Cross-Domain Risk View for SDoH Data Consistency
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Solution Overview
Problem
Existing healthcare systems are siloed, leading to fragmented care and inconsistent data across medical, behavioral, and social service domains, which negatively impacts vulnerable populations and wastes provider time with duplicative information gathering and introduces errors.
Innovation Solution
A Social Information Exchange (SIE) platform that collects, tags, and normalizes data from multiple domains to provide a unified view of an individual's Social Determinants of Health (SDoH) risks, using machine learning to update and visualize risks through a user interface.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If data is collected from multiple siloed domains (medical, behavioral, social services), then comprehensive view of individual risks is improved, but data consistency and coordination between domains deteriorates
Solution Approach 1:
The patent introduces a centralized data exchange platform that acts as an intermediary between siloed medical, behavioral, and social service domains. This platform standardizes data formats, implements common data elements, and facilitates coordinated information sharing across domains, thereby maintaining data consistency while enabling comprehensive risk assessment.
Solution Approach 2:
The system implements a universal data framework that can handle multiple types of data from different domains (medical, behavioral, social services) through standardized interfaces. This multi-functional approach allows the same platform to process diverse data types while maintaining consistency, enabling comprehensive risk views without sacrificing reliability.
2Reliability
If organizations limit exposure of sensitive personal data to reduce liability, then organizational risk is reduced, but care coordination and service delivery deteriorates
Solution Approach 1:
The platform serves as a secure intermediary that enables data sharing while protecting organizational liability. It implements standardized data exchange protocols, consent management, and access controls that allow coordinated care delivery without requiring organizations to directly expose their sensitive data systems to external entities.
Solution Approach 2:
The system segments data access and sharing responsibilities across different domains and organizations. Each organization maintains control over its own data while the platform provides coordinated access where needed, enabling efficient care coordination without compromising organizational liability protection boundaries.
3Loss of information
If providers gather duplicative information from clients across multiple organizations, then data completeness is improved, but provider time and client experience deteriorate
Solution Approach 1:
The centralized data exchange platform acts as an intermediary that automatically aggregates information from multiple organizations about each client. Providers can access comprehensive client data through the platform without manually gathering duplicative information, completing data collection while eliminating time-wasting duplicate assessments.
4Measurement precision
If multiple profiles are maintained across various information systems for the same client, then domain-specific data accuracy is improved, but data variability and error introduction increase
Solution Approach 1:
The system implements a universal data framework that maintains domain-specific data accuracy while ensuring cross-domain consistency. It uses common data elements and standardized formats that work across medical, behavioral, and social service domains, allowing accurate domain-specific information to be shared and coordinated without introducing variability or errors.
Data Source
AI summary
Determining risks based on data from a plurality of disassociated domains can comprise obtaining data regarding an individual from each of the plurality of disassociated domains. The data regarding the individual can have format and content specific to the domain from which it is obtained. The obtained data can be tagged based on the domain from which it is obtained and a set of predefined elements for each of the plurality of disassociated domains. The tagged data can be associated with one or more of a plurality of predefined groups. Each group can represent one or more of the plurality of disassociated domains. A diagnostic process identifying a risk for the individual in each of the domains can be performed on the tagged data and a user interface including a visual representation of the identified risk in each domain in each of the predefined groups can be provided.