HL7 Parser Engine for Real-Time Emergency Patient Tracking
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Solution Overview
Problem
Current electronic health record systems face challenges in generating real-time lists of patients in emergency departments, as they rely on manual processes and limited read-only historical data, leading to inefficiencies, inaccuracies, and inability to update patient information in real-time.
Innovation Solution
A computer-implemented system that parses Health Level 7 (HL7) messages in real-time to extract and combine patient data with historical analytics, providing a graphical user interface for a trackerboard that displays current patients in the emergency department, allowing for real-time updates and edits.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If manual processes and read-only historical database are used to generate patient lists, then system complexity is reduced, but productivity and timeliness of providing patient lists deteriorate
Solution Approach 1:
The patent introduces an intermediary system that sits between the EMR system and the historical batch database. This intermediary captures real-time HL7 messages from patient registration, parses them, and maintains a current patient list that can be quickly queried without accessing the resource-heavy historical database, thus improving timeliness while keeping the overall system architecture manageable
Solution Approach 2:
The system performs preliminary actions by capturing and storing patient registration data in real-time as HL7 messages when patients first enter the emergency department. This preliminary data capture creates a ready-to-query patient list that eliminates the need for manual compilation or complex historical database queries when generating patient lists later
2Measurement precision
If read-only historical batch database is accessed to generate patient lists, then data accuracy is improved, but resource consumption and time required deteriorate
Solution Approach 1:
Patient registration data is captured preliminarily in real-time through HL7 message interception during the registration process. This preliminary capture stores accurate patient information in a readily accessible format, eliminating the need for time-consuming historical database queries while maintaining data accuracy
3Use of energy by moving object
If manual identification of patients is performed, then system resource consumption is reduced, but productivity and accuracy of patient tracking deteriorate
Solution Approach 1:
The system performs self-service by automatically capturing patient registration information through HL7 message interception and parsing. The parser engine autonomously extracts patient data from incoming messages and maintains an updated patient list without requiring manual identification efforts from healthcare providers, thus improving productivity while keeping resource consumption moderate
4Ease of operation
If real-time data capture and writeback functionality are implemented, then ease of updating patient information is improved, but device complexity increases
Solution Approach 1:
The patent introduces an intermediary layer with parser engine and data storage components that mediates between the EMR system and the patient list display. This intermediary handles the complexity of real-time data capture, parsing, and writeback operations, making patient information updates easy for users while containing the technical complexity within the intermediary layer
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 combined with analytic results data and presented via a trackerboard with refresh and writeback capability, 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.