Network Architecture Aggregating HL7 Data
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Solution Overview
Problem
Existing hub-and-spoke network architectures face challenges in managing and processing vast amounts of real-time data from diverse sources, leading to overwhelming volumes of raw data and poor-quality information, which results in user fatigue and inefficient information delivery.
Innovation Solution
A network architecture that aggregates and filters data from multiple disparate sources, using a processor to curate and enhance HL7 ADT messages, providing tailored information to subscribing providers based on specific subscriber parameters, reducing unnecessary data transmission and improving data quality.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If a hub-and-spoke network architecture aggregates data from multiple disparate sources, then data quantity and coverage are improved, but data quality deteriorates due to overwhelming volumes of raw data
Solution Approach 1:
The patent extracts and filters only the relevant HL7 ADT messages from the overwhelming volume of raw data. The system selectively pulls out admission, discharge, and transfer messages that match specific subscriber parameters, discarding unnecessary data while maintaining data quality through targeted extraction.
Solution Approach 2:
The patent applies local quality by customizing data delivery for different subscribers based on their specific parameters and needs. Each subscriber receives tailored information filtered according to their unique requirements, ensuring high data quality relevance for each local recipient rather than uniform generic data distribution.
2Productivity
If the hub processes and routes data from multiple sources to multiple clients, then information delivery coverage is improved, but processor power consumption increases
Solution Approach 1:
The patent implements preliminary action by pre-configuring subscriber parameters and filtering criteria before data arrives. The system prepares subscription profiles, data types, and delivery preferences in advance, allowing efficient real-time filtering and routing without extensive processing when actual data messages are received and distributed.
3Loss of information
If the network transmits all incoming data to subscribers, then information completeness is improved, but bandwidth consumption increases
Solution Approach 1:
The patent extracts only the essential and relevant information from incoming data streams based on subscriber parameters. Instead of transmitting complete raw datasets, the system pulls out specific HL7 ADT messages that match subscriber interests, maintaining information completeness for each subscriber's needs while dramatically reducing overall bandwidth consumption.
4Loss of information
If the hub provides comprehensive data to all subscribers, then information availability is improved, but user fatigue increases due to overwhelming data volumes
Solution Approach 1:
The patent applies local quality by customizing data delivery for each subscriber based on their specific parameters, preferences, and needs. Each user receives a tailored information stream filtered to their local requirements, ensuring information availability for their specific context while eliminating overwhelming volumes of irrelevant data that cause user fatigue.
Data Source
AI summary
Network architectures are interfaced with diverse inputs like a hub with multiple spokes. A portion of these input are further interfaced as limited outputs. The inputs can be from wholly different users streaming unrelated data in different protocols. HL7 ADTs in these inputs can control network configuration, culling or screening output to communications that match user parameters. Network architectures can aggregate and enhance information that matches from among these inputs, including information across protocols such as HL7. Such information can be provided and stored back to other sources so as to add to or correct wide area data stores. All communications can be provided in real-time and with manageable volumes that do not fatigue users and better manage network resources.