Integrated Health Content Framework for Contextual EHR Recommendations
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Solution Overview
Problem
Current healthcare systems lack an integrated solution to provide contextually relevant health information by combining electronic health records (EHR) data with external data sources such as weather, location, and fitness habits, limiting the ability to generate personalized and effective treatment recommendations.
Innovation Solution
A system that integrates EHR data with external sources to apply treatment protocols, using an analyzer to identify consistent protocols, a verifier to validate them, and a generator to produce recommendations, while also considering additional data like environmental and demographic factors.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If EHR data is integrated with external data sources (weather, location, fitness habits), then the relevance and personalization of health information is improved, but the system complexity increases
Solution Approach 1:
The system divides the health information integration task into separate functional modules: an analyzer module that processes EHR data, an external data source module that fetches contextual information (weather, location, fitness), and a recommendation module that generates personalized health recommendations. This segmentation allows each module to handle specific data types independently, reducing overall system complexity while maintaining high contextual relevance.
Solution Approach 2:
The patent introduces a data processing layer that acts as an intermediary between EHR systems and external data sources. This intermediary layer standardizes data formats, handles data conflicts, and prepares integrated information for recommendation generation. The intermediary abstracts the complexity of data integration from the recommendation system, allowing the system to maintain simplicity while accessing diverse data sources.
2Adaptability or versatility
If multiple protocols are applied to EHR data to generate recommendations, then the comprehensiveness of treatment options is improved, but the time required for analysis increases
Solution Approach 1:
The system pre-loads and pre-processes multiple treatment protocols into a structured format before they are needed for recommendation generation. The analyzer module maintains a repository of validated protocols that are ready for immediate application to EHR data. This preliminary preparation eliminates the need for real-time protocol retrieval and parsing, significantly reducing analysis time while maintaining comprehensive treatment coverage.
Solution Approach 2:
The system creates a standardized representation of treatment protocols as data structures that can be rapidly applied to patient records. Instead of working with complex clinical guidelines in their original format, the system uses simplified protocol copies that capture essential treatment logic while being computationally efficient. This copying approach enables rapid protocol application across multiple clinical conditions without time penalty.
Data Source
AI summary
Embodiments herein disclose systems, methods, and computer-readable media for providing relevant content to users. Content from a plurality of disparate sources can be received and data from an electronic health record (EHR) can be leveraged such that relevant data or recommendations can be provided to users.