Automated Data Transformation for HL7 Schema Mapping
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Solution Overview
Problem
Mapping raw domain-specific data, such as HL7 messages, to a unified data model is a time-consuming and challenging task due to variations in representation across different systems, requiring custom analytics for each system, which is labor-intensive and error-prone.
Innovation Solution
A method that transforms semi-structured or unstructured data into a target schema by characterizing input data using predetermined metrics, determining contextual information, and mapping source fields to target fields based on confidence values, enabling automated mapping to a structured target schema like a unified data model.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If custom analytics are written for each individual health system, then data mapping accuracy is improved, but development time and complexity increase significantly
Solution Approach 1:
The system performs self-service by automatically analyzing incoming HL7 messages, identifying their structure and content characteristics, and generating appropriate mapping configurations without human intervention. The analytics engine autonomously characterizes data patterns and creates mapping rules, eliminating the need for manual custom analytics development for each health system.
Solution Approach 2:
The system changes parameters by dynamically adjusting mapping configurations based on the characterized properties of incoming data. Instead of using fixed mapping rules, the system modifies mapping parameters adaptively according to the specific characteristics of each health system's data, enabling accurate mapping without custom development.
2Measurement precision
If manual mapping of HL7 messages is performed, then mapping precision is maintained, but labor intensity and error rate increase
Solution Approach 1:
The system replaces the mechanical process of manual mapping with an automated analytics engine that uses characterization and pattern recognition. The mechanical task of manually analyzing and mapping HL7 messages is substituted by an automated system that performs the same function with higher precision and without human error.
Solution Approach 2:
The system introduces an intermediary analytics engine between the raw HL7 messages and the target data model. This intermediary automatically characterizes the incoming messages, determines appropriate mapping strategies, and performs the transformation, eliminating the need for manual intervention while maintaining high mapping precision.
3Productivity
If automated transformation is implemented, then productivity is improved, but handling of heterogeneous data formats becomes more difficult
Solution Approach 1:
The system segments the complex task of handling heterogeneous HL7 data into distinct phases: characterization of incoming messages, pattern recognition, mapping rule generation, and data transformation. By dividing the process into manageable segments, the system handles diverse data formats systematically while maintaining high transformation speed.
Solution Approach 2:
The system achieves universality by creating a single automated analytics engine that can handle multiple HL7 message types and formats from different health systems. The characterizer and mapping engine are designed to be format-agnostic, automatically adapting to various data structures without requiring separate processing logic for each format.
Data Source
AI summary
Embodiments generally relate transforming data for a target schema. In some embodiments, a method includes receiving input data, where the input data includes a plurality of segments, and where the segments include a plurality of source fields containing target data. The method further includes characterizing the input data based at least in part on a plurality of predetermined metrics, where the predetermined metrics determine a structure of the input data. The method further includes mapping the target data in the source fields of the segments to a plurality of target fields of a target schema based at least in part on the characterizing. The method further includes populating the target fields of the target schema with the target data from the source fields based at least in part on the mapping.