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Abstract:
Background: Chatbots using the Large Language Model (LLM) generate human responses to questions from all
categories. Due to staff shortages in healthcare systems, patients waiting for an appointment increasingly use
chatbots to get information about their condition. Given the number of chatbots currently available, assessing the
responses they generate is essential.
Methods: Five chatbots with free access were selected (Gemini, Microsoft Copilot, PiAI, ChatGPT, ChatSpot) and
blinded using letters (A, B, C, D, E). Each chatbot was asked questions about cardiology, oncology, and psoriasis.
Responses were compared to guidelines from the European Society of Cardiology, American Academy of
Dermatology and American Society of Clinical Oncology. All answers were assessed using readability scales
(Flesch Reading Scale, Gunning Fog Scale Level, Flesch-Kincaid Grade Level and Dale-Chall Score). Using a 3-
point Likert scale, two independent medical professionals assessed the compliance of the responses with the
guidelines.
Results: A total of 45 questions were asked of all chatbots. Chatbot C gave the shortest answers, 7.0 (6.0 – 8.0), and Chatbot A the longest 17.5 (13.0 – 24.5). The Flesch Reading Ease Scale ranged from 16.3 (12.2 – 21.9)
(Chatbot D) to 39.8 (29.0 – 50.4) (Chatbot A). Flesch-Kincaid Grade Level ranged from 12.5 (10.6 – 14.6) (Chatbot A) to 15.9 (15.1 – 17.1) (Chatbot D). Gunning Fog Scale Level ranged from 15.77 (Chatbot A) to 19.73 (Chatbot D). Dale-Chall Score ranged from 10.3 (9.3 – 11.3) (Chatbot A) to 11.9 (11.5 – 12.4) (Chatbot D).
Conclusion: This study indicates that chatbots vary in length, quality, and readability. They answer each question
in their own way, based on the data they have pulled from the web. Reliability of the responses generated by
chatbots is high. This suggests that people who want information from a chatbot need to be careful and verify the answers they receive, particularly when they ask about medical and health aspects.

Keywords:
Chatbots,Readability,Cardiovascular health,Oncology

Abstract:
Background. The coronavirus disease 2019 (COVID-19) pandemic has significantly accelerated the development and use of new healthcare technologies. While younger individuals may have been able to quickly embrace virtual advancements, older adults may still have different needs in terms of health communication.

Objectives. To identify areas of interest and preferred sources of information related to the COVID-19 pandemic among older adults and to verify their eHealth competencies.

Materials and methods. The study was conducted between February 2022 and July 2022. It included listeners from the University of the Third Age (U3A) and younger students. Both groups received information about the HealthBuddy+ chatbot, a questionnaire that addressed respondents’ interests about COVID-19, and the PL-eHEALS (eHealth Literacy Scale) questionnaire to measure their eHealth competencies.

Results. There were 573 participants in the study (U3A listeners – 303 participants, median age: 73 years (interquartile range (IQR): 69–77); young adult students – 270, median age: 24 years (IQR: 23–24). The primary source of information about COVID-19 for older adults was television (84.5%), and for younger adults, internet (84.4%). Among the older adults, only 17% ever interacted with a chatbot (younger adults – 78% respectively), and 19% considered it a trustworthy source of information on COVID-19 compared to 79% of younger respondents. Older adults and younger adults in our study were most interested in COVID-19 treatment methods (45.5% and 69.3%, respectively), symptoms of the disease (36.6% and 35.2%, respectively) and chronic diseases coexisting with COVID-19 (35.0% and 51.5%, respectively). However, their eHealth competencies were generally low (median (Me): 34; IQR: 30–39) compared to younger adults (Me: 42; IQR: 40–47).

Conclusions. Health education for older adults should be appropriately tailored to their current needs and differentiated. The level of eHealth competencies of older adults suggests that much work remains to narrow the gap between the eHealth competencies of the younger and older generations.

Keywords:
health education,older adults,information seeking,COVID-19