Node Graph Storage for Patient Data Across Clinics
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Solution Overview
Problem
Current healthcare systems face challenges in efficiently storing and retrieving patient data across multiple clinics, leading to repetitive data storage, slow data retrieval, and latency in accessing critical patient information, which hinders real-time diagnostics and patient care.
Innovation Solution
Implementing a node graph data structure that aggregates patient data from various clinics, using nodes and edges to store and retrieve data efficiently, prioritizing high-priority information, and maintaining a history of patient relationships over time to ensure accurate and timely access to patient records.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If patient data is stored in separate local databases at each clinic, then data storage is simple and decentralized, but data retrieval becomes slow and repetitive tests occur
Solution Approach 1:
The patent merges data from multiple clinic databases into a unified graph database structure where patients, clinics, and medical data are interconnected nodes. This allows the system to retrieve comprehensive patient data from all clinics simultaneously through graph traversal operations, eliminating the need to query each clinic's local database separately and reducing retrieval time while maintaining data decentralization at the source.
2Adaptability or versatility
If all patient data from multiple clinics is stored in a common database, then data accessibility improves, but database size grows excessively large with repetitive entries
Solution Approach 1:
The patent segments the database into distinct graph nodes representing patients, clinics, providers, and medical data, with edges representing relationships between them. Each node contains only the specific data attributes relevant to that entity, eliminating repetitive storage of patient demographic information across multiple clinic records while maintaining full accessibility through graph relationships.
Solution Approach 2:
Instead of storing patient data repeatedly in each clinic's records, the patent inverts the approach by storing each patient's data once in a central patient node and creating reference edges from each clinic node to the patient node. This allows any clinic to access the complete patient record through the graph relationship without duplicating the actual data.
3Device complexity
If conventional databases are used to store patient visits, then data structure is simple, but processing speed for retrieving patient information decreases
Solution Approach 1:
The patent transitions from traditional tabular database structures to a graph-based data structure that adds a dimensional layer of relationships between entities. Nodes represent entities (patients, clinics, providers) and edges represent relationships (visited, diagnosed, treated), enabling the system to traverse and retrieve related data across multiple dimensions simultaneously, dramatically improving processing speed for complex queries while maintaining manageable structural complexity through standardized graph operations.
Data Source
AI summary
A method for prioritized updating of a node graph data structure includes storing a node graph comprising a plurality of patient nodes and a plurality of attribute nodes; receiving, by the one or more processors, a set of data from one or more data sources, each of the one or more data sources corresponding to a different clinic visited by one or more patients represented in the node graph; inserting a first piece of data regarding a first patient having a first patient node in the node graph into a first queue and a second piece of data regarding a second patient having a second patient node in the node graph into a second queue; and updating the node graph with the first piece of data prior to updating the node graph with the second piece of data.