Automated Disease Risk Calculation System for Mobile Devices
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Solution Overview
Problem
Personalized medicine requires comprehensive patient information for disease risk assessment, but this information is not conveniently accessible to users on a user-friendly platform, limiting the ability of individuals to understand their health risks and take preventive measures.
Innovation Solution
An automated disease risk calculation system for mobile devices that allows users to input demographic and health data, perform risk assessments, and receive personalized disease risk calculations, along with suggestions for reducing risk and second opinions from experts, utilizing a mobile app and server-based database of disease risk factors.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If comprehensive patient information is collected for disease risk assessment, then measurement precision is improved, but device complexity increases
Solution Approach 1:
The system segments comprehensive patient information into distinct categories including demographic information, family history, medical history, genetic testing results, and laboratory test results. Each category is processed and stored separately, allowing the system to maintain high measurement precision for disease risk assessment while managing system complexity through modular organization of data collection and processing functions.
2Reliability
If specialized medical personnel have access to disease risk information, then reliability is improved, but ease of operation deteriorates
Solution Approach 1:
The system introduces a mobile device-based application as an intermediary between specialized medical personnel and patients. The application provides a user-friendly interface that allows patients to easily input their information and receive disease risk assessments without requiring direct interaction with complex medical systems. The application processes and presents information in an accessible format while maintaining the reliability of medical-grade risk assessment algorithms.
3Measurement precision
If personalized medicine approaches are implemented, then measurement precision is improved, but loss of information increases
Solution Approach 1:
The system implements feedback mechanisms where patients can review their disease risk assessments, view the specific factors contributing to their risk levels, and provide additional information or corrections. The system tracks changes in patient information over time and provides updates on how their health profile has changed. This feedback loop ensures that personalized medicine approaches maintain measurement precision while reducing information loss by allowing patients to verify and update their data.
Data Source
AI summary
A computer-implemented method including receiving, at a processing device of a disease risk calculation server, a request from a user mobile device for a disease risk calculation and including an answer, determining at the processing device, diseases the user is likely to contract, providing, from the processing device, the determined diseases, receiving, at the processing device, a user selection of a disease that user is likely to contract, providing, from the processing device, a question related to a proven risk factor of the user selected disease, receiving, at the processing device, a user answer, calculating, at the processing device, a risk that the user will contract the selected disease, providing, from the processing device, the calculated risk to the mobile device, and receiving and processing electronic payment from the user device after receipt by the user mobile device of the calculated risk, the electronic payment being governed by blockchain technology.