Sexually Transmitted Infection Symptom Reporting to AI-Based Virtual Triage and Care Referral

Objective: Evaluate the effectiveness of artificial intelligence-based virtual triage and care referral in describing, engaging and influencing care seeking behavior among patients with symptoms indicative of sexually transmitted infections. Methods: Evaluated 4,487,191 virtual triage encounters among patients aged 12+ years over 36 months for sexually transmitted infections ranked as top three conditions. Demographics and type of care seeking before and following virtual triage were quantified and relationships assessed for statistical significance. Results: A total 115,109 (2.6%) virtual triage encounters were consistent with a possible sexually transmitted infection. Most patients were female (64.5%) and 89.2% were aged 18–44. Over half of patients completed the virtual triage encounters in English or Spanish. Frequency of symptoms consistent with sexually transmitted infections reflected low detection rates due to the largely asymptomatic nature of these conditions. Of 5,717 patients completing pre- and post-triage care intent surveys, care seeking increased 6.1% post-triage (p<0.05); those uncertain about their care intent or planning no action decreased 20.0% (p<0.05). However, 46.3% of patients opted for contraindicated self-care measures post-triage, a 13.8% increase (p<0.05). Conclusions: Virtual triage substantially influenced care seeking among individuals reporting symptoms suggestive of a sexually transmitted infection. Patients unsure how to address their symptoms or who planned inaction decreased significantly, while users intending to seek care increased. Virtual triage reduced indecision among patients by converting passive symptom acknowledgement into proactive intent to seek care. Increase in patients opting for self-care measures after virtual triage indicated a possible sexually transmitted infection, and despite a recommendation to seek care, suggests that certain barriers to care seeking for these infections may be too deeply rooted to be addressed by virtual triage technology alone.

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Publication Details

Journal
Medical Research Archives
Published
2025-01-01
DOI
https://doi.org/10.18103/mra.v13i5.6475
Primary Topic
Machine Learning in Healthcare
Type
article
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article

Sexually Transmitted Infection Symptom Reporting to AI-Based Virtual Triage and Care Referral

Gabriel Gellert, Tim Price, Mateusz Palczewski, George A. Gellert et al.
Medical Research Archives
Machine Learning in Healthcare
article

Sexually Transmitted Infection Symptom Reporting to AI-Based Virtual Triage and Care Referral

Gabriel Gellert, Tim Price, Mateusz Palczewski, George A. Gellert, Katarzyna Trybucka, Maria Marecka
article en

Abstract

Objective: Evaluate the effectiveness of artificial intelligence-based virtual triage and care referral in describing, engaging and influencing care seeking behavior among patients with symptoms indicative of sexually transmitted infections. Methods: Evaluated 4,487,191 virtual triage encounters among patients aged 12+ years over 36 months for sexually transmitted infections ranked as top three conditions. Demographics and type of care seeking before and following virtual triage were quantified and relationships assessed for statistical significance. Results: A total 115,109 (2.6%) virtual triage encounters were consistent with a possible sexually transmitted infection. Most patients were female (64.5%) and 89.2% were aged 18–44. Over half of patients completed the virtual triage encounters in English or Spanish. Frequency of symptoms consistent with sexually transmitted infections reflected low detection rates due to the largely asymptomatic nature of these conditions. Of 5,717 patients completing pre- and post-triage care intent surveys, care seeking increased 6.1% post-triage (p<0.05); those uncertain about their care intent or planning no action decreased 20.0% (p<0.05). However, 46.3% of patients opted for contraindicated self-care measures post-triage, a 13.8% increase (p<0.05). Conclusions: Virtual triage substantially influenced care seeking among individuals reporting symptoms suggestive of a sexually transmitted infection. Patients unsure how to address their symptoms or who planned inaction decreased significantly, while users intending to seek care increased. Virtual triage reduced indecision among patients by converting passive symptom acknowledgement into proactive intent to seek care. Increase in patients opting for self-care measures after virtual triage indicated a possible sexually transmitted infection, and despite a recommendation to seek care, suggests that certain barriers to care seeking for these infections may be too deeply rooted to be addressed by virtual triage technology alone.

Medical Research ArchivesVol. 13(5)
Government Ayurvedic College and Hospital (IN)
Openalex Percentile: Top 94%
Machine Learning in Healthcare
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