Fuzz ratio similarity score (human-level accuracy)
Less time spent on fixing processing errors
Handling medical note labeling and processing
This healthcare technology and consulting company specializes in evidence-based clinical guidelines and software solutions for healthcare organizations.
However, their reliance on manual document analysis was inefficient and prone to errors. This led to issues when trying to maintain high standards of patient care and operational efficiency when manually labeling and processing medical notes.
They partnered with us to develop an AI Agent capable of accurately classifying and extracting data from healthcare documents.
Today, the client benefits from improved employee satisfaction and workflow efficiency, as it can process medical notes with exceptional accuracy and speed using a single AI Agent.
Medical note analysis and labeling indirectly lead to improved patient care. They allow for better data management and help healthcare companies quickly access patient information when required.
However, our client faced a problem with labeling and analyzing large volumes of medical notes. The task was time-consuming and highly prone to errors, as the staff had to manually review, label, and extract data from these notes.
Traditional solutions couldn’t sufficiently automate this use case. The complexity of medical notes, as well as the extremely high bar of accuracy required, made simple automation solutions ineffective. Medical data is varied and detailed, requiring a level of understanding and flexibility that traditional automation solutions simply do not offer.
For example, each provider’s records contain a unique structure and content, which non-AI solutions struggle to standardize and interpret correctly. A medical record from one provider might look drastically different from another’s, even though they contain similar types of information.
With all of this in mind, the client had two clear objectives:
To achieve the level of accuracy and flexibility needed, the client decided to invest in a more tailored solution. We helped them develop it.
To build a solution that met the client’s needs, we used a large language model that proficiently handles various linguistic and data processing tasks. However, we still wanted to improve its natural language capabilities and healthcare-specific knowledge. That’s why we performed so-called in-context learning using the client’s existing documents.
In-context learning helped us achieve two major goals:
Here is a brief overview of our process:
The AI Agent reached an impressive fuzz ratio similarity score of 0.925 on 98% of the text, presenting exceptional accuracy.
This score calculates the similarity between two texts by determining how many character edits are needed to transform the first text into the second. In this case, it shows how closely the original medical note text matches the AI Agent’s output in terms of accuracy and detail.
A score of 1 would indicate an exact match between the two texts.
This precision closely mirrors human accuracy, allowing the client to categorize critical medical information correctly and efficiently. Moreover, it boosts reliability by minimizing common errors in manual labeling processing.
Our AI Agent helped improve workflow efficiency within the company and set a benchmark for how AI can help handle sensitive medical data.
It shows how one AI Agent can massively improve data accuracy, processing speed, and cost efficiency - all factors that are key to improving the healthcare sector’s ongoing efforts to improve patient outcomes.
The client is currently assessing how best to implement the AI Agent within their operations. The focus is on further evaluating the AI Agent’s speed and overall performance to ensure it meets their demands for efficient medical note processing.