Young Adults’ Perspectives on the Use of Symptom Checkers for Self-Triage and Self-Diagnosis: Qualitative Study

Abstract

Background Young adults often browse the internet for self-triage and diagnosis. More sophisticated digital platforms such as symptom checkers have recently become pervasive; however, little is known about their use. Objective The aim of this study was to understand young adults’ (18-34 years old) perspectives on the use of the Google search engine versus a symptom checker, as well as to identify the barriers and enablers for using a symptom checker for self-triage and self-diagnosis. Methods A qualitative descriptive case study research design was used. Semistructured interviews were conducted with 24 young adults enrolled in a university in Ontario, Canada. All participants were given a clinical vignette and were asked to use a symptom checker (WebMD Symptom Checker or Babylon Health) while thinking out loud, and were asked questions regarding their experience. Interviews were audio-recorded, transcribed, and imported into the NVivo software program. Inductive thematic analysis was conducted independently by two researchers. Results Using the Google search engine was perceived to be faster and more customizable (ie, ability to enter symptoms freely in the search engine) than a symptom checker; however, a symptom checker was perceived to be useful for a more personalized assessment. After having used a symptom checker, most of the participants believed that the platform needed improvement in the areas of accuracy, security and privacy, and medical jargon used. Given these limitations, most participants believed that symptom checkers could be more useful for self-triage than for self-diagnosis. Interestingly, more than half of the participants were not aware of symptom checkers prior to this study and most believed that this lack of awareness about the existence of symptom checkers hindered their use. Conclusions Awareness related to the existence of symptom checkers and their integration into the health care system are required to maximize benefits related to these platforms. Addressing the barriers identified in this study is likely to increase the acceptance and use of symptom checkers by young adults.

Latent classes associated with the intention to use a symptom checker for self-triage

“…To our knowledge, our study is the first to merge the TAM and LCA literature to identify profiles among university students and regress these profiles on future symptom checker use. Interestingly, while young adults are perceived to be technology savvy, most of the participants recruited had not used a symptom checker in the past year–this may be due to the lack of awareness regarding the existence of these platforms [ 21 ]. Most had positive perspectives regarding the use of AI in health and symptom checkers’ functionality; however, some skepticism and issues related to perceived accessibility and functionality may hinder the future adoption and use of symptom checkers.…”

“…Moreover, to reduce respondent burden, not all factors included in the UTAUT were measured in the survey. A shortlist of factors was developed based on the UTAUT model and a ranking exercise conducted with 22 participants from the same target population (i.e., university students) as part of semi-structured interviews–this list is included in S3 Appendix and findings from this work can be found elsewhere [ 21 ].…”

Avey: An Accurate AI Algorithm for Self-Diagnosis

“…As an outcome, 400 vignettes were produced and standardized. To the best of our knowledge, this is the largest benchmark vignette 2 That is, we always ignore every earlier acceptance and rejection of a vignette if it gets changed at any point in time (no matter how big or small is the change) and start over the reviewing process of the vignette from scratch with all the 7 external doctors.…”

“…To the contrary of search engines, symptom checkers (referred henceforth to as checkers ) are patient-facing medical diagnostic tools that mimic clinical reasoning, especially if they use Artificial Intelligence (AI) [2, 27]. They are trained to make medical expertlike judgements on behalf of patients.…”

“…Nevertheless, the utility and promise of checkers cannot be materialized if they do not prove to be accurate in self-diagnosis [2]. A recent study has showed that most patients (more than 76%) use checkers solely for self-diagnosis [39].…”