Node Graph Storage for Unified Patient Data Retrieval
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Solution Overview
Problem
Storing patient data across different clinics in a healthcare system results in repetitive data entries, large memory requirements, and difficulty in real-time data retrieval due to the need to query multiple local databases, with protected health information often experiencing latency during upload.
Innovation Solution
Implementing a node graph data structure that stores patient data as nodes connected by edges, prioritizes data upload based on importance, and maintains relationship weights and statuses over time to enhance data retrieval efficiency and accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If patient data is stored in local databases at each clinic, then data storage is simple and decentralized, but data retrieval requires querying multiple databases and results in increased latency
Solution Approach 1:
The patent merges data from multiple local clinic databases into a centralized database that stores unified patient records. This consolidation eliminates the need to query multiple separate databases, thereby reducing data retrieval latency while maintaining centralized control over data access and management.
Solution Approach 2:
The patent introduces a centralized database as an intermediary layer between multiple clinics and patient data. This mediator consolidates data from various sources and provides a single point of access, reducing the time required to retrieve patient information while preserving the decentralized nature of clinic operations.
2Ease of operation
If all patient data is stored in a common database, then data retrieval is simplified, but the database size grows excessively large with repetitive entries
Solution Approach 1:
The patent segments patient data into distinct entities including patients, providers, locations, and visits, where each entity is stored once in the database. This segmentation eliminates repetitive data entries while maintaining efficient retrieval capabilities, as the database structure is optimized to access specific entity types without duplicating information across records.
Solution Approach 2:
The patent changes the fundamental parameter of data organization from repetition-based storage to entity-based storage. Instead of storing patient information repeatedly in each visit record, the system stores each patient as a single entity with a unique identifier, fundamentally reducing database size while improving retrieval efficiency through optimized query structures.
3Loss of information
If data is stored with full detail for each clinic visit, then complete patient history is maintained, but memory resources are significantly consumed
Solution Approach 1:
The patent segments patient information into discrete entities (patients, providers, locations, visits) where each entity is stored once with its unique attributes. This segmentation maintains complete patient history by preserving all necessary relationships and details while eliminating redundant storage, thereby reducing memory resource consumption significantly.
Solution Approach 2:
The patent uses lightweight references or foreign keys to connect related entities instead of copying full data records. Each entity stores only its essential identifying information, and relationships between entities are maintained through efficient reference structures, preserving complete patient history while minimizing memory usage.
Data Source
AI summary
The present disclosure describes a method for updating a node graph data structure, comprising storing a node graph data structure comprising a plurality of entity nodes and a plurality of attribute nodes; receiving, from a data source during a plurality of time periods, a plurality of data files comprising data for a first entity; identifying a plurality of edges between a first entity node of the plurality of entity nodes that identifies the first entity and an attribute node of the plurality of attribute nodes that identifies a first attribute of the first entity, each of the plurality of edges corresponding to a value and a different time period; and updating a value stored in a data structure for an edge that corresponds to a time period associated with a data file.