Pilot Testing of a Tool to Standardize the Assessment of the Quality of Health Information Generated by Artificial Intelligence-Based Models.

Background Artificial intelligence (AI)-based conversational models, such as Chat Generative Pre-trained Transformer (ChatGPT), Microsoft Bing, and Google Bard, have emerged as valuable sources of health information for lay individuals. However, the accuracy of the information provided by these AI models remains a significant concern. This pilot study aimed to test a new tool with key themes for inclusion as follows: Completeness of content, Lack of false information in the content, Evidence supporting the content, Appropriateness of the content, and Relevance, referred to as "CLEAR", designed to assess the quality of health information delivered by AI-based models. Methods Tool development involved a literature review on health information quality, followed by the initial establishment of the CLEAR tool, which comprised five items that aimed to assess the following: completeness, lack of false information, evidence support, appropriateness, and relevance. Each item was scored on a five-point Likert scale from excellent to poor. Content validity was checked by expert review. Pilot testing involved 32 healthcare professionals using the CLEAR tool to assess content on eight different health topics deliberately designed with varying qualities. The internal consistency was checked with Cronbach's alpha (α). Feedback from the pilot test resulted in language modifications to improve the clarity of the items. The final CLEAR tool was used to assess the quality of health information generated by four distinct AI models on five health topics. The AI models were ChatGPT 3.5, ChatGPT 4, Microsoft Bing, and Google Bard, and the content generated was scored by two independent raters with Cohen's kappa (κ) for inter-rater agreement. Results The final five CLEAR items were: (1) Is the content sufficient?; (2) Is the content accurate?; (3) Is the content evidence-based?; (4) Is the content clear, concise, and easy to understand?; and (5) Is the content free from irrelevant information? Pilot testing on the eight health topics revealed acceptable internal consistency with a Cronbach's α range of 0.669-0.981. The use of the final CLEAR tool yielded the following average scores: Microsoft Bing (mean=24.4±0.42), ChatGPT-4 (mean=23.6±0.96), Google Bard (mean=21.2±1.79), and ChatGPT-3.5 (mean=20.6±5.20). The inter-rater agreement revealed the following Cohen κ values: for ChatGPT-3.5 (κ=0.875, P<.001), ChatGPT-4 (κ=0.780, P<.001), Microsoft Bing (κ=0.348, P=.037), and Google Bard (κ=.749, P<.001). Conclusions The CLEAR tool is a brief yet helpful tool that can aid in standardizing testing of the quality of health information generated by AI-based models. Future studies are recommended to validate the utility of the CLEAR tool in the quality assessment of AI-generated health-related content using a larger sample across various complex health topics.

Catastrophic Costs among Tuberculosis-Affected Households in Egypt: Magnitude, Cost Drivers, and Coping Strategies.

Despite national programs covering the cost of treatment for tuberculosis (TB) in many countries, TB patients still face substantial costs. The end TB strategy, set by the World Health Organization (WHO), calls for "zero" TB households to be affected by catastrophic payments by 2025. This study aimed to measure the catastrophic healthcare payments among TB patients in Egypt, to determine its cost drivers and determinants and to describe the coping strategies. The study utilized an Arabic-validated version of the TB cost tool developed by the WHO for estimating catastrophic healthcare expenditure using the cluster-based sample survey with stratification in seven administrative regions in Alexandria. TB payments were considered catastrophic if the total cost exceeded 20% of the household's annual income. A total of 276 patients were interviewed: 76.4% were males, 50.0% were in the age group 18-35, and 8.3% had multidrug-resistant TB. Using the human capital approach, 17.0% of households encountered catastrophic costs compared to 59.1% when using the output approach. The cost calculation was carried out using the Egyptian pound converted to the United States dollars based on 2021 currency values. Total TB cost was United States dollars (USD) 280.28 ± 29.9 with a total direct cost of USD 103 ± 10.9 and a total indirect cost of USD 194.15 ± 25.5. The direct medical cost was the main cost driver in the pre-diagnosis period (USD 150.23 ± 26.89 pre diagnosis compared to USD 77.25 ± 9.91 post diagnosis, p = 0.013). The indirect costs (costs due to lost productivity) were the main cost driver in the post-diagnosis period (USD 4.68 ± 1.18 pre diagnosis compared to USD 192.84 ± 25.32 post diagnosis, p < 0.001). The households drew on multiple financial strategies to cope with TB costs where 66.7% borrowed and 25.4% sold household property. About two-thirds lost their jobs and another two-thirds lowered their food intake. Being female, delay in diagnosis and being in the intensive phase were significant predictors of catastrophic payment. Catastrophic costs were high among TB households in Alexandria and showed wide variation according to the method used for indirect cost estimation. The main cost driver before diagnosis was the direct medical costs, while it was the indirect costs, post diagnosis.

Assessment of Knowledge, Perception, Experience and Phobia toward Corticosteroids Use among the General Public in the Era of COVID-19: A Multinational Study.

BACKGROUND: Corticosteroids play a significant role in managing the vast majority of inflammatory and immunologic conditions. To date, population-based studies on knowledge and attitudes concerning corticosteroids are scarce. This study aims to comprehensively assess knowledge, perception, experience and phobia toward corticosteroid use among the general population in the era of COVID-19. METHODS: A cross-sectional self-administrated questionnaire was used to collect the data from 6 countries. Knowledge and corticophobia scores, descriptive statistics and logistic regression were computed. RESULTS: A total of 2354 participants were enrolled in this study; the majority were females (61.6%) with an average age of 30. Around 61.9% had been infected previously with COVID-19, and about one-third of the participants had experience with corticosteroid use. The mean knowledge score was relatively satisfactory (8.7 ± 4.5 out of 14), and Corticophobia ranked a high score in all countries. Age, female gender, and history of COVID-19 were positively correlated with developing corticophobia. CONCLUSION: Our study highlights that the general knowledge about steroids was satisfactory. However, the phobia toward its use upon indication is high. Therefore, enhancing awareness and providing essential counseling regarding the rational use of corticosteroids may reduce corticophobia.

Assessment of COVID-19 Molecular Testing Capacity in Jordan: A Cross-Sectional Study at the Country Level.

Coronavirus disease 2019 (COVID-19) pandemic control measures rely on the accurate and timely diagnosis of infected individuals. Real-time polymerase chain reaction (qPCR) remains the gold-standard method for laboratory diagnosis of the disease. Delayed diagnosis due to challenges that face laboratories performing COVID-19 testing can hinder public health control measures. Such challenges may be related to shortages in staff, equipment or materials, improper inventory management, flawed workflow, or long turnaround time (TAT). The aim of the current study was to assess the overall COVID-19 molecular testing capacity in Jordan as of April 2021. In addition, the study's objectives included the identification of potential defects that could comprise the utility of the COVID-19 molecular testing capacity in the country. All laboratories certified by the Ministry of Health (MoH) in Jordan to conduct molecular testing for SARS-CoV-2 were invited to participate in this study. Data were obtained from the participating laboratories (those which agreed to participate) by either telephone interviews or a self-reported written questionnaire with items assessing the key aspects of COVID-19 molecular testing. The full molecular testing capacity in each laboratory was self-reported considering 24 working hours. The total number of participating laboratories was 51 out of 77 (66.2%), with the majority being affiliated with MoH (n = 17) and private laboratories (n = 20). The total molecular COVID-19 testing capacity among the participating laboratories was estimated at 574,441 tests per week, while the actual highest number of tests performed over a single week was 310,047 (54.0%, reported in March 2021). Laboratories affiliated with the MoH were operating at a level closer to their maximum capacity (87.2% of their estimated full capacity for COVID-19 testing) compared to private hospital laboratories (41.3%, p = 0.004), private laboratories (20.8%, p < 0.001), and academic/research laboratories (14.7%, p < 0.001, ANOVA). The national average daily COVID-19 molecular testing was 349.2 tests per 100,000 people in April 2021. The average TAT over the first week of April 2021 for COVID-19 testing was 932 min among the participating laboratories, with the longest TAT among MoH laboratories (mean: 1959 min) compared to private laboratories (mean: 333 min, p < 0.001). Molecular COVID-19 testing potential in Jordan has not been fully utilized, particularly for private laboratories and those belonging to academic/research centers. Supply-chain challenges and shortages in staff were identified as potential obstacles hindering the exploitation of full molecular testing capacity for COVID-19 in the country.