Background: In response to the COVID-19 pandemic, telehealth has rapidly been adopted to deliver health care services around the world. To date, studies have not compared people’s experiences with telehealth services during the pandemic in Australia to their experiences with traditional in-person visits. Objective: This study aimed to compare participants’ perceptions of telehealth consults to their perceptions of traditional in-person visits and investigate whether people believe that telehealth services would be useful after the pandemic. Methods: A national, cross-sectional, community survey was conducted between June 5 and June 12, 2020 in Australia. In total, 1369 participants who were aged ≥18 years and lived in Australia were recruited via targeted advertisements on social media (ie, Facebook and Instagram). Participants responded to survey questions about their telehealth experience, which included a free-text response option. A generalized linear model was used to estimate the adjusted relative risks of having a poorer telehealth experience than a traditional in-person visit experience. Content analysis was performed to determine the reasons why telehealth experiences were worse than traditional in-person visit experiences. Results: Of the 596 telehealth users, the majority of respondents (n=369, 61.9%) stated that their telehealth experience was “just as good as” or “better than” their traditional in-person medical appointment experience. On average, respondents perceived that telehealth would be moderately useful to very useful for medical appointments after the COVID-19 pandemic ends (mean 3.67, SD 1.1). Being male (P=.007), having a history of both depression and anxiety (P=.016), and lower patient activation scores (ie, individuals’ willingness to take on the role of managing their health/health care) (P=.036) were significantly associated with a poor telehealth experience. In total, 6 overarching themes were identified from free-text responses for why participants’ telehealth experiences were poorer than their traditional in-person medical appointment experiences, as follows: communication is not as effective, limitations with technology, issues with obtaining prescriptions and pathology results, reduced confidence in their doctor, additional burden for complex care, and inability to be physically examined. Conclusions: Based on our sample’s responses, telehealth appointment experiences were comparable to traditional in-person medical appointment experiences. Telehealth may be worthwhile as a mode of health care delivery while the pandemic continues, and it may continue to be worthwhile after the pandemic.

Background: Misinformation about COVID-19 is common and has been spreading rapidly across the globe through social media platforms and other information systems. Understanding what the public knows about COVID-19 and identifying beliefs based on misinformation can help shape effective public health communications to ensure efforts to reduce viral transmission are not undermined. Objective: This study aimed to investigate the prevalence and factors associated with COVID-19 misinformation in Australia and their changes over time. Methods: This prospective, longitudinal national survey was completed by adults (18 years and above) across April (n=4362), May (n=1882), and June (n=1369) 2020. Results: Stronger agreement with misinformation was associated with younger age, male gender, lower education level, and language other than English spoken at home (P<.01 for all). After controlling for these variables, misinformation beliefs were significantly associated (P<.001) with lower levels of digital health literacy, perceived threat of COVID-19, confidence in government, and trust in scientific institutions. Analyses of specific government-identified misinformation revealed 3 clusters: prevention (associated with male gender and younger age), causation (associated with lower education level and greater social disadvantage), and cure (associated with younger age). Lower institutional trust and greater rejection of official government accounts were associated with stronger agreement with COVID-19 misinformation. Conclusions: The findings of this study highlight important gaps in communication effectiveness, which must be addressed to ensure effective COVID-19 prevention.

Background: Most health information does not meet the health literacy needs of our communities. Writing health information in plain language is time-consuming but the release of tools like ChatGPT may make it easier to produce reliable plain language health information. Objective: To investigate the capacity for ChatGPT to produce plain language versions of health texts. Design: Observational study of 26 health texts from reputable websites. Methods: ChatGPT was prompted to ‘rewrite the text for people with low literacy’. Researchers captured three revised versions of each original text. Main Measures: Objective health literacy assessment, including Simple Measure of Gobbledygook (SMOG), proportion of the text that contains complex language (%), number of instances of passive voice and subjective ratings of key messages retained (%). Key Results: On average, original texts were written at grade 12.8 (SD = 2.2) and revised to grade 11.0 (SD = 1.2), p < 0.001. Original texts were on average 22.8% complex (SD = 7.5%) compared to 14.4% (SD = 5.6%) in revised texts, p < 0.001. Original texts had on average 4.7 instances (SD = 3.2) of passive text compared to 1.7 (SD = 1.2) in revised texts, p < 0.001. On average 80% of key messages were retained (SD = 15.0). The more complex original texts showed more improvements than less complex original texts. For example, when original texts were ≥ grade 13, revised versions improved by an average 3.3 grades (SD = 2.2), p < 0.001. Simpler original texts (< grade 11) improved by an average 0.5 grades (SD = 1.4), p < 0.001. Conclusions: This study used multiple objective assessments of health literacy to demonstrate that ChatGPT can simplify health information while retaining most key messages. However, the revised texts typically did not meet health literacy targets for grade reading score, and improvements were marginal for texts that were already relatively simple.

Producing health information that people can easily understand is challenging and time-consuming. Existing guidance is often subjective and lacks specificity. With advances in software that reads and analyzes text, there is an opportunity to develop tools that provide objective, specific, and automated guidance on the complexity of health information. This paper outlines the development of the SHeLL (Sydney Health Literacy Lab) Health Literacy Editor, an automated tool to facilitate the implementation of health literacy guidelines for the production of easy-to-read written health information. Target users were any person or organization that develops consumer-facing education materials, with or without prior experience with health literacy concepts. Anticipated users included health professionals, staff, and government and nongovernment agencies. To develop this tool, existing health literacy and relevant writing guidelines were collated. Items amenable to programmable automated assessment were incorporated into the Editor. A set of natural language processing methods were also adapted for use in the SHeLL Editor, though the approach was primarily procedural (rule-based). As a result of this process, the Editor comprises 6 assessments: readability (school grade reading score calculated using the Simple Measure of Gobbledygook (SMOG)), complex language (percentage of the text that contains public health thesaurus entries, words that are uncommon in English, or acronyms), passive voice, text structure (eg, use of long paragraphs), lexical density and diversity, and person-centered language. These are presented as global scores, with additional, more specific feedback flagged in the text itself. Feedback is provided in real-time so that users can iteratively revise and improve the text. The design also includes a “text preparation” mode, which allows users to quickly make adjustments to ensure accurate calculation of readability. A hierarchy of assessments also helps users prioritize the most important feedback. Lastly, the Editor has a function that exports the analysis and revised text. The SHeLL Health Literacy Editor is a new tool that can help improve the quality and safety of written health information. It provides objective, immediate feedback on a range of factors, complementing readability with other less widely used but important objective assessments such as complex and person-centered language. It can be used as a scalable intervention to support the uptake of health literacy guidelines by health services and providers of health information. This early prototype can be further refined by expanding the thesaurus and leveraging new machine learning methods for assessing the complexity of the written text. User-testing with health professionals is needed before evaluating the Editor’s ability to improve the health literacy of written health information and evaluating its implementation into existing Australian health services.