Medical Diagnosis Support System for Chronic Illness Risk Analysis
Solution Overview
Problem
Medical information systems often hinder doctors from obtaining a comprehensive overview of patient data due to time constraints and limitations in graphical user interfaces, leading to missed information about chronic illnesses, medication, and complex diagnosis relationships, which can result in overlooked diagnoses and inefficient treatment processes.
Innovation Solution
A computer-implemented method that uses a data processing system with a graphical user interface to analyze patient data by applying rules for calculating diagnosis risks, allowing for the identification of primary and secondary risks through interactive symptom and guideline diagnostics, enabling doctors to quickly and accurately diagnose chronic illnesses and associated treatments.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If doctors manually review patient records to obtain comprehensive overview, then diagnostic accuracy improves, but time consumption increases significantly
Solution Approach 1:
The system performs preliminary analysis of patient data before the doctor reviews it. The computer automatically scans the entire patient record, identifies potential chronic illnesses, medications, and diagnostic relationships, and prepares a structured overview. This preliminary action allows the doctor to review pre-processed information rather than manually examining all raw data, thus maintaining diagnostic accuracy while significantly reducing time consumption.
2Ease of operation
If graphical user interface displays only recent diagnoses, then interface simplicity is maintained, but information completeness deteriorates
Solution Approach 1:
The information display is segmented into multiple hierarchical levels. The primary view shows recent diagnoses for quick overview, while additional segments provide access to chronic illnesses, medication history, and complex diagnostic relationships. The system segments information by relevance and time, allowing doctors to access comprehensive data through structured navigation without overwhelming the interface with all data at once.
Solution Approach 2:
The system adds temporal and categorical dimensions to the display. Instead of a simple linear list, information is organized in multiple dimensions: time (recent vs. historical), category (diagnoses, medications, symptoms), and relevance (confirmed vs. potential). This dimensional organization allows comprehensive information to be presented in a structured, navigable format that maintains interface simplicity while preventing information loss.
3Reliability
If doctors review entire patient history in detail, then comprehensive diagnosis is achieved, but time available for treatment decreases
Solution Approach 1:
The computer system performs the time-consuming review of entire patient history as a preliminary action before the doctor begins treatment. The automatic analysis identifies chronic illnesses, medication interactions, and diagnostic patterns, presenting key findings in a condensed format. This allows the doctor to achieve comprehensive diagnosis through the pre-analyzed information without spending excessive time on manual review, thereby preserving time for treatment activities.
4Reliability
If system provides detailed analysis of all patient data, then diagnostic quality improves, but system complexity increases
Solution Approach 1:
The diagnostic analysis is segmented into modular components: chronic illness detection, medication analysis, symptom-disease relationship mapping, and statistical evaluation. Each module handles a specific aspect of the analysis independently, making the overall system more manageable and maintainable. The segmented architecture allows the system to provide detailed analysis without becoming an unmanageable monolithic complexity.
Solution Approach 2:
The computer system acts as an intermediary between the complex patient data and the doctor. It processes raw medical records, applies diagnostic algorithms, and presents results in an interpretable format. This intermediary layer handles the complexity of data analysis internally while presenting simplified, actionable insights to the doctor, thereby improving diagnostic quality without exposing the full system complexity to the user.
Data Source
AI summary
The invention relates to a computer-implemented method for medical diagnosis support for patient data of a patient through a data processing system, wherein the data processing system comprises a graphical user interface and a database containing rules for calculating diagnosis risks.