Point-In-Time Architecture Database for Secure Event Prediction
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Solution Overview
Problem
Existing systems face challenges in managing and visualizing data in a way that is accessible, easy to interpret, and secure, particularly for sensitive information such as patient data, while ensuring compliance with regulatory standards.
Innovation Solution
A method and system utilizing a point-in-time architecture (PTA) database that manages and visualizes data by receiving and storing data from multiple sources, generating statistical metrics, predicting events, and ensuring patient content protection through redaction, while allowing for secure access and regulatory compliance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If data is stored and managed in traditional database systems, then data accessibility and ease of interpretation are improved, but data security and compliance with regulatory standards deteriorate
Solution Approach 1:
The patent segments data into multiple versions representing different points in time, allowing users to access historical data states while maintaining security controls. Each data version is stored separately in the PTA database, enabling selective access to appropriate data states without compromising overall security.
Solution Approach 2:
The system performs preliminary actions by pre-processing data into standardized formats and storing metadata about data versions before actual data access occurs. This includes pre-establishing security protocols and data transformation rules that automatically apply when data is retrieved, reducing the need for complex security checks during data access.
2Adaptability or versatility
If data from multiple sources is integrated and analyzed, then predictive capability and statistical analysis are improved, but system complexity and data management difficulty worsen
Solution Approach 1:
The PTA database serves multiple functions simultaneously: it stores data from diverse sources, maintains version history, performs statistical analysis, and supports predictive modeling. This multi-functional approach reduces the need for separate specialized systems while managing complexity through a unified architecture.
Solution Approach 2:
The patent introduces an intermediary layer that standardizes data from multiple sources before analysis. This intermediary processing layer transforms heterogeneous data into a common format, enabling predictive analytics without requiring complex point-to-point integration logic between different data sources.
3Measurement precision
If surveillance and monitoring of data are performed continuously, then event detection accuracy is improved, but computational resource consumption and processing time worsen
Solution Approach 1:
The system implements periodic surveillance at multiple levels: continuous monitoring of critical parameters, periodic analysis of aggregated data trends, and less frequent comprehensive reviews. This multi-tiered periodic approach maintains high detection accuracy while reducing overall computational burden by not processing all data at maximum intensity continuously.
Solution Approach 2:
The patent applies partial action by focusing surveillance resources on the most critical data parameters and time periods where events are most likely to occur. Rather than uniformly monitoring all data equally, the system concentrates computational effort on high-risk areas, achieving effective event detection with reduced overall resource consumption.
Data Source
AI summary
A method for managing data associated with a point-in-time architecture (PTA) database. The method includes receiving first data from a first data source. The first data comprises a first record and a second record. The first record associated with a first attribute and a first event. The second record is associated with a second attribute and a second event. The method further includes modifying the first data to be compatible with the first PTA database such that the first PTA database includes a first time and a second time. The method further includes executing a statistical operation to generate at least one statistical metric. The method further includes receiving reference data and predicting one or more events. The method further includes receiving, during the surveillance period of time, an update to at least one of the first data, the second data, or the reference data.