Hierarchical EHR Problem Lists for Real-Time Duplicate Removal
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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
1Reliability
If real-time updating of problem lists is implemented across all healthcare professionals, then completeness and accuracy of patient records is improved, but network congestion and data transmission delays worsen during peak usage times
Solution Approach 1:
The system segments the centralized problem list into multiple hierarchical derivative problem lists (DPLs) organized by specialty or care team. Each DPL contains a subset of problems relevant to specific clinicians, reducing the data volume each user must access and transmit while maintaining real-time updates for their specific domain.
Solution Approach 2:
The system extracts and stores problem list data locally in derivative problem lists at the server level before distribution. This pre-extraction eliminates the need for clinicians to retrieve entire problem lists repeatedly, reducing network traffic during peak times while maintaining access to updated information.
2Reliability
If all problem list data from multiple clinicians is stored centrally, then completeness of patient records is improved, but data redundancy and storage inefficiency worsen
Solution Approach 1:
The system merges problem list data from multiple clinicians into hierarchical derivative problem lists organized by specialty or care team. This consolidation eliminates duplicate entries across different clinicians' views while maintaining a comprehensive central repository, reducing overall data redundancy while preserving completeness.
Solution Approach 2:
The system automatically detects and discards duplicate problem entries when they appear in multiple derivative problem lists, keeping only one canonical version in the central problem list. This selective discarding of redundancies maintains data completeness while eliminating unnecessary duplication.
3Reliability
If hierarchical problem lists with access permissions are implemented, then data security and appropriate access control are improved, but system complexity and implementation difficulty worsen
Solution Approach 1:
The system segments access permissions by creating hierarchical derivative problem lists associated with specific specialties, departments, or care teams. Each DPL has defined access controls for relevant clinicians, providing fine-grained security without requiring a completely complex permission system across all users.
Solution Approach 2:
The hierarchical DPL structure serves multiple functions simultaneously: it organizes data by specialty, implements access control, reduces data transmission volume, and maintains security. This multi-functionality reduces overall system complexity by consolidating multiple concerns into a single architectural pattern.
4Loss of time
If complete problem list data is transmitted to all user devices, then real-time access to all patient information is improved, but network bandwidth consumption and transmission time worsen
Solution Approach 1:
The system segments the complete problem list into hierarchical derivative problem lists distributed to different user devices based on their roles and specialties. Each device receives only the subset of data relevant to its function, reducing network data volume while maintaining real-time access to necessary information.
Solution Approach 2:
The system implements local quality by providing each user device with a customized derivative problem list containing only the data relevant to that specific clinician's specialty or role. This localized data distribution reduces overall network traffic while ensuring each user has immediate access to their specific information needs.
Data Source
AI summary
Systems and methods for maintaining an electronic health records system, 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.