Medical Knowledge Packet Generation in Active Conversations
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Solution Overview
Problem
Accessing and retrieving relevant medical knowledge during conversations between medical professionals and patients is time-consuming and prone to inaccuracies due to the reliance on manual searches through extensive medical literature.
Innovation Solution
A method and system for generating a medical knowledge packet in an active conversation session using a machine learning model to determine the context of messages, sourcing information from both prestored medical libraries and external data sources, and assigning confidence scores to ensure accuracy and relevance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual searches through medical books and guidelines are used, then medical knowledge can be accessed, but it is time-consuming and leads to delays
Solution Approach 1:
The patent replaces manual mechanical search processes with an automated AI-based system. The machine learning model automatically queries medical knowledge bases, retrieves relevant information, and generates knowledge packets without requiring manual searching through medical books and guidelines, thus eliminating time delays while maintaining accuracy
Solution Approach 2:
The system enables self-service by automatically generating and providing medical knowledge packets in response to conversation queries. The AI model independently retrieves, processes, and formats medical information without requiring healthcare professionals to manually search through extensive medical literature, allowing instant access to accurate knowledge
2Productivity
If manual searches are performed, then medical information can be retrieved, but inaccuracies occur
Solution Approach 1:
The patent implements feedback mechanisms where the AI model continuously refines its responses based on the conversation context and medical knowledge base. The system validates retrieved information against multiple sources and adjusts its knowledge packet generation to ensure accuracy while maintaining high retrieval speed
Solution Approach 2:
The system replaces error-prone manual searching with automated AI-based retrieval that systematically queries multiple medical knowledge sources, cross-validates information, and generates standardized knowledge packets, thereby eliminating inaccuracies while improving retrieval speed
3Quantity of substance
If extensive medical literature is searched manually, then comprehensive knowledge is accessed, but the process is cumbersome
Solution Approach 1:
The patent extracts only the most relevant and essential medical information from extensive literature using AI-based filtering and selection algorithms. The machine learning model identifies and retrieves only the knowledge packets that are directly relevant to the conversation context, eliminating the need to manually navigate through vast amounts of medical literature while maintaining comprehensive coverage of important topics
Solution Approach 2:
The system replaces cumbersome manual navigation through extensive medical literature with automated AI-based information extraction and presentation. The machine learning model processes large volumes of medical knowledge, identifies relevant information, and presents it in formatted knowledge packets, making access to comprehensive medical information simple and efficient
4Reliability
If healthcare professionals manually search for medical knowledge, then they can find information, but they cannot stay up to date with latest developments
Solution Approach 1:
The patent implements preliminary action by continuously updating and maintaining the medical knowledge base with the latest medical developments, guidelines, and research. The AI model is pre-trained on current medical literature and continuously learns from new sources, ensuring that healthcare professionals receive the most up-to-date information instantly without requiring manual updates or research
Solution Approach 2:
The system replaces the manual process of staying updated with automated AI-based knowledge retrieval that continuously accesses and integrates the latest medical developments. The machine learning model automatically incorporates new medical information into its responses, enabling healthcare professionals to stay current with minimal effort while maintaining high reliability of the information provided
Data Source
AI summary
A system and a method for generating a medical knowledge packet in an active conversation session. The system receives a message associated with a conversation thread. The system determines a context of the message using a machine learning model. Further, one or more medical knowledge packets from one or more sources may be generated based on the message and the context. A confidence score to the medical knowledge packet is assigned based on one or more factors comprising relevance, accuracy, the one or more sources, and recency of the medical knowledge packet. The medical knowledge packet with a highest confidence score is modified by formatting, summarizing, highlighting, cross-referencing, and simplifying by using one or more text analysis algorithms.