Deidentified Patient Timeline Vectors for Federated Medical Data
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Solution Overview
Problem
Existing data analysis methods for medical research face challenges in combining diverse healthcare data sources while ensuring patient privacy and maintaining data utility, particularly in handling decentralized patient data across multiple systems.
Innovation Solution
A decentralized approach for deidentified data processing that allows querying and combining patient data across multiple sources without creating a unified dataset, using interval encoding and token identifiers to maintain privacy and avoid re-identification risks.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If patient data is centralized into a unified dataset for analysis, then data utility and research efficiency are improved, but patient privacy and re-identification risks worsen
Solution Approach 1:
The system segments patient data into distributed datasets across multiple worker nodes, with each node maintaining local data without centralized aggregation. This allows research queries to be executed across distributed data segments while preventing re-identification risks associated with centralized data storage
Solution Approach 2:
The system introduces an intermediary layer of federated query processing that mediates between research analysts and distributed patient data. This intermediary enables data utility through query execution while maintaining privacy by preventing direct access to raw patient information
2Adaptability or versatility
If data from multiple healthcare systems is combined, then comprehensiveness of medical research is improved, but data integration complexity and processing time worsen
Solution Approach 1:
The system implements a universal query interface that can operate across diverse healthcare data sources with different formats and structures. This multi-functional approach allows comprehensive research across multiple systems without requiring system-specific integration logic for each data source
Solution Approach 2:
The system performs preliminary actions by pre-processing and standardizing data at the source systems before queries are executed. This includes creating standardized data representations and indexes in advance, which reduces integration complexity during actual research queries
3Reliability
If deidentification is performed repeatedly on distributed data, then patient privacy is maintained, but processing time and computational resources worsen
Solution Approach 1:
Deidentification is performed as a preliminary action when data is initially ingested into the distributed system, rather than repeatedly during each query. This initial deidentification maintains patient privacy while avoiding the computational overhead of repeated processing
Solution Approach 2:
The system maintains continuous privacy protection through persistent deidentified data representations that remain protected throughout the query process. This continuous protection approach eliminates the need for repeated deidentification operations while maintaining reliability
Data Source
AI summary
Systems and methods for deidentified data processing are provided herein. Deidentified data processing techniques can include accessing data from one or more worker nodes and assembling accessed data into patient timeline vectors. Worker nodes can provide sub-vectors to a primary node, which can assemble sub-vectors across multiple worker nodes to build patient timeline vectors that include data from multiple sources. Data provided by worker nodes can be deidentified, and subjects across different worker nodes can be matched based on a hash or other unique identifier. In some implementations, worker nodes provide compressed data tables with subject data, and a primary node reconstructs tables from the compressed format. In some implementations, only relative dates are utilized to better preserve patient privacy.