Patient Perspectives on the Usefulness of an Artificial Intelligence-Assisted Symptom Checker: Cross-Sectional Survey Study

Recommendation endpoints and safety of an online self-triage for depression symptoms

INTRODUCTION

Online symptom checkers are a way to address patient concerns and potentially offload a burdened healthcare system. However, safety outcomes of self-triage are unknown, so we reviewed triage recommendations and outcomes of our institution's depression symptom checker.

METHODS

We examined endpoint recommendations and follow-up encounters seven days afterward during 2 December 2021 to 13 December 2022. Patients with an emergency department visit or hospitalization within seven days of self-triaging had a manual review of the electronic health record to determine if the visit was related to depression, suicidal ideation, or suicide attempt. Charts were reviewed for deaths within seven days of self-triage.

RESULTS

There were 287 unique encounters from 263 unique patients. In 86.1% (247/287), the endpoint was an instruction to call nurse triage; in 3.1% of encounters (9/287), instruction was to seek emergency care. Only 20.2% (58/287) followed the recommendations given. Of the 229 patients that did not follow the endpoint recommendations, 121 (52.8%) had some type of follow-up within seven days. Nearly 11% (31/287) were triaged to endpoints not requiring urgent contact and 9.1% (26/287) to an endpoint that would not need any healthcare team input. No patients died in the study period.

CONCLUSIONS

Most patients did not follow the recommendations for follow-up care although ultimately most patients did receive care within seven days. Self-triage appears to appropriately sort patients with depressed mood to emergency care. On-line self-triaging tools for depression have the potential to safely offload some work from clinic personnel.

AI and Aphasia in the Digital Age: A Critical Review

Aphasiology has a long and rich tradition of contributing to understanding how culture, language, and social environment contribute to brain development and function. Recent breakthroughs in AI can transform the role of aphasiology in the digital age by leveraging speech data in all languages to model how damage to specific brain regions impacts linguistic universals such as grammar. These tools, including generative AI (ChatGPT) and natural language processing (NLP) models, could also inform practitioners working with clinical populations in the assessment and treatment of aphasia using AI-based interventions such as personalized therapy and adaptive platforms. Although these possibilities have generated enthusiasm in aphasiology, a rigorous interrogation of their limitations is necessary before AI is integrated into practice. We explain the history and first principles of reciprocity between AI and aphasiology, highlighting how lesioning neural networks opened the black box of cognitive neurolinguistic processing. We then argue that when more data from aphasia across languages become digitized and available online, deep learning will reveal hitherto unreported patterns of language processing of theoretical interest for aphasiologists. We also anticipate some problems using AI, including language biases, cultural, ethical, and scientific limitations, a misrepresentation of marginalized languages, and a lack of rigorous validation of tools. However, as these challenges are met with better governance, AI could have an equitable impact.

Walking forward or on hold: Could the ChatGPT be applied for seeking health information in neurosurgical settings?

Self-management is important for patients suffering from cerebrovascular events after neurosurgical procedures. An increasing number of artificial intelligence (AI)-assisted tools have been used in postoperative health management. ChatGPT is a new trend dialog-based chatbot that could be used as a supplemental tool for seeking health information. Responses from ChatGPT version 3.5 and 4.0 toward 13 questions raised by experienced neurosurgeons were evaluated in this exploratory study for their consistency and appropriateness blindly by the other three neurosurgeons. The readability of response text was investigated quantitively by word count and the Gunning Fog and Flesch-Kincaid indices. Results showed that the chatbot could provide relatively stable output between the two versions on consistency and appropriateness (χ² = 0.348). As for readability, there was a higher demand for readers to comprehend the output text in the 4.0 version (more counts of words; lower Flesch-Kincaid reading ease score; and higher Flesch-Kincaid grade level). In general, the capacity of ChatGPT to deliver effective health information is still under debate.

Testing Two Online Symptom Checkers With Vulnerable Groups: Usability Study to Improve Cognitive Accessibility of eHealth Services

BACKGROUND

The popularity of eHealth services has surged significantly, underscoring the importance of ensuring their usability and accessibility for users with diverse needs, characteristics, and capabilities. These services can pose cognitive demands, especially for individuals who are unwell, fatigued, or experiencing distress. Additionally, numerous potentially vulnerable groups, including older adults, are susceptible to digital exclusion and may encounter cognitive limitations related to perception, attention, memory, and language comprehension. Regrettably, many studies overlook the preferences and needs of user groups likely to encounter challenges associated with these cognitive aspects.

OBJECTIVE

This study primarily aims to gain a deeper understanding of cognitive accessibility in the practical context of eHealth services. Additionally, we aimed to identify the specific challenges that vulnerable groups encounter when using eHealth services and determine key considerations for testing these services with such groups.

METHODS

As a case study of eHealth services, we conducted qualitative usability testing on 2 online symptom checkers used in Finnish public primary care. A total of 13 participants from 3 distinct groups participated in the study: older adults, individuals with mild intellectual disabilities, and nonnative Finnish speakers. The primary research methods used were the thinking-aloud method, questionnaires, and semistructured interviews.

RESULTS

We found that potentially vulnerable groups encountered numerous issues with the tested services, with similar problems observed across all 3 groups. Specifically, clarity and the use of terminology posed significant challenges. The services overwhelmed users with excessive information and choices, while the terminology consisted of numerous complex medical terms that were difficult to understand. When conducting tests with vulnerable groups, it is crucial to carefully plan the sessions to avoid being overly lengthy, as these users often require more time to complete tasks. Additionally, testing with vulnerable groups proved to be quite efficient, with results likely to benefit a wider audience as well.

CONCLUSIONS

Based on the findings of this study, it is evident that older adults, individuals with mild intellectual disability, and nonnative speakers may encounter cognitive challenges when using eHealth services, which can impede or slow down their use and make the services more difficult to navigate. In the worst-case scenario, these challenges may lead to errors in using the services. We recommend expanding the scope of testing to include a broader range of eHealth services with vulnerable groups, incorporating users with diverse characteristics and capabilities who are likely to encounter difficulties in cognitive accessibility.

Implications of Online Self-Diagnosis in Psychiatry

Online self-diagnosis of psychiatric disorders by the general public is increasing. The reasons for the increase include the expansion of Internet technologies and the use of social media, the rapid growth of direct-to-consumer e-commerce in healthcare, and the increased emphasis on patient involvement in decision making. The publicity given to artificial intelligence (AI) has also contributed to the increased use of online screening tools by the general public. This paper aims to review factors contributing to the expansion of online self-diagnosis by the general public, and discuss both the risks and benefits of online self-diagnosis of psychiatric disorders. A narrative review was performed with examples obtained from the scientific literature and commercial articles written for the general public. Online self-diagnosis of psychiatric disorders is growing rapidly. Some people with a positive result on a screening tool will seek professional help. However, there are many potential risks for patients who self-diagnose, including an incorrect or dangerous diagnosis, increased patient anxiety about the diagnosis, obtaining unfiltered advice on social media, using the self-diagnosis to self-treat, including online purchase of medications without a prescription, and technical issues including the loss of privacy. Physicians need to be aware of the increase in self-diagnosis by the general public and the potential risks, both medical and technical. Psychiatrists must recognize that the general public is often unaware of the challenging medical and technical issues involved in the diagnosis of a mental disorder, and be ready to treat patients who have already obtained an online self-diagnosis.

"That's just Future Medicine" - a qualitative study on users' experiences of symptom checker apps

BACKGROUND

Symptom checker apps (SCAs) are mobile or online applications for lay people that usually have two main functions: symptom analysis and recommendations. SCAs ask users questions about their symptoms via a chatbot, give a list with possible causes, and provide a recommendation, such as seeing a physician. However, it is unclear whether the actual performance of a SCA corresponds to the users' experiences. This qualitative study investigates the subjective perspectives of SCA users to close the empirical gap identified in the literature and answers the following main research question: How do individuals (healthy users and patients) experience the usage of SCA, including their attitudes, expectations, motivations, and concerns regarding their SCA use?

METHODS

A qualitative interview study was chosen to clarify the relatively unknown experience of SCA use. Semi-structured qualitative interviews with SCA users were carried out by two researchers in tandem via video call. Qualitative content analysis was selected as methodology for the data analysis.

RESULTS

Fifteen interviews with SCA users were conducted and seven main categories identified: (1) Attitudes towards findings and recommendations, (2) Communication, (3) Contact with physicians, (4) Expectations (prior to use), (5) Motivations, (6) Risks, and (7) SCA-use for others.

CONCLUSIONS

The aspects identified in the analysis emphasise the specific perspective of SCA users and, at the same time, the immense scope of different experiences. Moreover, the study reveals ethical issues, such as relational aspects, that are often overlooked in debates on mHealth. Both empirical and ethical research is more needed, as the awareness of the subjective experience of those affected is an essential component in the responsible development and implementation of health apps such as SCA.

TRIAL REGISTRATION

German Clinical Trials Register (DRKS): DRKS00022465. 07/08/2020.

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 .

Empowering the digital health workforce in Latin America in the context of the COVID-19 pandemic: the Peruvian case

The COVID-19 pandemic has exposed significant gaps in healthcare access, quality, and the urgent need for enhancing the capacity of digital health human resources, particularly in Latin America. During the pandemic, online courses and telehealth initiatives supported by governmental agencies, the Pan American Health Organization, and other public and private resources, have played a crucial role in meeting training demands. This article discusses the role of capacity building programs in digital health within the context of Latin America, with a specific focus on the Peruvian case. We highlight the development of digital health competencies and related policies, while also describing selected experiences related to capacity building in this field. Additionally, we discuss the pivotal role of collaborative partnerships among institutions and countries, emphasizing the importance of culturally relevant training programs in digital health. These initiatives have the potential to accelerate training and research opportunities in Latin America, drawing on the involvement of government agencies, non-governmental organizations, industry, universities, professional societies, and communities.

Beliefs, experiences and concerns of using artificial intelligence in healthcare: A qualitative synthesis

Objective

Artificial intelligence (AI) is a developing field in the context of healthcare. As this technology continues to be implemented in patient care, there is a growing need to understand the thoughts and experiences of stakeholders in this area to ensure that future AI development and implementation is successful. The aim of this study was to conduct a literature search of qualitative studies exploring the opinions of stakeholders such as clinicians, patients, and technology experts in order to establish the most common themes and ideas that have been presented in this research.

Methods

A literature search was conducted of existing qualitative research on stakeholder beliefs about the use of AI use in healthcare. Twenty-one papers were selected and analysed resulting in the development of four key themes relating to patient care, patient-doctor relationships, lack of education and resources, and the need for regulations.

Results

Overall, patients and healthcare workers are open to the use of AI in care and appear positive about potential benefits. However, concerns were raised relating to the lack of empathy in interactions of AI tools, and potential risks that may arise from the data collection needed for AI use and development. Stakeholders in the healthcare, technology, and business sectors all stressed that there was a lack of appropriate education, funding, and guidelines surrounding AI, and these concerns needed to be addressed to ensure future implementation is safe and suitable for patient care.

Conclusion

Ultimately, the results found in this study highlighted that there was a need for communication between stakeholder in order for these concerns to be addressed, mitigate potential risks, and maximise benefits for patients and clinicians alike. The results also identified a need for further qualitative research in this area to further understand stakeholder experiences as AI use continues to develop.

'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.

Experiences of using artificial intelligence in healthcare: a qualitative study of UK clinician and key stakeholder perspectives

OBJECTIVES

Artificial intelligence (AI) is a rapidly developing field in healthcare, with tools being developed across various specialties to support healthcare professionals and reduce workloads. It is important to understand the experiences of professionals working in healthcare to ensure that future AI tools are acceptable and effectively implemented. The aim of this study was to gain an in-depth understanding of the experiences and perceptions of UK healthcare workers and other key stakeholders about the use of AI in the National Health Service (NHS).

DESIGN

A qualitative study using semistructured interviews conducted remotely via MS Teams. Thematic analysis was carried out.

SETTING

NHS and UK higher education institutes.

PARTICIPANTS

Thirteen participants were recruited, including clinical and non-clinical participants working for the NHS and researchers working to develop AI tools for healthcare settings.

RESULTS

Four core themes were identified: positive perceptions of AI; potential barriers to using AI in healthcare; concerns regarding AI use and steps needed to ensure the acceptability of future AI tools. Overall, we found that those working in healthcare were generally open to the use of AI and expected it to have many benefits for patients and facilitate access to care. However, concerns were raised regarding the security of patient data, the potential for misdiagnosis and that AI could increase the burden on already strained healthcare staff.

CONCLUSION

This study found that healthcare staff are willing to engage with AI research and incorporate AI tools into care pathways. Going forward, the NHS and AI developers will need to collaborate closely to ensure that future tools are suitable for their intended use and do not negatively impact workloads or patient trust. Future AI studies should continue to incorporate the views of key stakeholders to improve tool acceptability.

TRIAL REGISTRATION NUMBER

NCT05028179; ISRCTN15113915; IRAS ref: 293515.

Assessment of a Digital Symptom Checker Tool's Accuracy in Suggesting Reproductive Health Conditions: Clinical Vignettes Study

BACKGROUND

Reproductive health conditions such as endometriosis, uterine fibroids, and polycystic ovary syndrome (PCOS) affect a large proportion of women and people who menstruate worldwide. Prevalence estimates for these conditions range from 5% to 40% of women of reproductive age. Long diagnostic delays, up to 12 years, are common and contribute to health complications and increased health care costs. Symptom checker apps provide users with information and tools to better understand their symptoms and thus have the potential to reduce the time to diagnosis for reproductive health conditions.

OBJECTIVE

This study aimed to evaluate the agreement between clinicians and 3 symptom checkers (developed by Flo Health UK Limited) in assessing symptoms of endometriosis, uterine fibroids, and PCOS using vignettes. We also aimed to present a robust example of vignette case creation, review, and classification in the context of predeployment testing and validation of digital health symptom checker tools.

METHODS

Independent general practitioners were recruited to create clinical case vignettes of simulated users for the purpose of testing each condition symptom checker; vignettes created for each condition contained a mixture of condition-positive and condition-negative outcomes. A second panel of general practitioners then reviewed, approved, and modified (if necessary) each vignette. A third group of general practitioners reviewed each vignette case and designated a final classification. Vignettes were then entered into the symptom checkers by a fourth, different group of general practitioners. The outcomes of each symptom checker were then compared with the final classification of each vignette to produce accuracy metrics including percent agreement, sensitivity, specificity, positive predictive value, and negative predictive value.

RESULTS

A total of 24 cases were created per condition. Overall, exact matches between the vignette general practitioner classification and the symptom checker outcome were 83% (n=20) for endometriosis, 83% (n=20) for uterine fibroids, and 88% (n=21) for PCOS. For each symptom checker, sensitivity was reported as 81.8% for endometriosis, 84.6% for uterine fibroids, and 100% for PCOS; specificity was reported as 84.6% for endometriosis, 81.8% for uterine fibroids, and 75% for PCOS; positive predictive value was reported as 81.8% for endometriosis, 84.6% for uterine fibroids, 80% for PCOS; and negative predictive value was reported as 84.6% for endometriosis, 81.8% for uterine fibroids, and 100% for PCOS.

CONCLUSIONS

The single-condition symptom checkers have high levels of agreement with general practitioner classification for endometriosis, uterine fibroids, and PCOS. Given long delays in diagnosis for many reproductive health conditions, which lead to increased medical costs and potential health complications for individuals and health care providers, innovative health apps and symptom checkers hold the potential to improve care pathways.

Multi-stakeholder preferences for the use of artificial intelligence in healthcare: A systematic review and thematic analysis

INTRODUCTION

Despite the proliferation of Artificial Intelligence (AI) technology over the last decade, clinician, patient, and public perceptions of its use in healthcare raise a number of ethical, legal and social questions. We systematically review the literature on attitudes towards the use of AI in healthcare from patients, the general public and health professionals' perspectives to understand these issues from multiple perspectives.

METHODOLOGY

A search for original research articles using qualitative, quantitative, and mixed methods published between 1 Jan 2001 to 24 Aug 2021 was conducted on six bibliographic databases. Data were extracted and classified into different themes representing views on: (i) knowledge and familiarity of AI, (ii) AI benefits, risks, and challenges, (iii) AI acceptability, (iv) AI development, (v) AI implementation, (vi) AI regulations, and (vii) Human - AI relationship.

RESULTS

The final search identified 7,490 different records of which 105 publications were selected based on predefined inclusion/exclusion criteria. While the majority of patients, the general public and health professionals generally had a positive attitude towards the use of AI in healthcare, all groups indicated some perceived risks and challenges. Commonly perceived risks included data privacy; reduced professional autonomy; algorithmic bias; healthcare inequities; and greater burnout to acquire AI-related skills. While patients had mixed opinions on whether healthcare workers suffer from job loss due to the use of AI, health professionals strongly indicated that AI would not be able to completely replace them in their professions. Both groups shared similar doubts about AI's ability to deliver empathic care. The need for AI validation, transparency, explainability, and patient and clinical involvement in the development of AI was emphasised. To help successfully implement AI in health care, most participants envisioned that an investment in training and education campaigns was necessary, especially for health professionals. Lack of familiarity, lack of trust, and regulatory uncertainties were identified as factors hindering AI implementation. Regarding AI regulations, key themes included data access and data privacy. While the general public and patients exhibited a willingness to share anonymised data for AI development, there remained concerns about sharing data with insurance or technology companies. One key domain under this theme was the question of who should be held accountable in the case of adverse events arising from using AI.

CONCLUSIONS

While overall positivity persists in attitudes and preferences toward AI use in healthcare, some prevalent problems require more attention. There is a need to go beyond addressing algorithm-related issues to look at the translation of legislation and guidelines into practice to ensure fairness, accountability, transparency, and ethics in AI.

Artificial Intelligence in Head and Neck Cancer: A Systematic Review of Systematic Reviews

INTRODUCTION

Several studies have emphasized the potential of artificial intelligence (AI) and its subfields, such as machine learning (ML), as emerging and feasible approaches to optimize patient care in oncology. As a result, clinicians and decision-makers are faced with a plethora of reviews regarding the state of the art of applications of AI for head and neck cancer (HNC) management. This article provides an analysis of systematic reviews on the current status, and of the limitations of the application of AI/ML as adjunctive decision-making tools in HNC management.

METHODS

Electronic databases (PubMed, Medline via Ovid, Scopus, and Web of Science) were searched from inception until November 30, 2022. The study selection, searching and screening processes, inclusion, and exclusion criteria followed the Preferred Reporting Items for Systematic Review and Meta-Analysis (PRISMA) guidelines. A risk of bias assessment was conducted using a tailored and modified version of the Assessment of Systematic Review (AMSTAR-2) tool and quality assessment using the Risk of Bias in Systematic Reviews (ROBIS) guidelines.

RESULTS

Of the 137 search hits retrieved, 17 fulfilled the inclusion criteria. This analysis of systematic reviews revealed that the application of AI/ML as a decision aid in HNC management can be thematized as follows: (1) detection of precancerous and cancerous lesions within histopathologic slides; (2) prediction of the histopathologic nature of a given lesion from various sources of medical imaging; (3) prognostication; (4) extraction of pathological findings from imaging; and (5) different applications in radiation oncology. In addition, the challenges in implementation of AI/ML models for clinical evaluations include the lack of standardized methodological guidelines for the collection of clinical images, development of these models, reporting of their performance, external validation procedures, and regulatory frameworks.

CONCLUSION

At present, there is a paucity of evidence to suggest the adoption of these models in clinical practice due to the aforementioned limitations. Therefore, this manuscript highlights the need for development of standardized guidelines to facilitate the adoption and implementation of these models in the daily clinical practice. In addition, adequately powered, prospective, randomized controlled trials are urgently needed to further assess the potential of AI/ML models in real-world clinical settings for the management of HNC.

Characteristics of Users and Nonusers of Symptom Checkers in Germany: Cross-Sectional Survey Study

BACKGROUND

Previous studies have revealed that users of symptom checkers (SCs, apps that support self-diagnosis and self-triage) are predominantly female, are younger than average, and have higher levels of formal education. Little data are available for Germany, and no study has so far compared usage patterns with people's awareness of SCs and the perception of usefulness.

OBJECTIVE

We explored the sociodemographic and individual characteristics that are associated with the awareness, usage, and perceived usefulness of SCs in the German population.

METHODS

We conducted a cross-sectional online survey among 1084 German residents in July 2022 regarding personal characteristics and people's awareness and usage of SCs. Using random sampling from a commercial panel, we collected participant responses stratified by gender, state of residence, income, and age to reflect the German population. We analyzed the collected data exploratively.

RESULTS

Of all respondents, 16.3% (177/1084) were aware of SCs and 6.5% (71/1084) had used them before. Those aware of SCs were younger (mean 38.8, SD 14.6 years, vs mean 48.3, SD 15.7 years), were more often female (107/177, 60.5%, vs 453/907, 49.9%), and had higher formal education levels (eg, 72/177, 40.7%, vs 238/907, 26.2%, with a university/college degree) than those unaware. The same observation applied to users compared to nonusers. It disappeared, however, when comparing users to nonusers who were aware of SCs. Among users, 40.8% (29/71) considered these tools useful. Those considering them useful reported higher self-efficacy (mean 4.21, SD 0.66, vs mean 3.63, SD 0.81, on a scale of 1-5) and a higher net household income (mean EUR 2591.63, SD EUR 1103.96 [mean US $2798.96, SD US $1192.28], vs mean EUR 1626.60, SD EUR 649.05 [mean US $1756.73, SD US $700.97]) than those who considered them not useful. More women considered SCs unhelpful (13/44, 29.5%) compared to men (4/26, 15.4%).

CONCLUSIONS

Concurring with studies from other countries, our findings show associations between sociodemographic characteristics and SC usage in a German sample: users were on average younger, of higher socioeconomic status, and more commonly female compared to nonusers. However, usage cannot be explained by sociodemographic differences alone. It rather seems that sociodemographics explain who is or is not aware of the technology, but those who are aware of SCs are equally likely to use them, independently of sociodemographic differences. Although in some groups (eg, people with anxiety disorder), more participants reported to know and use SCs, they tended to perceive them as less useful. In other groups (eg, male participants), fewer respondents were aware of SCs, but those who used them perceived them to be more useful. Thus, SCs should be designed to fit specific user needs, and strategies should be developed to help reach individuals who could benefit but are not aware of SCs yet.

Evaluation of a Medical Interview-Assistance System Using Artificial Intelligence for Resident Physicians Interviewing Simulated Patients: A Crossover, Randomized, Controlled Trial

Medical interviews are expected to undergo a major transformation through the use of artificial intelligence. However, artificial intelligence-based systems that support medical interviews are not yet widespread in Japan, and their usefulness is unclear. A randomized, controlled trial to determine the usefulness of a commercial medical interview support system using a question flow chart-type application based on a Bayesian model was conducted. Ten resident physicians were allocated to two groups with or without information from an artificial intelligence-based support system. The rate of correct diagnoses, amount of time to complete the interviews, and number of questions they asked were compared between the two groups. Two trials were conducted on different dates, with a total of 20 resident physicians participating. Data for 192 differential diagnoses were obtained. There was a significant difference in the rate of correct diagnosis between the two groups for two cases and for overall cases (0.561 vs. 0.393; p = 0.02). There was a significant difference in the time required between the two groups for overall cases (370 s (352-387) vs. 390 s (373-406), p = 0.04). Artificial intelligence-assisted medical interviews helped resident physicians make more accurate diagnoses and reduced consultation time. The widespread use of artificial intelligence systems in clinical settings could contribute to improving the quality of medical care.

Triage and Diagnostic Accuracy of Online Symptom Checkers: Systematic Review

BACKGROUND

In the context of a deepening global shortage of health workers and, in particular, the COVID-19 pandemic, there is growing international interest in, and use of, online symptom checkers (OSCs). However, the evidence surrounding the triage and diagnostic accuracy of these tools remains inconclusive.

OBJECTIVE

This systematic review aimed to summarize the existing peer-reviewed literature evaluating the triage accuracy (directing users to appropriate services based on their presenting symptoms) and diagnostic accuracy of OSCs aimed at lay users for general health concerns.

METHODS

Searches were conducted in MEDLINE, Embase, CINAHL, Health Management Information Consortium (HMIC), and Web of Science, as well as the citations of the studies selected for full-text screening. We included peer-reviewed studies published in English between January 1, 2010, and February 16, 2022, with a controlled and quantitative assessment of either or both triage and diagnostic accuracy of OSCs directed at lay users. We excluded tools supporting health care professionals, as well as disease- or specialty-specific OSCs. Screening and data extraction were carried out independently by 2 reviewers for each study. We performed a descriptive narrative synthesis.

RESULTS

A total of 21,296 studies were identified, of which 14 (0.07%) were included. The included studies used clinical vignettes, medical records, or direct input by patients. Of the 14 studies, 6 (43%) reported on triage and diagnostic accuracy, 7 (50%) focused on triage accuracy, and 1 (7%) focused on diagnostic accuracy. These outcomes were assessed based on the diagnostic and triage recommendations attached to the vignette in the case of vignette studies or on those provided by nurses or general practitioners, including through face-to-face and telephone consultations. Both diagnostic accuracy and triage accuracy varied greatly among OSCs. Overall diagnostic accuracy was deemed to be low and was almost always lower than that of the comparator. Similarly, most of the studies (9/13, 69 %) showed suboptimal triage accuracy overall, with a few exceptions (4/13, 31%). The main variables affecting the levels of diagnostic and triage accuracy were the severity and urgency of the condition, the use of artificial intelligence algorithms, and demographic questions. However, the impact of each variable differed across tools and studies, making it difficult to draw any solid conclusions. All included studies had at least one area with unclear risk of bias according to the revised Quality Assessment of Diagnostic Accuracy Studies-2 tool.

CONCLUSIONS

Although OSCs have potential to provide accessible and accurate health advice and triage recommendations to users, more research is needed to validate their triage and diagnostic accuracy before widescale adoption in community and health care settings. Future studies should aim to use a common methodology and agreed standard for evaluation to facilitate objective benchmarking and validation.

TRIAL REGISTRATION

PROSPERO CRD42020215210; https://tinyurl.com/3949zw83.

Artificial intelligence for secondary prevention of myocardial infarction: A qualitative study of patient and health professional perspectives

BACKGROUND

Artificial intelligence (AI) has potential to improve self-management of several chronic conditions. However, the perspective of patients and healthcare professionals regarding AI-enabled health management programs, which are key to successful implementation, remains poorly understood.

PURPOSE

To explore the opinions of people with a history of myocardial infarction (PHMI) and health professionals on the use of AI for secondary prevention of MI.

PROCEDURE

Three rounds of focus groups were conducted via videoconferencing with 38 participants: 22 PHMI and 16 health professionals.

FINDINGS

We identified 21 concepts stemming from participants' views, which we classified into five categories: Trust; Expected Functions; Adoption; Concerns; and Perceived Benefits. Trust covered the credibility of information and safety to believe health advice. Expected Functions covered tailored feedback and personalised advice. Adoption included usability features and overall interest in AI. Concerns originated from previous negative experience with AI. Perceived Benefits included the usefulness of AI to provide advice when regular contact with healthcare services is not feasible. Health professionals were more optimistic than PHMI about the usefulness of AI for improving health behaviour.

CONCLUSIONS

Altogether, our findings provide key insights from end-users to improve the likelihood of successful implementation and adoption of AI-enabled systems in the context of MI, as an exemplar of broader applications in chronic disease management.

Exploratory study: Evaluation of a symptom checker effectiveness for providing a diagnosis and evaluating the situation emergency compared to emergency physicians using simulated and standardized patients

BACKGROUND

The overloading of health care systems is an international problem. In this context, new tools such as symptom checker (SC) are emerging to improve patient orientation and triage. This SC should be rigorously evaluated and we can take a cue from the way we evaluate medical students, using objective structured clinical examinations (OSCE) with simulated patients.

OBJECTIVE

The main objective of this study was to evaluate the efficiency of a symptom checker versus emergency physicians using OSCEs as an assessment method.

METHODS

We explored a method to evaluate the ability to set a diagnosis and evaluate the emergency of a situation with simulation. A panel of medical experts wrote 220 simulated patients cases. Each situation was played twice by an actor trained to the role: once for the SC, then for an emergency physician. Like a teleconsultation, only the patient's voice was accessible. We performed a prospective non-inferiority study. If primary analysis had failed to detect non-inferiority, we have planned a superiority analysis.

RESULTS

The SC established only 30% of the main diagnosis as the emergency physician found 81% of these. The emergency physician was also superior compared to the SC in the suggestion of secondary diagnosis (92% versus 52%). In the matter of patient triage (vital emergency or not), there is still a medical superiority (96% versus 71%). We prove a non-inferiority of the SC compared to the physician in terms of interviewing time.

CONCLUSIONS AND RELEVANCE

We should use simulated patients instead of clinical cases in order to evaluate the effectiveness of SCs.

Artificial intelligence (AI) acceptance in primary care during the coronavirus pandemic: What is the role of patients' gender, age and health awareness? A two-phase pilot study

Background

Artificial intelligence (AI) is steadily entering and transforming the health care and Primary Care (PC) domains. AI-based applications assist physicians in disease detection, medical advice, triage, clinical decision-making, diagnostics and digital public health. Recent literature has explored physicians' perspectives on the potential impact of digital public health on key tasks in PC. However, limited attention has been given to patients' perspectives of AI acceptance in PC, specifically during the coronavirus pandemic. Addressing this research gap, we administered a pilot study to investigate criteria for patients' readiness to use AI-based PC applications by analyzing key factors affecting the adoption of digital public health technology.

Methods

The pilot study utilized a two-phase mixed methods approach. First, we conducted a qualitative study with 18 semi-structured interviews. Second, based on the Technology Readiness and Acceptance Model (TRAM), we conducted an online survey (n = 447).

Results

The results indicate that respondents who scored high on innovativeness had a higher level of readiness to use AI-based technology in PC during the coronavirus pandemic. Surprisingly, patients' health awareness and sociodemographic factors, such as age, gender and education, were not significant predictors of AI-based technology acceptance in PC.

Conclusions

This paper makes two major contributions. First, we highlight key social and behavioral determinants of acceptance of AI-enabled health care and PC applications. Second, we propose that to increase the usability of digital public health tools and accelerate patients' AI adoption, in complex digital public health care ecosystems, we call for implementing adaptive, population-specific promotions of AI technologies and applications.

A scoping review on the use and usefulness of online symptom checkers and triage systems: How to proceed?

Background

Patients are increasingly turning to the Internet for health information. Numerous online symptom checkers and digital triage tools are currently available to the general public in an effort to meet this need, simultaneously acting as a demand management strategy to aid the overburdened health care system. The implementation of these services requires an evidence-based approach, warranting a review of the available literature on this rapidly evolving topic.

Objective

This scoping review aims to provide an overview of the current state of the art and identify research gaps through an analysis of the strengths and weaknesses of the presently available literature.

Methods

A systematic search strategy was formed and applied to six databases: Cochrane library, NICE, DARE, NIHR, Pubmed, and Web of Science. Data extraction was performed by two researchers according to a pre-established data charting methodology allowing for a thematic analysis of the results.

Results

A total of 10,250 articles were identified, and 28 publications were found eligible for inclusion. Users of these tools are often younger, female, more highly educated and technologically literate, potentially impacting digital divide and health equity. Triage algorithms remain risk-averse, which causes challenges for their accuracy. Recent evolutions in algorithms have varying degrees of success. Results on impact are highly variable, with potential effects on demand, accessibility of care, health literacy and syndromic surveillance. Both patients and healthcare providers are generally positive about the technology and seem amenable to the advice given, but there are still improvements to be made toward a more patient-centered approach. The significant heterogeneity across studies and triage systems remains the primary challenge for the field, limiting transferability of findings.

Conclusion

Current evidence included in this review is characterized by significant variability in study design and outcomes, highlighting the significant challenges for future research.An evolution toward more homogeneous methodologies, studies tailored to the intended setting, regulation and standardization of evaluations, and a patient-centered approach could benefit the field.

Ethical, legal, and social aspects of symptom checker applications: a scoping review

Symptom Checker Applications (SCA) are mobile applications often designed for the end-user to assist with symptom assessment and self-triage. SCA are meant to provide the user with easily accessible information about their own health conditions. However, SCA raise questions regarding ethical, legal, and social aspects (ELSA), for example, regarding fair access to this new technology. The aim of this scoping review is to identify the ELSA of SCA in the scientific literature. A scoping review was conducted to identify the ELSA of SCA. Ten databases (e.g., Web of Science and PubMed) were used. Studies on SCA that address ELSA, written in English or German, were included in the review. The ELSA of SCA were extracted and synthesized using qualitative content analysis. A total of 25,061 references were identified, of which 39 were included in the analysis. The identified aspects were allotted to three main categories: (1) Technology; (2) Individual Level; and (3) Healthcare system. The results show that there are controversial debates in the literature on the ethical and social challenges of SCA usage. Furthermore, the debates are characterised by a lack of a specific legal perspective and empirical data. The review provides an overview on the spectrum of ELSA regarding SCA. It offers guidance to stakeholders in the healthcare system, for example, patients, healthcare professionals, and insurance providers and could be used in future empirical research to investigate the perspectives of those affected, such as users.

A Sociotechnical Systems Framework for the Application of Artificial Intelligence in Health Care Delivery

In the coming years, artificial intelligence (AI) will pervade almost every aspect of the health care delivery system. AI has the potential to improve patient safety (e.g. diagnostic accuracy) as well as reduce the burden on clinicians (e.g. documentation-related workload); however, these benefits are yet to be realized. AI is only one element of a larger sociotechnical system that needs to be considered for effective AI application. In this paper, we describe the current challenges of integrating AI into clinical care and propose a sociotechnical systems (STS) approach for AI design and implementation. We demonstrate the importance of an STS approach through a case study on the design and implementation of a clinical decision support (CDS). In order for AI to reach its potential, the entire work system as well as clinical workflow must be systematically considered throughout the design of AI technology.

Utilization patterns and efficiency gains from use of a fully EHR-integrated COVID-19 self-triage and self-scheduling tool: a retrospective analysis

OBJECTIVE

Symptom checkers can help address high demand for SARS-CoV2 (COVID-19) testing and care by providing patients with self-service access to triage recommendations. However, health systems may be hesitant to invest in these tools, as their associated efficiency gains have not been studied. We aimed to quantify the operational efficiency gains associated with use of an online COVID-19 symptom checker as an alternative to a telephone hotline.

METHODS

In our health system, ambulatory patients can either use an online symptom checker or a telephone hotline to be triaged and connected to COVID-19 care. We performed a retrospective analysis of adults who used either method between October 20, 2021 and January 10, 2022, using call logs, electronic health record data, and local wages to calculate labor costs.

RESULTS

Of the 15 549 total COVID-19 triage encounters, 1820 (11.7%) used only the telephone hotline and 13 729 (88.3%) used the symptom checker. Only 271 (2%) of the patients who used the symptom checker also called the hotline. Hotline encounters required more clinician time compared to those involving the symptom checker (17.8 vs 0.4 min/encounter), resulting in higher average labor costs ($24.21 vs $0.55 per encounter). The symptom checker resulted in over 4200 clinician labor hours saved.

CONCLUSION

When given the option, most patients completed COVID-19 triage and visit scheduling online, resulting in substantial efficiency gains. These benefits may encourage health system investment in such tools.

Online Symptom Checkers are Poor Tools for Diagnosing Men's Health Conditions

OBJECTIVE

To analyze the accuracy of the four most commonly used online symptom checkers (OSCs) in diagnosing erectile dysfunction (ED), scrotal pain (SP), Peyronie's disease (PD), and low testosterone (LT).

METHODS AND OUTCOMES

One-hundred and sixty artificial vignettes were created by de-identifying recent initial outpatient consults presenting to discuss ED (40), SP (40), PD (40), and LT (40). The vignettes were entered into the 4 most frequently used OSCs (WebMD, MedicineNet, EverydayHealth, and SutterHealth) as determined by web traffic analysis tools. The top 5 conditions listed in the OSC differential diagnosis were recorded and scored.

RESULTS

WebMD's accuracy for ED, SP, PD, and LT vignettes was 0%, 22.5%, 0%, and 95%, respectively. EverydayHealth was only able to diagnose SP 20% of the time, and failed to diagnose ED, PD, or LT on all occasions. MedicineNet diagnosed ED, PD, SP, and LT in 100%, 98%, 27.5%, and 0% of vignettes, respectively. SutterHealth correctly diagnosed ED, SP, and LT in 100%, 20%, and 80% of patients, respectively. Cumulatively, the OSCs were most accurate in diagnosing ED and least accurate in diagnosing SP when using the Top 1 (37.5% vs. 6.9%) and Top 5 (50% vs. 24.5%) of the suggested conditions.

CONCLUSIONS

No OSC could accurately diagnose all the conditions tested. The OSCs, on average, were poor at suggesting precise diagnoses for ED, PD, LT, SP. Patients and practitioners should be cautioned regarding the accuracy of OSCs.

Comparison of physician and artificial intelligence-based symptom checker diagnostic accuracy

Symptom checkers are increasingly used to assess new symptoms and navigate the health care system. The aim of this study was to compare the accuracy of an artificial intelligence (AI)-based symptom checker (Ada) and physicians regarding the presence/absence of an inflammatory rheumatic disease (IRD). In this survey study, German-speaking physicians with prior rheumatology working experience were asked to determine IRD presence/absence and suggest diagnoses for 20 different real-world patient vignettes, which included only basic health and symptom-related medical history. IRD detection rate and suggested diagnoses of participants and Ada were compared to the gold standard, the final rheumatologists’ diagnosis, reported on the discharge summary report. A total of 132 vignettes were completed by 33 physicians (mean rheumatology working experience 8.8 (SD 7.1) years). Ada’s diagnostic accuracy (IRD) was significantly higher compared to physicians (70 vs 54%, p = 0.002) according to top diagnosis. Ada listed the correct diagnosis more often compared to physicians (54 vs 32%, p < 0.001) as top diagnosis as well as among the top 3 diagnoses (59 vs 42%, p < 0.001). Work experience was not related to suggesting the correct diagnosis or IRD status. Confined to basic health and symptom-related medical history, the diagnostic accuracy of physicians was lower compared to an AI-based symptom checker. These results highlight the potential of using symptom checkers early during the patient journey and importance of access to complete and sufficient patient information to establish a correct diagnosis.

User initiated symptom assessment with an electronic symptom checker. Study protocol for mixed-methods validation. (Preprint)

BACKGROUND

The national Omaolo digital social welfare and health care service of Finland provides a symptom checker, Omaolo, which is a medical device (based on Duodecim Clinical Decision Support EBMEDS software) with a CE marking (risk class IIa), manufactured by the government-owned DigiFinland Oy. Users of this service can perform their triage by using the questions in the symptom checker. By completing the symptom checker, the user receives a recommendation for action and a service assessment with appropriate guidance regarding their health problems on the basis of a selected specific symptom in the symptom checker. This allows users to be provided with appropriate health care services, regardless of time and place.

OBJECTIVE

This study describes the protocol for the mixed methods validation process of the symptom checker available in Omaolo digital services.

METHODS

This is a mixed methods study using quantitative and qualitative methods, which will be part of the clinical validation process that takes place in primary health care centers in Finland. Each organization provides a space where the study and the nurse triage can be done in order to include an unscreened target population of users. The primary health care units provide walk-in model services, where no prior phone call or contact is required. For the validation of the Omaolo symptom checker, case vignettes will be incorporated to supplement the triage accuracy of rare and acute cases that cannot be tested extensively in real-life settings. Vignettes are produced from a variety of clinical sources, and they test the symptom checker in different triage levels by using 1 standardized patient case example.

RESULTS

This study plan underwent an ethics review by the regional permission, which was requested from each organization participating in the research, and an ethics committee statement was requested and granted from Pirkanmaa hospital district's ethics committee, which is in accordance with the University of Tampere's regulations. Of 964 clinical user-filled symptom checker assessments, 877 cases were fully completed with a triage result, and therefore, they met the requirements for clinical validation studies. The goal for sufficient data has been reached for most of the chief symptoms. Data collection was completed in September 2019, and the first feasibility and patient experience results were published by the end of 2020. Case vignettes have been identified and are to be completed before further testing the symptom checker. The analysis and reporting are estimated to be finalized in 2024.

CONCLUSIONS

The primary goals of this multimethod electronic symptom checker study are to assess safety and to provide crucial information regarding the accuracy and usability of the Omaolo electronic symptom checker. To our knowledge, this will be the first study to include real-life clinical cases along with case vignettes.

INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID)

DERR1-10.2196/41423.

Effects and utility of an online forward triage tool during the SARS-CoV-2 pandemic: a mixed method study and patient perspectives, Switzerland

OBJECTIVE

To assess the effects (quantitatively) and the utility (qualitatively) of a COVID-19 online forward triage tool (OFTT) in a pandemic context.

DESIGN

A mixed method sequential explanatory study was employed. Quantitative data of all OFTT users, between 2 March 2020 and 12 May 2020, were collected. Second, qualitative data were collected through key informant interviews (n=19) to explain the quantitative findings, explore tool utility, user experience and elicit recommendations.

SETTING

The working group e-emergency medicine at the emergency department developed an OFTT, which was made available online.

PARTICIPANTS

Participants included all users above the age of 18 that used the OFTT between 2 March 2020 and 12 May 2020.

INTERVENTION

An OFTT that displayed the current test recommendations of the Federal Office of Public Health on whether someone needed testing for COVID-19 or not. No diagnosis was provided.

RESULTS

In the study period, 6272 users consulted our OFTT; 40.2% (1626/4049) would have contacted a healthcare provider had the tool not existed. 560 participants consented to a follow-up survey and provided a valid email address. 31.4% (176/560) participants returned a complete follow-up questionnaire. 84.7% (149/176) followed the recommendations given. 41.5% (73/176) reported that their fear was allayed after using the tool. Qualitatively, seven overarching themes emerged namely (1) accessibility of tool, (2) user-friendliness of tool, (3) utility of tool as an information source, (4) utility of tool in allaying fear and anxiety, (5) utility of tool in medical decision-making (6) utility of tool in reducing the potential for onward transmissions and (7) utility of tool in reducing health system burden.

CONCLUSION

Our findings demonstrated that a COVID-19 OFTT does not only reduce the health system burden but can also serve as an information source, reduce anxiety and fear, reduce potential for cross infections and facilitate medical decision-making.

Analyzing the determinants to accept a virtual assistant and use cases among cancer patients: a mixed methods study

Background

Technological progress in artificial intelligence has led to the increasing popularity of virtual assistants, i.e., embodied or disembodied conversational agents that allow chatting with a technical system in a natural language. However, only little comprehensive research is conducted about patients' perceptions and possible applications of virtual assistant in healthcare with cancer patients. This research aims to investigate the key acceptance factors and value-adding use cases of a virtual assistant for patients diagnosed with cancer.

Methods

Qualitative interviews with eight former patients and four doctors of a Dutch radiotherapy institute were conducted to determine what acceptance factors they find most important for a virtual assistant and gain insights into value-adding applications. The unified theory of acceptance and use of technology (UTAUT) was used to structure perceptions and was inductively modified as a result of the interviews. The subsequent research model was triangulated via an online survey with 127 respondents diagnosed with cancer. A structural equation model was used to determine the relevance of acceptance factors. Through a multigroup analysis, differences between sample subgroups were compared.

Results

The interviews found support for all factors of the UTAUT: performance expectancy, effort expectancy, social influence and facilitating conditions. Additionally, self-efficacy, trust, and resistance to change, were added as an extension of the UTAUT. Former patients found a virtual assistant helpful in receiving information about logistic questions, treatment procedures, side effects, or scheduling appointments. The quantitative study found that the constructs performance expectancy (ß = 0.399), effort expectancy (ß = 0.258), social influence (ß = 0.114), and trust (ß = 0.210) significantly influenced behavioral intention to use a virtual assistant, explaining 80% of its variance. Self-efficacy (ß = 0.792) acts as antecedent of effort expectancy. Facilitating conditions and resistance to change were not found to have a significant relationship with user intention.

Conclusions

Performance and effort expectancy are the leading determinants of virtual assistant acceptance. The latter is dependent on a patient’s self-efficacy. Therefore, including patients during the development and introduction of a VA in cancer treatment is important. The high relevance of trust indicates the need for a reliable, secure service that should be promoted as such. Social influence suggests using doctors in endorsing the VA.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12913-022-08189-7.

Diagnostic accuracy of an artificial intelligence online engine in migraine: A multi‐center study

Objective

This study assesses the concordance in migraine diagnosis between an online, self‐administered, Computer‐based, Diagnostic Engine (CDE) and semi‐structured interview (SSI) by a headache specialist, both using International Classification of Headache Disorders, 3rd edition (ICHD‐3) criteria.

Background

Delay in accurate diagnosis is a major barrier to headache care. Accurate computer‐based algorithms may help reduce the need for SSI‐based encounters to arrive at correct ICHD‐3 diagnosis.

Methods

Between March 2018 and August 2019, adult participants were recruited from three academic headache centers and the community via advertising to our cross‐sectional study. Participants completed two evaluations: phone interview conducted by headache specialists using the SSI and a web‐based expert questionnaire and analytics, CDE. Participants were randomly assigned to either the SSI followed by the web‐based questionnaire or the web‐based questionnaire followed by the SSI. Participants completed protocols a few minutes apart. The concordance in migraine/probable migraine (M/PM) diagnosis between SSI and CDE was measured using Cohen’s kappa statistics. The diagnostic accuracy of CDE was assessed using the SSI as reference standard.

Results

Of the 276 participants consented, 212 completed both SSI and CDE (study completion rate = 77%; median age = 32 years [interquartile range: 28–40], female:male ratio = 3:1). Concordance in M/PM diagnosis between SSI and CDE was: κ = 0.83 (95% confidence interval [CI]: 0.75–0.91). CDE diagnostic accuracy: sensitivity = 90.1% (118/131), 95% CI: 83.6%–94.6%; specificity = 95.8% (68/71), 95% CI: 88.1%–99.1%. Positive and negative predictive values = 97.0% (95% CI: 91.3%–99.0%) and 86.6% (95% CI: 79.3%–91.5%), respectively, using identified migraine prevalence of 60%. Assuming a general migraine population prevalence of 10%, positive and negative predictive values were 70.3% (95% CI: 43.9%–87.8%) and 98.9% (95% CI: 98.1%–99.3%), respectively.

Conclusion

The SSI and CDE have excellent concordance in diagnosing M/PM. Positive CDE helps rule in M/PM, through high specificity and positive likelihood ratio. A negative CDE helps rule out M/PM through high sensitivity and low negative likelihood ratio. CDE that mimics SSI logic is a valid tool for migraine diagnosis.

Ethical, Legal, and Social Implications of Symptom Checker Apps in Primary Health Care (CHECK.APP): Protocol for an Interdisciplinary Mixed Methods Study

Background

Symptom checker apps (SCAs) are accessible tools that provide early symptom assessment for users. The ethical, legal, and social implications of SCAs and their impact on the patient-physician relationship, the health care providers, and the health care system have sparsely been examined. This study protocol describes an approach to investigate the possible impacts and implications of SCAs on different levels of health care provision. It considers the perspectives of the users, nonusers, general practitioners (GPs), and health care experts.

Objective

We aim to assess a comprehensive overview of the use of SCAs and address problematic issues, if any. The primary outcomes of this study are empirically informed multi-perspective recommendations for different stakeholders on the ethical, legal, and social implications of SCAs.

Methods

Quantitative and qualitative methods will be used in several overlapping and interconnected study phases. In study phase 1, a comprehensive literature review will be conducted to assess the ethical, legal, social, and systemic impacts of SCAs. Study phase 2 comprises a survey that will be analyzed with a logistic regression. It aims to assess the user degree of SCAs in Germany as well as the predictors for SCA usage. Study phase 3 will investigate self-observational diaries and user interviews, which will be analyzed as integrated cases to assess user perspectives, usage pattern, and arising problems. Study phase 4 will comprise GP interviews to assess their experiences, perspectives, self-image, and concepts and will be analyzed with the basic procedure by Kruse. Moreover, interviews with health care experts will be conducted in study phase 3 and will be analyzed by using the reflexive thematical analysis approach of Braun and Clark.

Results

Study phase 1 will be completed in November 2021. We expect the results of study phase 2 in December 2021 and February 2022. In study phase 3, interviews are currently being conducted. The final study endpoint will be in February 2023.

Conclusions

The possible ethical, legal, social, and systemic impacts of a widespread use of SCAs that affect stakeholders and stakeholder groups on different levels of health care will be identified. The proposed methodological approach provides a multifaceted and diverse empirical basis for a broad discussion on these implications.

Trial Registration

German Clinical Trials Register (DRKS) DRKS00022465; https://tinyurl.com/yx53er67

International Registered Report Identifier (IRRID)

DERR1-10.2196/34026

Triage Accuracy of Symptom Checker Apps: 5-Year Follow-up Evaluation

Background

Symptom checkers are digital tools assisting laypersons in self-assessing the urgency and potential causes of their medical complaints. They are widely used but face concerns from both patients and health care professionals, especially regarding their accuracy. A 2015 landmark study substantiated these concerns using case vignettes to demonstrate that symptom checkers commonly err in their triage assessment.

Objective

This study aims to revisit the landmark index study to investigate whether and how symptom checkers’ capabilities have evolved since 2015 and how they currently compare with laypersons’ stand-alone triage appraisal.

Methods

In early 2020, we searched for smartphone and web-based applications providing triage advice. We evaluated these apps on the same 45 case vignettes as the index study. Using descriptive statistics, we compared our findings with those of the index study and with publicly available data on laypersons’ triage capability.

Results

We retrieved 22 symptom checkers providing triage advice. The median triage accuracy in 2020 (55.8%, IQR 15.1%) was close to that in 2015 (59.1%, IQR 15.5%). The apps in 2020 were less risk averse (odds 1.11:1, the ratio of overtriage errors to undertriage errors) than those in 2015 (odds 2.82:1), missing >40% of emergencies. Few apps outperformed laypersons in either deciding whether emergency care was required or whether self-care was sufficient. No apps outperformed the laypersons on both decisions.

Conclusions

Triage performance of symptom checkers has, on average, not improved over the course of 5 years. It decreased in 2 use cases (advice on when emergency care is required and when no health care is needed for the moment). However, triage capability varies widely within the sample of symptom checkers. Whether it is beneficial to seek advice from symptom checkers depends on the app chosen and on the specific question to be answered. Future research should develop resources (eg, case vignette repositories) to audit the capabilities of symptom checkers continuously and independently and provide guidance on when and to whom they should be recommended.

Health Care Professionals' Experiences of Web-Based Symptom Checkers for Triage: Cross-sectional Survey Study

Background

Web-based symptom checkers are promising tools that provide help to patients seeking guidance on health problems. Many health organizations have started using them to enhance triage. Patients use the symptom checker to report their symptoms online and submit the report to the health care center through the system. Health care professionals (registered nurse, practical nurse, general physician, physiotherapist, etc) receive patient inquiries with urgency rating, decide on actions to be taken, and communicate these to the patients. The success of the adoption, however, depends on whether the tools can efficiently support health care professionals’ workflow and achieve their support.

Objective

This study explores the factors influencing health care professionals’ support for a web-based symptom checker for triage.

Methods

Data were collected through a web-based survey of 639 health care professionals using either of the two most used web-based symptom checkers in the Finnish public primary care. Linear regression models were fitted to study the associations between the study variables and health care professionals’ support for the symptom checkers. In addition, the health care professionals’ comments collected via survey were qualitatively analyzed to elicit additional insights about the benefits and challenges of the clinical use of symptom checkers.

Results

Results show that the perceived beneficial influence of the symptom checkers on health care professionals’ work and the perceived usability of the tools were positively associated with professionals’ support. The perceived benefits to patients and organizational support for use were positively associated, and threat to professionals’ autonomy was negatively associated with health care professionals’ support. These associations were, however, not independent of other factors included in the models. The influences on professionals’ work were both positive and negative; the tools streamlined work by providing preliminary information on patients and reduced the number of phone calls, but they also created extra work as the professionals needed to call patients and ask clarifying questions. Managing time between the use of symptom checkers and other tasks was also challenging. Meanwhile, according to health care professionals’ experience, the symptom checkers benefited patients as they received help quickly with a lower threshold for care.

Conclusions

The efficient use of symptom checkers for triage requires usable solutions that support health care professionals’ work. High-quality information about the patients’ conditions and an efficient way of communicating with patients are needed. Using a new eHealth tool also requires that health organizations and teams reorganize their workflows and work distributions to support clinical processes.

Patient's Perception of Digital Symptom Assessment Technologies in Rheumatology: Results From a Multicentre Study

Introduction

An increasing number of digital tools, including dedicated diagnostic decision support systems (DDSS) exist to better assess new symptoms and understand when and where to seek medical care. The aim of this study was to evaluate patient's previous online assessment experiences and to compare the acceptability, usability, usefulness and potential impact of artificial intelligence (AI)-based symptom checker (Ada) and an online questionnaire-based self-referral tool (Rheport).

Materials and Methods

Patients newly presenting to three German secondary rheumatology outpatient clinics were randomly assigned in a 1:1 ratio to complete consecutively Ada or Rheport in a prospective non-blinded multicentre controlled crossover randomized trial. DDSS completion time was recorded by local study personnel and perceptions on DDSS and previous online assessment were collected through a self-completed study questionnaire, including usability measured with the validated System Usability Scale (SUS).

Results

600 patients (median age 52 years, 418 women) were included. 277/600 (46.2%) of patients used an online search engine prior to the appointment. The median time patients spent assessing symptoms was 180, 7, and 8 min, respectively using online using search engines, Ada and Rheport. 111/275 (40.4%), 266/600 (44.3%) and 395/600 (65.8%) of patients rated the respective symptom assessment as very helpful or helpful, using online search engines, Ada and Rheport, respectively. Usability of both diagnostic decision support systems (DDSS) was “good” with a significantly higher mean SUS score (SD) of Rheport 77.1/100 (16.0) compared to Ada 74.4/100 (16.8), (p < 0.0001). In male patients, usability of Rheport was rated higher than Ada (p = 0.02) and the usability rating of older (52 years ≥) patients of both DDSS was lower than in younger participants (p = 0.005). Both effects were independent of each other. 440/600 (73.3%) and 475/600 (79.2%) of the patients would recommend Ada and Rheport to friends and other patients, respectively.

Conclusion

In summary, patients increasingly assess their symptoms independently online, however only a minority used dedicated symptom assessment websites or DDSS. DDSS, such as Ada an Rheport are easy to use, well accepted among patients with musculoskeletal complaints and could replace online search engines for patient symptom assessment, potentially saving time and increasing helpfulness.

Triage Capability of Laypersons: Retrospective, Exploratory Analysis (Preprint)

Background

Although medical decision-making may be thought of as a task involving health professionals, many decisions, including critical health–related decisions are made by laypersons alone. Specifically, as the first step to most care episodes, it is the patient who determines whether and where to seek health care (triage). Overcautious self-assessments (ie, overtriaging) may lead to overutilization of health care facilities and overcrowded emergency departments, whereas imprudent decisions (ie, undertriaging) constitute a risk to the patient’s health. Recently, patient-facing decision support systems, commonly known as symptom checkers, have been developed to assist laypersons in these decisions.

Objective

The purpose of this study is to identify factors influencing laypersons’ ability to self-triage and their risk averseness in self-triage decisions.

Methods

We analyzed publicly available data on 91 laypersons appraising 45 short fictitious patient descriptions (case vignettes; N=4095 appraisals). Using signal detection theory and descriptive and inferential statistics, we explored whether the type of medical decision laypersons face, their confidence in their decision, and sociodemographic factors influence their triage accuracy and the type of errors they make. We distinguished between 2 decisions: whether emergency care was required (decision 1) and whether self-care was sufficient (decision 2).

Results

The accuracy of detecting emergencies (decision 1) was higher (mean 82.2%, SD 5.9%) than that of deciding whether any type of medical care is required (decision 2, mean 75.9%, SD 5.25%; t_>90=8.4; P<.001; Cohen d=0.9). Sensitivity for decision 1 was lower (mean 67.5%, SD 16.4%) than its specificity (mean 89.6%, SD 8.6%) whereas sensitivity for decision 2 was higher (mean 90.5%, SD 8.3%) than its specificity (mean 46.7%, SD 15.95%). Female participants were more risk averse and overtriaged more often than male participants, but age and level of education showed no association with participants’ risk averseness. Participants’ triage accuracy was higher when they were certain about their appraisal (2114/3381, 62.5%) than when being uncertain (378/714, 52.9%). However, most errors occurred when participants were certain of their decision (1267/1603, 79%). Participants were more commonly certain of their overtriage errors (mean 80.9%, SD 23.8%) than their undertriage errors (mean 72.5%, SD 30.9%; t_>89=3.7; P<.001; d=0.39).

Conclusions

Our study suggests that laypersons are overcautious in deciding whether they require medical care at all, but they miss identifying a considerable portion of emergencies. Our results further indicate that women are more risk averse than men in both types of decisions. Layperson participants made most triage errors when they were certain of their own appraisal. Thus, they might not follow or even seek advice (eg, from symptom checkers) in most instances where advice would be useful.

Study protocol for a pilot prospective, observational study investigating the condition suggestion and urgency advice accuracy of a symptom assessment app in sub-Saharan Africa: the AFYA-'Health' Study

INTRODUCTION

Due to a global shortage of healthcare workers, there is a lack of basic healthcare for 4 billion people worldwide, particularly affecting low-income and middle-income countries. The utilisation of AI-based healthcare tools such as symptom assessment applications (SAAs) has the potential to reduce the burden on healthcare systems. The purpose of the AFYA Study (AI-based Assessment oF health sYmptoms in TAnzania) is to evaluate the accuracy of the condition suggestions and urgency advice provided by a user on a Swahili language Ada SAA.

METHODS AND ANALYSIS

This study is designed as an observational prospective clinical study. The setting is a waiting room of a Tanzanian district hospital. It will include patients entering the outpatient clinic with various conditions and age groups, including children and adolescents. Patients will be asked to use the SAA before proceeding to usual care. After usual care, they will have a consultation with a study-provided physician. Patients and healthcare practitioners will be blinded to the SAA's results. An expert panel will compare the Ada SAA's condition suggestions and urgency advice to usual care and study provided differential diagnoses and triage. The primary outcome measures are the accuracy and comprehensiveness of the Ada SAA evaluated against the gold standard differential diagnoses.

ETHICS AND DISSEMINATION

Ethical approval was received by the ethics committee (EC) of Muhimbili University of Health and Allied Sciences with an approval number MUHAS-REC-09-2019-044 and the National Institute for Medical Research, NIMR/HQ/R.8c/Vol. I/922. All amendments to the protocol are reported and adapted on the basis of the requirements of the EC. The results from this study will be submitted to peer-reviewed journals, local and international stakeholders, and will be communicated in editorials/articles by Ada Health.

TRIAL REGISTRATION NUMBER

NCT04958577.

Diagnostic Performance of an App-Based Symptom Checker in Mental Disorders: Comparative Study in Psychotherapy Outpatients

BACKGROUND

Digital technologies have become a common starting point for health-related information-seeking. Web- or app-based symptom checkers aim to provide rapid and accurate condition suggestions and triage advice but have not yet been investigated for mental disorders in routine health care settings.

OBJECTIVE

This study aims to test the diagnostic performance of a widely available symptom checker in the context of formal diagnosis of mental disorders when compared with therapists' diagnoses based on structured clinical interviews.

METHODS

Adult patients from an outpatient psychotherapy clinic used the app-based symptom checker Ada-check your health (ADA; Ada Health GmbH) at intake. Accuracy was assessed as the agreement of the first and 1 of the first 5 condition suggestions of ADA with at least one of the interview-based therapist diagnoses. In addition, sensitivity, specificity, and interrater reliabilities (Gwet first-order agreement coefficient [AC1]) were calculated for the 3 most prevalent disorder categories. Self-reported usability (assessed using the System Usability Scale) and acceptance of ADA (assessed using an adapted feedback questionnaire) were evaluated.

RESULTS

A total of 49 patients (30/49, 61% women; mean age 33.41, SD 12.79 years) were included in this study. Across all patients, the interview-based diagnoses matched ADA's first condition suggestion in 51% (25/49; 95% CI 37.5-64.4) of cases and 1 of the first 5 condition suggestions in 69% (34/49; 95% CI 55.4-80.6) of cases. Within the main disorder categories, the accuracy of ADA's first condition suggestion was 0.82 for somatoform and associated disorders, 0.65 for affective disorders, and 0.53 for anxiety disorders. Interrater reliabilities ranged from low (AC1=0.15 for anxiety disorders) to good (AC1=0.76 for somatoform and associated disorders). The usability of ADA was rated as high in the System Usability Scale (mean 81.51, SD 11.82, score range 0-100). Approximately 71% (35/49) of participants would have preferred a face-to-face over an app-based diagnostic.

CONCLUSIONS

Overall, our findings suggest that a widely available symptom checker used in the formal diagnosis of mental disorders could provide clinicians with a list of condition suggestions with moderate-to-good accuracy. However, diagnostic performance was heterogeneous between disorder categories and included low interrater reliability. Although symptom checkers have some potential to complement the diagnostic process as a screening tool, the diagnostic performance should be tested in larger samples and in comparison with further diagnostic instruments.

Data Integration Challenges for Machine Learning in Precision Medicine

A main goal of Precision Medicine is that of incorporating and integrating the vast corpora on different databases about the molecular and environmental origins of disease, into analytic frameworks, allowing the development of individualized, context-dependent diagnostics, and therapeutic approaches. In this regard, artificial intelligence and machine learning approaches can be used to build analytical models of complex disease aimed at prediction of personalized health conditions and outcomes. Such models must handle the wide heterogeneity of individuals in both their genetic predisposition and their social and environmental determinants. Computational approaches to medicine need to be able to efficiently manage, visualize and integrate, large datasets combining structure, and unstructured formats. This needs to be done while constrained by different levels of confidentiality, ideally doing so within a unified analytical architecture. Efficient data integration and management is key to the successful application of computational intelligence approaches to medicine. A number of challenges arise in the design of successful designs to medical data analytics under currently demanding conditions of performance in personalized medicine, while also subject to time, computational power, and bioethical constraints. Here, we will review some of these constraints and discuss possible avenues to overcome current challenges.

Beyond Dr. Google: Health information seeking from an intelligent online symptom checker: Cross-Sectional Questionnaire Study (Preprint)

Background

The ever-growing amount of health information available on the web is increasing the demand for tools providing personalized and actionable health information. Such tools include symptom checkers that provide users with a potential diagnosis after responding to a set of probes about their symptoms. Although the potential for their utility is great, little is known about such tools’ actual use and effects.

Objective

We aimed to understand who uses a web-based artificial intelligence–powered symptom checker and its purposes, how they evaluate the experience of the web-based interview and quality of the information, what they intend to do with the recommendation, and predictors of future use.

Methods

Cross-sectional survey of web-based health information seekers following the completion of a symptom checker visit (N=2437). Measures of comprehensibility, confidence, usefulness, health-related anxiety, empowerment, and intention to use in the future were assessed. ANOVAs and the Wilcoxon rank sum test examined mean outcome differences in racial, ethnic, and sex groups. The relationship between perceptions of the symptom checker and intention to follow recommended actions was assessed using multilevel logistic regression.

Results

Buoy users were well-educated (1384/1704, 81.22% college or higher), primarily White (1227/1693, 72.47%), and female (2069/2437, 84.89%). Most had insurance (1449/1630, 88.89%), a regular health care provider (1307/1709, 76.48%), and reported good health (1000/1703, 58.72%). Three types of symptoms—pain (855/2437, 35.08%), gynecological issues (293/2437, 12.02%), and masses or lumps (204/2437, 8.37%)—accounted for almost half (1352/2437, 55.48%) of site visits. Buoy’s top three primary recommendations split across less-serious triage categories: primary care physician in 2 weeks (754/2141, 35.22%), self-treatment (452/2141, 21.11%), and primary care in 1 to 2 days (373/2141, 17.42%). Common diagnoses were musculoskeletal (303/2437, 12.43%), gynecological (304/2437, 12.47%) and skin conditions (297/2437, 12.19%), and infectious diseases (300/2437, 12.31%). Users generally reported high confidence in Buoy, found it useful and easy to understand, and said that Buoy made them feel less anxious and more empowered to seek medical help. Users for whom Buoy recommended “Waiting/Watching” or “Self-Treatment” had strongest intentions to comply, whereas those advised to seek primary care had weaker intentions. Compared with White users, Latino and Black users had significantly more confidence in Buoy (P<.05), and the former also found it significantly more useful (P<.05). Latino (odds ratio 1.96, 95% CI 1.22-3.25) and Black (odds ratio 2.37, 95% CI 1.57-3.66) users also had stronger intentions to discuss recommendations with a provider than White users.

Conclusions

Results demonstrate the potential utility of a web-based health information tool to empower people to seek care and reduce health-related anxiety. However, despite encouraging results suggesting the tool may fulfill unmet health information needs among women and Black and Latino adults, analyses of the user base illustrate persistent second-level digital divide effects.

Attitudes and perception of artificial intelligence in healthcare: A cross-sectional survey among patients

Objective

The attitudes about the usage of artificial intelligence in healthcare are controversial. Unlike the perception of healthcare professionals, the attitudes of patients and their companions have been of less interest so far. In this study, we aimed to investigate the perception of artificial intelligence in healthcare among this highly relevant group along with the influence of digital affinity and sociodemographic factors.

Methods

We conducted a cross-sectional study using a paper-based questionnaire with patients and their companions at a German tertiary referral hospital from December 2019 to February 2020. The questionnaire consisted of three sections examining (a) the respondents’ technical affinity, (b) their perception of different aspects of artificial intelligence in healthcare and (c) sociodemographic characteristics.

Results

From a total of 452 participants, more than 90% already read or heard about artificial intelligence, but only 24% reported good or expert knowledge. Asked on their general perception, 53.18% of the respondents rated the use of artificial intelligence in medicine as positive or very positive, but only 4.77% negative or very negative. The respondents denied concerns about artificial intelligence, but strongly agreed that artificial intelligence must be controlled by a physician. Older patients, women, persons with lower education and technical affinity were more cautious on the healthcare-related artificial intelligence usage.

Conclusions

German patients and their companions are open towards the usage of artificial intelligence in healthcare. Although showing only a mediocre knowledge about artificial intelligence, a majority rated artificial intelligence in healthcare as positive. Particularly, patients insist that a physician supervises the artificial intelligence and keeps ultimate responsibility for diagnosis and therapy.

Performance of a new symptom checker in patient triage: Canadian cohort study

BACKGROUND

Computerized algorithms known as symptom checkers aim to help patients decide what to do should they have a new medical concern. However, despite widespread implementation, most studies on symptom checkers have involved simulated patients. Only limited evidence currently exists about symptom checker safety or accuracy when used by real patients. We developed a new prototype symptom checker and assessed its safety and accuracy in a prospective cohort of patients presenting to primary care and emergency departments with new medical concerns.

METHOD

A prospective cohort study was done to assess the prototype's performance. The cohort consisted of adult patients (≥16 years old) who presented to hospital emergency departments and family physician clinics. Primary outcomes were safety and accuracy of triage recommendations to seek hospital care, seek primary care, or manage symptoms at home.

RESULTS

Data from 281 hospital patients and 300 clinic patients were collected and analyzed. Sensitivity to emergencies was 100% (10/10 encounters). Sensitivity to urgencies was 90% (73/81) and 97% (34/35) for hospital and primary care patients, respectively. The prototype was significantly more accurate than patients at triage (73% versus 58%, p<0.01). Compliance with triage recommendations in this cohort using this iteration of the symptom checker would have reduced hospital visits by 55% but cause potential harm in 2-3% from delay in care.

INTERPRETATION

The prototype symptom checker was superior to patients in deciding the most appropriate treatment setting for medical issues. This symptom checker could reduce a significant number of unnecessary hospital visits, with accuracy and safety outcomes comparable to existing data on telephone triage.

Trust Me, I’m Not a Doctor! Determinants of Laypersons’ Trust in Medical Decision Aids: Experimental Study (Preprint)

Background

Objective

Methods

Results

Conclusions

Trial Registration

The Impact of Explanations on Layperson Trust in Artificial Intelligence-Driven Symptom Checker Apps: Experimental Study

BACKGROUND

Artificial intelligence (AI)-driven symptom checkers are available to millions of users globally and are advocated as a tool to deliver health care more efficiently. To achieve the promoted benefits of a symptom checker, laypeople must trust and subsequently follow its instructions. In AI, explanations are seen as a tool to communicate the rationale behind black-box decisions to encourage trust and adoption. However, the effectiveness of the types of explanations used in AI-driven symptom checkers has not yet been studied. Explanations can follow many forms, including why-explanations and how-explanations. Social theories suggest that why-explanations are better at communicating knowledge and cultivating trust among laypeople.

OBJECTIVE

The aim of this study is to ascertain whether explanations provided by a symptom checker affect explanatory trust among laypeople and whether this trust is impacted by their existing knowledge of disease.

METHODS

A cross-sectional survey of 750 healthy participants was conducted. The participants were shown a video of a chatbot simulation that resulted in the diagnosis of either a migraine or temporal arteritis, chosen for their differing levels of epidemiological prevalence. These diagnoses were accompanied by one of four types of explanations. Each explanation type was selected either because of its current use in symptom checkers or because it was informed by theories of contrastive explanation. Exploratory factor analysis of participants' responses followed by comparison-of-means tests were used to evaluate group differences in trust.

RESULTS

Depending on the treatment group, two or three variables were generated, reflecting the prior knowledge and subsequent mental model that the participants held. When varying explanation type by disease, migraine was found to be nonsignificant (P=.65) and temporal arteritis, marginally significant (P=.09). Varying disease by explanation type resulted in statistical significance for input influence (P=.001), social proof (P=.049), and no explanation (P=.006), with counterfactual explanation (P=.053). The results suggest that trust in explanations is significantly affected by the disease being explained. When laypeople have existing knowledge of a disease, explanations have little impact on trust. Where the need for information is greater, different explanation types engender significantly different levels of trust. These results indicate that to be successful, symptom checkers need to tailor explanations to each user's specific question and discount the diseases that they may also be aware of.

CONCLUSIONS

System builders developing explanations for symptom-checking apps should consider the recipient's knowledge of a disease and tailor explanations to each user's specific need. Effort should be placed on generating explanations that are personalized to each user of a symptom checker to fully discount the diseases that they may be aware of and to close their information gap.

Quality of condition suggestions and urgency advice provided by the Ada symptom assessment app evaluated with vignettes optimised for Australia

When people face a health problem, they often first ask, 'Is there an app for that?'. We investigated the quality of advice provided by the Ada symptom assessment application to address the question, 'How do I know the app on my phone is safe and provides good advice?'. The app was tested with 48 independently created vignettes developed for a previous study, including 18 specifically developed for the Australian setting, using an independently developed methodology to evaluate the accuracy of condition suggestions and urgency advice. The correct condition was listed first in 65% of vignettes, and in the Top 3 results in 83% of vignettes. The urgency advice in the app exactly matched the gold standard 63% of vignettes. The app's accuracy of condition suggestion and urgency advice is higher than that of the best-performing symptom assessment app reported in a previous study (61%, 77% and 52% for conditions suggested in the Top 1, Top 3 and exactly matching urgency advice respectively). These results are relevant to the application of symptom assessment in primary and community health, where medical quality and safety should determine app choice.

Mobile Health (mHealth) in Low- and Middle-Income Countries

This article reflects on current trends and proposes new considerations for the future of mobile technologies for health (mHealth). Our focus is predominantly on the value of and concerns with regard to the application of digital health within low- and middle-income countries (LMICs). It is in LMICs and marginalized communities that mHealth (within the wider scope of digital health) could be most useful and valuable. Peer-reviewed literature on mHealth in LMICs provides reassurance of this potential, often reflecting on the ubiquity of mobile phones and ever-increasing connectivity globally, reaching remote or otherwise disengaged populations. Efforts to adapt successful programs for LMIC contexts and populations are only just starting to reap rewards. Private-sector investment in mHealth offers value through enhanced capacity and advances in technology as well as the ability to meet increasing consumer demand for real-time, accessible, convenient, and choice-driven health care options. We examine some of the potential considerations associated with a private-sector investment, questioning whether a core of transparency, local ownership, equity, and safety are likely to be upheld in the current environment of health entrepreneurship. Expected final online publication date for the Annual Review of Public Health, Volume 43 is April 2022. Please see http://www.annualreviews.org/page/journal/pubdates for revised estimates.

Digital Approaches for a Reliable Early Diagnosis of Psoriatic Arthritis

Psoriatic arthritis (PsA) is a chronic inflammatory disease that develops in up to 30% of patients with psoriasis. In the vast majority of cases, cutaneous symptoms precede musculoskeletal complaints. Progression from psoriasis to PsA is characterized by subclinical synovio-entheseal inflammation and often non-specific musculoskeletal symptoms that are frequently unreported or overlooked. With the development of increasingly effective therapies and a broad drug armamentarium, prevention of arthritis development through careful clinical monitoring has become priority. Identifying high-risk psoriasis patients before PsA onset would ensure early diagnosis, increased treatment efficacy, and ultimately better outcomes; ideally, PsA development could even be averted. However, the current model of care for PsA offers only limited possibilities of early intervention. This is attributable to the large pool of patients to be monitored and the limited resources of the health care system in comparison. The use of digital technologies for health (eHealth) could help close this gap in care by enabling faster, more targeted and more streamlined access to rheumatological care for patients with psoriasis. eHealth solutions particularly include telemedicine, mobile technologies, and symptom checkers. Telemedicine enables rheumatological visits and consultations at a distance while mobile technologies can improve monitoring by allowing patients to self-report symptoms and disease-related parameters continuously. Symptom checkers have the potential to direct patients to medical attention at an earlier point of their disease and therefore minimizing diagnostic delay. Overall, these interventions could lead to earlier diagnoses of arthritis, improved monitoring, and better disease control while simultaneously increasing the capacity of referral centers.

Patient and general public attitudes towards clinical artificial intelligence: a mixed methods systematic review

Artificial intelligence (AI) promises to change health care, with some studies showing proof of concept of a provider-level performance in various medical specialties. However, there are many barriers to implementing AI, including patient acceptance and understanding of AI. Patients' attitudes toward AI are not well understood. We systematically reviewed the literature on patient and general public attitudes toward clinical AI (either hypothetical or realised), including quantitative, qualitative, and mixed methods original research articles. We searched biomedical and computational databases from Jan 1, 2000, to Sept 28, 2020, and screened 2590 articles, 23 of which met our inclusion criteria. Studies were heterogeneous regarding the study population, study design, and the field and type of AI under study. Six (26%) studies assessed currently available or soon-to-be available AI tools, whereas 17 (74%) assessed hypothetical or broadly defined AI. The quality of the methods of these studies was mixed, with a frequent issue of selection bias. Overall, patients and the general public conveyed positive attitudes toward AI but had many reservations and preferred human supervision. We summarise our findings in six themes: AI concept, AI acceptability, AI relationship with humans, AI development and implementation, AI strengths and benefits, and AI weaknesses and risks. We suggest guidance for future studies, with the goal of supporting the safe, equitable, and patient-centred implementation of clinical AI.