Multi-Channel Conversational AI for Accurate Healthcare Query Responses
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Solution Overview
Problem
The healthcare industry faces complexities in navigating insurance plans, leading to customer frustration due to high agent training needs and turnover, and existing automated conversational agents provide inaccurate and rigid responses.
Innovation Solution
A conversational AI agent system using finetuned machine learning models, integrated with retrieval-augmented generation and knowledge graphs, to provide personalized customer care across various communication channels, recognizing intents and generating contextually relevant responses.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Extent of automation
If rule-based automated conversational agents are used, then automation extent is improved, but response accuracy and relevance deteriorate
Solution Approach 1:
The patent replaces rule-based mechanical systems with neural network-based intelligent systems. The conversational agent uses trained neural networks to understand user intent and generate contextually relevant responses, substituting rigid if-then logic with adaptive pattern recognition and natural language processing capabilities.
Solution Approach 2:
The system changes the operational parameters of the conversational agent from fixed rules to dynamic parameters learned from training data. The neural network adjusts its response behavior based on patterns learned during training, allowing flexible adaptation to different query types and contexts while maintaining high accuracy.
2Reliability
If human agents are used, then response accuracy is improved, but training time and operational costs increase
Solution Approach 1:
The patent creates an artificial intelligence copy of human expert knowledge through neural network training. The system learns from training data containing examples of accurate responses to healthcare insurance queries, capturing expert knowledge in a digital model that can serve countless customers simultaneously without requiring ongoing human training.
Solution Approach 2:
The conversational agent performs self-learning through automated training processes. The neural network automatically adjusts its parameters and improves its responses through exposure to training data, eliminating the need for manual training of human agents while maintaining high response accuracy.
3Loss of information
If comprehensive insurance plan coverage is provided, then information completeness is improved, but system complexity increases
Solution Approach 1:
The patent introduces an intermediary layer between the complex insurance data and the user. The neural network acts as a mediator that automatically processes, filters, and presents only the relevant insurance plan information needed to answer specific user questions, shielding users from underlying system complexity while providing comprehensive information.
Solution Approach 2:
The system segments complex insurance plan information into manageable components based on user intent. The conversational agent breaks down comprehensive plan details into specific, relevant facts that directly address the user's question, presenting information in digestible portions rather than overwhelming users with complete plan documentation.
Data Source
AI summary
The disclosure relates to systems, methods, and computer-readable media for an artificial intelligence (AI) conversational agent configured for enhanced user interaction in multi-channel contact centers. An example method includes receiving a query regarding a particular property of an asset during a real-time conversation, classifying an intent of the query into a predefined category, triggering, based on the classifying of the intent, a finetuned large language model for the predefined category to answer the query, generating, by the finetuned large language model, a response to the query based on at least one of an information data model and a knowledge graph data store, and outputting the response to the query.