A Framework of AI-Based Approaches to Improving eHealth Literacy and Combating Infodemic

Health literacy in physical trauma patients : Evaluation of the general understanding of patients in an outpatient clinic using the German Short Test of Functional Health Literacy

BACKGROUND

Health literacy (HL) refers to the ability to understand and process information provided by the healthcare system and depends on various factors, such as language comprehension, education, and social environment. Low HL was recently associated with increased readmission, morbidity, and mortality. Little is known about HL levels in physical trauma patients. The aim of this study was to determine general HL in physical trauma patients in an outpatient setting and to evaluate possible differences based on demographic characteristics.

MATERIAL AND METHODS

A total of 100 physical trauma patients were recruited in the outpatient trauma facility of the Medical University of Vienna. All recruited patients completed the German Short Test of Functional Health Literacy (S-TOFHLA).

RESULTS

The evaluated HL index ranged between 20 and 36 points (highest achievable score: 36 points), with the mean value calculated at 34.3 (adequate). Out of 100 participants, 97 patients (97%) showed adequate HL and 3 patients (3%) reached a score corresponding to a marginal understanding. No patient showed inadequate HL utilizing the S‑TOFHLA tool. No significant differences were found between different demographic categories, including age, education level, native language, and injury location.

CONCLUSION

In this study, included outpatient trauma patients demonstrated an overall adequate understanding of healthcare related information. Age, sociodemographic background, and/or educational status did not influence performance, which leads to the question as to whether the German version of the S‑TOFHLA is valid to representatively measure HL in these patients. Furthermore, regarding the obvious shortcomings of the S‑TOFHLA, the education standard of the respective population should be taken into consideration when choosing an appropriate testing tool.

Evaluating Patients' Health Literacy to Improve Thyroid Pathology Reports

INTRODUCTION

As our growing population demonstrates a significant increase in the incidence of thyroid cancer, so does patient access to their medical records. Poor health literacy and understanding of disease severity, underscores the importance of effective and accessible patient-doctor communication. No previous studies on patient understanding of thyroid pathology reports exist; therefore, we sought to characterize health literacy in this population.

METHODS

Using a modified Delphi technique, a 12-question multiple-choice survey regarding common pathology terms with possible definitions for each term was synthesized and administered to patients in a high-volume endocrine surgery clinic. Survey results, patient demographics, history of prior thyroid procedure (biopsy or surgery), and self-reported health literacy were collected. Data analysis included t tests, chi-squared, and multivariable linear regression using R.

RESULTS

The survey was completed by 54 patients (response rate: 69.8%). On univariate analysis, White race, previous thyroid procedure, and at least a high school level education were all more likely to score higher on the survey than their counterparts (P < 0.05). On multivariable logistic regression for predicting a higher survey score, only race (est: 2.48 [95% confidence interval: 1.01-3.96]) and higher educational attainment (est: 3.98 [95% confidence interval: 2.32-5.64]) remained predictive (P < 0.05). The remaining demographic groups (age, health literacy confidence, and previous thyroid procedure) did not show a statistically significant difference.

CONCLUSIONS

Overall, terms on a thyroid pathology report are poorly understood by patients. This is exacerbated by non-White race and low educational attainment. There is a need for patient-facing pathology education.

Roles of Health Literacy in Relation to Social Determinants of Health and Recommendations for Informatics-Based Interventions: Systematic Review

BACKGROUND

Health literacy (HL) is the ability to make informed decisions using health information. As health data and information availability increase due to online clinic notes and patient portals, it is important to understand how HL relates to social determinants of health (SDoH) and the place of informatics in mitigating disparities.

OBJECTIVE

This systematic literature review aims to examine the role of HL in interactions with SDoH and to identify feasible HL-based interventions that address low patient understanding of health information to improve clinic note-sharing efficacy.

METHODS

The review examined 2 databases, Scopus and PubMed, for English-language articles relating to HL and SDoH. We conducted a quantitative analysis of study characteristics and qualitative synthesis to determine the roles of HL and interventions.

RESULTS

The results (n=43) were analyzed quantitatively and qualitatively for study characteristics, the role of HL, and interventions. Most articles (n=23) noted that HL was a result of SDoH, but other articles noted that it could also be a mediator for SdoH (n=6) or a modifiable SdoH (n=14) itself.

CONCLUSIONS

The multivariable nature of HL indicates that it could form the basis for many interventions to combat low patient understandability, including 4 interventions using informatics-based solutions. HL is a crucial, multidimensional skill in supporting patient understanding of health materials. Designing interventions aimed at improving HL or addressing poor HL in patients can help increase comprehension of health information, including the information contained in clinic notes shared with patients.

A Preliminary Checklist (METRICS) to Standardize the Design and Reporting of Studies on Generative Artificial Intelligence-Based Models in Health Care Education and Practice: Development Study Involving a Literature Review

BACKGROUND

Adherence to evidence-based practice is indispensable in health care. Recently, the utility of generative artificial intelligence (AI) models in health care has been evaluated extensively. However, the lack of consensus guidelines on the design and reporting of findings of these studies poses a challenge for the interpretation and synthesis of evidence.

OBJECTIVE

This study aimed to develop a preliminary checklist to standardize the reporting of generative AI-based studies in health care education and practice.

METHODS

A literature review was conducted in Scopus, PubMed, and Google Scholar. Published records with "ChatGPT," "Bing," or "Bard" in the title were retrieved. Careful examination of the methodologies employed in the included records was conducted to identify the common pertinent themes and the possible gaps in reporting. A panel discussion was held to establish a unified and thorough checklist for the reporting of AI studies in health care. The finalized checklist was used to evaluate the included records by 2 independent raters. Cohen κ was used as the method to evaluate the interrater reliability.

RESULTS

The final data set that formed the basis for pertinent theme identification and analysis comprised a total of 34 records. The finalized checklist included 9 pertinent themes collectively referred to as METRICS (Model, Evaluation, Timing, Range/Randomization, Individual factors, Count, and Specificity of prompts and language). Their details are as follows: (1) Model used and its exact settings; (2) Evaluation approach for the generated content; (3) Timing of testing the model; (4) Transparency of the data source; (5) Range of tested topics; (6) Randomization of selecting the queries; (7) Individual factors in selecting the queries and interrater reliability; (8) Count of queries executed to test the model; and (9) Specificity of the prompts and language used. The overall mean METRICS score was 3.0 (SD 0.58). The tested METRICS score was acceptable, with the range of Cohen κ of 0.558 to 0.962 (P<.001 for the 9 tested items). With classification per item, the highest average METRICS score was recorded for the "Model" item, followed by the "Specificity" item, while the lowest scores were recorded for the "Randomization" item (classified as suboptimal) and "Individual factors" item (classified as satisfactory).

CONCLUSIONS

The METRICS checklist can facilitate the design of studies guiding researchers toward best practices in reporting results. The findings highlight the need for standardized reporting algorithms for generative AI-based studies in health care, considering the variability observed in methodologies and reporting. The proposed METRICS checklist could be a preliminary helpful base to establish a universally accepted approach to standardize the design and reporting of generative AI-based studies in health care, which is a swiftly evolving research topic.

Comprehensiveness, Accuracy, and Readability of Exercise Recommendations Provided by an AI-Based Chatbot: Mixed Methods Study

BACKGROUND

Regular physical activity is critical for health and disease prevention. Yet, health care providers and patients face barriers to implement evidence-based lifestyle recommendations. The potential to augment care with the increased availability of artificial intelligence (AI) technologies is limitless; however, the suitability of AI-generated exercise recommendations has yet to be explored.

OBJECTIVE

The purpose of this study was to assess the comprehensiveness, accuracy, and readability of individualized exercise recommendations generated by a novel AI chatbot.

METHODS

A coding scheme was developed to score AI-generated exercise recommendations across ten categories informed by gold-standard exercise recommendations, including (1) health condition-specific benefits of exercise, (2) exercise preparticipation health screening, (3) frequency, (4) intensity, (5) time, (6) type, (7) volume, (8) progression, (9) special considerations, and (10) references to the primary literature. The AI chatbot was prompted to provide individualized exercise recommendations for 26 clinical populations using an open-source application programming interface. Two independent reviewers coded AI-generated content for each category and calculated comprehensiveness (%) and factual accuracy (%) on a scale of 0%-100%. Readability was assessed using the Flesch-Kincaid formula. Qualitative analysis identified and categorized themes from AI-generated output.

RESULTS

AI-generated exercise recommendations were 41.2% (107/260) comprehensive and 90.7% (146/161) accurate, with the majority (8/15, 53%) of inaccuracy related to the need for exercise preparticipation medical clearance. Average readability level of AI-generated exercise recommendations was at the college level (mean 13.7, SD 1.7), with an average Flesch reading ease score of 31.1 (SD 7.7). Several recurring themes and observations of AI-generated output included concern for liability and safety, preference for aerobic exercise, and potential bias and direct discrimination against certain age-based populations and individuals with disabilities.

CONCLUSIONS

There were notable gaps in the comprehensiveness, accuracy, and readability of AI-generated exercise recommendations. Exercise and health care professionals should be aware of these limitations when using and endorsing AI-based technologies as a tool to support lifestyle change involving exercise.

Relationship Between Electronic Health Literacy and Self-Management in People With Type 2 Diabetes Using a Structural Equation Modeling Approach

BACKGROUND

Electronic health (eHealth) literacy is a relatively new concept used to determine health outcomes. However, it is not well known how eHealth literacy relates to health outcomes such as diabetes self-management.

PURPOSE

This study was designed to examine the relationships among eHealth literacy, self-efficacy, social support, and self-management in people with Type 2 diabetes.

METHODS

A cross-sectional design was used to examine secondary data from a field survey of people with Type 2 diabetes recruited from outpatient clinics from August to December 2021 ( N = 453). A structural equation model was used that first analyzed the measurement model using confirmatory factor analysis and then tested the hypothesized structural model to estimate the expected relationships among the study variables. The significance of the statistical estimates for the model was assessed based on the 95% bias-corrected bootstrap confidence interval from 5,000 bootstrap resamples.

RESULTS

Significant, indirect relationships were found between eHealth literacy and self-management via self-efficacy (β = 0.26, B = 0.17, 95% CI [0.10, 0.24]) and via social support and, in turn, self-efficacy (β = 0.08, B = 0.05, 95% CI [0.04, 0.08]). eHealth literacy, social support, and self-efficacy together explained 58.1% of the variance in self-management.

CONCLUSION/IMPLICATIONS FOR PRACTICE

This study provides new evidence regarding how eHealth literacy relates to self-management in people with Type 2 diabetes via two indirect pathways, including self-efficacy alone and social support and self-efficacy in series. An eHealth literacy program for self-management should be developed in clinical practice that includes strategies for inducing synergistic effects from self-efficacy and social support on self-management in people with Type 2 diabetes.

Influence of eHealth Literacy and Health Promotion Behavior on Body Mass Index of Workers in the Public Sector

Background

Adequate eHealth literacy and health promotion behavior (HPB) are important to achieve good health-related quality of life. There is limited information on the influence of eHealth literacy and HPB on body mass index (BMI) in our setting and among public service workers.

Objectives

This study assessed the eHealth literacy, HPB, and BMI of public service workers and determined the influence of eHealth literacy and HPB on BMI.

Design

A descriptive cross-sectional design was adopted.

Methods

A simple random sampling technique was used to select 440 public service workers from civil service of redacted. A structured questionnaire was used to collect data on socio-demographics, eHealth literacy, and HPB. Weight and height were measured and BMI was calculated. Data were analyzed using frequency, percentage, mean, standard deviation, and logistic regression analysis. The significant level was set at 0.05.

Results

More than one quarter (28.2%) of respondents had low eHealth literacy, and more than one third (42.5%) had inadequate (30.0% fair and 12.5% poor) HPB. An average (50.5%) had a level of obesity that ranged from preobesity to type 2 obesity. There was a significant association between eHealth literacy and HPB with the BMI of respondents at p < .05.

Conclusion

There was inadequate eHealth literacy and HPB among public service workers. An average of the workers had a level of obesity that ranged from pre-obesity to type 2 obesity. There was a significant association between eHealth literacy and BMI and also between HPB and BMI of respondents. Community health professionals should assist public service workers to develop competencies and skills useful in evaluating health information on the Internet and applying such information to make informed decisions.

Infodemic Challenges During COVID-19 Pandemic and the Strategies to Deal with Them: A Review Article

Context: The phenomenon of infodemic following the outbreak of COVID-19 has led to several adverse public health consequences. Infodemic poses challenges at the community level, and identifying and adopting effective strategies against it can address many of these challenges. The present study aimed to determine the infodemic challenges of COVID-19 and the strategy to deal with them. Evidence acquisition: We searched PubMed and Scopus scientific databases using related keywords up to April 2022. The article selection process was based on the study’s inclusion and exclusion criteria. Data extraction was carried out using a data extraction form. We analyzed the extracted data through the content analyses method. Results: We identified infodemic challenges from related studies and classified them into nine main categories. These challenges included social hazards, improper health behavior, and scientific hazards. Other results show that strategies to deal with COVID-19 and other similar conditions can be classified into seven main categories, including the active confrontation with centers and scientific sources, the effective intervention of health care professionals, responsible participatory actions, actions of governments and authorities, monitoring and identifying incorrect information, heightening people’s awareness, and encouragement for vaccination. Conclusions: In this study, we identified and reported different coping strategies from all around the world that are very broad, and different countries and societies can use appropriate methods according to their situations and characteristics. Efforts to disseminate accurate information and prevent the propagation of incorrect information during a pandemic crisis can be vital.

Do Chinese netizens cross-verify the accuracy of unofficial social media information before changing health behaviors during COVID-19? A Web-based study in China

Background

As social media platforms have become significant sources of information during the pandemic, a significant volume of both factual and inaccurate information related to the prevention of COVID-19 has been disseminated through social media. Thus, disparities in COVID-19 information verification across populations have the potential to promote the dissemination of misinformation among clustered groups of people with similar characteristics.

Objective

This study aimed to identify the characteristics of social media users who obtained COVID-19 information through unofficial social media accounts and were (1) most likely to change their health behaviors according to web-based information and (2) least likely to actively verify the accuracy of COVID-19 information, as these individuals may be susceptible to inaccurate prevention measures and may exacerbate transmission.

Methods

An online questionnaire consisting of 17 questions was disseminated by West China Hospital via its official online platforms, between May 18, 2020, and May 31, 2020. The questionnaire collected the sociodemographic information of 14,509 adults, and included questions surveying Chinese netizens’ knowledge about COVID-19, personal social media use, health behavioral change tendencies, and cross-verification behaviors for web-based information during the pandemic. Multiple stepwise regression models were used to examine the relationships between social media use, behavior changes, and information cross-verification.

Results

Respondents who were most likely to change their health behaviors after obtaining web-based COVID-19 information from celebrity sources had the following characteristics: female sex (P=.004), age ≥50 years (P=.009), higher COVID-19 knowledge and health literacy (P=.045 and P=.03, respectively), non–health care professional (P=.02), higher frequency of searching on social media (P<.001), better health conditions (P<.001), and a trust rating score of more than 3 for information released by celebrities on social media (P=.005). Furthermore, among participants who were most likely to change their health behaviors according to social media information released by celebrities, female sex (P<.001), living in a rural residence rather than first-tier city (P<.001), self-reported medium health status and lower health care literacy (P=.007 and P<.001, respectively), less frequent search for COVID-19 information on social media (P<.001), and greater level of trust toward celebrities’ social media accounts with a trust rating score greater than 1 (P≤.04) were associated with a lack of cross-verification of information.

Conclusions

The findings suggest that governments, health care agencies, celebrities, and technicians should combine their efforts to decrease the risk in vulnerable groups that are inclined to change health behaviors according to web-based information but do not perform any fact-check verification of the accuracy of the unofficial information. Specifically, it is necessary to correct the false information related to COVID-19 on social media, appropriately apply celebrities’ star power, and increase Chinese netizens’ awareness of information cross-verification and eHealth literacy for evaluating the veracity of web-based information.

COVID-19 and the Infodemic: An Overview of the Role and Impact of Social Media, the Evolution of Medical Knowledge, and Emerging Problems

The infodemic is an important component of the cyber-risk in regard to the poor and uncontrolled dissemination of information related to the COVID-19 pandemic. The purpose of this study was to perform a narrative review based on three points of view to allow for an overall picture of this issue. The points of view focused on: (a) the volume of use of social media (a key element of the infodemic) and the position of international health domain bodies; (b) the evolution of scientific production in the life sciences; (c) emerging issues. The research methodology was based on Google and PubMed searches and a qualification process based on a standard checklist and an evaluation of eligibility based on parameters with five score levels applied by two experts (plus one in case of discrepancy). The three points of view stressed the key role of social media as a dissemination tool of the infodemic among citizens. The impact on citizens depends on various social factors and involves indirect (e.g., vaccine avoidance) and direct risks such as mental problems and the risk of suicide. The widespread diffusion of social media, conveyed by mobile technologies, also suggests their use as countermeasures, calibrated based on citizens’ level of both technological and health literacy. Effective and promising countermeasures in this direction are based both on initiatives of contact by apps or SMS and the collection of data based on surveys and finalized to the particular intervention. The review also suggests as further areas of in-depth research: (a) to combat high-level infodemic produced by scientific publications that are not yet official (preprint) or that have undergone peer review with bias/distortion; (b) focusing on the impact of the infodemic considering its spread in different languages.