Clinical Note NLP for Automatic Medical Form Population
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Solution Overview
Problem
Manual data entry in electronic medical forms by medical staff is time-consuming and prone to errors, failing to capture all relevant information.
Innovation Solution
A system utilizing natural language processing to automatically populate graphical user interfaces with voice inputs, employing a computer model trained on machine learning, including models like lexical parsers and named entity recognizers, to identify and extract relevant data fields and populate form fields.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual data entry is used by medical staff, then data can be entered into electronic forms, but the process is time-consuming and prone to errors
Solution Approach 1:
The patent replaces the manual mechanical data entry process with an automated natural language processing system. The system uses voice inputs and text processing to automatically extract and populate form fields, eliminating the need for manual typing and reducing both time consumption and error rates.
Solution Approach 2:
The system enables self-service by automatically processing clinical notes and populating forms without requiring medical staff to manually enter data. The natural language processing system autonomously identifies relevant information, extracts data fields, and fills in the appropriate form fields.
2Loss of information
If manual data entry is used, then information can be recorded, but not all relevant data is captured and user errors occur
Solution Approach 1:
The system incorporates feedback mechanisms where the natural language processing model continuously learns from training data and improves its accuracy over time. The system provides feedback on extracted entities and can be trained on additional data to enhance its ability to capture relevant information and reduce errors.
Solution Approach 2:
The automated NLP system replaces manual data entry, enabling comprehensive capture of relevant information from clinical notes through advanced text analysis, entity recognition, and pattern matching capabilities that exceed human manual processing.
3Productivity
If natural language processing is implemented, then automated population of form fields is achieved, but system complexity increases
Solution Approach 1:
The natural language processing system is segmented into multiple specialized models including lexical parsers, gender classifiers, part of speech taggers, named entity recognizers, and coreference resolution mappers. Each model handles a specific aspect of text processing, making the overall complex system manageable through modular decomposition.
Solution Approach 2:
The NLP system performs multiple functions including voice-to-text conversion, text analysis, entity recognition, form field identification, and data extraction. This multi-functional approach consolidates what would otherwise require multiple separate systems into a single integrated platform.
Data Source
AI summary
The present disclosure relates to systems, methods, and computer-readable media for performing natural language processing on a clinical note or audio information associated with medical personnel. A computer-implemented method performed by one or more processors for populating a graphical user interface with data associated with a voice input. The method may include receiving a voice input, generating a first text based on the voice input, comparing the text against a computer model, identifying a data field in the text, selecting a form field based on the identified data field, extracting a second text based on the generated text, and populating the second text in the selected form field.