EHR Genomics Rules Engine Ontology
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Solution Overview
Problem
Conventional Electronic Health Record (EHR) systems are not equipped to handle molecular or genomics data and primary care providers face difficulties in interpreting genetic test results.
Innovation Solution
A computer-executable application configured to process cross-clinical genomics data, which can be integrated with EHR systems, uses an ontology linking clinical and genetic terms, a knowledge base, and a rules engine to identify patients benefiting from genetic testing and provide interpretable results to clinicians.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If conventional EHR systems are used, then the system structure remains simple and easy to operate, but the system cannot handle molecular or genomics data
Solution Approach 1:
The system segments genomics data processing into distinct functional modules: data reception module, structured format validation module, interpretation module, and presentation module. Each module handles a specific aspect of genomics data processing, allowing the EHR system to handle molecular data without requiring complete system redesign.
Solution Approach 2:
The patent introduces an intermediary interpretation layer that translates complex genomics data into clinically actionable information. This intermediary component bridges the gap between raw molecular data and clinical decision-making, enabling the EHR system to process genomics data without direct complexity exposure to clinicians.
2Loss of information
If genetic test results are presented to primary care providers, then more genetic information becomes available, but providers have difficulty interpreting the results
Solution Approach 1:
The system employs an interpretation module that acts as an intermediary between raw genetic test results and clinical providers. This module automatically analyzes genetic variants, determines clinical significance, and presents results in standardized formats with clear actionable recommendations, eliminating the need for providers to directly interpret complex genetic data.
Solution Approach 2:
The patent transforms genetic data from its raw complex format into standardized clinical parameters. Genetic variants are converted into structured results with defined parameters such as pathogenicity classification, clinical significance, and recommended actions, making the information both accessible and interpretable for primary care providers.
3Adaptability or versatility
If unstructured genetic data is received, then data collection is flexible, but the data cannot be effectively processed or interpreted
Solution Approach 1:
The system performs preliminary structuring and validation of genetic data upon reception. The structured format requirement is enforced at the point of data entry, preparing data for subsequent processing. This preliminary action ensures data is ready for interpretation without requiring extensive post-reception processing.
Solution Approach 2:
The patent implements parameter transformation that converts unstructured genetic data into standardized structured formats with defined parameters. This transformation occurs systematically, changing data from flexible unstructured form into processed form with specific parameters that enable efficient interpretation and clinical application.
Data Source
AI summary
Described herein are various technologies pertaining an electronic health record application (EHR). The EHR has a rules engine that is configured to execute a plurality of rules corresponding to respective genetic disorders. The rules receive clinical data as input, and each rule is configured to output an indication as to whether or not a patient is a candidate for genetic testing for a genetic disorder.