Dynamic Medical Summary System for Interactive Data Filtering
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current medical summary systems fail to provide relevant medical information to users as the relevance of data varies based on user needs, patient complexity, and comorbidities, and do not allow users to interactively adjust the relevancy of medical data.
Innovation Solution
A dynamic medical summary system that allows users to tune parameters such as body part, disease state, and source of information, using a cognitive system and ontological repository to generate and update medical summaries based on user interactions, enabling the inclusion or exclusion of specific medical data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If medical data is summarized by removing non-relevant data, then the clarity and focus of the summary is improved, but the relevance of data to different users and patients deteriorates because a fixed filtering criterion cannot adapt to varying user needs and patient complexities
Solution Approach 1:
The system implements dynamic medical summaries that automatically adapt their content based on patient-specific factors (comorbidities, complexity) and user-specific factors (role, preferences). The summary composition changes dynamically rather than using a fixed template, resolving the contradiction between filtering noise and adapting to varying needs.
Solution Approach 2:
The system changes multiple parameters simultaneously including patient factors (age, comorbidities, complexity), user factors (role, preferences), and data attributes (source, type, recency) to determine relevance. This multi-parameter approach allows the system to maintain clarity while adapting to different users and patients by adjusting which data elements are included or excluded.
2Loss of information
If all medical data attributes are included in the summary, then the completeness of information is improved, but the complexity and difficulty of processing the summary deteriorates
Solution Approach 1:
The system applies different levels of detail and processing to different portions of the medical summary based on their relevance to the specific patient and user. High-relevance attributes receive more detailed processing and presentation, while lower-relevance attributes are summarized or omitted, reducing overall complexity while maintaining completeness of essential information.
Solution Approach 2:
The medical summary is segmented into multiple dimensions (patient factors, user factors, data attributes) that are independently evaluated and weighted. This segmentation allows the system to process and prioritize information in manageable segments rather than treating all data uniformly, reducing processing complexity while maintaining completeness.
3Adaptability or versatility
If the medical summary is customized for each user and patient, then the relevance and usability of information is improved, but the time and computational resources required to generate the summary deteriorates
Solution Approach 1:
The system performs preliminary actions by pre-processing and categorizing medical data according to multiple dimensions (patient factors, user factors, data attributes) before summary generation. This preliminary organization allows for rapid customization when summaries are actually generated, reducing the time required for real-time customization while maintaining high adaptability.
Solution Approach 2:
The system implements a universal framework that handles multiple customization dimensions simultaneously through a single integrated process. Rather than creating separate processing pipelines for each customization dimension, the system uses a multi-functional approach that evaluates all factors (patient, user, data attributes) in one unified relevance determination process, improving efficiency while maintaining comprehensive customization.
Data Source
AI summary
Methods and systems of summarizing medical data. One system includes an electronic processor configured to analyze medical data to extract a medical concept and a plurality of additional attributes of the medical concept and store the medical concept and the plurality of additional attributes. The electronic processor is configured to generate a first medical summary associated with the patient, where the first medical summary is based on the stored medical concept and at least a first additional attribute included in the stored plurality of additional attributes. The electronic processor is configured to receive a user interaction with the first medical summary. The electronic processor is configured to generate a second medical summary associated with the patient based on the user interaction, the second medical summary is based on the stored medical concept and at least a second additional attribute included in the stored plurality of additional attributes.