Persistent Health Data Collection with Multi-Level Prioritization
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Solution Overview
Problem
Modern health analysis systems face limitations in data connectivity, prioritization, and privacy, leading to unreliable and unattractive solutions for users, as they often restrict data collection to specific devices and lack flexible prioritization and maintenance methods for health and wellness calculations.
Innovation Solution
The system provides a computer-implemented method for persistent health data collection and multi-level prioritization, allowing users to select and prioritize devices and data through a graphical user interface, creating persistent connections to collect health data based on user input, and applying health data parameters to calculate personalized health values and recommendations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If health data is collected from multiple devices and sources, then data availability and reliability are improved, but system complexity and difficulty of managing connections increase
Solution Approach 1:
The patent segments the health data collection system into multiple independent data sources (wearable devices, mobile apps, electronic health records, laboratory systems) that can be connected and managed separately. Each device or source operates independently, allowing the system to handle multiple connections without overwhelming complexity by treating each as a separate module with standardized interfaces.
Solution Approach 2:
The system implements a universal data collection framework that can interface with multiple types of devices and sources through standardized protocols. This multi-functional approach allows a single system architecture to handle diverse health data sources (fitness trackers, medical devices, EHR systems) without requiring separate specialized systems for each, thereby improving reliability while controlling complexity.
2Measurement precision
If users can prioritize devices and data sources, then data accuracy and user control are improved, but ease of operation decreases
Solution Approach 1:
The system performs preliminary actions by automatically establishing default prioritization hierarchies among data sources during initial setup. Users can configure priority levels for different devices and data types in advance, allowing the system to automatically select the most reliable data sources without requiring users to make complex decisions during each health calculation or data collection event.
Solution Approach 2:
The system incorporates feedback mechanisms that monitor data quality and source reliability, automatically adjusting data selection based on performance metrics. Users receive feedback about which data sources are being used and can adjust priorities based on this information, creating an iterative process that improves accuracy while maintaining ease of operation through automated adjustments.
3Stability of the object's composition
If persistent connections are maintained for data collection, then data continuity is improved, but loss of time for connection management increases
Solution Approach 1:
The system maintains persistent connections with authorized data sources to enable continuous health data collection without requiring repeated authentication or connection establishment. Once a user authorizes a device or source, the connection remains active and data flows continuously, eliminating the need for repetitive connection management actions and ensuring uninterrupted data availability for health calculations.
4Adaptability or versatility
If the system allows flexible device and data prioritization, then adaptability is improved, but device complexity increases
Solution Approach 1:
The system implements dynamic prioritization where data source hierarchies can be adjusted in real-time based on user preferences, data quality metrics, and device availability. The architecture allows flexible reconfiguration of data collection priorities without requiring fundamental changes to the system structure, enabling adaptability while managing complexity through modular, dynamically-adjustable components rather than rigid hard-coded hierarchies.
Data Source
AI summary
Computer-implemented systems, methods, and computer-readable media are provided for persistent health data collection and multi-level prioritization. In accordance with one implementation, a method is provided that includes steps performed by at least one processor. The method may include providing at least one graphical user interface to a user that is configured to receive user input and creating, using application program interfaces (APIs), persistent connections to collect health data from one or more devices, wherein a first prioritization for the connected devices is stored based on user input received through the at least one graphical user interface. The method may further include storing a first prioritization for the data, the first prioritization for the data defining an order among the health data from the connected devices for selecting and applying one or more health data parameters, receiving health data from the devices in accordance with the first prioritization for the devices, and calculating a health value for the user using one more health data parameters based on the received health data, wherein the health data parameters are selected and applied according to the first prioritization for the data.