Cognitive Platform Cognifying Unstructured EMR Data
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Solution Overview
Problem
Current healthcare systems face inefficiencies in diagnosing medical conditions due to the time-consuming process of reviewing electronic medical records (EMRs) and the overwhelming amount of unstructured data, which can lead to incorrect diagnoses and waste of computational resources.
Innovation Solution
A cognitive intelligence platform that cognifies unstructured data from EMRs by using a knowledge graph and logical structure to generate summarized health information, allowing for efficient diagnosis and reducing the need for extensive data searches.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If physicians review extensive unstructured data in electronic medical records, then diagnostic completeness may improve, but time consumption and computational resource waste increase
Solution Approach 1:
The system performs preliminary processing of unstructured medical record data before the physician needs to review it. A natural language processing engine pre-analyzes the unstructured data, extracts relevant clinical information, and generates structured summaries. This preliminary action reduces the time physicians spend reviewing records while maintaining diagnostic accuracy by presenting pre-processed, relevant information.
Solution Approach 2:
The patent introduces an intermediary processing layer between the raw unstructured medical records and the physician. This intermediary consists of the natural language processing engine and information extraction module that translate unstructured text into structured, clinically relevant data. This mediator reduces the cognitive load on physicians while preserving all necessary diagnostic information.
2Reliability
If physicians review extensive unstructured data in electronic medical records, then diagnostic completeness may improve, but computational resource waste increases
Solution Approach 1:
The system extracts only the relevant structured information from extensive unstructured medical records using natural language processing and information extraction techniques. Instead of processing and presenting all raw data, the system selectively extracts clinically relevant findings, lab results, and patient history elements that are necessary for accurate diagnosis. This extraction approach maintains diagnostic accuracy while significantly reducing computational resource consumption.
Solution Approach 2:
The patent transforms unstructured text data into structured data with defined parameters and formats. By changing the parameter structure from unstructured free text to structured fields with specific data types, the system enables more efficient processing and retrieval. This parameter transformation allows the system to maintain comprehensive diagnostic information while reducing the computational overhead of handling unstructured data.
3Reliability
If unstructured data from EMRs is processed in detail, then diagnosis accuracy improves, but the complexity of the system increases
Solution Approach 1:
The patent segments the complex task of processing unstructured medical records into distinct functional modules: a natural language processing engine for text analysis, an information extraction module for identifying clinical entities, a structured data generation component, and a presentation layer. This segmentation allows each module to handle specific aspects of the processing, reducing overall system complexity while maintaining comprehensive diagnostic capabilities.
4Reliability
If physicians review extensive unstructured data, then comprehensive understanding of patient condition improves, but the burden on physicians increases
Solution Approach 1:
The system creates a structured copy or representation of the essential information from unstructured medical records. Instead of requiring physicians to read and interpret extensive unstructured text, the system generates a structured summary that replicates the critical diagnostic information in an easily consumable format. This copying approach maintains diagnostic accuracy while significantly improving ease of operation.
Data Source
AI summary
A method for diagnosing a medical condition through cognification of unstructured data is disclosed. The method may include receiving, at a server, an electronic medical record including notes pertaining to a patient. The method may also include generating cognified data using the notes, where the cognified data includes a health summary of the medical condition. The method may also include generating, based on the cognified data, a diagnosis of the medical condition of the patient, where the diagnosis at least identifies a type of the medical condition. The method may also include providing the diagnosis to a computing device for presentation on the computing device.