Hierarchical EHR Problem Lists with NLP Deduplication
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Solution Overview
Problem
Existing EHR systems face inefficiencies in real-time updating and management of problem lists due to incomplete documentation, data redundancy, and network congestion during peak usage times, leading to inaccurate and cumbersome patient records.
Innovation Solution
A centralized server system that implements a temporal-based data transmission policy, uses natural language processing to identify and remove duplicates, and manages hierarchical problem lists with access permissions, ensuring real-time updates and efficient data storage.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If all new problem list data is transmitted to the remote server in real-time, then data completeness and accuracy are improved, but network speed decreases and network congestion occurs during peak hours
Solution Approach 1:
The patent segments problem list data into hierarchical levels (master problem list at top level, derivative problem lists at lower levels) and applies selective transmission policies. Not all data is transmitted equally - only specific segments are sent to the server based on temporal policies, resolving the contradiction between complete data transmission and network speed maintenance.
Solution Approach 2:
The system transmits only partial data (keywords representing problem list entries) rather than all data to the server. This partial action approach maintains data completeness for critical information while reducing overall network traffic volume, thereby preserving network speed during peak usage times.
2Productivity
If multiple healthcare professionals can edit the master problem list simultaneously, then real-time collaboration is improved, but data redundancy and inaccuracies increase
Solution Approach 1:
The patent divides the problem list into hierarchical segments with different access rights. The master problem list can be viewed by all professionals but edited only by authorized users, while derivative problem lists can be edited by specific professionals. This segmentation enables collaborative viewing while preventing redundant or inaccurate edits to critical master data.
Solution Approach 2:
The system implements feedback mechanisms where edits to derivative problem lists must be approved or verified before affecting the master problem list. This feedback loop ensures data accuracy is maintained while still allowing collaborative work, as professionals can contribute to derivative lists without directly modifying the master list.
3Speed
If problem list data is stored locally at each user device, then access speed is improved, but data synchronization and consistency across network become difficult
Solution Approach 1:
The patent segments data storage into hierarchical levels with different synchronization requirements. Derivative problem lists can be stored and accessed locally at user devices for fast access during consultations, while the master problem list remains centrally managed. This segmentation allows local caching for speed while maintaining centralized control for consistency.
Solution Approach 2:
The system uses temporal-based data transmission policies that synchronize data periodically rather than continuously. Data is transmitted to the server at specific intervals or triggered by changes, balancing the need for fast local access with the need for network synchronization. This periodic approach reduces the complexity of continuous synchronization while maintaining data consistency.
Data Source
AI summary
Systems and methods for creating and managing an electronic health records system are provided, including a server storing a medical records database. Requests for medical data for a patient in a master problem list are received from a user device, and appropriate list permissions are determined and granted for hierarchical derivative problem lists. Annotated data is received at the server from a user device based on annotations made to a derivative problem list, and problem lists are updated with the received annotated data in real time. The received annotated data is compared with current entries using natural language processing to detect duplicate entries, the duplicate entries are iteratively detected and removed from the master problem list, and non-duplicate entries are stored in authorized derivative problem lists based on the natural language processing and list update permissions for each of the derivative problem lists.