Interactive Clinical Relationship Visualization System
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Solution Overview
Problem
In healthcare settings, determining and displaying clinical relationships between entities of interest and those that have had contact with them is time-consuming and inefficient, especially in real-time scenarios, and existing visualizations fail to convey dynamic information effectively for immediate decision-making and infection control.
Innovation Solution
A system and method for generating interactive visualizations of clinical relationships by determining entities with clinically-related contact, collecting data, filtering, applying algorithms to calculate clinical relevance scores, and assigning executable actions for dynamic displays that can be filtered and animated to show real-time trends and relationships.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If manual or electronic records are reviewed individually to determine clinical relationships, then complete information can be obtained, but the process is time-consuming and inefficient
Solution Approach 1:
The patent replaces manual review of electronic records with an automated computational system that uses algorithms to analyze clinical data and generate visualizations of clinical relationships. This substitution of mechanical/manual processes with automated computing resolves the contradiction by providing complete information extraction without the time penalty of manual review.
Solution Approach 2:
The system creates visual representations (copies) of clinical relationship data that make complex information accessible and analyzable without requiring direct examination of underlying records. These visualizations serve as simplified copies that convey essential relationship information instantly, eliminating the need to review individual records while maintaining information completeness.
2Measurement precision
If traditional visualizations show retrospective views of stored data, then data accuracy is maintained, but dynamic real-time information about relationships is lost
Solution Approach 1:
The patent transforms static retrospective visualizations into dynamic interactive displays that can represent real-time clinical relationships. The system processes and visualizes data in near-real-time, allowing the display to adapt and update as new clinical information becomes available, while maintaining accuracy through systematic data processing algorithms.
Solution Approach 2:
The system adds temporal and interactive dimensions to traditional static visualizations. By incorporating time-based processing and interactive capabilities, the visualization system can display both historical accuracy and dynamic real-time relationship information simultaneously, resolving the contradiction between data accuracy and adaptability.
3Loss of information
If all contact information is displayed, then complete relationship data is provided, but the most relevant information becomes difficult to identify
Solution Approach 1:
The patent applies local quality by differentiating and highlighting entities based on their clinical relevance to the patient. Rather than uniform display of all contact information, the system varies visual properties (such as size, color, or positioning) according to the clinical significance of each entity, making relevant information immediately distinguishable while preserving complete data availability.
Solution Approach 2:
The system extracts and separates the most clinically relevant entities from the complete set of contact information. By isolating and highlighting key entities that have the greatest impact on patient care, the system makes relevant information easily identifiable while maintaining access to the complete relationship data set for comprehensive analysis.
Data Source
AI summary
A system and method for generating interactive visualizations of clinical relationships is provided. The method includes determining what entities have had some clinical contact with an entity of interest and collecting data regarding each entity and each entity's contacts with the entity of interest. The collected data can be filtered to remove unwanted entities and properties. Algorithms are applied to generate clinical relevance scores, which represent the relationship between the entities and the entity of interest. In addition, each entity is assigned components that allow for some executable behavior when an entity is selected in a display. Finally, the appropriate visualization is selected and a display of the relationships is generated.