Automated Genetic Test Eligibility System for EMR Integration
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Solution Overview
Problem
Current healthcare systems lack the ability to effectively integrate genetic information to determine the efficacy of medications and treatment options, leading to inefficient prescribing practices and potential side effects.
Innovation Solution
A system and method that utilize genetic data to identify effective and ineffective treatment options by analyzing genetic markers, integrating this information with existing electronic medical record systems, and automating the ordering and authorization of genetic efficacy tests.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional prescribing methods based on historical effectiveness are used, then treatment options can be prescribed without genetic testing, but treatment efficacy is reduced and side effects increase due to lack of personalized medicine
Solution Approach 1:
The system performs genetic testing and analyzes genetic markers before prescribing treatment, allowing treatment recommendations to be prepared in advance based on the patient's genetic profile. This preliminary genetic analysis enables personalized treatment selection before the actual prescribing occurs, improving efficacy while managing complexity through automated preprocessing.
Solution Approach 2:
The patent introduces an intermediary system that bridges genetic data and treatment recommendations. This intermediary layer includes databases of genetic markers, their associations with treatment responses, and automated analysis algorithms that translate raw genetic data into actionable prescribing information, thereby managing the complexity between genetic testing and clinical decision-making.
2Object-affected harmful factors
If genetic efficacy testing is implemented, then treatment efficacy is improved and side effects are reduced, but system complexity and integration requirements increase
Solution Approach 1:
The system is designed to integrate with existing electronic medical record systems and healthcare workflows, serving multiple functions including genetic data storage, analysis, treatment recommendation generation, and provider alerting. This multi-functionality reduces the need for separate specialized systems, thereby managing integration complexity while delivering comprehensive personalized medicine capabilities.
Solution Approach 2:
The system automatically performs genetic data analysis, matches genetic markers with treatment efficacy data, generates treatment recommendations, and sends alerts to providers without requiring manual intervention. This automation reduces the operational complexity of implementing genetic testing programs and allows the system to serve itself in many routine tasks.
3Loss of information
If genetic data is integrated into existing systems, then information availability is improved, but data security and privacy risks increase
Solution Approach 1:
The system extracts and separates genetic data from general medical records into dedicated secure storage areas with specialized access controls. By isolating sensitive genetic information from the broader electronic medical record system, the patent reduces the attack surface for data breaches while maintaining information availability for authorized clinical use.
Data Source
AI summary
System and methods for suggesting insurance eligible genetic tests to a healthcare provider are disclosed. An EMR system is queried to retrieve insurance information, active medications, and diagnoses associated with a particular patient. An eligibility database is queried to retrieve insurance eligibly rules for a particular policy of a particular insurance providers based on the retrieved insurance information. A laboratory information system (“LIS”) applies the retrieved insurance eligibility rules to the retrieved active medications and diagnoses to determine eligible genetic tests. An electronic notification comprising an order form for each of the eligible genetic tests is transmitted from the LIS to the EMR.