Impact of Integrated Virtual and Live Nurse Triage on Patient Care Seeking and Health Care Delivery Effectiveness and Efficiency

Abstract

Objective: Evaluate if artificial intelligence (AI)-based virtual triage (VT) and care referral technology integrated with a live nurse triage workflow can improve care acuity alignment by appropriately altering patient-member post-triage health care intent and care seeking. Methods: Data were extracted from Infermedica’s AI-based call center triage application, implemented by the Médis health plan in their VT-informed nurse triage service over a 26-month period, from January 2022 through March 2024. Patient-member care seeking intent pre- and post-VT-informed nurse triage, as well as nurse triage recommendation, were grouped into five care acuity levels, and responses were compared to the output care recommendation of the VT process, including four kinds of medical consultation available for nurses to schedule. Pre- and post-triage care intent and care seeking behavior were compared and differences assessed for statistical significance. Analyses were conducted on a dataset of eligible patient-members interviews (N = 54,587) to examine if the use of VT influenced patient-member care seeking behavior. We examined if post-triage care seeking behavior aligned with that recommended, and if it changed as a result of triage and in what direction. To determine statistical significance of differences in care intent pre- and post-VT-informed nurse triage, Z-tests were performed. Results: The impact of VT-informed nurse triage recommendations was high with 83.9% of encounters influencing patient-member care seeking behavior, and 22.8% changing their care seeking intent as a result. Of these, 62.2% (14.2% of all patient-members) de-escalated care intent to a lower acuity care service, while 37.8% (8.6% of all patient-members) escalated to higher acuity care (p = 0.05). There was a substantial post-triage increase in patient-members intending to engage in self-care (+5.5 percentage points or PP or +39.5%), and a decrease in patient-members with a pre-triage intent to seek an urgent outpatient consultation within 24 h (-5.0 PP or -8.4%) (p = 0.05). The largest group of 14,109 patient-members (35.6%) decided to instead schedule a telemedicine consultation. One-fifth of encounters occurred outside usual operating hours, when patient-members were nearly twice as likely to initially intend to visit an emergency department, indicating strong after-hours patient-member care need and demand, with 48.4% of these cases de-escalated to lower care acuity settings. Conclusions: Integration of AI-based VT with a live triage nurse workflow was effective in yielding much improved care acuity alignment by changing patient post-triage care intent and care seeking, particularly in de-escalating care appropriately from in-person outpatient care to telemedical/virtual care delivery or self-care.