AI-Driven Virtual Triage for Behavioral Health: A Technical Review
DOI:
https://doi.org/10.15662/IJRAI.2023.0604010Keywords:
AI-Driven Behavioral Health Triage, Speech Emotion Recognition In Mental Health, Natural Language Processing For Crisis Detection, Predictive Risk Stratification In Psychiatry, Digital Mental Health InterventionAbstract
Behavioral health systems face compounding pressures from rising mental health demand, clinician workforce shortages, and triage infrastructures ill-equipped to detect crisis signals in real time. Traditional intake processes use a set series of questions that don't change based on a person's emotional state, which means that people in serious distress may not get the attention they need while less urgent cases take up valuable This article looks at how to create and use an AI-driven virtual triage platform that understands emotions, uses specialized language processing for behavioral health, and assesses risk in real time to change crisis response from being reactive to proactive. The platform looks at voice patterns, the meaning of what is said, and the caller's behavior over time during conversations, creating a flexible risk assessment that automatically prompts further help and directs Application domains that span crisis hotlines, emergency departments, telehealth providers, and managed care organizations. The technical architecture has four layers that depend on each other: The architecture includes emotion recognition, NLP-based semantic risk detection, a multi-factor scoring engine, and a FHIR-compliant integration framework. Each layer has its design trade-offs. Ethical governance issues, such as fairness in algorithms, clarity in processes, the involvement of humans in decision-making, and the ability A phased implementation helps bridge the difference between testing the system and using it on a large scale, while also evaluating its benefits in areas like healthcare, operations, finances, and social effects.
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