ArticleJMIR human factors2020
Patients' Utilization and Perception of an Artificial Intelligence-Based Symptom Assessment and Advice Technology in a British Primary Care Waiting Room: Exploratory Pilot Study.
Article in JMIR human factors, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 62 papers, 3 of them syntheses that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
62 citing papers in PubMed, 3 syntheses or guidelines pooled it, 133 citations in OpenAlex.
- Opportunities, challenges, and requirements for Artificial Intelligence (AI) implementation in Primary Health Care (PHC): a systematic review.BMC primary care · 2025Pooled it
- Triage and Diagnostic Accuracy of Online Symptom Checkers: Systematic Review.Journal of medical Internet research · 2023Pooled it
- Challenges in evaluating the accuracy of AI-containing digital triage systems: A systematic review.PloS one · 2022Pooled it
- Comparison of Two Symptom Checkers (Ada and Symptoma) in the Emergency Department: Randomized, Crossover, Head-to-Head, Double-Blinded Study.Journal of medical Internet research · 2024Trial
- Diagnostic Accuracy of a Mobile AI-Based Symptom Checker and a Web-Based Self-Referral Tool in Rheumatology: Multicenter Randomized Controlled Trial.Journal of medical Internet research · 2024Trial
- Can clinical decision support systems be an asset in medical education? An experimental approach.BMC medical education · 2023Trial
- Patient's Perception of Digital Symptom Assessment Technologies in Rheumatology: Results From a Multicentre Study.Frontiers in public health · 2022Trial
- Accuracy, patient-perceived usability, and acceptance of two symptom checkers (Ada and Rheport) in rheumatology: interim results from a randomized controlled crossover trial.Arthritis research & therapy · 2021Trial
- Functionality Review of Mobile Apps for the Tracking and Self-Management of Fatigue: Systematic Search in App Stores and Content Analysis.JMIR mHealth and uHealth · 2026Article
- Real-World Evaluation of the Eye+Dot Online Triage Support Tool in Community Optometry Practices: Mixed Methods Evaluation Study.JMIR human factors · 2026Article
- Exploring Patient Perspectives, Engagement, and Output Quality in Doctor-Supervised Use of Artificial Intelligence During Informed Consent Consultation With ChatGPT and Retrieval Augmented Generation (RAG): Quantitative Exploratory Study.Journal of medical Internet research · 2025Article
- Factors Influencing the Use of Online Symptom Checkers in the United Kingdom: Cross-Sectional Study.JMIR formative research · 2025Article
- Article
- AI and Primary Care: Scoping Review.Journal of medical Internet research · 2025Article
- Dr AI will see you now: could artificial intelligence replace general practitioners?Irish journal of medical science · 2025Article
- Article
- The status and challenges of online consultation service in internet hospitals operated by physical hospitals in China: a large-scale pooled analysis of multicenter data.BMC health services research · 2025Article
- Exploring Laypersons' Experiences With a Mobile Symptom Checker App as an Interface Between eHealth Literacy, Health Literacy, and Health-Related Behavior: Qualitative Interview Study.JMIR formative research · 2025Article
- Technology-supported self-triage decision making.npj health systems · 2025Article
- Contrasting rule and machine learning based digital self triage systems in the USA.NPJ digital medicine · 2024Review
2 more citing papers are in PubMed but not listed here.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundWhen someone needs to know whether and when to seek medical attention, there are a range of options to consider. Each will have consequences for the individual (primarily considering trust, convenience, usefulness, and opportunity costs) and for the wider health system (affecting clinical throughput, cost, and system efficiency). Digital symptom assessment technologies that leverage artificial intelligence may help patients navigate to the right type of care with the correct degree of urgency. However, a recent review highlighted a gap in the literature on the real-world usability of these technologies.
objectiveWe sought to explore the usability, acceptability, and utility of one such symptom assessment technology, Ada, in a primary care setting.
methodsPatients with a new complaint attending a primary care clinic in South London were invited to use a custom version of the Ada symptom assessment mobile app. This exploratory pilot study was conducted between November 2017 and January 2018 in a practice with 20,000 registered patients. Participants were asked to complete an Ada self-assessment about their presenting complaint on a study smartphone, with assistance provided if required. Perceptions on the app and its utility were collected through a self-completed study questionnaire following completion of the Ada self-assessment.
resultsOver a 3-month period, 523 patients participated. Most were female (n=325, 62.1%), mean age 39.79 years (SD 17.7 years), with a larger proportion (413/506, 81.6%) of working-age individuals (aged 15-64) than the general population (66.0%). Participants rated Ada's ease of use highly, with most (511/522, 97.8%) reporting it was very or quite easy. Most would use Ada again (443/503, 88.1%) and agreed they would recommend it to a friend or relative (444/520, 85.3%). We identified a number of age-related trends among respondents, with a directional trend for more young respondents to report Ada had provided helpful advice (50/54, 93%, 18-24-year olds reported helpful) than older respondents (19/32, 59%, adults aged 70+ reported helpful). We found no sex differences on any of the usability questions fielded. While most respondents reported that using the symptom checker would not have made a difference in their care-seeking behavior (425/494, 86.0%), a sizable minority (63/494, 12.8%) reported they would have used lower-intensity care such as self-care, pharmacy, or delaying their appointment. The proportion was higher for patients aged 18-24 (11/50, 22%) than aged 70+ (0/28, 0%).
conclusionsIn this exploratory pilot study, the digital symptom checker was rated as highly usable and acceptable by patients in a primary care setting. Further research is needed to confirm whether the app might appropriately direct patients to timely care, and understand how this might save resources for the health system. More work is also needed to ensure the benefits accrue equally to older age groups.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.