Patient Complexity Classification via NLP on DICOM Metadata
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Solution Overview
Problem
Artificial intelligence tools in healthcare struggle to accurately assess patients with complex medical conditions that differ significantly from their training data, leading to potential misclassification and inappropriate treatment recommendations, especially when integrated with electronic medical records (EMR) systems that vary in format and consistency.
Innovation Solution
A patient complexity classification (PCC) system that utilizes natural language processing (NLP) on medical image metadata and image analytics to determine patient complexity, routing medical image data to appropriate evaluation systems without requiring EMR integration, employing AI filtering mechanisms agnostic to EMR data and using DICOM header information to extract features indicative of complexity.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If AI tools are integrated with EMR systems to assess patient complexity, then more comprehensive patient information can be analyzed, but the system becomes more complex and vulnerable to format inconsistencies and integration issues
Solution Approach 1:
The patent extracts the essential features needed for patient complexity assessment directly from medical imaging data and metadata, separating this function from EMR system integration. By taking out only the necessary imaging-related features (DICOM headers, image characteristics) and processing them independently through NLP and image analytics, the system achieves reliable complexity assessment without the complexity of full EMR integration.
2Adaptability or versatility
If AI tools are trained on diverse EMR data from multiple sources, then they can handle varying formats and improve generalization, but the data integration and standardization process becomes more difficult
Solution Approach 1:
The patent segments the patient assessment process into distinct components: imaging data extraction, metadata processing through NLP, image analytics, and complexity classification. Each component processes specific types of data independently using standardized methods (DICOM standards for imaging, structured NLP for metadata), avoiding the need to integrate and standardize diverse EMR formats while maintaining adaptability to different imaging sources.
3Measurement precision
If the system performs comprehensive analysis of both EMR data and medical imaging data, then more accurate complexity classification can be achieved, but the processing time and computational resources increase
Solution Approach 1:
The patent performs preliminary extraction and processing of imaging features and metadata before the main complexity classification task. By pre-processing the medical imaging data to extract relevant features (through NLP on metadata and image analytics) and preparing them in advance, the system reduces the computational burden during actual classification, achieving precise results without excessive processing time.
4Loss of information
If the system requires integration with EMR systems to access patient history and clinical data, then more complete patient context is available, but the ease of deployment and operation is reduced
Solution Approach 1:
The patent enables the imaging analysis system to self-sufficiently extract and process all necessary information from medical imaging data and metadata without requiring external EMR system connections. The system uses DICOM headers and image characteristics that are inherently present in the imaging data itself, allowing independent deployment and operation while still achieving accurate complexity assessment.
Data Source
AI summary
Mechanisms are provided for implementing a patient complexity classification (PCC) computing system. The PCC computing system receives medical image study data for a patient that comprises one or more medical image data structures and one or more corresponding medical image metadata data structures. A natural language processing engine of the PCC computing system performs natural language processing on the medical image metadata data structure to extract features indicative of at least one of patient or medical image characteristics. A complexity classifier of the PCC computing system evaluates the extracted features to determine a patient complexity indicating a complexity of a medical condition of the patient. Routing logic associated with the PCC computing system routes the one or more medical image data structures and one or more corresponding medical image metadata data structures to one or more downstream patient evaluation computing systems based on the determined patient complexity.