Knowledge Graph Medical Data Summarization for Faster EMR Review
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Solution Overview
Problem
Existing medical data systems require physicians to manually review extensive electronic medical records (EMRs) for patient notes, leading to inefficiencies and potential misdiagnosis due to the time-consuming nature of this process, which also wastes computing and network resources.
Innovation Solution
A cognitive intelligence platform that integrates and consolidates data from various sources, using a knowledge graph and artificial intelligence to cognify unstructured patient notes into summarized, cognified data, providing targeted health-related information to physicians and patients, thereby reducing the need for extensive EMR reviews.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If physicians manually review extensive electronic medical records for patient notes, then comprehensive patient information is obtained, but time consumption increases and efficiency decreases
Solution Approach 1:
The system extracts relevant patient information from extensive electronic medical records using natural language processing and machine learning algorithms. The extraction module identifies and pulls out key clinical data, symptoms, and diagnostic information, presenting only the most relevant details to physicians rather than requiring review of all raw data.
Solution Approach 2:
The system performs preliminary analysis of patient notes before physician review. AI algorithms pre-process electronic medical records to generate summarized patient profiles, identify potential diagnostic patterns, and highlight critical information that requires attention, allowing physicians to start their review with pre-processed insights already in place.
2Reliability
If extensive electronic medical records are manually reviewed, then diagnostic accuracy may be maintained, but computing and network resources are wasted
Solution Approach 1:
The system applies different processing qualities to different portions of medical records based on their diagnostic relevance. High-priority information such as acute symptoms, critical lab values, and urgent medications receive intensive AI analysis and validation, while routine or less critical data receives lighter processing, optimizing computing resource allocation across the entire record set.
Solution Approach 2:
The system performs partial analysis on all records but intensive validation only on suspicious or critical findings. AI algorithms scan entire electronic medical records for patterns, then apply more computationally expensive verification methods only to identified anomalies or high-risk diagnostic areas, reducing overall computing resource consumption while maintaining diagnostic accuracy.
Data Source
AI summary
A method for controlling distribution of information pertaining to a medical condition 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 processing the notes to obtain indicia. The method may also include identifying a possible medical condition of the patient by identifying a similarity between the indicia and a knowledge graph representing knowledge pertaining to the possible medical condition, wherein the knowledge graph includes a set of nodes representing the information pertaining to the possible medical condition. The method may also include providing, at a first time, first information of the information to a computing device of the patient for presentation on the computing device, the first information being associated with a root node of the set of nodes.