Real-time HL7 Parser Engine for EMR Analytics
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Solution Overview
Problem
Existing electronic medical record (EMR) systems face challenges in providing analytic data quickly and efficiently to medical professionals, often requiring significant time to process and present relevant information.
Innovation Solution
The implementation of a parser engine that processes Health Level 7 (HL7) messages in real-time or near real-time to extract and store necessary EMR data, combined with an optimally designed relational database, enables the rapid determination and presentation of analytic results, utilizing both current and historical data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of time
If traditional EMR systems process and present analytic data, then complete analysis can be achieved, but significant time is required to process and present relevant information
Solution Approach 1:
The parser engine performs preliminary extraction and storage of structured EMR data from HL7 messages in real-time or near real-time, preparing data beforehand for rapid analytical queries. This preliminary structuring eliminates time-consuming data processing steps when clinicians need analytic results, directly reducing the time loss while maintaining complete data availability.
Solution Approach 2:
The system segments the EMR data processing into distinct components: the parser engine handles data extraction and initial structuring, the database stores organized data for efficient retrieval, and the analytics engine performs specialized analyses. This segmentation allows each component to optimize its function, with the parser operating independently in real-time to improve overall productivity without compromising analysis completeness.
2Productivity
If a parser engine processes HL7 messages in real-time to extract and store EMR data, then data availability speed is enhanced, but system complexity increases
Solution Approach 1:
The parser engine serves as an intermediary component between the HL7 message input and the database/analytics systems. It translates unstructured HL7 messages into structured formats and stores them in a standardized database schema, enabling rapid data retrieval and analysis. This intermediary layer manages the complexity by handling data transformation centrally, allowing the rest of the system to operate with simplified data access patterns while maintaining high productivity.
Data Source
AI summary
The present invention provides systems and methods for use with electronic records, such as Electronic Medical Records (EMRs). A parser engine may receive a stream of Health Level 7 (HL7) messages containing EMR data and, using parsing logic, parse the HL7 messages to identify and extract specified EMR data therefrom. The extracted EMR data may be utilized in determining analytic results data that may be presented, or made available for presentation, to a medical professional or medical staff member, in real time or near real time relative to entry of the EMR data into an EMR system.