Healthcare Data Cloud System Resolving Database Compatibility
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Solution Overview
Problem
Current healthcare systems face significant challenges in identifying individuals and associating varying identifiers across different databases, leading to inefficiencies, fraud, and high costs due to the lack of a comprehensive identification system.
Innovation Solution
The Healthcare Data Cloud System employs a cloud-based reference service that collects, stores, analyzes, and processes multiple variations of identifying information to uniquely identify individuals and associate these variations with distributed systems' records, using AI and machine learning to enhance identification accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If multiple different databases from different software manufacturers are used to store healthcare records, then data coverage and record completeness are improved, but system compatibility and communication between databases deteriorate
Solution Approach 1:
The patent introduces a standardized interface layer and common data exchange protocol that acts as an intermediary between different healthcare databases. This intermediary enables seamless communication and data sharing across heterogeneous systems without requiring changes to the underlying database structures, thus maintaining data coverage while resolving compatibility issues.
Solution Approach 2:
The system implements a universal data model and standardized identifier system that can work across multiple different database platforms. This universal layer allows the same identification and data retrieval mechanisms to function effectively whether the underlying database is from manufacturer A, B, or C, thereby achieving versatility without sacrificing data coverage.
2Measurement precision
If additional identifying information is collected to distinguish individuals with the same name, then identification accuracy is improved, but data collection complexity and processing overhead increase
Solution Approach 1:
The patent combines multiple identifying attributes (name, date of birth, gender, address, social security number) into a single composite identification key. This merged approach allows the system to leverage the power of multiple data points for accurate identification while presenting a unified, simplified interface to applications, thereby reducing perceived complexity.
Solution Approach 2:
The system performs preliminary data validation and standardization during the data entry phase, ensuring that all identifying information is collected and formatted correctly before it needs to be used for identification. This preliminary action reduces later processing complexity and prevents identification errors.
3Reliability
If unique identifiers are updated across multiple databases when individual information changes, then record link accuracy is improved, but system synchronization complexity and update overhead increase
Solution Approach 1:
The patent implements a feedback mechanism where the centralized identification system monitors for changes in identifying information across databases and automatically triggers updates. This feedback loop ensures that record links remain accurate without requiring complex manual synchronization protocols, as the system self-corrects when changes are detected.
Solution Approach 2:
The system enables databases to self-update their identifier mappings by providing them with access to the centralized identification service. When an individual's information changes, the affected databases can independently query the central system and update their local records, eliminating the need for complex centralized coordination.
4Adaptability or versatility
If comprehensive identification systems access distributed database records, then identification capability is improved, but privacy security concerns increase
Solution Approach 1:
The patent extracts only the minimum necessary identifying information from distributed databases to perform identification matching, rather than accessing or storing complete personal records. This extraction approach enables comprehensive identification capability while minimizing privacy exposure by limiting data access to what is strictly necessary for the identification function.
Solution Approach 2:
The system implements differential access rights where different components of the identification system have different levels of access to sensitive information. Critical identification logic can access detailed records when necessary, while routine operations work with anonymized or aggregated data, thereby providing identification capability while maintaining privacy security through localized data quality control.
Data Source
AI summary
In some embodiments, the system integrates different unique identifiers from agencies and organizations and associated the different unique identifiers with each other in a table. In some embodiments, the table links all different unique identifiers to a single individual so that a search for any one identifier returns links to all records and data associated with the individual. In some embodiments, the system collects the different unique identifiers from organizations such as patient medical services, local and federal law enforcement databases, and private company records. In some embodiments, the system parses each component of each identifier and stores them as variations. In some embodiments, each parsed identifier is associated with a master identifier. In some embodiments, the system links the master identifier to all records and data across multiple organizations and agencies.