Automating Healthcare Documentation Using AI-Powered Analysis

Summary

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.

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Healthcare Companies Require Specialized AI Agents

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:

1. Implement an AI solution capable of processing complex medical notes, focusing on accuracy in document classification and data extraction.
2. Turn their document processing workflow into a single, efficient system that can handle the demands of a fast-paced healthcare environment.
   

To achieve the level of accuracy and flexibility needed, the client decided to invest in a more tailored solution. We helped them develop it.

Tailored Solutions to Boost Accuracy and Reliability in Healthcare 

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:

• Improve the model’s understanding and processing of the complex language found in healthcare documents, and
• Adapt the model to different types of medical notes and terminologies without extensive retraining.
  

Here is a brief overview of our process:

• Dataset curation: We used 23 documents provided by the client..
• Embeddings calculations: To boost the AI Agent’s ability to find relevant examples during the in-context learning process, we calculated the embeddings of the documents and stored them in a vector database. Embeddings are numerical representations of text data that capture the semantic meaning of words and phrases.
• Vector database for embedding storage: We then stored these embeddings in a vector database. When a new medical note is processed, its embedding is compared against the stored values to identify the most similar examples within the database.
• In-context learning execution: We made several API calls to the foundation model using the most relevant document examples, feeding these examples into the model to provide context. This process helps the model ‘learn’ from specific instances, helping to align its output more closely with the expected results.
• Result concatenation: The outputs from the foundation model were then concatenated to form a final document.
  

Automating Medical Note Processing With Exceptional Accuracy

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.