Using LLMs to assist with data extraction
Description
We are investigating the ability of Large Language Models (LLMs) to automate the extraction of discrete data elements from clinical notes. The National Radiation Oncology Program (NROP) office is using Quality Measures to evaluate the quality of care to veterans receiving radiotherapy inside of the VA and in the community. While a much value was found in the data collected in prior projects, the manual data extraction process was expensive, time consuming and still error prone. The purpose of this work is to determine the feasibility of using modern AI technology to reduce the burden of collecting this information from clinical notes. AI will be used to automate data extraction from free text notes which is not possible at scale because of the cost and the level of human labor required.
Detailed example
The primary output of this system will be discrete data found in free text clinical notes.
AI / analytics pattern
Natural Language Processing: AI that processes, interprets, and shares information in human language.
Automation level / stage
a) Pre-deployment – The use case is in a development or acquisition status.
Expected benefit
Extracting this discrete data will help to track changes in treatment quality over time, identify care gaps and provide data which could later be used for building predictive models to improve care and become a resource for VA researchers.
Controls / human review
ATO: Not reported; PIA: Not published