ML Template System for Customized Medical Notes
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Manual entry of patient attributes in medical records is time-consuming and inefficient, especially in hospitals without standard electronic medical records, and existing automated systems struggle to generate customized summarized notes that cater to different contexts and languages.
Innovation Solution
A computer-implemented method using machine learning models to select templates with translatable string resources and variables, which are populated with key attributes from historical notes, allowing for the generation of customized summarized notes tailored to specific contexts, languages, and user preferences.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If manual entry of patient attributes is used, then data can be entered in hospitals without standard electronic medical records, but the process becomes time-consuming and inefficient
Solution Approach 1:
The system automatically extracts key attributes from historical notes using NLP and populates templates without requiring manual data entry. The machine learning model self-services by identifying and extracting relevant patient attributes, distributing them according to learned patterns, and generating summarized notes automatically.
Solution Approach 2:
The patent replaces the manual mechanical process of data entry with an automated machine learning system. The ML model automatically performs attribute extraction, template selection, and note generation, substituting human manual operations with intelligent automated processing.
2Reliability
If structured data is used to analyze patient cases, then treatment options can be suggested, but it becomes time-consuming for care teams to sort through various categories of information
Solution Approach 1:
The system extracts only the most relevant key attributes from the structured data and places them in a customized summarized note format. Instead of presenting all structured data categories, the ML model identifies and extracts essential information needed for treatment decisions, reducing information overload while maintaining reliability.
Solution Approach 2:
The patent applies local quality by customizing the note format and content based on specific user needs and contexts. Different care team members receive tailored summaries with different emphases and formats, allowing each user to quickly access the specific information most relevant to their role without sifting through all structured data.
3Stability of the object's composition
If standardized templates are used for summarized notes, then consistency can be maintained, but customization for different contexts and languages becomes difficult
Solution Approach 1:
The system uses dynamic template selection where the template structure and content are adapted based on the specific context, user preferences, and language requirements. The machine learning model dynamically determines which template to apply and how to populate it, maintaining consistency within each context while allowing flexibility across different contexts and languages.
Solution Approach 2:
The patent creates a universal template system that can serve multiple functions and contexts. The same underlying template framework is used across different languages and user needs, with the machine learning model adapting the template content and structure to meet specific requirements, allowing one system to handle diverse customization needs.
Data Source
AI summary
Provided are techniques for generating and customizing summarized notes. A template is selected from a plurality of templates based on a context using a machine learning model. The template includes one or more translatable string resources with variables to represent key attributes extracted from historical notes. A summarized note is generated using values of the key attributes for the variables in the translatable string resources of the template.