Haitham Alzghaibi

Background: The diffusion of large-language-model (LLM) chatbots such as ChatGPT has introduced a new layer to the digital health information ecosystem, alongside search engines, social media, and national mobile-health (mHealth) platforms. The actual prevalence of AI chatbot-based self-diagnosis among Saudi university students, the ecosystem of tools they choose, and the factors that predict use are not well characterised. Objectives: To estimate AI chatbot use for self-diagnosis among Saudi university students, map the ecosystem of tools used, characterise attitudes and perceived superiority over traditional search, identify predictors through logistic regression, and surface qualitative themes. Methods: A cross-sectional online survey was administered to 511 Saudi university students between April and May 2024. Instruments included a binary item on prior self-diagnosis use, a multi-response item on AI tools used, an 8-item AI Chatbot Perceived Superiority scale, a 15-item AI Doctor Chatbot Attitudes and Trust scale, five multi-response items, and three open-ended items analysed thematically. Data were analysed using descriptive statistics, multivariable logistic regression, Mann–Whitney U/Kruskal-Wallis tests, tool co-use analysis, and latent class analysis. The study received IRB approval from Qassim University (No. 23-19-02). Results: ChatGPT was the dominant tool ( n = 355), followed by Google Gemini ( n = 197), Microsoft Copilot ( n = 166), and the Ministry of Health’s Sehhaty AI chatbot ( n = 154). Multivariable logistic regression identified three independent predictors: gender [female vs. male, adjusted OR = 0.32, 95% CI (0.20, 0.52), p < 0.001], age (per step OR = 1.24, p = 0.080), and specialty (Health vs. Education OR = 6.71, p < 0.001). AI was rated easier and faster than search engines ( M = 3.51) and more comprehensive (M = 3.43). Privacy concerns were prominent ( M = 3.69); 65.1% endorsed institutionally verified AI chatbots. Tool co-use analysis showed the seven AI tools were used largely independently (max phi = 0.12). Conclusion: AI doctor chatbots have already become mainstream tools for self-diagnosis among Saudi university students, with ChatGPT dominating but the Sehhaty AI chatbot already in active use. Male students and those in Health, Management, Social Science, and Engineering are substantially more likely to use AI. The strong endorsement of institutionally verified chatbots provides a clear policy signal for embedding safe, MOH-aligned LLM tools within Sehhaty under Vision 2030.

Background: People living with multiple sclerosis (MS) are increasingly turning to the Internet for information about their condition and its treatment. However, online health information varies widely in quality, and patients often lack the eHealth literacy needed to distinguish reliable from unreliable sources. Objective: This study examined how people with MS in the United Kingdom seek, evaluate, and judge the quality of online health information, with particular attention to information about medicines, and explored the features that they would value in a curated, quality-assessed information resource. Methods: A cross-sectional online survey was distributed via the MS Trust to adults with MS or their carers in the United Kingdom. The 55-item instrument, adapted from a previously validated questionnaire, captured demographic characteristics, Internet use, eHealth confidence, perceived quality indicators, assessment difficulties, and preferences for a curated information resource. Descriptive statistics summarised the sample, and Spearman’s rank-order correlation tested associations between confidence, perceived importance of quality, and information-checking behaviours. Results: One hundred and fifty-two participants completed the survey. Because the number of individuals reached through the MS Trust’s distribution channels was not recorded, a true response rate could not be calculated; the achieved sample represented approximately 38–40% of the a priori target of 400. Almost all participants (99%) used the Internet to find MS-related information, and 84% sought information about their medicines online. Recommendation by a healthcare professional was the strongest indicator of perceived information quality (17.8%), and MS specialists were the most trusted source overall (32.9%). However, 54.4% of participants expressed concerns about online information quality, and only 47.9% believed that search engines reliably returned high-quality websites. Confidence in evaluating online medicine information correlated strongly and positively with the perceived importance of information quality (ρ = 0.869, p < 0.001, n = 73) and with active checking behaviour (ρ = 0.677, p < 0.001, n = 73). Eighty-eight percent of participants endorsed the development of a single, quality-assessed website for MS-related medicine information, and 80% wished for visible details of how each source had been assessed. Conclusions: Despite high engagement with online information, people with MS remain uncertain about its quality and find independent assessment time-consuming and complex. A curated, transparently assessed information resource, ideally with clinician endorsement and visible quality indicators, was endorsed by the subset of participants who reached these items and represents a candidate direction for future digital health interventions in MS care. Because several preference items were answered by small subgroups (n = 35–79) drawn from an engaged charity membership, these findings are exploratory and require confirmation in more representative samples.

Background: Digital health services hold real promise for older adults managing chronic conditions, yet this population consistently struggles to engage with them meaningfully. In Saudi Arabia, the national Sehhaty mHealth platform has been deployed at scale under Vision 2030, but there is limited evidence on why older users do or do not engage, and what structural factors drive those patterns. Aim: This study examined the structural determinants of digital health service engagement among Saudi adults aged 55 and above, focusing on the role of access barriers, technology anxiety, digital health literacy, facilitating conditions, and social influence in shaping behavioural intention to use Sehhaty and AI-enabled virtual physician chatbots. Methods: A cross-sectional survey was conducted with 316 community-dwelling adults aged 55 years and above across Saudi Arabia. Participants completed a 28-item validated questionnaire covering nine constructs adapted from the Technology Acceptance Model (TAM) and the Unified Theory of Acceptance and Use of Technology 2 (UTAUT2). Internal consistency, composite reliability, and average variance extracted were assessed for each construct. Structural Equation Modelling (SEM) with bootstrapped mediation analysis and non-parametric group comparisons (Mann–Whitney U ; Kruskal-Wallis) were conducted. The SEM model demonstrated good fit: CFI = 0.947, TLI = 0.938, RMSEA = 0.064 [90% CI: 0.055–0.073], SRMR = 0.071. Results: The model explained 61.0% of variance in behavioural intention ( R ² = 0.610) and 54.0% in digital health literacy ( R ² = 0.540). Technology anxiety was the dominant inhibitor, directly suppressing both engagement intention ( B = −0.44, p < 0.001) and digital health literacy ( B = −0.60, p < 0.001). Literacy partially mediated the anxiety-intention relationship (indirect B = −0.281; 95% CI [−0.246, −0.104]; 25.9% of total effect). One finding was unexpected: Arabic-language smartphone users reported substantially lower technology anxiety than English-language users ( r = 0.404, medium effect), despite lower average digital proficiency. Conclusion: Technology anxiety and digital health literacy are the primary targets for improving older adult digital health engagement. The Arabic-language anxiety difference carries direct implications for platform design: native language interface concordance appears to reduce anxiety independently of technical skill. Scaffolded onboarding, community literacy programmes, and structured clinician endorsement are recommended as practical complements.

Adults living with chronic disease increasingly depend on online sources for day-to-day self-management, yet digital environments expose them to health mis

Background: Advances in digital health technologies have transformed how individuals with chronic diseases seek and use health information. Patients increasingly rely on online sources, including search engines, social media, and messaging applications, to understand symptoms and manage chronic conditions. However, these digital environments can also expose users to misinformation or conflicting advice. Artificial intelligence (AI) enabled tools and mobile health (mHealth) services have emerged to assist patients in identifying symptoms, verifying health information, and supporting timely health decisions. Despite these developments, limited conceptual work has examined how individuals with chronic diseases integrate such tools into their health information–seeking and decision-making processes. Objective: This study aimed to develop and empirically illustrate a model explaining how individuals with chronic diseases seek and verify digital health information using AI-enabled tools and how these processes influence trust and health-related decision-making. Methods: A cross-sectional survey was conducted among adults aged ≥ 18 years diagnosed with diabetes or hypertension. Participants were recruited through chronic disease support groups on Facebook and WhatsApp. The survey assessed digital health information–seeking behavior, verification practices, trust in AI-enabled physician chatbots and national mHealth services, and their role in health-related decision-making. Descriptive statistics and visualization analyses were conducted using R. Results: Health information seeking occurred across multiple digital platforms, with considerable overlap between messaging applications, social media, and web-based sources. Participants reported using AI physician chatbots and national mHealth services mainly to verify health information encountered online. Trust in AI diagnostic support tools was moderate, indicating cautious but active engagement. Most participants used these tools to support clinical consultations rather than replace professional medical advice. Conclusion: Verification behavior and trust play key roles in how individuals with chronic diseases engage with digital health information. AI-enabled mHealth tools may function as complementary decision-support resources that help patients verify information and interpret symptoms while supporting informed health decisions alongside traditional healthcare services.

BACKGROUND People living with chronic diseases increasingly rely on online sources to support ongoing self-management. While digital environments expand access to health information, they also expose patients to misinformation of varying credibility. Prior studies have largely described information-seeking behaviours or misinformation exposure separately, with limited integration of verification processes. OBJECTIVE This study examined the interrelationships between online health information seeking (HIS), verification behaviour (VER), and misinformation-related perceptions (MIS) among individuals with chronic diseases using a behaviourally integrated framework. METHODS A cross-sectional online survey was conducted among adults with self-reported chronic diseases. The questionnaire assessed health information seeking, verification practices, and perceptions of health misinformation using Likert-scale measures. Data were analysed using descriptive statistics, visual analytics, and structural equation modelling (SEM) to evaluate direct and indirect associations between constructs. RESULTS Participants reported frequent engagement with online health information and widespread use of verification strategies. SEM analysis revealed a strong positive association between HIS and VER (β = 0.81), indicating that active information seeking was closely linked to credibility assessment behaviours. HIS was positively associated with MIS (β = 0.41), suggesting that greater engagement increased awareness of misleading content. VER demonstrated a modest negative association with MIS (β = −0.29), consistent with a buffering effect whereby verification practices partially attenuated misinformation-related perceptions. CONCLUSIONS Findings support a mechanistic interpretation in which online health information seeking promotes verification behaviour, and verification practices may mitigate the perceived impact of misinformation. These results extend beyond descriptive accounts by demonstrating how information-seeking and evaluative behaviours interact within misinformation-rich digital environments. Interventions that reinforce verification strategies and embed credibility cues within commonly used platforms may strengthen safe digital health engagement among chronic disease populations

Background: The Saudi government has allocated four billion Saudi Riyals (approximately $1,066 million) to establish the National Electronic Health Record (NEHR) and advance its e-health strategy. Over seventy projects have been identified to achieve this vision. Following the failure of previous initiatives, the Ministry of Health (MoH) in Saudi Arabia is prioritizing the implementation of Electronic Health Record Systems (EHRS) in all Primary Healthcare Centres (PHCs). This study evaluates the implementation of EHRS in PHCs at two phases: pre-implementation and post-implementation.Methods: A quantitative, self-reported questionnaire was employed at two distinct timescales (pre- and post-implementation) to assess user attitudes and experiences. The study population included all clinical and administrative staff working in Saudi PHCs (n = 38,514). A multi-stage cluster sampling technique was used, resulting in data from total 834 participants in both pre and post implementation phases.Results: Participants demonstrated a high level of awareness regarding the perceived usefulness of EHRS during the pre-implementation phase. In the post-implementation phase, agreement toward EHRS usefulness increased, with 96.6% of participants endorsing the system’s implementation. However, dissatisfaction emerged regarding training and technical support mechanisms. Negative attitudes were also expressed, particularly regarding the time required to assist less experienced users. Variability in user attitudes was observed across scales measuring perceived usefulness, training and support, and negative attitudes. These findings highlight evolving perceptions influenced by direct system use and organizational support.Conclusion: End-user attitudes toward EHRS implementation vary over time and are influenced by system usability, organizational support, and the scale of the project. Addressing training deficiencies, improving technical support, and involving end-users in the implementation process are critical to fostering positive attitudes and ensuring successful EHRS adoption in PHCs.

Health informatics is a multidisciplinary field that supports the use and management of health data to enhance the quality and efficiency of healthcare delivery. As digital transformation accelerates in healthcare systems, particularly in Saudi Arabia, there is a growing need to ensure that undergraduate education programs in this field meet both national priorities and international standards. This study aimed to evaluate and compare the content of undergraduate health informatics programs in Saudi universities with international curriculum recommendations, and to explore professional stakeholder perceptions on essential educational components. A mixed-methods design was used, combining a national cross-sectional survey of healthcare and academic professionals with document analysis of curricula from five universities offering undergraduate degrees in health informatics. The survey assessed perspectives on six educational domains, including technical, clinical, behavioural, managerial, and foundational knowledge areas. The document review evaluated program structures against globally recognized curriculum guidelines. A total of 127 experts completed the survey. Most respondents strongly supported the inclusion of a structured internship, with six months identified as the optimal duration. While all universities covered core technical and health content, there was substantial variation in how each emphasized different domains. For instance, one university focused heavily on data and computing, while others gave greater attention to health sciences or foundational principles. Differences were also observed between stakeholder expectations and international benchmarks, especially regarding applied skills, behavioural sciences, and leadership training. Undergraduate health informatics programs in Saudi Arabia show encouraging progress but vary widely in structure and focus. The findings highlight the need for more standardized educational models that integrate practical skills and international best practices while responding to local healthcare needs. This study offers actionable insights for policymakers and educators aiming to improve health informatics education and workforce readiness in a global context. Not applicable.