Clinical Report Data Model Generator for Structured Ingestion
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Solution Overview
Problem
Integration of clinical reports from various sources into comprehensive medical systems is challenging due to variations in structure, format, and visual representation, limiting the ability of report integration systems to process and standardize patient-specific genomics data efficiently.
Innovation Solution
A method and system that guide users through a sequential mapping process to generate a clinical report template and data capture mechanism, enabling the creation of custom data models for various clinical report types, allowing for efficient mapping and standardization of incoming reports using a graphical user interface and data ingestion tool.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Extent of automation
If Named Entity Recognition (NER) is used to capture clinical report content, then automated extraction is achieved without expert-guided curation, but the system requires vast datasets for training and fails to extract specific information from highly customized document structures
Solution Approach 1:
The system performs preliminary action by guiding users to define custom data models and mapping templates before processing clinical reports. Users select and map specific fields from reference information models to the actual report structure, establishing the extraction rules in advance. This allows the automated system to reliably extract specific information from customized documents without requiring vast training datasets.
Solution Approach 2:
The system introduces an intermediary layer between the standardized reference information model and the customized clinical report structure. The custom data model acts as a mediator that translates between the generic reference model and the specific report format, enabling reliable extraction of targeted information while maintaining automation.
2Stability of the object's composition
If technical standards are applied to integrate clinical reports, then integration consistency is improved, but adoption remains sparse and limited
Solution Approach 1:
The system provides a universal framework that can handle multiple report types and formats through a single interface. The custom data model mechanism allows the same system to adapt to different clinical report structures without requiring separate specialized tools for each report type, thereby improving both consistency and versatility.
Solution Approach 2:
The system dynamically adapts to different report structures by allowing users to define custom mappings for each report type. Rather than requiring rigid pre-defined standards, the system can flexibly configure data models to match various report formats, improving adoption across diverse clinical domains while maintaining integration consistency.
3Measurement precision
If manual curation and expert guidance are used to create data models, then extraction accuracy is improved, but time and human resources are significantly increased
Solution Approach 1:
The system performs preliminary action by providing pre-defined reference information models that contain standardized fields and data types. Users only need to select and map the relevant fields rather than creating models from scratch, significantly reducing the time required while maintaining accuracy through the structured reference framework.
Solution Approach 2:
The system uses copying by allowing users to select from pre-existing reference information model elements and replicate them in the custom data model. This enables users to leverage established standardized fields rather than recreating them, reducing manual effort and time while preserving extraction accuracy.
4Measurement precision
If customized data models are created for each report type, then data-specific accuracy is improved, but system complexity increases
Solution Approach 1:
The system segments the data model creation process into modular components: reference information models, custom data models, and mapping templates. Each segment can be independently configured and reused, allowing customization for specific report types without requiring complete system redesign. This modular approach maintains accuracy while managing complexity.
Solution Approach 2:
The system provides a universal platform that handles multiple report types through a single interface and set of tools. The same custom data model mechanism works across different clinical domains, reducing overall system complexity compared to having separate specialized systems for each report type while maintaining data-specific accuracy.
Data Source
AI summary
The present disclosure describes systems configured to guide users through a sequential mapping process to extract targeted information from received clinical report documents. The systems are configured to utilize the extracted information to generate a comprehensive, flexible report data model used to process incoming clinical report documents having the same document structure. Systems are uniquely configured to map incoming reports by utilizing the source code of PDF files, including the displayed text, information that determines how the text appears, and the absolute position of the text within each document constituting a clinical report.