AI Message Intent Routing Across Multi-Regional Healthcare Channels
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Solution Overview
Problem
Healthcare organizations face challenges in managing healthcare-related requests across multiple channels due to siloed interactions, lack of channel integration, and inefficient processing of incoming messages, leading to missed opportunities and increased human intervention.
Innovation Solution
A system utilizing advanced natural language processing (NLP) and artificial intelligence (AI)/machine learning (ML) models to identify message intents and automate routing, integrating multiple channels for seamless transitions and prioritization.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If multiple communication channels are used for patient interactions, then patient access and convenience are improved, but channel integration and message organization deteriorate
Solution Approach 1:
The system segments the complex message processing task into distinct functional components: an NLP engine that extracts intent and entities from messages, a routing engine that determines destination based on intent, and separate processing paths for different message types. This segmentation allows each component to handle specific aspects independently, reducing overall system complexity while maintaining multi-channel capability
Solution Approach 2:
The patent introduces an intermediary processing layer (the intelligent message processing system) that sits between multiple communication channels and the healthcare organization's internal systems. This intermediary standardizes and structures incoming messages from various channels into a common format, enabling integrated processing without requiring changes to the underlying channel infrastructure or target systems
2Productivity
If manual processing is used for incoming messages, then message accuracy and understanding are improved, but processing time and resource consumption increase
Solution Approach 1:
The system implements self-service through automated NLP-based intent detection and entity extraction that processes messages without human intervention. The routing engine automatically determines the appropriate destination based on extracted intent, and the system can trigger automated responses or actions directly. This self-service capability dramatically increases processing speed while maintaining accuracy through sophisticated natural language understanding algorithms
3Measurement precision
If advanced NLP and AI/ML models are deployed for intent detection, then message understanding accuracy is improved, but system complexity and computational resources increase
Solution Approach 1:
The complex NLP processing is segmented into distinct stages: text preprocessing, intent classification, entity extraction, and routing decision generation. Each stage is handled by specialized AI/ML models or algorithms that can be independently optimized and managed. This segmentation reduces the apparent system complexity by breaking down the monolithic NLP task into manageable, modular components
Solution Approach 2:
The patent implements a universal message processing framework that handles multiple message types (chat, email, SMS, voice transcripts) and multiple intents (appointment scheduling, prescription refills, information requests) through a single integrated system. The NLP engine and routing engine serve multiple functions across different communication channels and message purposes, reducing overall system complexity through multi-functionality rather than requiring separate specialized systems for each message type
Data Source
AI summary
A system for multi-regional intelligent classification and routing based on processing a message uses an artificial intelligence platform. Artificial Intelligence (AI) and machine learning (ML) based approaches significantly optimize the user experience and efficiently utilize care coordination and delivery workflows in healthcare organizations. Trained machine learning models are used to intelligently route messages from a patient member seeking access to care, determining an intent and entities from the message to perform an action in response to their message.