Medical Diagnostic Aid for Unstructured Clinical Data Coding
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current medical diagnostic systems face challenges in processing non-standardized and unstructured clinical data, leading to inefficiencies in retrieving useful information from patient records and doctor-patient conversations, which can impact treatment quality.
Innovation Solution
A medical diagnostic apparatus utilizing a receiver, analyser and parser, synonym mapping engine, automatic coding solver, and enrichment engine to process unstructured data, identify medical terms, and generate standardized medical codes, diagnoses, and treatment suggestions, with features like synonym mapping and string matching algorithms to enhance accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If automated NLP processing is implemented to analyze unstructured clinical data, then productivity and information retrieval efficiency are improved, but device complexity and implementation difficulty increase
Solution Approach 1:
The system segments the complex NLP processing task into distinct functional modules: a receiver for unstructured input data, an analyser and parser for text processing, a synonym mapping engine for standardization, an automatic coding solver for code generation, and an enrichment engine for diagnostic support. This modular architecture improves information retrieval efficiency while managing system complexity through organized functional decomposition.
2Measurement precision
If manual processing of free-text medical notes is performed by medical professionals, then measurement precision and diagnostic accuracy are improved, but loss of time and productivity decrease
Solution Approach 1:
The automated diagnostic system acts as an intermediary between unstructured clinical notes and standardized medical codes/diagnoses. The system processes free-text notes through NLP techniques to extract medical terms, map them to standard codes, and generate diagnostic suggestions, thereby maintaining diagnostic accuracy while significantly reducing the time medical professionals spend on manual note processing.
3Measurement precision
If comprehensive synonym mapping is implemented in the knowledge graph, then measurement precision of medical term matching is improved, but device complexity and processing requirements increase
Solution Approach 1:
The synonym mapping engine performs preliminary action by pre-processing and annotating the medical knowledge graph with synonym relationships before the matching process. This advance preparation of synonym mappings enables more accurate medical term matching while managing complexity through pre-computed relationship structures that can be efficiently queried during diagnosis.
4Productivity
If automated coding and diagnosis generation is implemented, then productivity and time efficiency are improved, but reliability and potential for errors may worsen
Solution Approach 1:
The system implements feedback mechanisms where the automatic coding solver generates potential medical codes based on detected medical terms, which are then validated and refined by the enrichment engine that cross-references multiple data sources including the synonym-mapped knowledge graph. This iterative feedback process improves diagnostic reliability while maintaining high productivity through automation.
Data Source
Figure 1
Figure 2A
Figure 2B
AI summary
Diagnostic aids, methods for assisting medical personnel in performing a diagnosis, and computer readable media comprising code which, when executed by a computer, cause the computer to execute a method for assisting medical personnel in performing a diagnosis, wherein the diagnostic aids comprise: a receiver configured to receive an unstructured input; an analyser and parser configured to split the unstructured input into a plurality of logical components, and to detect medical terms in the plurality of logical components; a mapping engine configured to receive a medical classification hierarchy of medical standard codes in the form of a knowledge graph, and semantically annotate the knowledge graph with synonyms of medical terms used in the medical standard codes; an automatic coding solver configured to analyse the medical terms detected in the plurality of logical components by the analyser and parser, to generate a list of potential matching medical standard codes for each of the medical terms, to compare the lists of potential matching medical standard codes, and to output top matching medical standard codes based on the comparison; and an enrichment engine comprising a database of diagnoses linked to symptoms and treatments, wherein the enrichment engine is configured to compare the top matching medical standard codes output by the automatic coding solver against entries in the database of diagnoses, and to output diagnoses, symptoms and treatments linked to each of the top matching medical standards codes for assisting medical personnel in providing a diagnosis.