Health Data Normalization Using AI for Privacy and Data Utility
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Solution Overview
Problem
Healthcare data from diverse sources often lacks uniformity in structure and terminology, leading to biased insights and inefficiencies in processing and research, while conventional de-identification techniques remove too much information or are not suited for varied data formats.
Innovation Solution
A health data platform utilizing AI, machine learning, and natural language processing automates data normalization, converting diverse data into a unified format, leveraging clinical experts for monitoring and updating, and employing metadata-driven transforms for improved consistency and debugging.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional de-identification techniques are used to protect patient privacy, then patient confidentiality is improved, but data utility is reduced due to excessive information removal
Solution Approach 1:
The patent applies parameter changes by transitioning from conventional de-identification methods that remove information to a normalization approach that transforms and standardizes data parameters. The system changes the structural parameters of health data (formats, terminologies, code sets) while preserving the semantic content and clinical meaning, thereby maintaining both patient confidentiality and data utility for research purposes.
Solution Approach 2:
The patent substitutes the mechanical removal process (conventional de-identification) with an intelligent transformation process using AI, machine learning, and natural language processing. Instead of mechanically deleting data elements, the system intelligently normalizes diverse health data formats into a unified structure, preserving information while enabling standardized analysis.
2Quantity of substance
If health data from multiple diverse sources is processed using standard techniques, then data volume is increased, but processing complexity and time consumption increase significantly
Solution Approach 1:
The patent implements universality by creating a unified data model that can handle multiple diverse health data formats, terminologies, and structures through a single normalization framework. The AI-powered system provides multi-functional capability to process electronic health records, claims data, and other health information formats simultaneously, reducing the need for separate processing pipelines for each data type.
Solution Approach 2:
The patent introduces an intermediary normalization layer that mediates between diverse source data formats and the standardized analysis requirements. This intermediary system uses AI and natural language processing to translate various health data formats into a common structure, simplifying downstream processing and reducing overall system complexity.
3Quantity of substance
If health data from multiple diverse sources is processed using standard techniques, then data volume is increased, but processing time is excessive
Solution Approach 1:
The patent applies preliminary action by performing data normalization proactively as data enters the system, rather than processing it later. The AI-powered normalization engine pre-processes and standardizes health data formats, terminologies, and structures in advance, enabling faster retrieval and analysis of ready-to-use standardized data when research queries are executed.
Solution Approach 2:
The patent substitutes traditional mechanical data processing methods with AI-driven intelligent processing. Machine learning models and natural language processing algorithms automatically recognize and normalize diverse health data formats, significantly reducing the time required compared to conventional rule-based or manual processing approaches.
4Reliability
If conventional de-identification techniques are applied to varied data formats, then privacy protection is improved, but adaptability to different data formats is reduced
Solution Approach 1:
The patent implements dynamics by creating an adaptive normalization system that dynamically adjusts to different data formats, terminologies, and structures while maintaining consistent privacy protection. The AI-powered engine learns from diverse data sources and dynamically updates its normalization rules, enabling it to handle new and varying data formats while preserving patient confidentiality through standardized transformation rather than format-specific de-identification.
Data Source
AI summary
Systems and methods for data normalization are disclosed. The disclosed systems and methods train one or more machine learning model based on a plurality of annotated records and apply these trained models to new or updated records. The models are configured to analyze the records and generate classifications or concepts from the records and corresponding confidence scores. If the confidence scores exceed a threshold, the corresponding classifications or concepts are appended to the appropriate record.