Clinical Data Workflow Engine for Secure Multi-Format Integration
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing clinical data integration systems face challenges in adhering to HIPAA regulations, managing large data volumes, and ensuring secure and efficient transmission and translation between diverse medical data formats and systems, particularly in larger organizations with specific customization needs.
Innovation Solution
A workflow engine that processes clinical data as a directed acyclic graph, allowing dynamic reconfiguration without programming, and includes a mechanism for uniform data processing, mapping, and transmission using a cloud-based IoT configuration service, with discrete components managing queue states to handle various medical data formats and protocols.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If clinical data is transmitted between diverse medical systems using multiple standards and protocols, then interoperability is improved, but device complexity and transmission efficiency deteriorate due to the variety of formats and translation requirements
Solution Approach 1:
The patent implements a translation service as an intermediary component that mediates between diverse medical data standards and protocols. This service receives data in various formats (DICOM, HL7, FHIR, CDA) and translates them into a unified internal representation, eliminating the need for direct point-to-point translation between all possible standard combinations and reducing overall system complexity
Solution Approach 2:
The workflow engine is designed as a universal platform that can handle multiple data standards, protocols, and transmission methods through a single unified interface. It provides multi-functional capabilities including data ingestion, transformation, routing, and exception handling that work across all supported medical data formats without requiring separate specialized systems for each standard
2Adaptability or versatility
If data transmission protocols are diversified to support multiple medical standards, then adaptability is improved, but transmission speed and efficiency worsen due to translation overhead
Solution Approach 1:
The system performs preliminary actions by pre-configuring translation rules, mapping dictionaries, and workflow templates before data transmission occurs. This allows the translation service to quickly match incoming data against known patterns and apply predefined transformations, reducing real-time translation overhead and improving transmission speed
Solution Approach 2:
The translation service dynamically adjusts translation parameters and optimization levels based on data type, priority, and destination system requirements. It can change transmission parameters such as compression level, translation depth, and protocol selection to balance speed and compatibility for different data scenarios
3Manufacturing precision
If mapping dictionaries are customized for larger organizations with specific demands, then manufacturing precision is improved, but ease of manufacture deteriorates due to increased complexity in altering dictionaries
Solution Approach 1:
The mapping dictionary is segmented into modular, hierarchical components that can be independently configured and customized. Organization-specific mappings are separated from standard mappings, allowing precise customization of only the necessary portions while reusing standard mappings elsewhere, thereby maintaining high mapping accuracy without overwhelming customization complexity
4Reliability
If workflows are configured to handle complex data movement scenarios, then reliability is improved, but ease of operation worsens due to the need to manage sequence of activities and exception handling
Solution Approach 1:
The workflow engine implements self-service capabilities by automatically detecting data issues, selecting appropriate exception handling procedures, and recovering from errors without requiring manual intervention. It autonomously manages workflow state, tracks data lineage, and ensures data integrity through built-in validation and reconciliation mechanisms, improving reliability while reducing operational complexity
Data Source
AI summary
Systems and method are described to share large amounts of data in a secure and hierarchical manner across computer systems. The sharing process includes techniques to manage access to data and to manage physical storage locations throughout a hierarchy of computer systems. An intermediary level of storage may be provided to “cache” large data files to minimize repeated transfer of large data files throughout a given level of the hierarchy. For example, access from client devices will be served, when available, from an intermediary level rather than from a parent cloud system of stored data.