Medical Record Linking of Diagnosis Notes and Examination Results
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Solution Overview
Problem
The cumbersome process of manually registering examination results as the basis for diagnosis/treatment records in medical data systems makes it difficult to identify the underlying examination results, especially when they are not initially recorded.
Innovation Solution
A medical information processing apparatus and method that uses machine learning models to automatically extract and correlate diagnosis/treatment data with relevant examination data, displaying them together on the same screen to facilitate identification of the examination results used in creating diagnosis/treatment records.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual registration of examination results is implemented, then the accuracy of linking diagnosis/treatment records with examination results is improved, but the ease of operation deteriorates due to cumbersome operations
Solution Approach 1:
The system automatically extracts examination results that serve as bases for diagnosis/treatment records without requiring manual registration by medical providers. The extraction unit autonomously performs the linking by analyzing the relationship between examination data and diagnosis/treatment data, making the system self-servicing and eliminating cumbersome manual operations while maintaining accurate linking.
Solution Approach 2:
The patent replaces the mechanical manual registration process with an automated information processing system. The extraction unit uses computational methods to identify and link examination results to diagnosis/treatment records, substituting the manual mechanical operation with an automated electronic system that achieves both accuracy and operational efficiency.
2Ease of operation
If no examination result is registered at the time of entering diagnosis/treatment record, then the ease of operation is improved, but the ability to identify the examination result serving as basis deteriorates
Solution Approach 1:
The extraction unit performs preliminary extraction of examination results from examination data before the diagnosis/treatment record is finalized. By proactively identifying and extracting the relevant examination results that serve as bases, the system ensures that this information is captured and stored in advance, preventing information loss while maintaining ease of operation during record entry.
Solution Approach 2:
The system provides feedback by displaying the extracted examination results in association with the diagnosis/treatment record. This feedback mechanism allows medical providers to verify and identify the examination results that serve as bases for their diagnoses and treatments, ensuring information completeness without requiring manual registration during the entry process.
3Productivity
If automatic extraction using machine learning is implemented, then the productivity is improved by eliminating manual registration, but the device complexity increases due to machine learning models
Solution Approach 1:
The patent introduces an extraction unit as an intermediary component that bridges examination data and diagnosis/treatment data. This intermediary performs the complex machine learning-based extraction and linking operations, isolating the complexity from the main medical information processing system. The extraction unit acts as a specialized mediator that handles the sophisticated analysis while presenting simplified results to the rest of the system.
Data Source
AI summary
A medical information processing apparatus includes processing circuitry. The processing circuitry extracts, from diagnosis/treatment data in which a diagnosis/treatment record of a patient is written, first data indicating the diagnosis/treatment record; extracts a piece of examination data including second data indicating an examination result relevant to the first data, from one or more pieces of examination data in each of which an examination result of the patient is written; judges a correspondence relationship between an item included in the extracted first data and an item included in the second data of the extracted piece of examination data; and causes the diagnosis/treatment data and the extracted piece of examination data to be displayed on mutually the same screen, and also causes the items determined as having the correspondence relationship with each other to be displayed in an identifiable manner.