Clinical Note Augmentation via NLP Identifier Insertion

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Solution Overview

Problem

Clinical notes in electronic medical records often contain incorrect or incomplete information due to user mistakes, duplication of data, and difficulties in accessing pertinent information, which leads to inefficiencies and errors in a time-pressured clinical environment.

Innovation Solution

A method using natural language processing algorithms to identify relevant data types in clinical notes, inserting identifiers for target subject information locations, thereby reducing the need for manual duplication and improving access to relevant data.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Reliability

If clinicians manually duplicate or summarize patient information in clinical notes, then the notes contain relevant information, but this leads to additional work, time consumption, and increased error rates

Engineering Contradiction:

Improveaccuracy of clinical note informationVSAvoidtime spent writing clinical notes

Core Design Contradiction:

ReliabilityVSLoss of time

Solution Approach 1:

The system automatically copies relevant patient information from the EMR into clinical notes using natural language processing algorithms, eliminating manual duplication by clinicians while ensuring accurate information transfer

Inventive Principle:

Principle #26Copying

Solution Approach 2:

The system performs self-service by automatically generating and inserting relevant patient information into clinical notes without requiring clinician intervention, thereby reducing both time consumption and error rates

Inventive Principle:

Principle #25Self-service

2Loss of information

If clinicians write detailed clinical notes with all relevant information, then the notes are comprehensive, but reading and accessing pertinent information takes significant time

Engineering Contradiction:

Improvecompleteness of clinical note informationVSAvoidtime spent reading clinical notes

Core Design Contradiction:

Loss of informationVSLoss of time

Solution Approach 1:

The system extracts only the most pertinent patient information from the EMR and inserts it into clinical notes, eliminating the need for clinicians to read through lengthy text while ensuring all critical information is captured

Inventive Principle:

Principle #2Taking out (Extraction)

Solution Approach 2:

The system segments clinical information by identifying and separating key data elements (vital signs, lab results, imaging findings) from general text, allowing for efficient extraction and insertion of only relevant portions into notes

Inventive Principle:

Principle #1Segmentation

3Loss of information

If clinicians access information distributed across multiple systems, then all pertinent data is available, but this process is time-consuming and complex

Engineering Contradiction:

Improveavailability of pertinent informationVSAvoidease of accessing information

Core Design Contradiction:

Loss of informationVSEase of operation

Solution Approach 1:

The system merges information from multiple distributed EMR systems into a unified view, automatically retrieving and integrating data from various sources (vital signs, labs, imaging) without requiring clinicians to navigate multiple systems

Inventive Principle:

Principle #5Merging (Combining)

Data Source

PatentUS20230065999A1Method and system for augmenting clinical notes

Publication Date: 2023.03.02 KONINKLIJKE PHILIPS NV

AI summary

Provided are concepts for generating an augmented clinical note by determining target subject information based on an identified data type of the clinical note, and inserting an identifier to a location of the target information in the clinical note. In particular, a natural language processing algorithm is used to process content of the clinical note in order to identify a data type, which is used to determine target subject information. Using this concept, clinical notes with automatically generated identifiers to locations of target subject information may be obtained. This may obviate the need for manual duplication of information, ultimately reducing errors and a time taken for a user to produce clinical notes, as well as making target subject information easier for a reader to obtain.