Medical Diagnostic Platform Data Extraction
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Solution Overview
Problem
Current methods for accessing and extracting medical, financial, and demographic data from disparate databases are time-consuming and labor-intensive, making it inefficient for medical diagnostics and predictive analysis.
Innovation Solution
A system and method for selective data extraction and user correlation that utilizes decentralized computing resources to preprocess and transmit only pertinent data, normalizing, de-identifying, and delimiting data before extraction, creating an encoded data file for efficient transmission and processing.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If conventional methods are used to access and extract data from disparate databases, then complete medical, financial, and demographic information can be obtained, but the process becomes time-consuming and labor-intensive
Solution Approach 1:
The system performs preliminary actions by pre-processing and pre-filtering data at the source databases before extraction. Data is normalized, de-identified, and filtered for relevance before being transferred to the target system, eliminating the need for time-consuming post-extraction processing and significantly reducing data extraction time while maintaining completeness
Solution Approach 2:
The system segments the data extraction process into distinct phases: identification of relevant data at source, pre-processing and normalization, selective extraction, and targeted delivery to the target system. This segmentation allows parallel processing and optimization of each phase, reducing overall extraction time while ensuring complete retrieval of necessary medical, financial, and demographic information
2Reliability
If all source data is extracted and transmitted to the target server, then comprehensive analysis can be performed, but resource consumption and transmission time increase significantly
Solution Approach 1:
The system extracts only the essential and relevant features from the source data during the pre-processing phase. By identifying and extracting only pertinent medical, financial, and demographic attributes needed for predictive analysis, the system reduces data volume for transmission and processing while maintaining the reliability and accuracy required for comprehensive analysis at the target server
Solution Approach 2:
The system applies local quality by differentiating data processing at different locations: source servers perform normalization and de-identification, the transmission channel handles encoded data efficiently, and the target server focuses on predictive analysis. This distributed quality approach ensures comprehensive analysis capability while optimizing resource consumption at each stage
3Productivity
If data is preprocessed and filtered before extraction using decentralized computing resources, then transmission time and resource consumption are reduced, but system complexity increases
Solution Approach 1:
The system implements self-service by enabling source databases to autonomously perform pre-processing, normalization, and filtering of their own data using decentralized computing resources. Each source system independently prepares its data for extraction without requiring centralized control, simplifying the overall system architecture while dramatically improving extraction speed and reducing transmission requirements
Data Source
AI summary
Embodiments of the invention are directed to a system, method, or computer program product for a medical diagnostic platform. The system accesses data collected on one or more source server systems and selectively extracts user information according to the desired criteria of an operator or user. The system generates a secure, user database, wherein the user database comprises the selectively extracted user information, such as medical, financial, and demographic information, from multiple source server systems creating a centralized database of user information stored in a single location. The system further generates a medical diagnostic analysis of the user in comparison to similar users and displays recommended and extrapolated results for diagnoses, procedures, treatments, and costs for the user based on the history of the similar users.