Dynamic Document Field Selection for Medical Diagnostics
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Solution Overview
Problem
Current systems for generating document content in medical diagnostics face inefficiencies due to the need to store multiple documents with varying diagnoses, leading to increased computing resources and memory requirements, and often include irrelevant information that complicates the diagnosis process.
Innovation Solution
A system that analyzes input information to dynamically select and update document fields based on relevance, using rules to determine additional fields and correlation scores to ensure the document is complete and focused on the correct diagnosis, thereby reducing data storage needs and improving diagnostic accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If multiple documents are stored to accommodate different diagnoses, then diagnostic coverage is improved, but memory requirements and computing resources increase
Solution Approach 1:
The patent applies universality by creating a single dynamic document template that can adapt to multiple diagnoses through programmable fields. Instead of storing separate documents for each diagnosis type, the system uses one universal template that automatically configures itself based on the detected diagnosis, eliminating the need for multiple specialized documents while maintaining comprehensive diagnostic coverage
Solution Approach 2:
The patent implements dynamics by making the document template programmable and adaptive. The fields and questions in the document dynamically change based on the diagnosed condition, allowing the same document structure to serve multiple purposes. This dynamic configuration reduces storage needs while maintaining versatility across different diagnostic scenarios
2Reliability
If all possible diagnosis questions are included in the document, then diagnostic completeness is improved, but relevance and diagnostic accuracy decrease due to irrelevant information
Solution Approach 1:
The patent applies segmentation by dividing the document into modular, programmable fields that can be selectively activated. Instead of presenting all possible questions at once, the system segments the document into diagnostic categories and only displays relevant fields based on the detected diagnosis, reducing complexity while maintaining accuracy
Solution Approach 2:
The patent implements local quality by customizing specific fields and questions based on the diagnosed condition. Each field can be locally adapted to match the relevant diagnostic criteria, ensuring that only pertinent information is presented to the user while maintaining overall document coherence and diagnostic accuracy
3Adaptability or versatility
If static document templates are used for all diagnoses, then system simplicity is maintained, but adaptability to specific diagnostic needs is reduced
Solution Approach 1:
The patent applies self-service by enabling the document template to automatically configure itself based on the detected diagnosis. The system performs self-configuration by selecting and activating appropriate fields without requiring manual intervention, achieving adaptability while minimizing operational complexity
Solution Approach 2:
The patent implements parameter changes by dynamically modifying document fields based on diagnostic parameters. The system changes field configurations, question formulations, and field visibility based on the detected diagnosis parameters, achieving high adaptability through programmable parameter adjustment rather than complex structural changes
Data Source
AI summary
Systems and methods for generating document content using data analysis. For example, a system may store data representing one or more documents, where a document is associated with a type of user and fields. The system may send the data representing the document to an electronic device. Additionally, the system may receive, from the electronic device, data representing information input into the document. Using the information, the system may select various fields for the document and send, to the electronic device, data representing the fields. Furthermore, the system may analyze the information to determine a score associated with the document. If the score does not satisfy a threshold score, the system may continue to select fields using the information and send, to the electronic device, data representing the fields. However, if the score satisfies the threshold score, the system may determine that the document is complete.