AI Caller Stress Tracking for Real-Time Mental Health Calls
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Solution Overview
Problem
Mental health call centers face challenges in managing longer calls that require tracking changes in a caller's emotional state, which is crucial for effective crisis intervention.
Innovation Solution
An AI-supported system that analyzes audio and text features from calls to calculate and track stress levels, providing visual indications to call takers about when to continue or end calls based on stress levels and identified stressors.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual tracking of emotional state is used, then call taker autonomy is maintained, but tracking precision and reliability are insufficient
Solution Approach 1:
An AI system acts as an intermediary between the call taker and the caller, automatically analyzing audio and text data to track emotional state. This intermediary handles the complex measurement task, providing precise emotional state tracking without requiring the call taker to manually monitor and interpret emotional cues, thus resolving the contradiction between measurement precision and operational simplicity.
2Reliability
If call duration is extended to ensure positive outcomes, then intervention effectiveness improves, but loss of time increases
Solution Approach 1:
The system implements real-time feedback by continuously monitoring emotional state and providing alerts to call takers when positive outcomes are detected or when calls should be ended. This feedback mechanism enables dynamic adjustment of call duration based on actual emotional state changes, ensuring interventions are extended only as long as necessary to achieve positive outcomes, thus resolving the contradiction between intervention effectiveness and time loss.
3Productivity
If real-time emotional state analysis is implemented, then call management quality improves, but computational energy consumption increases
Solution Approach 1:
The system applies partial action by focusing computational resources on analyzing only the most relevant emotional and contextual features from audio and text data, rather than processing all possible data points. This selective analysis approach maintains high call management quality while reducing overall computational energy consumption by concentrating processing power on critical indicators of emotional state and call outcome readiness.
Data Source
AI summary
A communication system and method provide for tracking emotional state of a caller during a call center communication using artificial intelligence. A mental health answering point (102) interoperates with an artificial intelligence server (108) wherein the AI server is configured to: extract audio and text features from the call in real time. The AI server calculates and stores stress levels associated with the caller, from the extracted audio features, over time and synchronizes the stored stress levels with the extracted text features from the call. The stress levels are monitored during the call, and visual indicators are provided for high/increasing stress levels and low/decreasing stress levels.