Imaging Related Clinical Context System for Radiology Workflow
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Solution Overview
Problem
In healthcare environments, radiologists face challenges in accessing and interpreting relevant patient data due to its siloed nature across various systems, leading to inefficiencies in diagnosis and treatment, as current technologies fail to effectively aggregate and present clinically relevant information in real-time during imaging studies.
Innovation Solution
The implementation of an Imaging Related Clinical Context (IRCC) system that uses natural language processing and machine learning to identify and emphasize relevant clinical data, automatically aggregating information from disparate sources and presenting it to radiologists within their workflow, thereby reducing the need for manual searching and enhancing data retrieval.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If manual searching and reviewing of patient data is performed across multiple siloed systems, then comprehensive clinical information can be accessed, but the time required for diagnosis increases and workflow efficiency decreases
Solution Approach 1:
The system performs preliminary actions by automatically retrieving, aggregating, and organizing relevant patient data from multiple sources before the radiologist needs it. The IRCC system proactively compiles clinical context information including patient history, prior imaging, and relevant test results, making it immediately available when the radiologist begins their review, thereby eliminating manual searching time while ensuring comprehensive information access
Solution Approach 2:
The IRCC system acts as an intermediary layer between disparate healthcare information systems (PACS, RIS, EMR, LIS) and the radiologist. It aggregates data from these siloed systems, processes it through natural language processing and machine learning to identify relevant clinical context, and presents it in a unified format, thereby providing comprehensive information without requiring the radiologist to access multiple separate systems
2Loss of information
If all available patient data is presented to radiologists, then complete clinical context is provided, but the complexity of information processing increases and relevant information becomes harder to identify
Solution Approach 1:
The system extracts only the most relevant clinical information from the vast amount of available patient data using natural language processing and machine learning algorithms. It identifies and extracts key elements such as clinically significant findings, relevant patient history, and important diagnostic context, while filtering out redundant or less relevant information, thereby providing complete clinical context in a simplified, manageable format
Solution Approach 2:
The IRCC system applies local quality by providing different levels and types of information emphasis based on clinical relevance. It highlights critical findings and organizes information according to its importance for the specific diagnostic task at hand, allowing radiologists to quickly identify the most pertinent clinical context without being overwhelmed by uniform presentation of all data
3Ease of manufacture
If multiple separate healthcare information systems are used to store and manage patient data, then data can be organized by function, but accessing and correlating information across systems becomes difficult and time-consuming
Solution Approach 1:
The IRCC system provides multi-functionality by serving as a universal interface that can access and integrate data from multiple specialized healthcare information systems including PACS, RIS, EMR, and LIS. It performs multiple functions simultaneously: retrieving data from different sources, processing and correlating the information, and presenting it in a unified view, thereby maintaining the functional organization benefits of separate systems while enabling easy cross-system information retrieval
Data Source
AI summary
Systems, methods, and apparatus provide facilitate detection, processing, and relevancy analysis of clinical data including imaging related clinical context are disclosed and described herein. An example imaging related clinical context apparatus includes a processor to: analyze a plurality of documents to identify a subset of relevant documents in the plurality of document by: applying natural language processing to identify terms in the plurality of documents, a subset of the identified terms forming tagged concepts; processing the identified terms using a machine learning model with respect to a relevancy criterion for an examination to select the subset of relevant documents; and adding an emphasis to the tagged concepts found in the subset of relevant documents. The processor is to output the subset of relevant documents including emphasized tagged concepts.