Natural Language Query Translation for Electronic Health Records
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Solution Overview
Problem
Current electronic health record (EHR) systems face challenges in merging data from different sources, extracting relevant information, and generating clinical queries due to inconsistencies and the need for specialized database skills, which hinders efficient data analysis and healthcare assessments.
Innovation Solution
A system and method for extracting data from multiple EHR systems by translating plain language query phrases into database queries using predefined syntax, allowing for quick access to desired data subsets, generating risk scores, and providing interactive visualization tools for healthcare providers.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If complex database queries are crafted to extract information from EHR systems, then data extraction capability is improved, but time consumption and operational complexity increase
Solution Approach 1:
The patent introduces a natural language processing intermediary that translates plain language queries into database queries. This mediator layer allows users to interact with the system using natural language rather than requiring complex SQL or database query syntax, thereby maintaining data extraction capability while dramatically reducing time consumption and operational complexity.
Solution Approach 2:
The patent replaces the mechanical process of manually crafting complex database queries with an automated natural language processing system. Instead of requiring users to manually construct SQL queries with multiple joins, aggregations, and filters, the system automatically generates the necessary database queries from simple natural language statements, eliminating the time-consuming manual query construction process.
2Measurement precision
If specialized database skills are required for query generation, then query accuracy is improved, but ease of operation deteriorates
Solution Approach 1:
The natural language processing intermediary serves as a bridge between users without specialized database skills and the complex database system. It handles the translation and optimization of queries automatically, ensuring accurate data extraction while making the system accessible to users with minimal training.
Solution Approach 2:
The system performs self-service by automatically generating, optimizing, and executing database queries based on natural language input. The system independently handles query construction, table joining, and result aggregation without requiring user expertise in database operations, thereby maintaining accuracy while improving ease of operation.
3Quantity of substance
If data is merged from multiple EHR systems with limited consistency, then data comprehensiveness is improved, but system complexity increases
Solution Approach 1:
The patent applies homogeneity by standardizing data from multiple EHR systems into a unified schema. The system normalizes different data formats, structures, and conventions from various EHR vendors into a consistent internal representation, enabling comprehensive data aggregation while managing complexity through standardization.
Solution Approach 2:
The system implements universality by creating a multi-functional data integration layer that can handle data from multiple EHR systems with different structures and formats. This universal interface provides consistent data access and query capabilities across diverse sources, comprehensiveness achieved while complexity is abstracted away.
Data Source
AI summary
A method of generating a clinically supplemented risk score using data from an electronic health record system can include collecting data from a plurality of electronic health records; parsing the data into defined fields; comparing the parsed data to at least one look-up table to generate an inferred diagnostic condition; comparing the inferred diagnostic condition to a documented diagnostic condition; mapping the inferred diagnostic condition to at least one condition category; refining the at least one mapped inferred diagnostic condition into a hierarchy to generate a hierarchal mapped conditioned category; and determining via a processor, a risk score in response to the inferred diagnostic condition for the patient in response to the hierarchal mapped conditioned category, the risk score representing an expected total cost of care for the patient relative to the average per-patient cost of care over an entire population.