Healthcare Data Model with Rule-Based Validation
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Solution Overview
Problem
Current healthcare information management systems face challenges in efficiently exchanging and maintaining compatible data across different systems, particularly when multiple specialists with different record-keeping systems are involved, leading to inefficiencies in clinical trials, patient record management, and complaint tracking.
Innovation Solution
A data model that maintains a state of healthcare information by receiving transactions, evaluating them against rules, and updating the model accordingly, ensuring compliance with predefined conditions and permissions, allowing for the addition or alteration of rules and data, and utilizing a hierarchical structure for data representation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If multiple data systems are used to manage healthcare information for different specialists, then the system can accommodate diverse data needs and requirements, but the systems become incompatible and information exchange becomes inefficient
Solution Approach 1:
The patent introduces a rules engine as an intermediary layer between diverse data systems and the centralized data model. This rules engine translates and validates data from multiple incompatible systems into a unified CDISC-ODM standard format, enabling information exchange efficiency while maintaining the adaptability to handle diverse data needs from different specialists and organizations.
2Reliability
If manual data verification is performed to ensure compliance with standards, then data accuracy can be maintained, but the process becomes time-consuming and labor-intensive
Solution Approach 1:
The patent implements self-service through automated rule-based validation. The rules engine automatically verifies data compliance with CDISC-ODM standards by evaluating transactions against predefined rules, eliminating the need for manual verification. This automated approach maintains high data reliability while significantly reducing the time and labor required for compliance checking.
Solution Approach 2:
The system incorporates feedback mechanisms where the rules engine continuously monitors and validates data transactions in real-time. When data violates predefined compliance rules, the system provides immediate feedback by rejecting the transaction or requesting corrections, ensuring data accuracy without requiring manual intervention and reducing verification time.
3Loss of information
If comprehensive data tracking is implemented for all healthcare information, then completeness of information can be ensured, but the system complexity and difficulty of data management increase
Solution Approach 1:
The patent segments data management complexity by organizing data into a hierarchical structure based on CDISC-ODM standards. This segmentation divides comprehensive healthcare information into standardized categories and levels, making the data more manageable while ensuring completeness. The rules engine then operates on this segmented structure, reducing overall system complexity.
Data Source
AI summary
Healthcare information data is managed by, on a computer, maintaining a model of a state of a system based on the healthcare information, receiving a transaction representing a change to the system, determining that the change complies with a rule, and changing the model according to the transaction.