Patient Data Consent Adjustment for Machine Learning Inputs
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Medical systems and facilities are slow to adopt newer technologies due to patient safety concerns and a preference for traditional practices, hindering the implementation of advanced surgical procedures and data management systems.
Innovation Solution
A data propagation reporting system that tracks and annotates patient data utilization, generates reports on data access and contribution, manages consent keys for external access, and adjusts data based on consent changes using machine learning models.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If patient data is stored in external systems for broader access and utilization, then data value and accessibility are improved, but patient privacy and security risks worsen
Solution Approach 1:
The patent introduces a consent management system that acts as an intermediary between patient data and external systems. This system uses consent keys and propagation tracking to mediate data access, allowing data to be shared externally while maintaining patient privacy through controlled access mechanisms and audit trails.
Solution Approach 2:
The patent implements feedback mechanisms through data propagation reporting that tracks and reports how patient data is accessed and utilized across different systems. This feedback loop enables continuous monitoring and control of data usage, allowing the system to respond to access patterns and maintain security while enabling data sharing.
2Reliability
If traditional data management practices are maintained for patient safety, then reliability is improved, but productivity and innovation worsen
Solution Approach 1:
The patent replaces traditional manual consent management and data tracking mechanisms with automated computer-implemented systems. Machine learning models automatically track data propagation, generate reports, and manage consent keys, substituting manual processes with automated technological solutions that improve both efficiency and accuracy.
Solution Approach 2:
The system enables self-service capabilities where the data management system automatically monitors, tracks, and reports data usage without requiring manual intervention. The propagation reporting system autonomously generates utilization reports and manages consent verification, reducing administrative burden while maintaining rigorous safety standards.
3Loss of information
If comprehensive data tracking and reporting systems are implemented, then data utilization transparency is improved, but system complexity worsens
Solution Approach 1:
The patent segments the data tracking and reporting functionality into distinct modular components: consent management module, data propagation tracking module, report generation module, and machine learning analysis module. This segmentation allows each component to handle specific aspects of transparency independently, reducing overall system complexity while maintaining comprehensive tracking capabilities.
Data Source
AI summary
A data system may adjust the input data to a machine learning model based on a change in a consent associated with a patient. The data system may detect the change in the consent associated with the patient. The data system may identify private data associated with the change in the consent. The data system may identify a machine learning model to which the private data has contributed. The data system may determine input data that has contributed to the machine learning model. The input data may include the private data. The data system may determine, based on the change in the consent associated with the private data, whether to replace the private data in the input data with replacement data. The data system may adjust the input data based on the determination of whether to replace the private data in the input data with replacement data.