AI Audio Processing for Early Disease Identification
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Solution Overview
Problem
Current healthcare systems face delays in diagnosing diseases, leading to postponed outreach and less desirable outcomes due to subjective evaluations and ineffective preparations by customer service representatives (CSRs), resulting in missed opportunities for timely intervention and increased costs.
Innovation Solution
Implementing real-time audio processing using artificial intelligence to analyze interactions between CSRs and members, generating text data, predicting topics, detecting conditions, and automatically identifying actions to improve engagement and early disease identification, thereby enhancing member care and reducing lag times in treatment.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If real-time audio processing using AI is implemented, then early disease identification accuracy is improved, but system complexity increases
Solution Approach 1:
The system segments the complex audio processing task into distinct functional modules: audio signal reception, speech-to-text conversion, text analysis, condition detection, and action identification. Each module handles a specific aspect of the processing pipeline, making the overall system more manageable and maintainable while achieving high diagnostic accuracy
Solution Approach 2:
The patent introduces an intermediary speech-to-text conversion layer that transforms audio signals into text data before analysis. This intermediary step decouples the audio processing from the diagnostic logic, allowing each component to be optimized independently and reducing overall system complexity
2Productivity
If AI-based audio processing is used, then member engagement effectiveness is improved, but processing time and computational resources increase
Solution Approach 1:
The system performs preliminary speech-to-text conversion and text analysis during the interaction itself rather than after completion. This allows real-time detection of conditions and generation of actionable insights while the member is still engaged, eliminating post-processing delays and enabling immediate intervention
Solution Approach 2:
The audio processing and analysis operations continue throughout the duration of the member interaction without interruption. The system processes audio streams continuously, maintaining constant surveillance for diagnostic indicators while the CSR engages the member, ensuring no valuable information is missed
Data Source
AI summary
A system and method for audio processing using artificial intelligence during a voice interaction is configured to improve early identification of disease and case management of a diagnosed member in a healthcare system. Audio data is generated from a voice call between customer service representative (CSR) of the healthcare system and then processed by one or more of a speech-to-text recognition model, a topic detection model, a speaker recognition model, a tone model, an intent identifier, and a sentiment identifier. Then the output of the audio processing is used to detect a condition and automatically perform an action.