Health Score Algorithm for Privacy-Safe Data Sharing
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current methods for assessing and improving individual health are inefficient, as they lack rapid, cost-effective, and timely solutions, especially in areas with limited access to healthcare, and existing health information sharing mechanisms are restricted by privacy laws and cumbersome, making it difficult for individuals to track and share health data effectively.
Innovation Solution
A system and method for computing a Health Score using health and extrinsic data, processed by an algorithm to provide a user-friendly interface with color-coded bands and trend simulations, incorporating a cardiovascular risk model and lifestyle model, allowing for real-time feedback and notification systems to promote healthy behaviors.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional health assessment methods (doctor visits, lab tests) are used, then health information accuracy is improved, but accessibility and cost-effectiveness deteriorate
Solution Approach 1:
The patent creates a virtual copy of the healthcare system through a computational platform that replicates doctor assessments, lab test interpretations, and health advice delivery. The system copies the essential functions of traditional healthcare into a digital environment, allowing users to access health assessments remotely while maintaining accuracy through algorithmic processing of health data.
Solution Approach 2:
The patent introduces a computational platform as an intermediary between users and health information. This platform processes health data, generates assessments, and delivers recommendations without requiring direct doctor-patient interaction. The intermediary maintains accuracy through structured algorithms while improving accessibility by removing geographical and scheduling barriers.
2Productivity
If health information is shared widely to improve public health outcomes, then health improvement effectiveness is improved, but privacy protection deteriorates
Solution Approach 1:
The patent applies different levels of privacy protection to different types of health information. Sensitive personal identifiers are protected with higher security measures, while anonymized health data can be shared for public health research. The system selectively applies privacy protection based on the specific data element and its intended use, allowing beneficial sharing while maintaining individual privacy where critical.
Solution Approach 2:
The patent segments health information into different categories with different sharing permissions. Personal identifiable information is separated from health metrics, and health data is further divided into sensitive and non-sensitive categories. This segmentation allows the system to share necessary health information for public health improvement while protecting private information through structural separation of data elements.
3Loss of information
If manual health tracking methods (FFQs, paper logs) are used, then data collection completeness is improved, but user burden and time consumption deteriorate
Solution Approach 1:
The patent replaces manual mechanical tracking methods (writing in food frequency questionnaires, filling out paper logs) with automated electronic systems. Sensors, mobile applications, and digital interfaces automatically capture health data such as dietary intake, physical activity, and vital signs, eliminating the need for manual recording while maintaining complete data collection through automated prompting and validation.
Data Source
AI summary
A system and method are disclosed for computing a Health Score. Health data and extrinsic data are received that are parameters for computation of the Health Score. The received data can be combined using an algorithm being implemented as code executing in a processor so as to compute the Health Score of the individual wherein parameters comprising one portion of the data interacts with parameters comprising another portion of the data. Further, the computed Health Score is output to an interface of the user device. Information concerning the parameters' interaction are selectively output to the interface that explain which changes in the parameters are significant drivers of the change in the Health Score.


