Distributed Behavior Data Network for Secure Symptom Tracking
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Solution Overview
Problem
Current systems lack the capability to securely track and monitor the behavior and symptoms of individuals with conditions such as ADHD, depression, and other neuropsychological disorders over time, leading to inadequate personalization and relevance in managing and treating these conditions.
Innovation Solution
A system and method for securely collecting, analyzing, and sharing behavior and symptom data using a distributed network, employing a graphical user interface, authentication, analytics, and reporting modules to provide personalized and contextually relevant information to patients, caregivers, and healthcare providers.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a distributed network system is implemented for secure data collection and analysis, then measurement precision and reliability of symptom tracking are improved, but device complexity increases
Solution Approach 1:
The system is divided into multiple independent components including data collection modules, authentication modules, analytics modules, and reporting modules that operate across a distributed network. Each module performs a specific function and can be independently deployed and maintained, reducing overall system complexity while enabling precise symptom tracking through coordinated operation of specialized components.
Solution Approach 2:
An authentication module acts as an intermediary between users and the data collection/analysis system, managing secure access and data sharing permissions. This intermediary layer simplifies the overall system architecture by centralizing security functions and enabling trusted data exchange between patients, caregivers, and healthcare providers without requiring complex point-to-point security implementations.
2Adaptability or versatility
If personalized tracking and monitoring of symptoms is implemented, then adaptability to individual patient needs is improved, but device complexity increases
Solution Approach 1:
The system dynamically adapts to individual patient needs by collecting and analyzing personal symptom data, behavior patterns, and treatment responses. The analytics module processes this data to generate personalized insights and treatment recommendations that evolve over time based on the patient's unique profile, enabling high adaptability without requiring manual reconfiguration of system parameters.
Solution Approach 2:
The analytics module automatically processes collected data to identify patterns, generate insights, and provide treatment recommendations without requiring manual intervention. The system self-adjusts by learning from accumulated patient data, reducing the complexity of personalized tracking while maintaining high adaptability to individual needs through automated data-driven decision support.
3Reliability
If secure data sharing across multiple platforms is enabled, then reliability of information exchange is improved, but device complexity increases
Solution Approach 1:
The authentication module provides universal security functionality that enables reliable data sharing across multiple platforms and user roles (patients, caregivers, healthcare providers). This single multi-functional component handles authentication, authorization, and data protection for all users and platforms, improving data sharing reliability while reducing complexity compared to implementing separate security systems for each platform.
Data Source
AI summary
Computer-implemented methods and systems for managing the collection of and access to behavior assessment data. In an embodiment, a first user having authority to act on behalf of an individual under study identifies a second and third user role, specifies behavior data, symptom measurement data, and/or medicine regimen data associated with the individual under study, and defines access permissions for the second and third user roles with respect to the behavior data and symptom measurement data. The symptoms and behaviors to be measured are specified based on a condition of the individual. Users provide behavior data and symptom measurement data observed from the individual. An analytics module performs computational analysis on the behavior data and symptom measurement data, thereby producing behavior assessment data. A reporting module presents the behavior assessment data to the users in a manner consistent with the defined access permissions.