Entity Recognition Models for EMR Summaries
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Physicians face inefficiencies in sorting and extracting relevant information from large volumes of Electronic Medical Records (EMRs), particularly for patients with chronic illnesses, leading to a risk of missing crucial data spread across numerous records.
Innovation Solution
A method involving multiple entity recognition models trained on specific entities within EMRs to label and aggregate relevant data, generating a summary that reduces the time spent by caregivers reviewing medical reports and increases the availability of relevant information.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If physicians manually sort and extract information from large volumes of EMRs, then comprehensive review of patient records is achieved, but time consumption increases significantly
Solution Approach 1:
The patent introduces an intermediary system comprising entity recognition models and natural language processing components that act as a mediator between the large volume of EMR data and the physician. This intermediary automatically extracts, labels, and summarizes relevant patient information, delivering comprehensive reviews without requiring significant physician time investment.
Solution Approach 2:
The patent replaces the mechanical manual process of information extraction and review with an automated computational system. Machine learning models and NLP algorithms substitute for the physician's manual sorting and extraction activities, maintaining comprehensive review quality while dramatically reducing time consumption.
2Loss of information
If multiple entity recognition models are used to label and aggregate data, then retrieval of relevant information is enhanced, but system complexity increases
Solution Approach 1:
The patent segments the complex task of information extraction into multiple specialized entity recognition models, each focused on specific medical entities (e.g., diagnoses, medications, procedures). This segmentation allows each model to be optimized for its specific domain while the aggregation layer integrates their outputs, enhancing information retrieval without overwhelming complexity.
Solution Approach 2:
The patent creates a universal aggregation framework that handles outputs from multiple specialized entity recognition models. This aggregation layer serves multiple functions: consolidating results, resolving conflicts, prioritizing information, and presenting unified summaries. The universal system manages the complexity of multiple models while providing enhanced information retrieval.
Data Source
AI summary
Various methods and systems are provided for generating and displaying summaries of patient information extracted from one or more medical reports stored in an electronic medical record (EMR) of a patient. In one example, a method includes receiving text data of a patient; entering the text data as input into a plurality of entity recognition models, each entity recognition model of the plurality of entity recognition models trained to label instances of a respective entity in the text data; aggregating the labeled text data outputted by each entity recognition model; generating a summary of the text data based on the aggregated labeled text data; and displaying and/or saving the summary and/or the aggregated labeled text data.