Mental Health Symptom Reporting to a Virtual Triage Engine Prior to and During the COVID-19 Pandemic

Abstract

Objective: To examine patient-user symptom reporting to an AI-based online virtual triage (VT) and care-referral engine to assess patterns of mental health symptoms (MHS) reporting prior to and during the COVID-19 pandemic. Methods: The frequencies of 11 MHS reported through VT were analyzed over three time intervals: one year prior to the WHO declaring a global COVID-19 emergency; from pandemic declaration to a mid-point in US vaccine distribution/uptake; and one year thereafter. Results: A total of 4,346,987 VT encounters/interviews presenting somatic and MHS occurred, increasing over time and peaking in the COVID-19 post-vaccine interval with 2,257,553 encounters (51.9%). In 866,218 encounters (19.9%), at least one MHS was reported. MHS reporting declined across subsequent time intervals, was lowest in the COVID-19 post-vaccine period (19.1%), and slightly higher in the pre-pandemic and COVID-19 pre-vaccine intervals (p = 0.05). The most frequently reported symptoms were anxiety, sleep disorder, general anxiety, irritability, and nervousness. Women reported anxiety less often and nervousness and irritability more often. Individuals aged 60+ years reported anxiety and nervousness less frequently, insomnia and sleep disorder more often than individuals 18–39 and 40–59 years old, and sleep disorder more often than those aged 40–59 years in all periods (all p = 0.05). Conclusions: Overall VT usage for somatic and mental health symptom reporting and care referral increased dramatically during the pandemic. VT effectively screened and provided care referral for patient-users presenting with MHS. Virtual triage offers a valuable additional vehicle to detect mental health symptoms and potentially accelerate care referral for patients needing care.

Keywords

• COVID-19
• SARS-CoV-2
• virtual clinical triage
• artificial intelligence
• digital triage
• mental health symptoms