Dynamic Guideline Tree for Therapy Visualization
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Solution Overview
Problem
Current methods for determining optimal therapies in healthcare are time-consuming, expensive, and biased, with published literature expanding rapidly, making it impossible for healthcare providers to aggregate and interpret all relevant data, leading to outdated guidelines and potential conflicts with new therapies or interactions.
Innovation Solution
A method involving the generation of a guideline tree based on therapies and guidelines, where each leaf node represents a therapy and each edge represents a guideline, allowing for a visual depiction that can be modified based on patient attributes, enabling healthcare providers to quickly identify suitable therapies and understand their interactions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual review of published literature is performed by subject-matter experts, then guidance and best practices can be provided, but the process becomes time-consuming, expensive, and inherently biased
Solution Approach 1:
The patent replaces the manual mechanical review process with an automated computational system that uses natural language processing and machine learning algorithms to analyze published literature, extract therapy information, and generate evidence-based recommendations, thereby eliminating time consumption and human bias while maintaining or improving accuracy
Solution Approach 2:
The patent introduces an intermediary computational layer between the published literature and the decision-makers, which automatically processes and synthesizes the literature into structured guidance documents and best practices, reducing the need for direct manual review by experts
2Loss of information
If all published literature is aggregated and interpreted, then comprehensive and up-to-date guidelines can be generated, but the enormous amount of data makes it impossible for healthcare providers to evaluate all potential therapies
Solution Approach 1:
The patent segments the enormous body of published literature into manageable units by automatically extracting and organizing therapy information into structured formats with standardized schemas, making the data processable and interpretable without overwhelming healthcare providers
Solution Approach 2:
The patent creates a universal computational framework that can process diverse types of published literature (clinical trials, observational studies, guidelines) using the same automated extraction and synthesis methods, enabling comprehensive analysis without proportionally increasing complexity
3Stability of the object's composition
If traditional static guidelines are used, then established best practices can be followed, but the guidelines become universally outdated and potentially conflict with newly discovered therapies or interactions
Solution Approach 1:
The patent transforms static guidelines into dynamic, continuously updating recommendations by implementing automated systems that monitor new published literature, extract emerging therapy information, and refresh guidance documents in real-time, ensuring guidelines remain current while maintaining structural stability
Solution Approach 2:
The patent implements feedback loops where the automated system continuously monitors new published literature and emerging therapies, compares them against existing guidelines, and automatically updates recommendations to resolve conflicts and incorporate new evidence, maintaining both stability and adaptability
Data Source
AI summary
Techniques for dynamic visualization of data are provided. A plurality of therapies is received, where each of the plurality of therapies is associated with a respective plurality of guidelines. A guideline tree is generated based on the plurality of therapies, where each leaf node in the guideline tree represents a respective therapy, and where each edge in the guideline tree represents a respective guideline. A visual depiction of the guideline tree is generated. Further, a first plurality of attributes associated with a first patient is received, and a first modified visual depiction of the guideline tree is generated based on the first plurality of attributes.