AI EMR Conversion With Blockchain for Cross-Provider Records
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Solution Overview
Problem
Chronic patients face fragmented and inaccurate medical records due to interoperability issues between disparate electronic medical record (EMR) systems, leading to suboptimal care, delays, and medical errors.
Innovation Solution
A computing device equipped with an AI engine and blockchain technology to convert, confirm, and manage medical records across different EMR systems, ensuring seamless integration, data integrity, and real-time accessibility.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If disparate EMR systems are used by different care providers, then each provider can maintain their own record-keeping standards and systems, but patient information becomes fragmented and inaccurate across providers
Solution Approach 1:
The patent implements an intermediary conversion service that acts as a mediator between disparate EMR systems. This service receives medical information from one EMR system, converts it to the format required by another EMR system, and transmits it. The conversion service includes trained AI models that learn the specific data formats, structures, and requirements of different EMR systems, enabling accurate translation of patient information without loss or fragmentation.
Solution Approach 2:
The conversion service is designed with universal functionality to handle multiple EMR system formats and protocols. The AI models are trained on diverse datasets from various EMR systems and can perform bidirectional conversions between different formats. This multi-functional capability allows the same conversion service to accommodate numerous different EMR systems without requiring system-specific customizations.
2Productivity
If manual processes are used for medical record collection and conversion, then care providers can maintain control over data accuracy, but unnecessary time is spent on tests, questionnaires, and paperwork
Solution Approach 1:
The system implements self-service automation where the conversion service automatically receives, processes, and converts medical information without requiring manual intervention. The AI models autonomously perform the conversion tasks by learning from training data and applying the learned patterns to new information. This eliminates the need for care providers to manually complete forms, questionnaires, and paperwork, as the system handles these tasks automatically.
Solution Approach 2:
The patent replaces manual mechanical processes with automated AI-based conversion. Instead of care providers manually collecting and transcribing medical information across different systems, the AI conversion service automatically performs the data transformation. The AI models learn from training datasets and apply this knowledge to automatically convert information between EMR systems, substituting human manual labor with intelligent automation.
3Extent of automation
If AI automation is implemented for medical information conversion, then productivity and time efficiency improve, but the complexity of the system increases
Solution Approach 1:
The system is segmented into distinct functional modules: the conversion service that receives information, the AI models that perform conversion, and the transmission mechanism that sends converted data. Each module has a specific function and can be independently trained, deployed, and maintained. The AI models themselves are segmented into different specialized models for different EMR system pairs, allowing for modular complexity management where each segment handles a specific conversion task.
Data Source
AI summary
Implementations including methods and systems for processing electronic medical records (EMR), including receiving first medical information from a first care provider, generating, automatically, second medical information with an artificial intelligence (AI) engine based on the first medical information, the AI engine including an AI model trained to convert the first medical information into the second medical information. The methods and systems transmitting the generated second medical information to the first care provider, receiving confirmation of the generated second medical information from the first care provider, generating metadata from the confirmed second medical information, and transmitting the metadata and the confirmed second medical information to a second care provider.