Self-diagnosing the end of pregnancy after medication abortion.

This qualitative study conducted between November 2020 and March 2021 in the US state of Mississippi examines the experiences of 25 people who obtained medication abortion at the state's only abortion facility. We conducted in-depth interviews with participants after their abortions until concept saturation was reached, and then analysed the content using inductive and deductive analysis. We assessed how people use embodied knowledge about their individual physical experiences such as pregnancy symptoms, a missed period, bleeding, and visual examinations of pregnancy tissue to identify the beginning and end of pregnancy. We compared this to how people use biomedical knowledge such as pregnancy tests, ultrasounds, and clinical examinations to confirm their self-diagnoses. We found that most people felt confident that they could identify the beginning and end of pregnancy through embodied knowledge, especially when combined with the use of home pregnancy tests that confirmed their symptoms, experiences, and visual evidence. All participants concerned about symptoms sought follow-up care at a medical facility, whereas people who felt confident of the successful end of the pregnancy did so less often. These findings have implications for settings of restricted abortion access that have limited options for follow-up care after medication abortion.

Does an App a Day Keep the Doctor Away? AI Symptom Checker Applications, Entrenched Bias, and Professional Responsibility.

The growing prominence of artificial intelligence (AI) in mobile health (mHealth) has given rise to a distinct subset of apps that provide users with diagnostic information using their inputted health status and symptom information-AI-powered symptom checker apps (AISympCheck). While these apps may potentially increase access to health care, they raise consequential ethical and legal questions. This paper will highlight notable concerns with AI usage in the health care system, further entrenchment of preexisting biases in the health care system and issues with professional accountability. To provide an in-depth analysis of the issues of bias and complications of professional obligations and liability, we focus on 2 mHealth apps as examples-Babylon and Ada. We selected these 2 apps as they were both widely distributed during the COVID-19 pandemic and make prominent claims about their use of AI for the purpose of assessing user symptoms. First, bias entrenchment often originates from the data used to train AI systems, causing the AI to replicate these inequalities through a "garbage in, garbage out" phenomenon. Users of these apps are also unlikely to be demographically representative of the larger population, leading to distorted results. Second, professional accountability poses a substantial challenge given the vast diversity and lack of regulation surrounding the reliability of AISympCheck apps. It is unclear whether these apps should be subject to safety reviews, who is responsible for app-mediated misdiagnosis, and whether these apps ought to be recommended by physicians. With the rapidly increasing number of apps, there remains little guidance available for health professionals. Professional bodies and advocacy organizations have a particularly important role to play in addressing these ethical and legal gaps. Implementing technical safeguards within these apps could mitigate bias, AIs could be trained with primarily neutral data, and apps could be subject to a system of regulation to allow users to make informed decisions. In our view, it is critical that these legal concerns are considered throughout the design and implementation of these potentially disruptive technologies. Entrenched bias and professional responsibility, while operating in different ways, are ultimately exacerbated by the unregulated nature of mHealth.

Only the anxious ones? Identifying characteristics of symptom checker app users: a cross-sectional survey.

BACKGROUND: Symptom checker applications (SCAs) may help laypeople classify their symptoms and receive recommendations on medically appropriate actions. Further research is necessary to estimate the influence of user characteristics, attitudes and (e)health-related competencies. OBJECTIVE: The objective of this study is to identify meaningful predictors for SCA use considering user characteristics. METHODS: An explorative cross-sectional survey was conducted to investigate German citizens' demographics, eHealth literacy, hypochondria, self-efficacy, and affinity for technology using German language-validated questionnaires. A total of 869 participants were eligible for inclusion in the study. As n = 67 SCA users were assessed and matched 1:1 with non-users, a sample of n = 134 participants were assessed in the main analysis. A four-step analysis was conducted involving explorative predictor selection, model comparisons, and parameter estimates for selected predictors, including sensitivity and post hoc analyses. RESULTS: Hypochondria and self-efficacy were identified as meaningful predictors of SCA use. Hypochondria showed a consistent and significant effect across all analyses OR: 1.24-1.26 (95% CI: 1.1-1.4). Self-efficacy OR: 0.64-0.93 (95% CI: 0.3-1.4) showed inconsistent and nonsignificant results, leaving its role in SCA use unclear. Over half of the SCA users in our sample met the classification for hypochondria (cut-off on the WI of 5). CONCLUSIONS: Hypochondria has emerged as a significant predictor of SCA use with a consistently stable effect, yet according to the literature, individuals with this trait may be less likely to benefit from SCA despite their greater likelihood of using it. These users could be further unsettled by risk-averse triage and unlikely but serious diagnosis suggestions. TRIAL REGISTRATION: The study was registered in the German Clinical Trials Register (DRKS) DRKS00022465, DERR1- https://doi.org/10.2196/34026 .

The Potential of ChatGPT as a Self-Diagnostic Tool in Common Orthopedic Diseases: Exploratory Study.

BACKGROUND: Artificial intelligence (AI) has gained tremendous popularity recently, especially the use of natural language processing (NLP). ChatGPT is a state-of-the-art chatbot capable of creating natural conversations using NLP. The use of AI in medicine can have a tremendous impact on health care delivery. Although some studies have evaluated ChatGPT's accuracy in self-diagnosis, there is no research regarding its precision and the degree to which it recommends medical consultations. OBJECTIVE: The aim of this study was to evaluate ChatGPT's ability to accurately and precisely self-diagnose common orthopedic diseases, as well as the degree of recommendation it provides for medical consultations. METHODS: Over a 5-day course, each of the study authors submitted the same questions to ChatGPT. The conditions evaluated were carpal tunnel syndrome (CTS), cervical myelopathy (CM), lumbar spinal stenosis (LSS), knee osteoarthritis (KOA), and hip osteoarthritis (HOA). Answers were categorized as either correct, partially correct, incorrect, or a differential diagnosis. The percentage of correct answers and reproducibility were calculated. The reproducibility between days and raters were calculated using the Fleiss κ coefficient. Answers that recommended that the patient seek medical attention were recategorized according to the strength of the recommendation as defined by the study. RESULTS: The ratios of correct answers were 25/25, 1/25, 24/25, 16/25, and 17/25 for CTS, CM, LSS, KOA, and HOA, respectively. The ratios of incorrect answers were 23/25 for CM and 0/25 for all other conditions. The reproducibility between days was 1.0, 0.15, 0.7, 0.6, and 0.6 for CTS, CM, LSS, KOA, and HOA, respectively. The reproducibility between raters was 1.0, 0.1, 0.64, -0.12, and 0.04 for CTS, CM, LSS, KOA, and HOA, respectively. Among the answers recommending medical attention, the phrases "essential," "recommended," "best," and "important" were used. Specifically, "essential" occurred in 4 out of 125, "recommended" in 12 out of 125, "best" in 6 out of 125, and "important" in 94 out of 125 answers. Additionally, 7 out of the 125 answers did not include a recommendation to seek medical attention. CONCLUSIONS: The accuracy and reproducibility of ChatGPT to self-diagnose five common orthopedic conditions were inconsistent. The accuracy could potentially be improved by adding symptoms that could easily identify a specific location. Only a few answers were accompanied by a strong recommendation to seek medical attention according to our study standards. Although ChatGPT could serve as a potential first step in accessing care, we found variability in accurate self-diagnosis. Given the risk of harm with self-diagnosis without medical follow-up, it would be prudent for an NLP to include clear language alerting patients to seek expert medical opinions. We hope to shed further light on the use of AI in a future clinical study.

Impacts of Symptom Checkers for Laypersons' Self-diagnosis on Physicians in Primary Care: Scoping Review.

BACKGROUND: Symptom checkers (SCs) for laypersons' self-assessment and preliminary self-diagnosis are widely used by the public. Little is known about the impact of these tools on health care professionals (HCPs) in primary care and their work. This is relevant to understanding how technological changes might affect the working world and how this is linked to work-related psychosocial demands and resources for HCPs. OBJECTIVE: This scoping review aimed to systematically explore the existing publications on the impacts of SCs on HCPs in primary care and to identify knowledge gaps. METHODS: We used the Arksey and O'Malley framework. We based our search string on the participant, concept, and context scheme and searched PubMed (MEDLINE) and CINAHL in January and June 2021. We performed a reference search in August 2021 and a manual search in November 2021. We included publications of peer-reviewed journals that focused on artificial intelligence- or algorithm-based self-diagnosing apps and tools for laypersons and had primary care or nonclinical settings as a relevant context. The characteristics of these studies were described numerically. We used thematic analysis to identify core themes. We followed the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews) checklist to report the study. RESULTS: Of the 2729 publications identified through initial and follow-up database searches, 43 full texts were screened for eligibility, of which 9 were included. Further 8 publications were included through manual search. Two publications were excluded after receiving feedback in the peer-review process. Fifteen publications were included in the final sample, which comprised 5 (33%) commentaries or nonresearch publications, 3 (20%) literature reviews, and 7 (47%) research publications. The earliest publications stemmed from 2015. We identified 5 themes. The theme finding prediagnosis comprised the comparison between SCs and physicians. We identified the performance of the diagnosis and the relevance of human factors as topics. In the theme layperson-technology relationship, we identified potentials for laypersons' empowerment and harm through SCs. Our analysis showed potential disruptions of the physician-patient relationship and uncontested roles of HCPs in the theme (impacts on) physician-patient relationship. In the theme impacts on HCPs' tasks, we described the reduction or increase in HCPs' workload. We identified potential transformations of HCPs' work and impacts on the health care system in the theme future role of SCs in health care. CONCLUSIONS: The scoping review approach was suitable for this new field of research. The heterogeneity of technologies and wordings was challenging. We identified research gaps in the literature regarding the impact of artificial intelligence- or algorithm-based self-diagnosing apps or tools on the work of HCPs in primary care. Further empirical studies on HCPs' lived experiences are needed, as the current literature depicts expectations rather than empirical findings.

Prevalence Of Cyberchondria Among University Students: An Emerging Challenge Of The 21st Century.

OBJECTIVE: To investigate the prevalence of cyberchondria among university students, and to explore their self diagnosis behaviour. METHODS: The cross-sectional study was conducted in different cities of Pakistan from September 2021 to July 2022. Participants were approached through purposive sampling at different institutions of higher education and were asked about access to internet. Data was collected using a demographic proforma and through the self-reporting Cyberchondria Severity Scale-Short Version. Data was analysed using SPSS 26. RESULTS: Of the 500 subjects, 248(49.6%) were male and 252(50.4%) were female. The overall mean age of the sample was 24.14±3.68 years (range: 18-45 years). Of the total, 286(57.2%) subjects were diagnosed with some medical condition, 214(42.8%) self-diagnosed themselves, 302(60.4%) rated their health status as fair, 123(24.6%) rated their health status as good, and 320(64%) said they did not check the accuracy of health-related information. The prevalence of cyberchondria was moderate 252(50.4%) to high 119(23.80%) which indicates the severe severity level of cyberchondria among students. The prevalence of cyberchondria was moderate in women 151(60%) compared to men 101(40.7%). Mean scores of women on cyberchondria severity scale were higher than men (p<0.01). Cyberchondria was more prevalent among individuals with diagnosed medical condition (p<0.01) and those who self-diagnose their symptoms via the internet (p<0.001). CONCLUSIONS: Cyberchondria must be seen as a serious public health concern in Pakistan. Since it is associated with distress and worry, measures need to be adopted to evaluate, prevent, and treat it at the population level.

Early diagnosis of Parkinson's disease using Continuous Convolution Network: Handwriting recognition based on off-line hand drawing without template.

The examination of patients' handwriting has become an important auxiliary method for the diagnosis and treatment of Parkinson's disease which can be used for early self-diagnosis of patients with Parkinson's disease. However, at present, the recognition of writing disorders based on artificial intelligence technology mainly relies on pattern templates and intelligent dynamic acquisition equipment, which has some design limitations. And professional acquisition equipment is not suitable for ordinary home patients. In order to facilitate the diagnosis of Parkinson's disease and get more accurate diagnostic results, this paper is devoted to studying various features of spiral hand drawing of Parkinson's disease and developing an auxiliary diagnosis scheme based on hand drawing. Firstly, through the ablation experiment with open dataset, it is verified that the visual information of hand drawing can better reflect the characteristics of hand drawing of patients with Parkinson's disease than the original dynamic information. Secondly, an Archimedes spiral hand drawing dataset is established that can accurately reflect the tremor, shape and spacing characteristics of the image, with no limitation of the application scenario. Finally, Continuous Convolution Network (CC-Net) is proposed to reduce the pooling layer. Compared with the traditional classification network, CC-Net can accurately extract diversified features of hand drawings and maximize the retention of image information, and obtain a higher classification accuracy with qualified stability (the classification accuracy on the dataset of this paper is about 89.3%, MCC is about 0.733, and average AUC is about 0.934).

Monkeypox self-diagnosis abilities, determinants of vaccination and self-isolation intention after diagnosis among MSM, the Netherlands, July 2022.

Monkeypox is a zoonotic disease and leads to a smallpox-like disease in humans. The current epidemic in European countries requires informed responses. We investigated the ability to self-diagnose a potential infection, and determinants of vaccination and self-isolation intention after diagnosis among 394 MSM in the Netherlands. We found that about half were able to self-diagnose monkeypox, that 70% had a high intention to get vaccinated and 44% to self-isolate after monkeypox diagnosis. Determinants went beyond mere risk behaviour criteria.

An analysis of the diagnostic accuracy and peer-to-peer health information provided on online health forums for heart failure.

AIMS: To examine the accuracy of diagnostic responses and types of information provided on online health forums. DESIGN: Qualitative descriptive study. METHODS: This paper reports the findings of a thematic analysis of peer responses to posts included on heart failure online health forums, to understand the quality and types of information provided. Responses posted between March 2016 and March 2019 were screened, collected and analysed thematically using Braun & Clarke. Themes were conceptually underpinned by Normalization Process Theory. Responses were assessed for quality against the NICE and SIGN guidelines to determine whether they were evidence based or not. RESULTS: The total number of responses collected for analysis was 639. Five main themes were identified: diagnostic, experiential, informational, peer relations and relationships with healthcare professionals. Out of 298 diagnostic responses, 5% were guideline evidence-based and 6% had information that were partly evidence-based. Non-evidence based and potentially dangerous responses were 10%. Experiential responses were 10%; 23% included advice that was not supported with any clinical evidence; and 46% signposted users to other online references/healthcare professionals. CONCLUSION: Online health communication largely focuses on provision of experiential responses to assist those in need of pre- or post-diagnosis advice and support. However, there is evidence of inaccurate information provision which suggests the use of a moderator would be beneficial. IMPACT: This study suggests heart failure online health forums are a source of support, however, there are potential risks. Increasing nurses and other health care professional's awareness of online health forums will be important. Additional training is needed to help them learn more about patient's use of online health forums, to gain a better understanding about the types of information sought, and how best to address such knowledge deficits. Healthcare systems must ensure sufficient time and resources are available to meet information needs for people with heart failure.

Utilization of Self-Diagnosis Health Chatbots in Real-World Settings: Case Study.

BACKGROUND: Artificial intelligence (AI)-driven chatbots are increasingly being used in health care, but most chatbots are designed for a specific population and evaluated in controlled settings. There is little research documenting how health consumers (eg, patients and caregivers) use chatbots for self-diagnosis purposes in real-world scenarios. OBJECTIVE: The aim of this research was to understand how health chatbots are used in a real-world context, what issues and barriers exist in their usage, and how the user experience of this novel technology can be improved. METHODS: We employed a data-driven approach to analyze the system log of a widely deployed self-diagnosis chatbot in China. Our data set consisted of 47,684 consultation sessions initiated by 16,519 users over 6 months. The log data included a variety of information, including users' nonidentifiable demographic information, consultation details, diagnostic reports, and user feedback. We conducted both statistical analysis and content analysis on this heterogeneous data set. RESULTS: The chatbot users spanned all age groups, including middle-aged and older adults. Users consulted the chatbot on a wide range of medical conditions, including those that often entail considerable privacy and social stigma issues. Furthermore, we distilled 2 prominent issues in the use of the chatbot: (1) a considerable number of users dropped out in the middle of their consultation sessions, and (2) some users pretended to have health concerns and used the chatbot for nontherapeutic purposes. Finally, we identified a set of user concerns regarding the use of the chatbot, including insufficient actionable information and perceived inaccurate diagnostic suggestions. CONCLUSIONS: Although health chatbots are considered to be convenient tools for enhancing patient-centered care, there are issues and barriers impeding the optimal use of this novel technology. Designers and developers should employ user-centered approaches to address the issues and user concerns to achieve the best uptake and utilization. We conclude the paper by discussing several design implications, including making the chatbots more informative, easy-to-use, and trustworthy, as well as improving the onboarding experience to enhance user engagement.

The incorporation of HIV self-testing as an exclusive option among men who have sex with men in Spain: results of an online cross-sectional study.

BACKGROUND: We assessed to what extent HIV self-testing would be incorporated by men who have sex with men (MSM) with previous testing history as their exclusive testing option and describe what actions they would take in the case of obtaining a reactive self-test. METHODS: We conducted an online survey among Spanish resident MSM recruited mainly in gay dating apps and analyze 6171 ever tested individuals. We used Poisson regression to estimate factors associated with the incorporation of self-testing as the exclusive testing option. Among those who would incorporate self-testing as their exclusive option, we described actions taken if obtaining a reactive self-test by number of tests in the past. RESULTS: Nearly half of the participants (48.3%) were > =35 years old, 84.6% were born in Spain, 57.9% had attained a university degree, 55.1% lived in a municipality of ≤500.000 and 86.4% self-identified as homosexual. For 37.2%, self-testing would become their exclusive testing option. The incorporation of self-testing as the exclusive option increased with age 25-34 (PR:1.1, 95%CI:1.0-1.3), 35-44 (PR:1.3, 95%CI:1.2-1.5), 45-49 (PR:1.5, 95%CI:1.3-1.7) and > 50 (PR:1.5, 95%CI:1.3-1.8) and in those who reported unprotected anal intercourse (PR:1.1, 95%CI:1.0-1.2) or having paid for sex (PR:1.2, 95%CI:1.0-1.3) in the last 12 months. It was also associated with having had < 10 HIV test in the past (2-9 tests (PR:1.3, 95%CI:1.1-1.4); 1 test (PR:1.5, 95%CI:1.3-1.7)), and having been tested ≥2 years (PR:1.4, 95%CI:1.3-1.5) or between 1 and 2 years ago (PR:1.1, 95%CI:1.0-1.2). Of participants who would use self-testing exclusively 76.6% would confirm their result in case of obtaining a reactive self-test and only 6.1% wouldn't know how to react. Only one individual expressed that he would do nothing at all. CONCLUSION: HIV self-testing could become the exclusive testing option for more than a third of our participants. It was chosen as the exclusive option especially by older, at risk and under-tested MSM. Self-testing strategies need to especially consider the linkage to care process. In this sense, only a small fraction would not know how to react and virtually nobody reported taking no action if obtaining a reactive result.

Diagnosis of Sleep-Disordered Breathing in the Home Environment.

Polysomnography, a gold standard for the diagnosis of sleep-disordered breathing, is a complex investigation requiring access to the sleep laboratory. Thus, sleep-disordered breathing could be underdiagnosed. The aim of this paper was to investigate the feasibility and effectiveness of self-performed investigation of obstructive sleep apnea (OSA) in the home setting, using a portable device, and to assess the comfort and simplicity of the procedure from the patient's perspective. The study included 68 middle-aged patients (21 women and 47 men), who were examined at home with the ApneaLink Air device in search for the underlying reason of reported nighttime snoring and occasionally disordered breathing pattern. The apnea-hypopnea index was quantified and matched with body mass index (BMI), age, and other characteristics. OSA was diagnosed in 37 patients (27 men and 10 women): 22 had mild, 4 had moderate, and 11 patients had severe OSA. All cases of severe OSA were present in men. Patients with severe OSA had significantly higher BMI than those from the other groups. All of the patients pointed to the comfort and ease of the diagnostic device. We conclude that home diagnosis of OSA is a relatively easy and cost-effective way to substitute for the hospital-linked polysomnography, particularly in severely ill patients who have a movement difficulty. A wider implementation of home-based diagnosis of OSA may substantially increase the number of patients investigated in a short time span, also leading to the plausibly upward correction of the disease prevalence.

A mixed methods systematic review of the effects of patient online self-diagnosing in the 'smart-phone society' on the healthcare professional-patient relationship and medical authority.

BACKGROUND: As technology continues to advance, the internet is becoming increasingly popular. Self-diagnosis and health information seeking online is growing more common and it will be important to understand the influence this may have on the patient-healthcare professional relationship. METHODS: A mixed-method systematic review of quantitative, qualitative and mixed method studies concerning the public and healthcare professionals' perceptions of online self-diagnosis and health information seeking and how this can impact the patient-healthcare professional relationship. We searched MEDLINE, EMBASE, CINAHL, ACM & SCOPUS between 2007 and 2018. Relevant data were extracted, and a thematic analysis was conducted and conceptualised using the Normalisation Process Theory framework. RESULTS: Of 6107 records identified, 25 articles met the review eligibility criteria which included 16 qualitative, 8 quantitative and 1 mixed method study. The findings indicated that patients found the internet as a complementary information source alongside healthcare professionals. Health care professionals were perceived to be the most reliable and valued information source. People feel responsible for their own health and find the internet to be a source that provides information rapidly with accessibility at their convenience. Most healthcare professionals agreed on the importance of collaboration with patients and the need to develop a partnership and shared decision-making process but struggled to find time in the consultation to do so efficiently. Some healthcare professionals felt that the internet was advantageous for patients looking after their own health, while others felt it was due to a lack of trust in their expertise. Patients tended to present information to the healthcare professional to support the therapeutic relationship rather than to challenge it and to become more involved in the decision-making process of their healthcare. CONCLUSION: The results of this review suggests that patients value healthcare professionals as a source of medical advice more than the internet. While health professionals' views were mixed our findings indicate that online health information seeking can potentially improve the patient-healthcare professional relationship as patients reported they usually conducted an online search to form a partnership with the healthcare professional as opposed to trying to prove them wrong.

Self-diagnosis of malaria by travellers: a cohort study on the use of malaria rapid diagnostic tests provided by a Swiss travel clinic.

BACKGROUND: The WHO recommends that all suspect malaria cases be tested before receiving treatment. Rapid diagnostic tests (RDT) for malaria can be performed reliably by community health workers with no formal medical background and thus, RDTs could also be provided to travellers for self-diagnosis during visits to endemic regions. METHODS: RDTs were proposed during pre-travel consultations to pre-defined categories of travellers. A training run on their own blood was performed and, if carried out correctly, the traveller was given a written procedure on how to perform the test and act on its result. The travellers were then proposed to buy a malaria RDT kit and were interviewed upon their return. RESULTS: From February 2012 to February 2017, 744 travellers were proposed RDTs and 692 performed the training run (one could not complete it due to a hand tremor). Among the 691 subjects included, 69% travelled to moderate- or low-risk areas of malaria, 18% to high-risk areas and 13% to mixed-risk areas. The two most frequent categories of travellers to whom RDTs were proposed were long-term travellers (69%) and those travelling to remote areas (57%). 543 travellers (79%) were interviewed upon return. During their trip, 17% (91/543) had a medical problem with fever and 12% (65/543) without fever. Among 91 febrile patients, 57% (52/91) performed an RDT, 22% (20/91) consulted immediately without using the test, and 21% (19/91) did neither. Four RDTs (4/52; 8%) were positive: 2 in low-risk and 2 in high-risk areas (0.7% attack rate of self-documented malaria). Two travellers could not perform the test correctly and attended a facility or took standby emergency treatment. Four travellers with negative results repeated the test after 24 h; all were still negative. Carrying RDTs made travellers feel more secure, especially when travelling with children. CONCLUSIONS: 1/6 travellers experienced fever and 4/5 of those reacted appropriately: more than half used RDTs and a quarter consulted immediately. Four travellers (including 2 from low-risk areas) diagnosed themselves with malaria and self-treated successfully. This strategy allows prompt treatment for malaria in high-risk groups and may avoid over-diagnosis (and subsequent inappropriate treatment) of malaria on-site.

Psychological, situational and application-related determinants of the intention to self-test: a factorial survey among students.

BACKGROUND: The Internet enables an unprecedented opportunity to access a broad range of self-tests (e.g. testing for HIV, cancer, hepatitis B/C), which can be conducted by lay consumers without the help of a health professional. However, there is only little knowledge about the determinants of the use of self-tests. Thus, the aims of this study were (1) to experimentally investigate the impact of situational and application-related characteristics on the intention to use a self-test (ST), compared to being tested by a health professional at home (HPH) or at a doctor's office (HPD), (2) to examine the applicability of social-cognitive health behaviour theories on self-testing, and (3) to explore the advantages of integrating technological affinity into social-cognitive health behaviour models to predict self-testing. METHODS: In a factorial survey, 1248 vignettes were rated by 208 students. The core concepts of social-cognitive health behaviour theories, technological affinity, and different situational and application-related characteristics were investigated. RESULTS: Intention to ST was only predicted by the medical expertise of the tested person, while HPH and HPD were also associated with the application purpose of the test and the presence of an emotionally supporting person. Perceived severity and outcome-expectancy significantly predicted intention to self-test. Technological enthusiastic people had a higher intention to use a self-test. CONCLUSIONS: Intention to ST, HPH and HPD were predicted by different situational and application-related characteristics. Social-cognitive health behaviour theories can be applied to predict self-testing and do not need to be extended by technological affinity.

Forecasting the Incidence of Dementia and Dementia-Related Outpatient Visits With Google Trends: Evidence From Taiwan.

BACKGROUND: Google Trends has demonstrated the capability to both monitor and predict epidemic outbreaks. The connection between Internet searches for dementia information and dementia incidence and dementia-related outpatient visits remains unknown. OBJECTIVE: This study aimed to determine whether Google Trends could provide insight into trends in dementia incidence and related outpatient visits in Taiwan. We investigated and validated the local search terms that would be the best predictors of new dementia cases and outpatient visits. We further evaluated the nowcasting (ie, forecasting the present) and forecasting effects of Google Trends search trends for new dementia cases and outpatient visits. The long-term goal is to develop a surveillance system to help early detection and interventions for dementia in Taiwan. METHODS: This study collected (1) dementia data from Taiwan's National Health Insurance Research Database and (2) local Internet search data from Google Trends, both from January 2009 to December 2011. We investigated and validated search terms that would be the best predictors of new dementia cases and outpatient visits. We then evaluated both the nowcasting and the forecasting effects of Google Trends search trends through cross-correlation analysis of the dementia incidence and outpatient visit data with the Google Trends data. RESULTS: The search term "dementia + Alzheimer's disease" demonstrated a 3-month lead effect for new dementia cases and a 6-month lead effect for outpatient visits (r=.503, P=.002; r=.431, P=.009, respectively). When gender was included in the analysis, the search term "dementia" showed 6-month predictive power for new female dementia cases (r=.520, P=.001), but only a nowcasting effect for male cases (r=.430, P=.009). The search term "neurology" demonstrated a 3-month leading effect for new dementia cases (r=.433, P=.008), for new male dementia cases (r=.434, P=.008), and for outpatient visits (r=.613, P<.001). CONCLUSIONS: Google Trends established a plausible relationship between search terms and new dementia cases and dementia-related outpatient visits in Taiwan. This data may allow the health care system in Taiwan to prepare for upcoming outpatient and dementia screening visits. In addition, the validated search term results can be used to provide caregivers with caregiving-related health, skills, and social welfare information by embedding dementia-related search keywords in relevant online articles.

Enhancing Awareness and Self-diagnosis of Obstructive Sleep Apnea Using AI-Powered Chatbots: The Role of ChatGPT in Revolutionizing Healthcare.

Since OpenAI (San Francisco, CA) released its generative AI chatbot, ChatGPT, we are on the cusp of technological transformation. The tool is capable of generating text according to the input that the user adds to it. Due to its ability to imitate human speech tone while extracting encyclopedic knowledge, ChatGPT can be a platform for personalized patient interaction. Thus, it has the potential to revolutionize the healthcare system. Our study aims to evaluate how ChatGPT can answer the queries of patients suffering from obstructive sleep apnea and aid in self-diagnosis. By analyzing symptoms and guiding patients' behavior toward prevention, ChatGPT can play a major role in avoiding serious health repercussions that develop in the later course of obstructive sleep apnea.

Digital Healthcare and Internet Derived Information Obstruction Treatment (IDIOT) Syndrome: Understanding the Link Between Clicks and Consequences.

This article explores the phenomenon of Internet Derived Information Obstruction Treatment (IDIOT) syndrome, highlighting the impact of internet-derived health information on individuals' treatment decisions. Drawing on recent studies, including the rise of IDIOT syndrome due to increased internet use and the potential risks associated with self-medication based on online information, the editorial emphasizes the importance of critically evaluating health information. Insights from research conducted in the last few years highlight the complexity of health conditions and the necessity of seeking professional medical guidance to address the various clinical conditions and their consequences. This article sets the stage for a detailed examination of the IDIOT syndrome and its implications for healthcare decision-making in the digital era.

Critical review of self-diagnosis of mental health conditions using artificial intelligence.

The advent of artificial intelligence (AI) has revolutionised various aspects of our lives, including mental health nursing. AI-driven tools and applications have provided a convenient and accessible means for individuals to assess their mental well-being within the confines of their homes. Nonetheless, the widespread trend of self-diagnosing mental health conditions through AI poses considerable risks. This review article examines the perils associated with relying on AI for self-diagnosis in mental health, highlighting the constraints and possible adverse outcomes that can arise from such practices. It delves into the ethical, psychological, and social implications, underscoring the vital role of mental health professionals, including psychologists, psychiatrists, and nursing specialists, in providing professional assistance and guidance. This article aims to highlight the importance of seeking professional assistance and guidance in addressing mental health concerns, especially in the era of AI-driven self-diagnosis.

'Better see a doctor?' Status quo of symptom checker apps in Germany: A cross-sectional survey with a mixed-methods design (CHECK.APP).

Background: Symptom checker apps (SCAs) offer symptom classification and low-threshold self-triage for laypeople. They are already in use despite their poor accuracy and concerns that they may negatively affect primary care. This study assesses the extent to which SCAs are used by medical laypeople in Germany and which software is most popular. We examined associations between satisfaction with the general practitioner (GP) and SCA use as well as the number of GP visits and SCA use. Furthermore, we assessed the reasons for intentional non-use. Methods: We conducted a survey comprising standardised and open-ended questions. Quantitative data were weighted, and open-ended responses were examined using thematic analysis. Results: This study included 850 participants. The SCA usage rate was 8%, and approximately 50% of SCA non-users were uninterested in trying SCAs. The most commonly used SCAs were NetDoktor and Ada. Surprisingly, SCAs were most frequently used in the age group of 51-55 years. No significant associations were found between SCA usage and satisfaction with the GP or the number of GP visits and SCA usage. Thematic analysis revealed skepticism regarding the results and recommendations of SCAs and discrepancies between users' requirements and the features of apps. Conclusion: SCAs are still widely unknown in the German population and have been sparsely used so far. Many participants were not interested in trying SCAs, and we found no positive or negative associations of SCAs and primary care.