EHR-Driven DSS for Reproductive Care Prediction
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Solution Overview
Problem
Current decision support systems (DSSs) and electronic health records (EHRs) lack integration and validation, failing to provide personalized prognostic information for reproductive care and other health conditions, and are not optimized for extracting accurate data from complex medical histories intertwined with lifestyle factors.
Innovation Solution
An EHR-driven DSS is developed using clinic-specific, region-specific, and population-specific prediction models based on demographic, clinical, and laboratory variables, validated through machine learning algorithms, and integrated with EHR platforms to provide personalized health predictions and data management.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If DSS and EHR systems are integrated with validated prediction models, then personalized prognostic information and healthcare quality are improved, but system complexity and implementation costs increase
Solution Approach 1:
The patent combines DSS prediction models with EHR data collection and management functions into an integrated platform. The system merges clinical data extraction, prediction model execution, and result delivery into a unified system that works seamlessly with existing EHR infrastructure, thereby improving reliability without proportionally increasing complexity.
Solution Approach 2:
The patent introduces an intermediary layer between EHR data and prediction models that handles data extraction, validation, and formatting. This intermediary component simplifies the integration process by standardizing data interfaces and reducing the complexity of direct system-to-system connections.
2Measurement precision
If EHR platforms are designed to extract data from complex medical histories intertwined with lifestyle factors, then data accuracy for reproductive care is improved, but ease of operation and user burden increase
Solution Approach 1:
The patent enables patients to self-enter lifestyle and medical history data through user-friendly interfaces. The system provides structured forms and guidance that help patients accurately input complex information without requiring extensive medical knowledge or professional assistance, thereby maintaining data accuracy while improving ease of operation.
Solution Approach 2:
The patent implements pre-structured data collection forms and templates that are prepared in advance based on clinical requirements. These preliminary preparations guide patients through the data entry process systematically, ensuring all necessary information is captured accurately without overwhelming users during the actual input process.
3Reliability
If validated prediction models are implemented for reproductive care, then personalized prognostic information is improved, but loss of time for validation and implementation increases
Solution Approach 1:
The patent performs validation of prediction models in advance during the system development and deployment phase. By completing validation studies before widespread implementation, the system establishes reliability upfront, avoiding the need for time-consuming validation processes during clinical use and reducing the time loss associated with ongoing verification.
4Ease of operation
If DSS tools are made freely accessible to consumers, then ease of operation and accessibility are improved, but quality control and validation recognition decrease
Solution Approach 1:
The patent creates a universal platform that serves both consumer self-assessment and professional clinical decision-making. The same validated prediction models are made accessible to both groups through different interfaces, ensuring quality control is maintained while improving accessibility. The system functions simultaneously as a consumer health tool and a clinical decision support system.
Data Source
AI summary
Provided are methods of delivering decision support systems (DSSs) to healthcare providers, patients, and/or consumers with or without integrated electronic health records (EHRs) for reproductive care and other health conditions. The DSS platforms of the present invention include predication models based upon de-identified data sets and customized algorithms that may be clinic specific, region specific, and/or population specific. The DSS platforms of the present invention also include methods of providing third party payments of an individual's medical bills, wherein the third party is not capable of viewing the personal health identifiers of the individual.