Health Data Aggregation Platform for Personalized Treatment Recommendations
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Solution Overview
Problem
Current automated medical treatment systems are unable to process and normalize complex individual patient data to provide personalized and tailored reports and educational materials effectively, making it difficult to consistently provide high-quality care for heart attack, stroke, and diabetes prevention due to time limitations in fast-paced medical environments.
Innovation Solution
A system that collects and aggregates medical data from various sources, normalizes it, and uses adaptive rule-based analysis to identify patient medical risk levels, generating dynamic patient health reporting interfaces with interactive elements for treatment recommendations and educational plans, allowing for personalized patient reports and adjustments based on user input.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If automated medical treatment systems process complex individual patient data, then personalization and quality of care improve, but system complexity and data processing requirements increase
Solution Approach 1:
The system segments complex medical data into distinct categories (demographic data, clinical data, laboratory data, imaging data, genetic data, lifestyle data) and processes each segment through specialized modules. This segmentation allows the system to manage complexity by dividing the overall data processing task into smaller, more manageable components while maintaining high personalization quality for each patient.
Solution Approach 2:
The patent introduces a normalization layer as an intermediary between diverse data sources and the analysis engine. This normalization component standardizes incongruent data structures from multiple sources into a unified format, enabling complex data integration without proportionally increasing system complexity. The intermediary handles data transformation and reconciliation, simplifying downstream processing.
2Measurement precision
If comprehensive medical data from multiple sources is collected and analyzed, then patient risk identification accuracy improves, but data processing time and resource requirements increase
Solution Approach 1:
The system performs preliminary normalization and validation of medical data from multiple sources before detailed analysis. By pre-processing and standardizing data structures in advance, the system reduces the computational burden during risk identification, maintaining high accuracy while decreasing actual processing time when clinical decisions are needed.
Solution Approach 2:
The patent transforms diverse medical data into standardized parameters and metrics that can be efficiently processed. By converting incongruent data structures into uniform parameter formats, the system enables faster computational analysis while preserving the precision needed for accurate risk identification across multiple disease conditions.
3Reliability
If normalized medical data is analyzed using adaptive rule-based systems, then treatment recommendation accuracy improves, but computational requirements and processing complexity increase
Solution Approach 1:
The system changes the parameters of medical data into standardized formats that are optimized for rule-based analysis. By transforming raw medical data into normalized parameters with consistent structures and units, the adaptive rule-based system can operate more efficiently, reducing computational energy requirements while maintaining or improving recommendation accuracy through better data quality.
4Ease of operation
If dynamic patient health reporting interfaces are generated with interactive elements, then user engagement and personalization improve, but system complexity and development requirements increase
Solution Approach 1:
The patent implements a universal dynamic reporting interface framework that serves multiple functions through a single system. The interface can display normalized medical data, present risk assessments, provide treatment recommendations, and offer educational materials across different disease conditions and patient types. This multi-functionality reduces overall system complexity by avoiding the need for separate interfaces for each function while maintaining high user engagement through personalization.
Data Source
AI summary
Some embodiments described herein are directed to a universal health platform that may efficiently provide sophisticated health data processing and treatment recommendations. In some embodiments, the platform may be configured to allow a user (e.g. a patient or health professional) to input or import a vast and varied collection of data including health, medical, lifestyle, historical, and other relevant data. The platform may be configured to process this data through a multifaceted and dynamically updated decision-making protocol to create a personalized patient report. In some embodiments, the personalized patient report may comprise various medications, treatment, preventative, health, lifestyle, and behavioral recommendations and/or plans. In some embodiments, the data input and personalized patient report features are communicated to a user through a dynamic user interface configured to automatically update and adjust based on newly inputted data or user selection and/or rejection of recommendations in the personalized patient report.