Adaptive Medical UI Prioritizing Data via AI Interaction Learning
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Solution Overview
Problem
Physicians face challenges in efficiently analyzing and presenting large amounts of medical data from various sources, including patient sensors and aggregated clinical data, which can lead to time-consuming and error-prone manual reviews, making it difficult to determine relevant information for patient treatment planning.
Innovation Solution
A system that monitors the interaction journey of healthcare providers with medical devices and feeds this data, along with patient parameters, into a model trained on correlations from other healthcare providers' interactions and patient data, to adapt the user interface (UI) and present the most relevant information for treatment planning.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If physicians manually review patient data and published research, then comprehensive analysis can be performed, but the process becomes time-consuming and error-prone
Solution Approach 1:
The system enables self-service by automatically analyzing patient data and generating treatment recommendations without requiring manual physician review. The AI model processes medical records, sensor data, and published research autonomously, allowing the system to serve itself in performing tasks traditionally requiring human intervention.
Solution Approach 2:
The patent replaces the mechanical system of manual data review with an automated AI-based system. The machine learning model substitutes human physicians' manual analysis process, using computational algorithms to process and interpret medical data, thereby eliminating the time-consuming and error-prone manual review process.
2Loss of information
If all medical data is presented on the UI, then complete information is available, but the interface becomes complex and difficult to navigate
Solution Approach 1:
The system extracts and separates relevant information from the complete medical data set. The AI model identifies and extracts only the most relevant data points for treatment planning, presenting them on the UI while keeping the complete data available in the background. This extraction process maintains information completeness while simplifying the interface.
Solution Approach 2:
The patent applies local quality by making different parts of the UI have different levels of detail and importance. Critical treatment-related information is highlighted and prioritized in the interface, while less relevant data is presented in a more subdued manner. This creates a hierarchy of information quality within the UI, making it easier to navigate while preserving access to all data.
3Ease of operation
If the UI is customized for each patient, then relevant information is prioritized, but the system requires complex adaptation mechanisms
Solution Approach 1:
The system implements dynamic UI customization that automatically adapts to each patient's specific needs. The AI model dynamically generates treatment plans and adjusts the UI presentation based on real-time analysis of patient data, making the interface flexible and responsive without requiring complex manual configuration. The customization is driven by the dynamic nature of the AI-generated treatment recommendations.
Data Source
AI summary
There is provided a method of adapting a user interface (UI) for presenting medical data of a target patient, comprising: monitoring an interaction journey of a healthcare provider with at least one medical device that performs at least one member of the group consisting of: storing data of a target patient, monitoring the target patient, presenting medical data of the target patient, and treating the target patient, monitoring at least one patient parameter of the target patient, feeding the interaction journey and the at least one patient parameter into a model trained according to computed at least correlation between interaction journeys of at least one of sample healthcare providers with respective medical devices and at least one patient parameter of at least one sample patient, and outputting an adaptation to the UI by the model.