Intelligent Health Data Filtering via Terminology Conversion
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Solution Overview
Problem
Healthcare providers face challenges in efficiently filtering and retrieving relevant health-related information from a vast amount of data encoded in multiple standard terminologies, which can hinder timely and accurate treatment decisions.
Innovation Solution
An intelligent filtering system that converts health-related information from external standard terminologies into internal medical terminology, using a data extraction engine, terminology conversion engine, relevancy search engine, and user interface engine to identify and present relevant information to caregivers.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If health-related information is stored in multiple external standard terminologies for comprehensive data collection, then the quantity and completeness of health data is improved, but the complexity of filtering and retrieving relevant information increases
Solution Approach 1:
The patent introduces an intermediary component (terminology conversion engine or mapping layer) that translates between multiple external standard terminologies and an internal medical terminology. This intermediary handles the complexity of multi-terminology data integration, allowing the system to maintain comprehensive data from various sources while simplifying the retrieval process by converting all data to a common internal representation that can be efficiently searched and filtered.
2Loss of information
If all health-related data is made available to caregivers, then the completeness of information for treatment decisions is improved, but the time required to locate relevant data increases
Solution Approach 1:
The patent extracts and highlights only the relevant health information from the comprehensive data set based on the caregiver's query or the patient's current condition. The system identifies and pulls out specific data elements (such as current medications, allergies, or relevant lab results) from the vast repository of health information, presenting them in a prioritized manner that reduces the time caregivers need to search while ensuring all critical information is captured.
Solution Approach 2:
The system performs preliminary organization and indexing of health data according to multiple external standards and relationships before the caregiver needs to access it. By pre-processing the data into structured formats with established relationships between different data elements, the system prepares the information in advance so that when a caregiver queries for specific information, the relevant data can be rapidly retrieved without requiring real-time analysis of the entire data set.
3Ease of operation
If data is converted into internal medical terminology for standardized processing, then the ease of information retrieval is improved, but the processing time for data conversion increases
Solution Approach 1:
The system performs terminology conversion in advance during data ingestion and storage, converting external standard terminologies to internal medical terminology before the data needs to be retrieved. This preliminary conversion action eliminates the need for real-time translation during query operations, making information retrieval fast and efficient while accepting the conversion time cost during the initial data processing phase.
Data Source
AI summary
Intelligent filtering of health-related information includes receiving health-related information including items encoded in one or more external standard terminologies. The health-related information is converted from the external standard terminologies into an internal medical terminology. Items within the health-related information are then identified that are related to a selected term of the internal medical terminology.