Unified Clinical Model Interface for Treatment Outcome Visualization
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Solution Overview
Problem
Medical professionals face difficulties in collating and comparing information from multiple clinical models to select the most appropriate treatment for patients, as existing models are often cumbersome and spread across different applications or webpages.
Innovation Solution
A computer-implemented method and system that generates a graphical representation of predicted treatment effectiveness by combining indicators from clinical models using machine learning, allowing medical professionals to make informed decisions more efficiently.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If multiple clinical models are used to provide comprehensive treatment indicators, then the reliability and completeness of treatment information is improved, but the device complexity and ease of operation deteriorate due to needing to interact with multiple disparate applications or webpages
Solution Approach 1:
The patent combines multiple clinical models into a single integrated application that displays indicators from different models (e.g., nomograms, risk calculators) within one unified interface. This merging eliminates the need for clinicians to switch between multiple disparate applications or webpages while maintaining access to comprehensive treatment information from various clinical models simultaneously.
Solution Approach 2:
The application provides multi-functionality by incorporating multiple clinical models and indicators within a single platform. It can display survival outcomes, pathological outcomes, functional outcomes, and other treatment indicators from different clinical models, making one application serve multiple purposes that previously required separate tools.
2Reliability
If multiple clinical models from different sources are used, then the comprehensiveness of treatment indicators is improved, but the ease of operation deteriorates due to difficulty in collating and comparing information
Solution Approach 1:
The application merges indicators from multiple clinical models into a single unified display interface. Different indicators (survival outcomes, pathological outcomes, functional outcomes) from various clinical models are presented together in one view, eliminating the need for manual collation and making comparison straightforward.
Solution Approach 2:
The application organizes multiple indicators from different clinical models into a structured multi-dimensional display format. Indicators are arranged by category (survival, pathological, functional outcomes) and model source, creating an organized information architecture that facilitates easy comparison across different dimensions without overwhelming the user.
3Reliability
If comprehensive treatment information from multiple models is provided, then the quality of decision making is improved, but the time required for treatment selection increases due to the cumbersome nature of existing models
Solution Approach 1:
The application performs preliminary organization and integration of data from multiple clinical models before presentation to the clinician. All indicators are pre-calculated, pre-organized by category and model, and pre-displayed in a unified interface, eliminating the time-consuming manual processes of data collection, collation, and comparison that previously occurred after treatment selection began.
Solution Approach 2:
By merging multiple clinical models into one application with a unified interface, the system allows clinicians to access all necessary treatment indicators simultaneously in one location. This eliminates the time lost to switching between applications, webpages, or tools, while maintaining comprehensive information for high-quality decision-making.
Data Source
AI summary
A computer implemented method for generating a graphical representation of a predicted effectiveness of a first treatment. The method comprises using (102) a clinical model to determine at least one indicator related to an outcome of a first treatment. An effectiveness of the first treatment is then predicted (104) based on the at least one indicator. The predicted effectiveness of the first treatment is then displayed (106) to a user, using a first graphical representation.