Factors Influencing the Use of Online Symptom Checkers in the United Kingdom: Cross-Sectional Study
Bibliographic record
Abstract
Background: The National Health Service (NHS) faces increasing strain. Concurrently, demand for health information, consumer empowerment, and health awareness continues to grow. These trends, coupled with the ubiquity of smartphones and internet access, are positioning online symptom checkers (OSCs) as promising tools for preliminary diagnosis and triage. While there is increasing data on the demographics, motivations, and perspectives of current and potential users of OSCs globally, no study has yet quantified or ranked the various factors associated with the use of OSCs in the United Kingdom. Objective: This study aimed to assess key trends and user perceptions on the usability and effectiveness of OSC in the United Kingdom. We also sought to identify concerns related to the privacy, security, and accuracy of OSCs and to quantify the weight of these various factors on the use of OSCs. Methods: A cross-sectional survey of UK adults was conducted using an electronic questionnaire. A convenience sample was recruited between February and March 2024 through web-based platforms and personal networks. The survey included questions on awareness, use, perceptions, and concerns regarding OSCs, as well as respondents' demographics. Responses were pseudo-anonymized and analyzed using univariable and multivariable logistic regression models to assess relationships between demographic factors; perceived usability, reliability, and risks; and OSC use. Results: The survey collected responses from 634 participants. The majority (543/634, 85.7%) had used OSCs, primarily the NHS 111 service (498/634, 78.6%). Younger age (<46 years old), being female (adjusted odds ratio [aOR] 1.79, 95% CI 1.05-3.06), and having children (aOR 3.19, 95% CI 1.56-6.51) were associated with higher odds of using OSCs. Key motivations for using OSCs included understanding symptoms (501/634, 79.0%) and determining the need for medical care (491/634, 77.4%). Key concerns negatively impacting use related to privacy (aOR 0.58, 95% CI 0.35-0.97) and fear of replacing traditional, face-to-face consultations (aOR 0.47, 95% CI 0.26-0.87). The most important factor found to affect the decision to use OSCs was the perceived ease of use (aOR 8.17, 95% CI 4.25-15.71), followed by the perceived helpfulness in decision-making (aOR 2.96, 95% CI 1.62-5.42), and respondents' trust in their diagnostic accuracy (aOR 2.24, 95% CI 1.32-3.79). Conclusions: OSCs are widely used in the United Kingdom, particularly the NHS 111 service, driven primarily by ease of use and perceived helpfulness in decision support. However, privacy and security concerns, as well as fears of OSCs replacing traditional consultations, pose significant barriers. Addressing these concerns is crucial for enhancing user trust and maximizing the benefits of OSCs in supporting self-care and improving health care efficiency.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".