misinformation, structure and dynamics of online communities, idiosyncrasies of social media channels, motivation and profile of people seeking health information, content and framing of health messages, and context in which misinformation is shared, are critical to understanding the dynamics of health misinformation through these platforms. For instance, although the role of social bots in spreading misinformation through social media platforms during political campaigns and election periods is widely recognized, health debates on social media are also affected by social bots [ 24 ]. At present, social bots are used to promote certain products in order to increase company profits, as well as to benefit certain ideological positions or contradict health evidence (eg, in the case of vaccines) [ 25 ]. Third, a key challenge in epidemiology and public health research is to determine not only the effective impact of these tools in the dissemination of health misinformation but also their impact on the development and reproduction of unhealthy or dangerous behaviors. Finally, regarding health interventions, we need to know which strategies are the best in fighting and reducing the negative impact of health misinformation without reducing the inherent communicative potential to propagate health information with these same tools.

In line with the abovementioned gaps, a recent report represents one of the first steps forward in the comparative study of health misinformation on social media [ 16 ]. Through a systematic review of the literature, this study offers a general characterization of the main topics, areas of research, methods, and techniques used for the study of health misinformation. However, despite the commendable effort made to compose a comprehensible image of this highly complex phenomenon, the lack of objective indicators that make it possible to measure the problem of health misinformation is still evident today.

Taking into account this wide set of considerations, this systematic review aimed to specifically address the knowledge gap. In order to guide future studies in this field of knowledge, our objective was to identify and compare the prevalence of health misinformation topics on social media platforms, with specific attention paid to the methodological quality of the studies and the diverse analytical techniques that are being implemented to address this public health concern.

This systematic review was conducted according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines [ 26 ].

Inclusion Criteria

Studies were included if (1) the objectives were to address the study of health misinformation on social media, search systematically for health misinformation, and explicitly discuss the impact, consequences, or purposes of misinformation; (2) the results were based on empirical results and the study used quantitative, qualitative, and computational methods; and (3) the research was specifically focused on social media platforms (eg, Twitter, Facebook, Instagram, Flickr, Sina Weibo, VK, YouTube, Reddit, Myspace, Pinterest, and WhatsApp). For comparability, we included studies written in English that were published after 2000 until March 2019.

Exclusion Criteria

Articles were excluded if they addressed health information quality in general or if they partially mentioned the existence of health misinformation without providing empirical findings. We did not include studies that dealt with content posted on other social media platforms. During the screening process, papers with a lack of methodological quality were also excluded.

Search Strategy

We searched MEDLINE and PREMEDLINE in March 2019 using the PubMed search engine. Based on previous findings [ 16 ], the query searched for MeSH terms and keywords (in the entire body of the manuscript) related to the following three basic analytical dimensions that articulated our research objective: (1) social media, (2) health, and (3) misinformation. The MeSH terms were social media AND health (ie, this term included health behaviors) AND (misinformation OR information seeking behavior OR communication OR health knowledge, attitudes, practice). Based on the results obtained through this initial search, we added some keywords that (having been extracted from the articles that met the inclusion criteria) were specifically focused on the issue of health misinformation on social media. The search using MeSH terms was supplemented with the following keywords: social media (eg, “Twitter” OR “Facebook” OR “Instagram” OR “Flickr” OR “Sina Weibo” OR “YouTube” OR “Pinterest”) AND health AND misinformation (eg, “inaccurate information” OR “poor quality information” OR “misleading information” OR “seeking information” OR “rumor” OR “gossip” OR “hoax” OR “urban legend” OR “myth” OR “fallacy” OR “conspiracy theory”). This initial search retrieved 1693 records. Additionally, this search strategy was adapted for its use in Scopus (3969 records) and Web of Science (1541 records). A full description of the search terms can be found in Multimedia Appendix 1 .

Study Selection

In total, we collected 5018 research articles. After removing duplicates, we screened 3563 articles and retrieved 226 potentially eligible articles. In the next stage, we independently carried out a full-text selection process for inclusion (k=0.89). Discrepancies were shared and resolved by mutual agreement. Finally, a total of 69 articles were included in this systematic review ( Figure 1 ).

Preferred Reporting Items for Systematic Reviews and Meta-Analyses flow chart.

Data Extraction

In the first phase, the data were extracted by VSL and then checked by VSL and JAG. In order to evaluate the quality of the selected studies and given the wide variety of methodologies and approaches found in the articles, we composed an extraction form based on previous work [ 27 - 29 ]. Each extraction form contained 62 items, most of which were closed questions that could be answered using predefined forms (yes/good, no/poor, partially/fair, etc). Following this coding scheme, we extracted the following four different fields of information: (1) descriptive information (27 items), (2) search strategy evaluation (eight items), (3) information evaluation (six items), and (4) the quality and rigor of methodology and reporting (15 items) for either quantitative or qualitative studies ( Multimedia Appendix 1 ). Questions in field 2, which have been used in previous studies [ 27 ], assessed the quality of information provided to demonstrate how well reported, systematic, and comprehensive the search strategy was (S score). The items in field 3 measured how rigorous the evaluation was (E score) for health-related misinformation [ 27 ]. Field 4 contained items designed for the general evaluation of quality in the research process, whether quantitative [ 28 ] or qualitative [ 29 ]. This Q-score approach takes into account general aspects of the research and reporting, such as the study, methodology, and quality of the discussion. For each of the information fields, we calculated the raw score as the sum of each of the items by equating “yes” or “good” as 1 point, “fair” as 0.5 points, and “no” or “poor” as 0 points ( Multimedia Appendix 2 ). The purpose of these questions is to guarantee the quality of the selected studies.

Furthermore, in order to be able to compare the methods used in the selected studies, the studies were classified into several categories. The studies classified as “content/text analysis” used methods related to textual and content analysis, emphasizing the word/topic frequency, linguistic inquiry word count, n-grams, etc. The second category “evaluating content” grouped together studies whose methods were focused on the evaluation of content and information. In general, these studies analyzed different dimensions of the information published on social media. The third category “evaluating quality” included studies that analyzed the quality of the information offered in a global way. This category considered other dimensions in addition to content, such as readability, accuracy, usefulness, and sources of information. The fourth category “sentiment analysis” included studies whose methods were focused on sentiment analysis techniques (ie, methods measuring the reactions and the general tone of the conversation on social media). Finally, the “social network analysis” category included those studies whose methods were based on social network analysis techniques. These studies focused on measuring how misinformation spreads on social media, the relationship between the quality of information and its popularity on these social platforms, the relationship between users and opinions, echochambers effects, and opinion formation.

Of the 226 studies available for full-text review, 157 were excluded for various reasons, including research topics that were not focused on health misinformation (n=133). We also excluded articles whose research was based on websites rather than social media platforms (n=16), studies that did not assess the quality of health information (n=6) or evaluated institutional communication (n=5), nonempirical studies (n=2), and research protocols (n=1). In addition, two papers were excluded because of a lack of quality requirements (Q score <50%). Finally, the protocol of this review was registered at the International Prospective Register of Systematic Reviews (PROSPERO CRD42019136694).

Prevalence of Health Misinformation

Ultimately, 69 studies were identified as eligible, and they covered a wide range of health topics and social media platforms, with the most common data source being Twitter (29/69, 43%), followed by YouTube (25/69, 37%) and Facebook (6/69, 9%). The less common sources were Instagram, MySpace, Pinterest, Tumblr, WhatsApp, and VK or a combination of these. Overall, 90% (61/69) of the studies were published in health science journals, and only 7% (5/69) of the studies were published in communication journals. The vast majority of articles analyzed posts written exclusively in one language (63/69, 91%). Only a small percentage assessed posts in more than one language (6/69, 10%).

Table 1 classifies the studies by topic and social media platform [ 30 - 97 ]. It also includes the prevalence of health misinformation posts. The topics were articulated around the following six principal categories: vaccines (22/69, 32%), drugs or smoking issues (16/69, 22%), noncommunicable diseases (13/69, 19%), pandemics (7/69, 10%), eating disorders (6/69, 9%), and medical treatments (5/69, 7%). The quality assessment results for the S score, E score, and Q score are reported in Multimedia Appendix 3 .

Summary of the prevalence of misinformation by topic and social media platform.

a N/A: not applicable.

b EDs: eating disorders.

c NCDs: noncommunicable diseases.

Figure 2 shows the prevalence of health misinformation grouped by different topics and social media typology. Studies are ordered according to the percentage of health misinformation posts found in the studies selected. These works were also classified according to the type of social media under study. In this way, papers focused on Twitter, Tumblr, or Myspace were categorized as “microblogging.” Additionally, papers focused on YouTube, Pinterest, or Instagram were classified within “media sharing” platforms. Moreover, papers focused on Facebook, VK, or WhatsApp were included within the group of “social network” platforms. While all topics were present on all the different social media platforms, we found some differences in their prevalence. On one hand, vaccines, drugs, and pandemics were more prevalent topics on microblogging platforms (ie, Twitter or MySpace). On the other hand, on media sharing platforms (ie, YouTube, Instagram, or Pinterest) and social network platforms (ie, Facebook, VK, or WhatsApp), noncommunicable diseases and treatments were the most prevalent topics. More specifically, Twitter was the most used source for work on vaccines (10/69), drugs or smoking products (10/69), pandemics (4/69), and eating disorders (3/69). For studies on noncommunicable diseases (9/69) or treatments (5/69), YouTube was the most used social media platform.

Prevalence of health misinformation grouped by different topics and social media type.

Overall, health misinformation was most prevalent in studies related to smoking products, such as hookah and water pipes [ 33 , 59 , 71 ], e-cigarettes, and drugs, such as opioids and marijuana [ 45 , 70 , 97 ]. Health misinformation about vaccines was also very common. However, studies reported different levels of health misinformation depending on the type of vaccine studied, with the human papilloma virus (HPV) vaccine being the most affected [ 67 , 68 ]. Health misinformation related to diets or pro–eating disorder arguments were moderate in comparison to the aforementioned topics [ 35 , 93 ]. Studies focused on diseases (ie, noncommunicable diseases and pandemics) also reported moderate misinformation rates [ 56 , 85 ], especially in the case of cancer [ 76 , 96 ]. Finally, the lowest levels of health misinformation were observed in studies evaluating the presence of health misinformation regarding medical treatments. Although first-aid information on burns or information on dental implants was limited in quantity and quality, the prevalence of misinformation for these topics was low. Surgical treatment misinformation was the least prevalent. This was due to the fact that the content related to surgical treatments mainly came from official accounts, which made the online information complete and reliable.

Regarding the methods used in the different studies, there were some differences between the diverse social media platforms. We classified the studies based on the methods applied into the following five categories: social network analysis (19/69), evaluating content (18/69), evaluating quality (16/69), content/text analysis (12/69), and sentiment analysis (4/69). Figure 3 shows the different methods applied in the studies classified by the type of social media platform and ordered by the percentage of misinformation posts. Among platforms, such as YouTube and Instagram, methods focused on the evaluation of health information quality and content were common, representing 22% (15/69) and 12% (8/69), respectively. On microblogging platforms, such as Twitter and Tumblr, social network analysis was the method most used by 19% (13/69) of the studies. Finally, on social media platforms, such as Facebook, VK, and WhatsApp, studies whose methods were related to social network analysis represented 3% (2/69) of the included studies and those focused on the evaluation of content represented 4% (3/69) of the included studies.

Prevalence of health misinformation grouped by methods and social media type.

Misinformation Topics and Methods

Overall, 32% (22/69) of the studies focused on vaccines or vaccination decision-making–related topics. Additionally, 14% (10/69) of the selected articles focused on social media discussion regarding the potential side effects of vaccination [ 23 , 36 , 48 , 53 , 55 , 60 , 65 , 77 , 87 , 88 ], 12% (8/69) were centered on the debate around the HPV vaccine [ 42 , 49 - 51 , 67 , 68 , 79 , 94 ], and 3% (2/69) were centered on the antivaccine movement [ 39 , 43 ]. According to social media platforms, 9% (6/69) of the studies were focused on the debate and narratives about vaccines in general on Twitter, and 6% (4/69) specifically analyzed the HPV debate on this platform. Papers focused on YouTube also followed a similar trend, and they were centered on the HPV debate and on the public discussion on vaccine side effects and risks for specific population groups (eg, autism in children). Regarding Facebook, all studies were particularly focused on vaccination decision-making.

Most authors studied differences in language use, the effect of a heterogeneous community structure in the propagation of health misinformation, and the role played by fake profiles or bots in the spread of poor quality, doubtful, or ambiguous health content. In line with these concerns, authors pointed out the need to further study the circumstances surrounding those who adopt these arguments [ 49 ], and whether alternative strategies to education could improve the fight against antivaccine content [ 51 ]. Authors also recommended paying close attention to social media as these tools are assumed to play a fundamental role in the propagation of misinformation. For instance, the role played by the echochamber or the heterogeneous community structure on Twitter has been shown to skew the information to which users are exposed in relation to HPV vaccines [ 49 ]. In this sense, it is widely acknowledged that health professionals should pay more attention to antivaccine arguments on social media, so that they can better respond to patients’ concerns [ 36 , 43 , 65 , 77 ]. Furthermore, governmental organizations could also use social media platforms to reach a greater number of people [ 39 , 55 ].

Drugs and Smoking

Several studies (16/69, 22%) covered misuse and misinformation about e-cigarettes, marijuana, opioid consumption, and prescription drug abuse. Studies covering the promotion of e-cigarette use and other forms of smoking, such as hookah (ie, water pipes or narghiles) represented 7% (5/69) of the articles analyzed. The rest (16%, 11/69) were focused on the analysis of drug misinformation.

According to topic, regarding drug and opioid use, studies investigated the dissemination of misinformation through social media platforms [ 32 , 45 , 46 , 70 , 97 ], the consumption of misinformation related to these products, drug abuse, and the sale of online medical products [ 61 , 66 ]. These studies highlighted the risk, especially for young people, caused by the high rate of misinformation related to the dissemination of drug practice and misuse (predominantly marijuana and opioids) [ 45 ]. In addition, social media platforms were identified as a potential source of illegal promotion of the sale of controlled substances directly to consumers [ 66 ]. Most drug-related messages on social media were potentially misleading or false claims that lacked credible evidence to support them [ 32 ]. Other studies pointed to social media as a potential source of information that illegally promotes the sale of controlled prescription drugs directly to consumers [ 66 ]. In the case of cannabinoids, there was often content that described, encouraged, promoted [ 54 ], or even normalized the consumption of illicit substances [ 70 ].

Unlike drug studies, most of the papers analyzed how e-cigarettes and hookah [ 33 , 34 , 59 , 71 , 73 , 78 , 82 , 95 ] are portrayed on social media and/or the role of bots in promoting e-cigarettes. Regarding e-cigarettes, studies pointed out the high prevalence of misinformation denying health damage [ 95 ]. In this sense, it is worth noting the importance of sources of misinformation. While in the case of vaccines, the source of health misinformation was mainly individuals or groups of people with a particular interest (eg, antivaccine movement), social media was found to be frequently contaminated by misinformation from bots (ie, software applications that autonomously run tasks such as spreading positive discourse about e-cigarettes and other tobacco products) [ 78 ]. In fact, these fake accounts may influence the online conversation in favor of e-cigarettes given the scientific appearance of profiles [ 78 ]. Some of the claims found in this study denied the harmfulness of e-cigarettes. In line with these findings, other studies pointed to the high percentage of messages favoring e-cigarettes as an aid to quitting smoking [ 95 ].

We found that 10% (7/69) of the studies used methods focused on evaluating the content of the posts. These studies aimed to explore the misperceptions of drug abuse or alternative forms of tobacco consumption. Along these lines, another study (1/69, 1%) focused on evaluating the quality of content. The authors evaluated the truthfulness of claims about drugs. In particular, we found that 7% (5/69) of the studies used social network analysis techniques. These studies analyzed the popularity of messages based on whether they promoted illegal access to drugs online and the interaction of users with this content. Other studies (3/69, 3%) used content analysis techniques. These studies evaluated the prevalence of misinformation on platforms and geographically, as a kind of “toxicosurveillance” system [ 34 , 46 ].

Noncommunicable Diseases

A relevant proportion (13/69, 19%) of studies assessed noncommunicable diseases, such as cancer, diabetes, and epilepsy. Most of the studies focused on the objective evaluation of information quality on YouTube [ 38 , 56 , 57 , 69 , 72 , 74 , 76 , 80 , 85 ]. Overall, 13% (9/69) of these studies used methods to assess the quality of the information. The authors analyzed the usefulness and accuracy of the information. Moreover, 4% (3/69) of the studies used methods related to content assessment. The main objective of these studies was to analyze which are the most common misinformation topics. Furthermore, 3% (2/69) used social network analysis, and the main objective of the analysis was to study the information dissemination patterns or the social spread of scientifically inaccurate health information.

Some studies evaluated the potential of this platform as a source of information specially for health students or self-directed education among the general public. Unfortunately, the general tone of research findings was that YouTube is not an advisable source for health professionals or health information seekers. Regarding diabetes, the probability of finding misleading videos was high [ 56 ]. Misleading videos promoted cures for diabetes, negated scientific arguments, or provided treatments with no scientific basis. Furthermore, misleading videos related to diabetes were found to be more popular than those with evidence-based health information [ 74 ], which increased the probability of consuming low-quality health content. The same misinformation pattern was detected for other chronic diseases such as hypertension [ 72 ], prostate cancer [ 76 ], and epilepsy [ 80 ].

Pandemics and Communicable Diseases

Results indicated that 10% (7/69) of the studies covered misinformation related to pandemics and communicable diseases such as H1N1 [ 31 , 47 ], Zika [ 40 , 89 ], Ebola [ 58 , 84 ], and diphtheria [ 86 ]. All these studies analyzed how online platforms were used by both health information seekers and health and governmental authorities during the pandemic period.

We found that 14% (10/69) of the studies on this topic evaluated the quality of the information. To achieve this, most of the studies used external instruments such as DISCERN and AAD7 Self-Care Behaviors. Overall, 9% (6/69) of the papers evaluated the content of the information. These studies were focused on analysis of the issues of misinformation. Another 4% (3/69) used social media analysis to observe the propagation of misinformation. Finally, 3% (2/69) used textual analysis as the main method. These studies focused on the study of the prevalence of health misinformation.

These studies identified social media as a public forum for free discussion and indicated that this freedom might lead to rumors on anecdotal evidence and misunderstandings regarding pandemics. Consequently, although social media was described as a forum for sharing health-related knowledge, these tools are also recognized by researchers and health professionals as a source of misinformation that needs to be controlled by health experts [ 83 , 84 ]. Therefore, while social media serves as a place where people commonly share their experiences and concerns, these platforms can be potentially used by health professionals to fight against false beliefs on communicable diseases (eg, as it is happening today during the COVID-19 pandemic). Accordingly, social media platforms have been found to be powerful tools for health promotion among governmental institutions and health-related workers, and new instruments that, for instance, are being used to increase health surveillance and intervention against false beliefs and misinformation [ 31 , 89 ]. In fact, different authors agreed that governmental/health institutions should increase their presence on social media platforms during pandemic crises [ 47 , 58 , 84 , 86 ].

Diet/Eating Disorders

Studies focusing on diet and eating disorders represented 9% (6/69) of the included studies. This set of studies identified pro–eating disorder groups and discourses within social media [ 35 ], and how pro–eating disorder information was shared and spread on these platforms [ 91 ]. Anorexia was the most studied eating disorder along with bulimia. Furthermore, discourses promoting fitness or recovery after an eating disorder were often compared with those issued by pro–eating disorder groups [ 41 , 62 , 92 , 93 ]. In general, the authors agreed on the relevance of pro–eating disorder online groups, the mutual support among members, and the way they reinforce their opinions and health behaviors [ 35 ].

Overall, 4% (3/69) of the studies used social network analysis techniques. The authors focused on analyzing the existing connections between individuals in the pro–eating disorder community and their engagement, or comparing the cohesion of these communities with other communities, such as the fitness community, that promote healthier habits. Moreover, 3% (2/69) of the studies evaluated the quality of the content and particularly focused on informative analysis of the videos, that is, the content was classified as informative when it described the health consequences of anorexia or proana if, on the contrary, anorexia was presented as a fashion or a source of beauty. Furthermore, only one study used content analysis techniques. The authors classified the posts according to the following categories: proana, antiana, and prorecovery. Pro–eating disorder pages tended to identify themselves with body-associated pictures owing to the importance they attributed to motivational aspects of pro–eating disorder communities [ 92 ]. The pro–eating disorder claims contained practices about weight loss, wanting a certain body type or characteristic of a body part, eating disorders, binge eating, and purging [ 62 ]. Pro–eating disorder conversations also had a high content of social support in the form of tips and tricks (eg, “Crunch on some ice chips if you are feeling a hunger craving. This will help you feel as if you are eating something substantial” and “How do you all feel about laxatives?”) [ 92 ].

Regarding eating disorders on social media, paying attention to community structure is important according to authors. Although it is widely acknowledged that communities can be positive by providing social support, such as recovery and well-being, certain groups on social media may also reaffirm the pro–eating disorder identity [ 35 ]. In fact, polarized pro/anti–eating disorder communities can become closed echochambers where community members are selectively exposed to the content they are looking for and therefore only hear the arguments they want to hear. In this case, the echochamber effect might explain why information campaigns are limited in scope and often encourage polarization of opinion, and can even reinforce existing divides in pro–eating disorder opinions [ 88 ].

Treatments and Medical Interventions

Finally, we found that 7% (5/69) of the studies assessed the quality of health information regarding different medical treatments or therapies recommended through social media [ 63 , 81 ]. According to method, 6% (4/69) of the studies evaluated the quality of information related to the proposed treatments and therapies. In this sense, the fundamental goal of these studies was aimed at assessing the quality and accuracy of the information.

As in the case of noncommunicable diseases, professionals scanned social networks, especially YouTube, and evaluated the quality of online health content as an adequate instrument for self-care or for health student training. There were specific cases where information was particularly limited in quality and quantity, such as dental implants and first-aid information on burns [ 30 , 44 ]. However, most surgical treatments or tools were found to have a sufficient level of quality information on YouTube [ 52 , 81 ]. In relation to this topic, it is worth pointing out the source of the misinformation. In this particular case, most of the posts were published by private companies. They used the platforms to promote their medical products. Therefore, the amount of misinformation was considerably low compared with other topics, such as eating disorders and vaccines, that are closely linked to the general public. In general, the videos were accurate, were well presented, and framed treatments in a useful way for both health workers and health information seekers.

A full description of the objectives and main conclusions of the reviewed articles is presented in Multimedia Appendix 4 .

Main Findings

This work represents, to our knowledge, the first effort aimed at finding objective and comparable measures to quantify the extent of health misinformation in the social media ecosystem. Our study offers an initial characterization of dominant health misinformation topics and specifically assesses their prevalence on different social platforms. Therefore, our systematic review provides new insights on the following unanswered question that has been recurrently highlighted in studies of health misinformation on social media: How prevalent is health misinformation for different topics on different social platform types (ie, microblogging, media sharing, and social networks)?

We found that health misinformation on social media is generally linked to the following six topical domains: (1) vaccines, (2) diets and eating disorders, (3) drugs and new tobacco products, (4) pandemics and communicable diseases, (5) noncommunicable diseases, and (6) medical treatments and health interventions.

With regard to vaccines, we found some interesting results throughout the different studies. Although antivaccine ideas have been traditionally linked to emotional discourse against the rationality of the scientific and expert community, we curiously observed that in certain online discussions, antivaccine groups tend to incorporate scientific language in their own discourse with logically structured statements and/or with less usage of emotional expressions [ 53 ]. Thus, the assimilation of the scientific presentation and its combination with anecdotal evidence can rapidly spread along these platforms through a progressive increment of visits and “likes” that can make antivaccine arguments particularly convincing for health information seekers [ 53 , 55 ]. Furthermore, we found that the complex and heterogeneous community structure of these online groups must be taken into account. For instance, those more exposed to antivaccine information tend to spread more negative concerns about vaccines (ie, misinformation or opinions related to vaccine hesitancy) than users exposed to positive or neutral opinions [ 49 ]. Therefore, negative/positive opinions are reinforced through the network structure of particular social media platforms. Moreover, fake profiles tend to amplify the debate and discussion, thereby undermining the possible public consensus on the effectiveness and safety of vaccines, especially in the case of HPV; measles, mumps, and rubella (MMR); and influenza [ 23 ].

As observed in our review, health topics were omnipresent over all social media platforms included in our study; however, the health misinformation prevalence for each topic varied depending on platform characteristics. Therefore, the potential effect on population health was ambivalent, that is, we found both positive and negative effects depending on the topic and on the group of health information seekers. For instance, content related to eating disorders was frequently hidden or not so evident to the general public, since pro–eating disorder communities use their own codes to reach specific audiences (eg, younger groups) [ 98 ]. To provide a simple example, it is worth mentioning the usage of nicknames, such as proana for proanorexia and promia for probulimia, as a way to reach people with these health conditions and make it easier for people to talk openly about their eating disorders. More positively, these tools have been useful in prevention campaigns during health crises. For example, during the H1N1, Ebola, and Zika pandemics, and, even more recently, with the ongoing COVID-19 pandemic, platforms, such as Twitter, have been valuable instruments for spreading evidence-based health knowledge, expert recommendations, and educative content aimed at avoiding the propagation of rumors, risk behaviors, and diseases [ 31 , 89 ].

Throughout our review, we found different types of misinformation claims depending on the topic. Concerning vaccines, misinformation was often framed with a scientific appearance against scientific evidence [ 53 ]. Drug-related misinformation promoted the consumption and abuse of these substances [ 66 ]. However, these statements lacked scientific evidence to support them [ 32 ]. As with vaccines, false accounts that influenced the online conversation did so with a scientific appearance in favor of e-cigarettes [ 82 ]. In this sense, most accounts tended to promote the use and abuse of these items. With beauty as the final goal, misinformation about eating disorders promoted changes in the eating habits of social media users [ 91 ]. Furthermore, we found that social media facilitated the development of pro–eating disorder online communities [ 35 ]. In general, the results indicated that this type of content promoted unhealthy practices while normalizing eating disorders. In contrast, epidemic/pandemic-related misinformation was not directly malicious. Misinformation on this topic involved rumors, misunderstandings, and doubts arising from a lack of scientific knowledge [ 31 ]. The statements were within the framework of the health emergency arising from the pandemic. In line with these findings, we noted findings related to noncommunicable diseases. Messages that focused on this topic promoted cures for chronic diseases or for conditions with no cure through fallacies or urban legends [ 85 ].

In this study, we focused on analysis of the results obtained and the conclusions of the authors. Some of our findings are in line with those obtained in recent works [ 16 ]. The reviewed studies indicate, on one hand, the difficulty in characterizing and evaluating the quality of health information on social media [ 1 ] and, on the other, the conceptual fuzziness that can result from the convergence of multiple disciplines trying to apprehend the multidisciplinary and complex phenomenon of health misinformation on social media. This research field is being studied by health and social scientists [ 70 , 73 ], as well as by researchers from the fields of computer science, mathematics, sociophysics, etc [ 99 , 100 ]. Therefore, we must understand that the inherent multidisciplinary and methodological diversity of studies and the highly dynamic world of social media are a perfect match for making it more difficult to identify comprehensive and transversal solutions to the problem of health misinformation. In fact, as we have found, misinformation on vaccines, drugs, and new smoking products is more prevalent on media-sharing platforms (eg, YouTube) and microblogging applications (eg, Twitter), while misinformation on noncommunicable diseases is particularly prevalent on media sharing platforms where users can widely describe disease symptoms, medical treatments, and therapies [ 76 , 85 ]. Platforms, such as YouTube, owing to their characteristics, allow more space for users to share this type of information, while the natural dynamism of Twitter makes it an ideal medium for discussion among online communities with different political or ideological orientations (eg, pro/antivaccination communities).

Finally, we should mention that the current results are limited to the availability and quality of social media data. Although the digitalization of social life offers researchers an unprecedented amount of health and social information that can be used to understand human behaviors and health outcomes, accessing this online data is becoming increasingly difficult, and some measures have to be taken to mitigate bias [ 40 , 43 , 67 , 79 ]. Over the last few years, new concerns around privacy have emerged and led governments to tighten regulations around data access and storage [ 101 , 102 ]. Consequently, in response to these new directives, as well as scandals involving data sharing and data breaches such as the Cambridge Analytica case, social media companies are developing new controls and barriers to data in their platforms. This is why free access to application programming interfaces (APIs) is becoming increasingly difficult and the range of social data accessible via APIs is gradually decreasing. These difficulties in accessing data are also determining which platforms are most frequently used by researchers, which are not used, and which will be used in the near future.

Limitations and Strengths

The present study has some limitations. First, the conceptual definition of health misinformation is one limitation. In any case, taking into account that we were facing a new field of study, we considered a broad definition in order to be more inclusive and operative in the selection of studies. Therefore, we included as many papers as possible for the review in order to perform an analysis of the largest number of possible topics. Second, from a methodological perspective, our findings are limited to research published in English language journals and do not cover all the social media platforms that exist. Besides, we discovered some technical limitations when conducting this systematic review. Owing to the newness of this research topic, our study revealed difficulties in comparing different research studies characterized by specific theoretical approaches, working definitions, methodologies, data collection processes, and analytical techniques. Some studies selected involved observational designs (using survey methods and textual analysis), whereas others were based on the application of automatic or semiautomatic computational procedures with the aim of classifying and analyzing health misinformation on social media. Finally, taking into account the particular features of each type of social media (ie, microblogging service, video sharing service, or social network) and the progressive barriers in accessing social media data, we need to consider the information and selection bias when studying health misinformation on these platforms. According to these biases, we should ponder which users are behind these tools and how we can extrapolate specific findings (ie, applied to certain groups and social media platforms) to a broader social context.

Despite the limitations described above, it is necessary to mention the strengths of our work. First, we believe that this study represents one of the first steps in advancing research involving health misinformation on social media. Unlike previous work, we offer some measures that can serve as guidance and a comparative baseline for subsequent studies. In addition, our study highlights the need to redirect future research toward social media platforms, which, perhaps due to the difficulties of automatic data collection, are currently being neglected by researchers. Our study also highlights the need for both researchers and health professionals to explore the possibility of using these digital tools for health promotion and the need for them to progressively colonize the social media ecosystem with the ultimate goal of combating the waves of health misinformation that recurrently flood our societies.

Health misinformation was most common on Twitter and on issues related to smoking products and drugs. Although we should be aware of the difficulties inherent in the dynamic magnitude of online opinion flows, our systematic review offers a comprehensive comparative framework that identifies subsequent action areas in the study of health misinformation on social media. Despite the abovementioned limitations, our research presents some advances when compared with previous studies. Our study provides (1) an overview of the prevalence of health misinformation identified on different social media platforms; (2) a methodological characterization of studies focused on health misinformation; and (3) a comprehensive description of the current research lines and knowledge gaps in this research field.

According to the studies reviewed, the greatest challenge lies in the difficulty of characterizing and evaluating the quality of the information on social media. Knowing the prevalence of health misinformation and the methods used for its study, as well as the present knowledge gaps in this field will help us to guide future studies and, specifically, to develop evidence-based digital policy action plans aimed at combating this public health problem through different social media platforms.

Acknowledgments

We would like to acknowledge the support of the University Research Institute on Social Sciences (INDESS, University of Cadiz) and the Ramon & Cajal Program. JAG was subsidized by the Ramon & Cajal Program operated by the Ministry of Economy and Business (RYC-2016-19353) and the European Social Fund.

Abbreviations

Multimedia appendix 1, multimedia appendix 2, multimedia appendix 3, multimedia appendix 4.

Conflicts of Interest: None declared.

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• 12 May 2024

Is the Internet bad for you? Huge study reveals surprise effect on well-being

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A global, 16-year study 1 of 2.4 million people has found that Internet use might boost measures of well-being, such as life satisfaction and sense of purpose — challenging the commonly held idea that Internet use has negative effects on people’s welfare.

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“It’s an important piece of the puzzle on digital-media use and mental health,” says psychologist Markus Appel at the University of Würzburg in Germany. “If social media and Internet and mobile-phone use is really such a devastating force in our society, we should see it on this bird’s-eye view [study] — but we don’t.” Such concerns are typically related to behaviours linked to social-media use, such as cyberbullying, social-media addiction and body-image issues. But the best studies have so far shown small negative effects, if any 2 , 3 , of Internet use on well-being, says Appel.

The authors of the latest study, published on 13 May in Technology, Mind and Behaviour , sought to capture a more global picture of the Internet’s effects than did previous research. “While the Internet is global, the study of it is not,” said Andrew Przybylski, a researcher at the University of Oxford, UK, who studies how technology affects well-being, in a press briefing on 9 May. “More than 90% of data sets come from a handful of English-speaking countries” that are mostly in the global north, he said. Previous studies have also focused on young people, he added.

To address this research gap, Pryzbylski and his colleagues analysed data on how Internet access was related to eight measures of well-being from the Gallup World Poll , conducted by analytics company Gallup, based in Washington DC. The data were collected annually from 2006 to 2021 from 1,000 people, aged 15 and above, in 168 countries, through phone or in-person interviews. The researchers controlled for factors that might affect Internet use and welfare, including income level, employment status, education level and health problems.

Like a walk in nature

The team found that, on average, people who had access to the Internet scored 8% higher on measures of life satisfaction, positive experiences and contentment with their social life, compared with people who lacked web access. Online activities can help people to learn new things and make friends, and this could contribute to the beneficial effects, suggests Appel.

The positive effect is similar to the well-being benefit associated with taking a walk in nature, says Przybylski.

However, women aged 15–24 who reported having used the Internet in the past week were, on average, less happy with the place they live, compared with people who didn’t use the web. This could be because people who do not feel welcome in their community spend more time online, said Przybylski. Further studies are needed to determine whether links between Internet use and well-being are causal or merely associations, he added.

The study comes at a time of discussion around the regulation of Internet and social-media use , especially among young people. “The study cannot contribute to the recent debate on whether or not social-media use is harmful, or whether or not smartphones should be banned at schools,” because the study was not designed to answer these questions, says Tobias Dienlin, who studies how social media affects well-being at the University of Vienna. “Different channels and uses of the Internet have vastly different effects on well-being outcomes,” he says.

doi: https://doi.org/10.1038/d41586-024-01410-z

Vuorre, M. & Przybylski, A. K. Technol. Mind Behav . https://doi.org/10.1037/tmb0000127 (2024).

Article   Google Scholar  

Heffer, T. et al. Clin. Psychol. Sci. 7 , 462–470 (2018).

Coyne, S. M., Rogers, A. A., Zurcher, J. D., Stockdale, L. & Booth, M. Comput. Hum. Behav . 104 , 106160 (2020).

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Internet use statistically associated with higher wellbeing, finds new global Oxford study

Links between internet adoption and wellbeing are likely to be positive, despite popular concerns to the contrary, according to a major new international study from researchers at the Oxford Internet Institute, part of the University of Oxford.

The study encompassed more than two million participants psychological wellbeing from 2006-2021 across 168 countries, in relation to internet use and psychological well-being across 33,792 different statistical models and subsets of data, 84.9% of associations between internet connectivity and wellbeing were positive and statistically significant. 

The study analysed data from two million individuals aged 15 to 99 in 168 countries, including Latin America, Asia, and Africa and found internet access and use was consistently associated with positive wellbeing.   

Assistant Professor Matti Vuorre, Tilburg University and Research Associate, Oxford Internet Institute and Professor Andrew Przybylski, Oxford Internet Institute carried out the study to assess how technology relates to wellbeing in parts of the world that are rarely studied.

Professor Przybylski said: 'Whilst internet technologies and platforms and their potential psychological consequences remain debated, research to date has been inconclusive and of limited geographic and demographic scope. The overwhelming majority of studies have focused on the Global North and younger people thereby ignoring the fact that the penetration of the internet has been, and continues to be, a global phenomenon'. 

'We set out to address this gap by analysing how internet access, mobile internet access and active internet use might predict psychological wellbeing on a global level across the life stages. To our knowledge, no other research has directly grappled with these issues and addressed the worldwide scope of the debate.' 

The researchers studied eight indicators of well-being: life satisfaction, daily negative and positive experiences, two indices of social well-being, physical wellbeing, community wellbeing and experiences of purpose.   

Commenting on the findings, Professor Vuorre said, “We were surprised to find a positive correlation between well-being and internet use across the majority of the thousands of models we used for our analysis.”

Whilst the associations between internet access and use for the average country was very consistently positive, the researchers did find some variation by gender and wellbeing indicators: The researchers found that 4.9% of associations linking internet use and community well-being were negative, with most of those observed among young women aged 15-24yrs.

Whilst not identified by the researchers as a causal relation, the paper notes that this specific finding is consistent with previous reports of increased cyberbullying and more negative associations between social media use and depressive symptoms among young women. 

Adds Przybylski, 'Overall we found that average associations were consistent across internet adoption predictors and wellbeing outcomes, with those who had access to or actively used the internet reporting meaningfully greater wellbeing than those who did not'.

'We hope our findings bring some greater context to the screentime debate however further work is still needed in this important area.  We urge platform providers to share their detailed data on user behaviour with social scientists working in this field for transparent and independent scientific enquiry, to enable a more comprehensive understanding of internet technologies in our daily lives.' 

In the study, the researchers examined data from the Gallup World Poll, from 2,414,294 individuals from 168 countries, from 2006-2021.  The poll assessed well-being with face-to-face and phone surveys by local interviewers in the respondents’ native languages.  The researchers applied statistical modelling techniques to the data using wellbeing indicators to test the association between internet adoption and wellbeing outcomes. 

Watch the  American Psychological Association (APA) video  highlighting the key findings from the research.

Download the paper ‘ A multiverse analysis of the associations between internet use and well-being ’ published in the journal Technology, Mind and Behaviour, American Psychological Association.

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Teens and Video Games Today

85% of u.s. teens say they play video games, and about four-in-ten do so daily. teens see both positive and negative sides of video games – from problem-solving and making friends to harassment and sleep loss, table of contents.

• Who plays video games?
• How often do teens play video games?
• What devices do teens play video games on?
• Social media use among gamers
• Teen views on how much they play video games and efforts to cut back
• Are teens social with others through video games?
• Do teens think video games positively or negatively impact their lives?
• Why do teens play video games?
• Bullying and violence in video games
• Appendix A: Detailed charts
• Acknowledgments
• Methodology

Pew Research Center conducted this analysis to better understand teens’ use of and experiences with video games.

The Center conducted an online survey of 1,453 U.S. teens from Sept. 26 to Oct. 23, 2023, through Ipsos. Ipsos recruited the teens via their parents, who were part of its KnowledgePanel . The KnowledgePanel is a probability-based web panel recruited primarily through national, random sampling of residential addresses. The survey was weighted to be representative of U.S. teens ages 13 to 17 who live with their parents by age, gender, race and ethnicity, household income, and other categories.

This research was reviewed and approved by an external institutional review board (IRB), Advarra, an independent committee of experts specializing in helping to protect the rights of research participants.

Here are the questions used for this analysis , along with responses, and  its methodology .

There are long-standing debates about the impact of video games on youth. Some credit them for helping young people form friendships and teaching them about teamwork and problem-solving . Others say video games expose teenagers to violent content, negatively impact their sleep and can even lead to addiction.

With this in mind, Pew Research Center surveyed 1,423 U.S. teens ages 13 to 17 about their own video game habits – from how often they play to the friends they’ve made and whether it gets in the way of them doing well in school or getting a good night’s sleep. 1

Key findings from the survey

• Video games as a part of daily teen life: 85% of U.S. teens report playing video games, and 41% say they play them at least once a day. Four-in-ten identify as a gamer.
• Gaming as a social experience: 72% of teens who play video games say that a reason why they play them is to spend time with others. And some have even made a friend online from playing them – 47% of teen video game players say they’ve done this.
• Helpful with problem-solving, less so for sleep: Over half of teens who play video games say it has helped their problem-solving skills, but 41% also say it has hurt their sleep.
• Bullying is a problem: 80% of all teens think harassment over video games is a problem for people their age. And 41% of those who play them say they’ve been called an offensive name when playing.
• Boys’ and girls’ experiences differ: Most teen boys and girls play video games, but larger shares of boys identify as gamers (62% vs. 17%) and play every day (61% vs. 22%). Boys who play them are also more likely to experience positive things from it, like making friends, and more troubling things like harassment.

Jump to read about: Who plays video games | Socializing over video games | Views about video games’ impact | Harassment and violence in video games      

Playing video games is widespread among teens. The vast majority of U.S. teens (85%) say they play them. Just 15% say they never do, according to the survey conducted Sept. 26-Oct. 23, 2023.

In addition to asking whether teens play video games, we also wanted to learn whether they consider themselves gamers. Overall, four-in-ten U.S. teens think of themselves as gamers. Just under half of teens (45%) play video games but do not think of themselves as gamers.

Nearly all boys (97%) say they play video games, compared with about three-quarters of teen girls. There is a substantial gap by gender in whether teens identify as gamers: 62% of teen boys do, compared with 17% of girls. 2

By gender and age

Younger teen girls are more likely than older girls to say they play video games: 81% of girls ages 13 to 14 compared with 67% of those ages 15 to 17. But among boys, nearly all play video games regardless of age. 

Similar shares of teens play video games across different racial and ethnic groups and among those who live in households with different annual incomes. Go to Appendix A for more detail on which teens play video games and which teens identify as gamers.

We also asked teens how often they play video games. About four-in-ten U.S. teens say they play video games daily, including 23% who do so several times a day.

Another 22% say they play several times a week, while 21% play them about once a week or less.

Teen boys are far more likely than girls to say they play video games daily (61% vs. 22%). They are also much more likely to say they play them several times a day (36% vs. 11%).

By whether someone identifies as a gamer

About seven-in-ten teens who identify as gamers (71%) say they play video games daily. This drops to 30% among those who play them but aren’t gamers.

By household income

Roughly half of teens living in households with an annual income of less than $30,000 (53%) say they play video games at least daily. This is higher than those in households with an annual income of $30,000 to $74,999 (42%) and $75,000 or more (39%).

Go to Appendix A to see more details about who plays video games and identifies as a gamer by gender, age, race and ethnicity, and household income.

Most teens play video games on a gaming console or a smartphone. When asked about five devices, most teens report playing video games on a gaming console (73%), such as PlayStation, Switch or Xbox. And 70% do so on a smartphone. Fewer – though still sizable shares – play them on each of the following:

• 49% say they play them on a desktop or laptop computer
• 33% do so on a tablet  
• 24% play them on a virtual reality (VR) headset such as Oculus, Meta Quest or PlayStation VR

Many teens play video games on multiple devices. About a quarter of teens (27%) do so on at least four of the five devices asked about, and about half (49%) play on two or three of them. Just 8% play video games on one device.

Teen boys are more likely than girls to play video games on four of the five devices asked about – all expect tablets. For instance, roughly nine-in-ten teen boys say they ever play video games on a gaming console, compared with 57% of girls. Equal shares of teen boys and girls play them on tablets.  

Teens who consider themselves gamers are more likely than those who play video games but aren’t gamers to play on a gaming console (95% vs. 78%), desktop or laptop computer (72% vs. 45%) or a virtual reality (VR) headset (39% vs. 19%). Similar shares of both groups play them on smartphones and tablets.

One way that teens engage with others about video games is through online platforms. And our survey findings show that teen gamers stand out for their use of two online platforms that are known for their gaming communities – Discord and Twitch :

• 44% of teen gamers say they use Discord, far higher than video game players who don’t identify as gamers or those who use the platform but do not play video games at all. About three-in-ten teens overall (28%) use Discord.
• 30% of teens gamers say they use Twitch. About one-in-ten other teens or fewer say the same; 17% of teens overall use the platform.

Previous Center research shows that U.S. teens use online platforms at high rates .

Teens largely say they spend the right amount of time playing video games. When asked about how much time they spend playing them, the largest share of teens (58%) say they spend the right amount of time. Far fewer feel they spend too much (14%) or too little (13%) time playing them.

Teen boys are more likely than girls to say they spend too much time playing video games (22% vs. 6%).

By race and ethnicity

Black (17%) and Hispanic (18%) teens are about twice as likely than White teens (8%) to say they spend too little time playing video games. 3

A quarter of teens who consider themselves gamers say they spend too much time playing video games, compared with 9% of those who play video games but don’t identify as gamers. Teen gamers are also less likely to think they spend too little time playing them (19% vs. 10%).

Fewer than half of teens have reduced how much they play video games. About four-in-ten (38%) say they have ever chosen to cut back on the amount of time they spend playing them. A majority (61%) report that they have not cut back at all.

This share is on par with findings about whether teenagers have cut back with their screen time – on social media or their smartphone.

Although boys are more likely to say they play video games too much, boys and girls are on par for whether they have ever cut back. About four-in-ten teen boys (39%) and girls (38%) say that they have ever cut back.

And gamers are as likely to say they have cut back as those who play video games but don’t identify as gamers (39% and 41%).

A main goal of our survey was to ask teens about their own experiences playing video games. For this section of the report, we focus on teens who say they play video games.

Socializing with others is a key part of the video game experience. Most teens who play video games do so with others, and some have developed friendships through them.

About nine-in-ten teen video game players (89%) say they play them with other people, in person or online. Far fewer (11%) play them only on their own.

Additionally, about half (47%) report that they have ever made a friend online because of a video game they both play. This equals 40% of all U.S. teens who have made a friend online because of a video game.

These experiences vary by:  

• Gender: Most teen boy and girl video game players play them with others, though it’s more common among boys (94% vs. 82%). Boys who play video games are much more likely to say they have made a friend online because of a video game (56% vs. 35%).
• Race and ethnicity: Black (55%) and Hispanic (53%) teen video game players are more likely than White teen video game players (43%) to say they have made a friend online because of them.
• Whether someone identifies as a gamer: Nearly all teen gamers report playing video games with others (98%). Fewer – though still most – of those who play video games but aren’t gamers (81%) also play them with others. And about seven-in-ten (68%) say they have made a friend online because of a video game, compared with 29% of those who play them but don’t identify as gamers.

Teens who play video games are particularly likely to say video games help their problem-solving skills. More than half of teens who play video games (56%) say this.

Additionally, more think that video games help, rather than hurt, three other parts of their lives that the survey asked about. Among teens who play video games:

• Roughly half (47%) say it has helped their friendships
• 41% say it has helped how they work with others
• 32% say it has helped their mental health

No more than 7% say playing video games has hurt any of these.

More teens who play video games say it hurts, rather than helps, their sleep. Among these teens, 41% say it has hurt how much sleep they get, while just 5% say it helps. And small shares say playing video games has impacted how well they do in school in either a positive or a negative way.

Still, many teens who play video games think playing them doesn’t have much an impact in any of these areas. For instance, at least six-in-ten teens who play video games say it has neither a positive nor a negative impact on their mental health (60%) or their school performance (72%). Fewer (41%) say this of their problem-solving skills.

Teen boys who play video games are more likely than girls to think playing them has helped their problem-solving skills, friendships and ability to work with others. For instance, 55% of teen boys who play video games say this has helped their friendships, compared with 35% of teen girls.

As for ways that it may hurt their lives, boys who play them are more likely than girls to say that it has hurt the amount of sleep they get (45% vs. 37%) and how well they do in school (21% vs. 11%). 

Teens who consider themselves gamers are more likely than those who aren’t gamers but play video games to say video games have helped their friendships (60% vs. 35%), ability to work with others (52% vs. 32%), problem-solving skills (66% vs. 47%) and mental health (41% vs. 24%).

Gamers, though, are somewhat more likely to say playing them hurt their sleep (48% vs. 36%) and how well they do in school (20% vs. 14%).

By whether teens play too much, too little or the right amount

Teens who report playing video games too much stand out for thinking video games have hurt their sleep and school performance. Two-thirds of these teens say it has hurt the amount of sleep they get, and 39% say it hurt their schoolwork. Far fewer of those who say they play the right amount (38%) or too little (32%) say it has hurt their sleep, or say it hurt their schoolwork (12% and 16%).

Teens who play video games say they largely do so to be entertained. And many also play them to be social with and interact with others. Teens who play video games were asked about four reasons why they play video games. Among those who play video games:

• Nearly all say fun or entertainment is a major or minor reason why they play video games – with a large majority (87%) saying it’s a major reason.
• Roughly three-quarters say spending time with others is a reason, and two-thirds say this of competing with others. Roughly three-in-ten say each is a major reason.
• Fewer – 50% – see learning something as a reason, with just 13% saying it’s a major reason.

While entertainment is by far the most common reason given by teens who play video games, differences emerge across groups in why they play video games.

Teens who identify as gamers are particularly likely to say each is major reason, especially when it comes to competing against others. About four-in-ten gamers (43%) say this is a major reason, compared with 13% of those who play video games but aren’t gamers.

Teen boys who play video games are more likely than girls to say competing (36% vs. 15%), spending time with others (36% vs. 27%) and entertainment (90% vs. 83%) are major reasons they play video games.

Black and Hispanic teens who play video games are more likely than White teens to say that learning new things and competing against others are major reasons they play them. For instance, 29% of Black teen video game players say learning something new is a major reason, higher than 17% of Hispanic teen video game players. Both are higher than the 7% of White teen video game players who say the same.

Teens who play video games and live in lower-income households are especially likely to say competing against others and learning new things are major reasons. For instance, four-in-ten teen video game players who live in households with an annual income of less than $30,000 say competing against others is a major reason they play. This is higher than among those in households with annual incomes of $30,000 to $74,999 (29%) and $75,000 or more (23%).

Cyberbullying can happen in many online environments, but many teens encounter this in the video game world.

Our survey finds that name-calling is a relatively common feature of video game life – especially for boys. Roughly four-in-ten teen video game players (43%) say they have been harassed or bullied while playing a video game in one of three ways: 

• 41% have been called an offensive name
• 12% have been physically threatened
• 8% have been sent unwanted sexually explicit things

Teen boys are particularly likely to say they have been called an offensive name. About half of teen boys who play video games (48%) say this has happened while playing them, compared with about a third of girls (32%). And they are somewhat more likely than girls to have been physically threatened (15% vs. 9%).

Teen gamers are more likely than those who play video games but aren’t gamers to say they been called and offensive name (53% vs. 30%), been physically threatened (17% vs. 8%) and sent unwanted sexually explicit things (10% vs. 6%).

Teens – regardless of whether they’ve had these experiences – think bullying is a problem in gaming. Eight-in-ten U.S. teens say that when it comes to video games, harassment and bullying is a problem for people their age. This includes 29% who say it is a major problem.

It’s common for teens to think harassment while playing video games is a problem, but girls are somewhat more likely than boys to say it’s a major problem (33% vs. 25%).

There have also been decades-long debates about how violent video games can influence youth behavior , if at all – such as by encouraging or desensitizing them to violence. We wanted to get a sense of how commonly violence shows up in the video games teens are playing.

Just over half of teens who play video games (56%) say at least some of the games they play contain violence. This includes 16% who say it’s in all or most of the games they play.

Teen boys who play video games are far more likely than girls to say that at least some of the games they play contain violence (69% vs. 37%).

About three-quarters of teen gamers (73%) say that at least some of the games they play contain violence, compared with 40% among video game players who aren’t gamers.   

• Throughout this report, “teens” refers to those ages 13 to 17. ↩
• Previous Center research of U.S. adults shows that men are more likely than women to identify as gamers – especially the youngest adults. ↩
• There were not enough Asian American respondents in the sample to be broken out into a separate analysis. As always, their responses are incorporated into the general population figures throughout the report. ↩

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ABOUT PEW RESEARCH CENTER  Pew Research Center is a nonpartisan fact tank that informs the public about the issues, attitudes and trends shaping the world. It conducts public opinion polling, demographic research, media content analysis and other empirical social science research. Pew Research Center does not take policy positions. It is a subsidiary of  The Pew Charitable Trusts .

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This paper is in the following e-collection/theme issue:

Published on 16.5.2024 in Vol 26 (2024)

Implementation of Inpatient Electronic Consultations During the COVID-19 Crisis and Its Sustainability Beyond the Pandemic: Quality Improvement Study

Authors of this article:

Original Paper

• Anna S Aledia, BS   ; 
• Amish A Dangodara, MD   ; 
• Aanya A Amin   ; 
• Alpesh N Amin, MD, MBA  

Department of Medicine & Hospital Medicine, University of California, Irvine, Orange, CA, United States

Corresponding Author:

Alpesh N Amin, MD, MBA

Department of Medicine & Hospital Medicine

University of California, Irvine

333 City Boulevard West

Orange, CA, 92868

United States

Phone: 1 714 456 3785

Fax:1 714 456 3871

Email: [email protected]

Background: Limiting in-person contact was a key strategy for controlling the spread of the highly infectious novel coronavirus (COVID-19). To protect patients and staff from the risk of infection while providing continued access to necessary health care services, we implemented a new electronic consultation (e-consult) service that allowed referring providers to receive subspecialty consultations for patients who are hospitalized and do not require in-person evaluation by the specialist.

Objective: We aimed to assess the impact of implementing e-consults in the inpatient setting to reduce avoidable face-to-face referrals during the COVID-19 pandemic.

Methods: This quality improvement study evaluated all inpatient e-consults ordered from July 2020 to December 2022 at the University of California Irvine Medical Center. The impact of e-consults was assessed by evaluating use (eg, number of e-consults ordered), e-consult response times, and outcome of the e-consult requests (eg, resolved electronically or converted to the in-person evaluation of patient).

Results: There were 1543 inpatient e-consults ordered across 11 participating specialties. A total of 53.5% (n=826) of requests were addressed electronically, without the need for a formal in-person evaluation of the patient. The median time between ordering an e-consult and a specialist documenting recommendations in an e-consult note was 3.7 (IQR 1.3-8.2) hours across all specialties, contrasted with 7.3 (IQR 3.6-22.0) hours when converted to an in-person consult ( P <.001). The monthly volume of e-consult requests increased, coinciding with surges of COVID-19 cases in California. After the peaks of the COVID-19 crisis subsided, the use of inpatient e-consults persisted at a rate well above the precrisis levels.

Conclusions: An inpatient e-consult service was successfully implemented, resulting in fewer unnecessary face-to-face consultations and significant reductions in the response times for consults requested on patients who are hospitalized and do not require an in-person evaluation. Thus, e-consults provided timely, efficient delivery of inpatient consultation services for appropriate problems while minimizing the risk of direct transmission of the COVID-19 virus between health care providers and patients. The service also demonstrated its value as a tool for effective inpatient care coordination beyond the peaks of the pandemic leading to the sustainability of service and value.

Introduction

When the novel coronavirus (COVID-19), the disease caused by SARS-CoV-2, began to quickly spread around the world, the high transmissibility of this disease urged health care systems to explore alternatives to face-to-face interactions that would reduce the risk of exposure for both the patient and the provider. Electronic consultations (e-consults) are asynchronous, non–face-to-face, provider-to-provider exchanges that have been shown to improve patient access to specialty care for appropriate referral problems that do not require an in-person evaluation of the patient by the specialist [ 1 - 3 ]. The rapid rise in COVID-19 cases induced a demand for the adoption of e-consult services and triggered an increase in the use of e-consults [ 4 ]. Although its use in the outpatient setting is well established [ 5 , 6 ], e-consults in the inpatient arena are relatively new.

As the only academic health system in the sixth largest county in the United States, University of California Irvine (UCI) Health has been a leader in the advancement of telehealth technologies that expand access to care and improve health care efficiency and resource use [ 7 ]. UCI already has a well-developed e-consults program in the ambulatory setting [ 8 ], and to complement this existing service, we expanded e-consults to patients who were hospitalized to further help reduce in-person contacts between consulting providers and patients, thereby minimizing disease transmission and conserving scarce personal protective equipment (PPE) during the COVID-19 crisis. Other health systems have implemented similar e-consult services for inpatients, but provider use of e-consults was temporary in response to the pandemic, favoring in-person consultative care instead [ 9 ]; inpatient e-consults were offered by only a single specialty consulting service [ 10 , 11 ]; and use cases involved early inpatient e-consult models [ 12 , 13 ]. In this study, we describe our rapid implementation of inpatient e-consults in multiple specialties and its sustained use beyond the peaks of the pandemic.

The inpatient e-consult service was implemented at the UCI Medical Center, a 478-bed acute care hospital providing tertiary and quaternary care, ambulatory and specialty medical clinics, behavioral health care, and rehabilitation services. Located in Orange County, California, it serves a diverse population of close to 4 million persons with broad health care needs. With more than 500 specialty and primary care physicians, UCI offers a full scope of acute and general care services. It is also the primary teaching location for UCI medical and nursing students, medical residents, and fellows, and it is home to Orange County’s only adult level 1 and pediatric level 2 trauma centers, a National Cancer Institute–designated comprehensive cancer center, a regional burn center, the county’s only hematopoietic stem cell and bone marrow transplant program, and the region’s only high-risk perinatal and neonatal program and maternal-fetal transport system. In winter 2020, UCI Medical Center opened a temporary mobile field hospital that added up to 50 acute care beds in response to a surge of patients with COVID-19.

Implementation

The design and implementation of inpatient e-consults were guided by a steering committee, which included the Chair of the Department of Medicine and Executive Director of Hospital Medicine (who was the lead to design and develop e-consults at the UCI), a clinical informaticist, specialty physician leads, an IT build team, representatives from the Compliance and Privacy Office and Physicians Billing Group, and a project manager. Early on, members of this committee engaged UCI leadership to affirm support for the new service and obtain the IT resources needed to build the inpatient e-consults workflow. Regular steering committee meetings were established to discuss the design of the inpatient e-consults workflow and develop a process for provider reimbursement or credit. Prior to the go-live, the inpatient e-consult service was publicized by members of the steering committee through email communications with house staff. Steering committee members also hosted Zoom training (Zoom Technologies) and orientation sessions with participating consulting services, and they distributed tip sheets summarizing the steps to complete the requesting provider and responding consultant workflows.

Our IT team was able to efficiently implement our inpatient e-consult service by designing workflows similar to those for traditional in-person consults. Thus, the processes for requesting and responding to inpatient e-consults were not unfamiliar to providers ( Figure 1 ). To request an e-consult, the inpatient service or team places a consult order in the electronic health record (EHR), indicating that the request is for an e-consult. The patient is then added to the physician e-consults system list of the appropriate specialty. A follow-up call or page is also sent to the specialty by the requesting team to alert the inpatient consulting team of the e-consult and, if necessary, provide them with any additional details. To respond to the e-consult request, the inpatient consulting team reviews the relevant clinical information available in the EHR and documents their assessment and recommendations in a consult note. If the case is deemed too complex to be addressed electronically, the consulting team converts the e-consult to a traditional in-person consultation and the patient is examined before documenting guidance in the EHR. The requesting provider and responding consultant are each credited with 0.7 work relative value units (a measure of the provider’s time and effort required to perform the service) for every completed e-consult that does not result in an in-person evaluation of the patient by the consulting service, while usual billing or relative value unit credit applies for in-person consultations.

Measurement and Analysis

We conducted a retrospective evaluation of all inpatient e-consults ordered at the UCI Medical Center from implementation in July 2020 to December 2022 to assess use, outcomes, and response times. Use was tracked by examining the volume of e-consults ordered per specialty over the 2.5-year period and comparing it with the volume of traditional in-person consults ordered for the specialties offering inpatient e-consults. To assess outcomes, we categorized the result of each e-consult order as either “resolved electronically” if the consulting team addressed the request without a face-to-face evaluation of the patient or “converted to in-person” if the consulting team deemed the case too complex and the patient required a physical examination. The response time was defined as the interval between the documented consult order in the EHR and the consulting team filing recommendations in a consult note. We calculated the median response time and the IQR in hours for each specialty and performed nonparametric Mann-Whitney U tests in SPSS (version 28; IBM Corp) to compare the median response times of requests resolved electronically and converted to in person. All P values were 2-tailed, and P <.05 was considered statistically significant.

Ethical Considerations

Our implementation and retrospective analysis of the inpatient e-consults service constituted as quality improvement activities and not human subjects research. Thus, our study did not require institutional review board review. This study followed the Standards for Quality Improvement Reporting Excellence guidelines.

UCI’s e-consults service was launched in 11 total specialties (allergy and immunology, cardiology, dermatology, endocrinology, infectious diseases, nephrology, palliative care, pediatric endocrinology, pulmonary and critical care, radiation oncology, and rheumatology). Over a 2.5-year period, 1543 e-consults were requested out of 14,974 total consult orders (e-consult and traditional in-person consults) across the 11 participating specialties ( Figure 2 ). Thus, the average proportion of consult orders requested as e-consults is 10.3%, although this proportion varied widely among participating specialties. The specialty with the lowest e-consult proportion was pulmonary and critical care, which had 1.5% (13/850) of total consult orders requested as e-consults, while the specialty with the highest e-consult proportion was pediatric endocrinology, which had 48% (12/25) of total consult orders requested as e-consults. However, with only 25 total consult orders, pediatric endocrinology had the fewest number of total consult orders of all participating specialties.

The most requested e-consult specialties were infectious diseases (which received 574/1543, 37.2% of the e-consult requests), cardiology (261/1543, 16.9% of the e-consult requests), endocrinology (229/1543, 14.8% of the e-consult requests), and dermatology (226/1543, 14.6% of the e-consult requests; Table 1 ). A total of 53.5% (826/1543) of e-consult requests across all participating specialties were addressed without the need for an in-person evaluation of the patient by the consulting team. The specialty with the fewest e-consult requests resolved electronically was pulmonary and critical care, which completed 0% (0/13) of requests electronically, while the specialty with the most e-consult requests resolved electronically was pediatric endocrinology, which completed 100% (12/12) of e-consult requests, without needing to physically examine the patient. However, both specialties had the smallest volumes of e-consult requests of all participating specialties.

We found that the overall median response time of e-consult requests resolved electronically was significantly lower than requests converted to an in-person consultation ( Figure 3 ). The median time between ordering an e-consult and a specialist documenting recommendations in a consult note was 3.7 (IQR 1.3-8.2) hours across all specialties when resolved electronically, contrasted with 7.3 (IQR 3.6-22.0) hours when converted to an in-person consult ( P <.001). Over half (6/11, 55%) of the participating specialties had significantly faster median e-consult response times for requests resolved electronically compared to requests converted to an in-person consultation. The specialties with the fastest e-consult response times were dermatology and radiation oncology, which had median response times of 1.3 (IQR 0.4-3.0) hours and 0.9 (IQR 0.3-1.5) hours when resolved electronically, respectively. However, radiation oncology had one of the smallest volumes of e-consult requests among participating specialties.

The overall response times of e-consult requests were much faster than the turnaround goal mandated by our institutional guidelines, which require a same-day response by 8 PM if the consult is ordered before noon or a response by the following morning if ordered after noon. For reference, the overall median response time for completion of a traditional in-person consult by the same 11 specialties during the same 2.5-year period is 25.8 (IQR 10.8-65.7) hours ( Multimedia Appendix 1 ). Thus, regardless of whether an e-consult request was resolved electronically or converted to an in-person consult, e-consults significantly improved the turnaround times for inpatient consultations.

The average volume of requests was 19 inpatient e-consults per month during the first 5 months that inpatient e-consults were live ( Figure 4 ). Then, California experienced surges of COVID-19 cases throughout the pandemic and we saw corresponding increases in inpatient e-consults use. During the winter 2020 surge, the average volume of requests increased to 52 inpatient e-consults per month. Then, the Delta variant wave arrived in summer 2021, and the average volume of requests increased to 61 inpatient e-consults per month. When the Omicron variant wave emerged in winter 2021, the average volume of requests peaked at 75 inpatient e-consults per month. During a sustained wave in spring-summer 2022 driven by Omicron subvariants, the average volume of requests was 62 inpatient e-consults per month. After these surges subsided and COVID-19 cases declined, the use of inpatient e-consults remained at a high-level baseline with an average of 53 inpatient e-consults per month. Interestingly, similar patterns of increased e-consults use were observed in the ambulatory setting.

Principal Findings

In response to the COVID-19 crisis, we successfully implemented an inpatient e-consult service that offered providers the option of requesting a subspecialty consultation for patients who are hospitalized and do not require an in-person evaluation by the specialist. Strong engagement by the clinical champions and technology partners in our steering committee, along with support from UCI’s leadership which provided us with dedicated IT, compliance, and billing teams, contributed to the successful design and implementation of our inpatient e-consults service. In addition, we were able to rapidly launch the service by leveraging our experiences with implementing e-consults in the ambulatory setting and capitalizing on existing infrastructure for inpatient consults. Instead of creating unique e-consult orders, configuring our existing inpatient consult order reduced the build components for our IT team, allowing us to quickly and effectively launch the inpatient e-consults service. Because we used workflows similar to those for traditional in-person consults, the processes for requesting and responding to inpatient e-consults were not new for providers. This strategy, along with provider familiarity with our well-established e-consults service in the ambulatory setting, likely helped to foster the adoption of inpatient e-consults. Although the COVID-19 crisis provided the key stimulus, these factors may have also contributed to the more rapid adoption of inpatient e-consults in comparison to the initial uptake of our ambulatory e-consults.

We found that the e-consult services helped to significantly reduce the response time for consults requested on patients who are hospitalized and do not require an in-person evaluation. In fact, the overall median response time of e-consult requests resolved electronically was approximately half of the response time for requests converted to an in-person consultation and nearly 7 times faster than the response time for traditional in-person consults. This time saving was critical during surges of COVID-19 cases when emergency departments and inpatient units were overwhelmed, leading to prolonged wait times for patients who were hospitalized to receive consultative care. e-Consults helped to streamline the inpatient consultation process and enabled the consulting team to promptly and efficiently provide recommendations on patients not needing a physical examination.

Although some diagnoses require in-person evaluation of the patient, lower complexity problems can be managed effectively using e-consults. Indeed, we found that over half of e-consult requests were addressed electronically without the need for an in-person evaluation of the patient by the consulting team. By reducing unnecessary in-person consultations, e-consults likely helped to limit the use of scarce PPE; minimize disease transmission; and free up specialists for other activities, such as examining patients with more complicated conditions and performing procedures. This improved resource use may also translate to potential cost savings associated with avoided in-person consultations and increased productivity. Future work should aim to analyze the cost-effectiveness of inpatient e-consults.

After the peaks of the COVID-19 crisis subsided in California, we discovered that provider use of inpatient e-consults persisted at a rate well above the precrisis levels. This sustained use implies positive provider experiences with the service and suggests a preference for e-consults when addressing lower complexity problems. Developing workflows for the inpatient e-consults service that were familiar to providers and significantly improving the turnaround times for inpatient consultations also likely helped to facilitate this sustainability. Thus, while case numbers and death rates associated with the COVID-19 pandemic have declined, e-consults continued to be an important part of our health care delivery.

Although relatively new, there have been a few reports of e-consults in the inpatient setting. The earliest examples involved the unexpected use of the ambulatory e-consult platform in the inpatient setting [ 13 ] and the design of an inpatient e-consult protocol that provided subspecialty consultations to inpatients at a remote hospital that lacked access to these clinical services [ 12 ]. Other reports described the feasibility and use of inpatient e-consults for only 1 specific specialty consulting service [ 10 , 11 ]. While 1 health system reported their implementation of an inpatient e-consult program in several specialties, provider adoption was temporary in response to the COVID-19 crisis [ 9 ]. Our experience with inpatient e-consults uniquely contrasts with these other health systems because we not only successfully implemented inpatient e-consults in multiple specialties but also demonstrated its sustained use beyond the pandemic.

Limitations

Although anecdotal provider feedback has been positive, limitations to this study include the absence of a formal assessment of user experiences with the inpatient e-consults service. In addition, the volumes of e-consult requests and total consult orders were low for some specialties; thus, caution must be applied in the interpretation of results from these low-volume specialties. Nevertheless, we believe our unique development of inpatient e-consults is easily translatable to other institutions interested in implementing it and will lead to a positive user experience and greater use since we fit the e-consult process into already existing and common workflows of requesting a consultation. Additionally, although the implementation of our inpatient e-consults service was in a single academic health system, we successfully demonstrated that the use of e-consults in the inpatient setting is a promising approach to expediting patient care and reporting our experience in designing and implementing inpatient e-consults may provide guidance to other health systems considering similar telehealth models.

Conclusions

Our implementation of e-consults in the inpatient setting highlighted an innovative use for e-consults in the era of COVID-19. It allowed for timely, efficient delivery of inpatient consultation services while reducing the unnecessary exposure of health care workers to potential infection. Consequently, inpatient e-consults likely helped to conserve precious PPE, minimize disease transmission, and enhance our ability to deal with surges in COVID-19 cases by expediting rapid assessment and management of lower complexity referrals. Although the COVID-19 emergency served as motivation to expand our ambulatory e-consults program to the inpatient setting, the service has become a vital component of our regular practices and will remain an essential part of our health care delivery, both in the ambulatory and inpatient settings, beyond the current pandemic, achieving sustainability and value.

Acknowledgments

The authors thank our steering committee members (Dr Byron Allen and Dr Nathan Rojek) and IT build team (Donna Jackson, Brian Lambertson, Elizabeth Burrows, Jaymee Zillgitt, and Tanya Sickles) for their contributions to the design and implementation of our inpatient e-consults. We also thank additional team members Kathy LaPierre, Jennifer Rios, and Debra Webb Torres for their guidance with compliance and billing issues.

Conflicts of Interest

ANA has been a principal investigator or coinvestigator of clinical trials sponsored by the National Institutes of Health/National Institute of Allergy and Infectious Diseases, NeuroRx Pharma, Pulmotect, Blade Therapeutics, Novartis, Takeda, Humanigen, Eli Lilly, PTC Therapeutics, OctaPharma, Fulcrum Therapeutics, and Alexion, as well as a speaker and consultant for BMS, Pfizer, BI, Portola, Sunovion, Mylan, Salix, Alexion, AstraZeneca, Novartis, Nabriva, Paratek, Bayer, Tetraphase, Achogen LaJolla, Ferring, Seres, Spero, Eli Lilly, Gilead, Millenium, HeartRite, Aseptiscope, and Sprightly; these relationships were unrelated to the current work. ASA, AAD, and AAA have no conflicts of interest to report.

Median (IQR) response times by specialty for traditional in-person consults, compared with median (IQR) response times for e-consults converted to in-person and e-consults resolved electronically.

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Abbreviations

Edited by T de Azevedo Cardoso, S He; submitted 19.12.23; peer-reviewed by J Chen, I Moroz; comments to author 14.02.24; revised version received 06.03.24; accepted 27.03.24; published 16.05.24.

©Anna S Aledia, Amish A Dangodara, Aanya A Amin, Alpesh N Amin. Originally published in the Journal of Medical Internet Research (https://www.jmir.org), 16.05.2024.

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