Misinformation: A Major Threat to Public Health, Healthcare Services, and Patient Outcomes

A special issue of Healthcare (ISSN 2227-9032).

Deadline for manuscript submissions: 2 July 2026 | Viewed by 1190

Special Issue Editor

Special Issue Information

Dear Colleagues,

The worldwide dissemination of misinformation has a palpable negative impact on public health, healthcare services, and patient outcomes. The online propagation of false information has emerged as a global issue with profound economic, political, and social repercussions. Misinformation leads to various negative effects, such as swaying people's choices and diminishing trust in authoritative bodies. For example, in healthcare settings, misinformation has obstructed the acceptance of COVID-19 vaccines and related preventive measures. Additionally, conspiracy theories surrounding COVID-19 vaccines have been associated with heightened vaccine hesitancy and reduced willingness to get vaccinated.

Consequently, misinformation can mislead the public and undermine public health initiatives. Furthermore, health-related misinformation, covering topics like vaccines, infectious diseases, nutrition, climate change, cancer, and smoking, is rampant on major social media platforms. The misinformation problem stems from multiple interconnected factors, including the rapid dissemination of information by non-experts, the impact of bots, and social media algorithms.

Moreover, the global reach of social media and swift technological progress hinder efforts to enhance the quality and accuracy of information in this context. In summary, addressing misinformation necessitates a coordinated effort involving social media users, content creators, companies, and governments. Special attention should be given to healthcare services since misinformation among healthcare workers and clients may reduce the quality of care and increase negative patient outcomes.

In this Special Issue, original research articles and reviews are welcome. Research areas may include (but are not limited to) the following:

  • Accurate measurement and detection of health-related misinformation in clinical and community settings;
  • Patient-specific and system-level predictors of vulnerability to health misinformation;
  • Quantifiable impacts of misinformation on patient health behaviors, treatment adherence, and clinical outcomes;
  • Health system consequences of misinformation, including effects on provider–patient communication and public health initiatives;
  • Evidence-based strategies for healthcare professionals to identify and counteract misinformation in clinical practice;
  • The role of social media and digital platforms in propagating health misinformation and healthcare policy interventions for effective mitigation.

I look forward to receiving your contributions.

Dr. Petros Galanis
Guest Editor

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Keywords

  • health misinformation
  • medical fake news
  • infodemic
  • healthcare services
  • healthcare communication
  • patient outcomes
  • susceptibility
  • measurement
  • health literacy
  • trust in healthcare

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Published Papers (1 paper)

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Research

22 pages, 927 KB  
Article
The Online Misinformation Susceptibility Scale: Development and Initial Validation
by Aglaia Katsiroumpa, Ioannis Moisoglou, Polyxeni Mangoulia, Olympia Konstantakopoulou, Parisis Gallos, Maria Tsiachri and Petros Galanis
Healthcare 2025, 13(17), 2252; https://doi.org/10.3390/healthcare13172252 - 8 Sep 2025
Viewed by 750
Abstract
Background/Objectives: Although it is known that widespread online misinformation has negative consequences, there is no scale to measure susceptibility to online misinformation. Thus, our aim was to develop and validate a tool for measuring susceptibility to online misinformation: the Online Misinformation Susceptibility [...] Read more.
Background/Objectives: Although it is known that widespread online misinformation has negative consequences, there is no scale to measure susceptibility to online misinformation. Thus, our aim was to develop and validate a tool for measuring susceptibility to online misinformation: the Online Misinformation Susceptibility Scale (OMISS). Methods: A comprehensive literature review was conducted to generate a preliminary pool of items. Subsequently, a multidisciplinary panel of experts assessed the content validity of these items. To establish face validity, cognitive interviews were performed. Both exploratory and confirmatory factor analyses were performed to verify the underlying construct structure of the OMISS. We examined the concurrent validity of the OMISS by using a fake news detection scale, the Trust in Scientists Scale, a single-item trust in scientists scale, the Conspiracy Mentality Questionnaire, and a single-item conspiracy belief scale. Reliability was rigorously examined using multiple indices, including Cronbach’s alpha, McDonald’s Omega, Cohen’s kappa, and intraclass correlation coefficient. Results: The psychometric evaluation using both exploratory and confirmatory factor analyses supported a one-factor nine-item model for the OMISS. The OMISS demonstrated strong concurrent validity, evidenced by statistically significant correlations with the five scales mentioned above. Cronbach’s alpha and McDonald’s Omega were 0.920 and 0.921, respectively. The intraclass correlation coefficient for the OMISS was 0.994. Conclusions: Our comprehensive psychometric evaluations confirmed the OMISS as a valid tool for measuring online misinformation susceptibility. The OMISS holds promise as an effective tool for identifying susceptibility to misinformation and could support policymakers, health educators, healthcare professionals, and stakeholders in pinpointing high-risk groups. Full article
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