NLP Diagnosis Indicator Extraction from Clinical Notes
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Solution Overview
Problem
Current methods lack an automated way to interpret and score clinical notes in behavioral health, hindering the ability to measure patient progress and operational efficiency within the field.
Innovation Solution
A computer-implemented method using machine learning algorithms trained with natural language processing to identify diagnosis indicators from unstructured clinical notes, transforming them into visual representations for clinical decision-making and health engagement outreach.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual interpretation of clinical notes is performed, then diagnostic accuracy can be maintained, but time consumption and labor intensity increase significantly
Solution Approach 1:
The patent replaces the mechanical manual interpretation process with an automated machine learning system that uses natural language processing to extract diagnosis indicators from clinical notes. This substitution maintains diagnostic accuracy through trained algorithms while eliminating the time-consuming manual review process, allowing rapid analysis of unstructured clinical data.
Solution Approach 2:
The patent introduces an intermediary machine learning model that acts as a bridge between unstructured clinical notes and structured diagnosis indicators. This intermediary automatically processes and transforms raw narrative data into actionable diagnostic information, reducing both time consumption and manual labor while preserving diagnostic precision through learned patterns.
2Productivity
If automated machine learning interpretation is implemented, then processing speed and efficiency increase, but system complexity and development requirements increase
Solution Approach 1:
The patent implements a universal machine learning platform that can process multiple types of clinical notes and generate various diagnosis indicators through a single system. This multi-functional approach increases processing speed across different clinical scenarios while managing system complexity through shared underlying architecture and reusable components.
Solution Approach 2:
The patent enables the system to automatically train and improve itself through continuous learning from clinical data. The self-service capability allows the machine learning model to refine its own performance over time, reducing the need for manual system adjustments and complexity management while maintaining high processing speeds.
3Reliability
If comprehensive analysis of all diagnosis indicators is performed, then diagnostic thoroughness is improved, but computational resources and processing time increase
Solution Approach 1:
The patent extracts only the most relevant diagnosis indicators from clinical notes using the machine learning model, rather than analyzing all possible indicators comprehensively. This selective extraction approach maintains diagnostic thoroughness by focusing on high-value indicators while significantly reducing computational resource consumption and processing requirements.
Solution Approach 2:
The patent applies partial analysis by prioritizing and deeply analyzing the most critical diagnosis indicators while performing lighter analysis on secondary indicators. This differentiated approach ensures diagnostic reliability for key conditions while optimizing computational resource usage across the full range of potential indicators.
Data Source
AI summary
A computer implemented method for diagnosis indicator identification and analytics includes receiving a narrative note describing a first patient visit with a first patient and including unstructured data, applying a machine learning algorithm trained to use natural language processing to identify diagnosis indicators to the unstructured data to identify diagnosis indicators in the narrative note, performing at least one analysis based on information regarding the diagnosis indicators to thereby identify at least one possible patient diagnosis, and outputting a visual representation of results of the analysis for display on a display device of the end user device, wherein the result is associated with the at least one possible patient diagnosis and the visual representation facilitates clinical decision making.