Customer Overview

AIRA was recently engaged with a major healthcare institution to assist them in integrating Machine Learning into their case preparation process. 

The organization can automatically arrange the massive case files into single-page medical narratives by utilizing natural language processing to integrate their excess data into a scalable solution.

Natural language processing for the Healthcare industry

Natural language processing is the ability of computers to comprehend the most recent human speech words and texts. It’s employed in today’s technology to assist with spam email privacy, personal voice assistants, and language translation apps.

NLP is becoming a more widely used technology in the healthcare industry because of their ability to search, analyze, and understand massive amounts of data. Machine learning and NLP technology in the healthcare industry can extract meaningful insights and concepts from data that was previously thought to be buried in text form.

The client

Our client is a medical necessity reviewer who acts as a go-between for payers and providers on pre-authorization and medical necessity reviews.

The Challenge

Our client has thousands of relevant medical documents that they uploaded on their application portal, which they must read to reach a decision.

The organization will get case-related information, such as the patient’s medical records and test results, and documents relating to the insurance company, its policies, and other extenuating circumstances.

To make matters even more complicated, the data can be in a variety of formats, including printed text, scanned handwritten notes, photographs, and computer-generated EHR dumps, all of which can contain mistakes or be incomplete.

Our clients and its clinical staff must turn this disorganized data into a timely and accurate conclusion.

How is AIRA making a difference?

AIRA created a scalable, containerized data engineering solution to arrange their patent files using NLP to summarize the case, reducing the number of hours their in-house medical team had to spend examining case files significantly.

Step 1: Gather data and organize it

Our initial step was to arrange the deluge of content they were receiving and convert it into a standardized, organized data collection that our AI system might eventually analyze.

To accomplish this, we created a service that uses digital extraction and optical character recognition to convert every word on every page into something that our AI system could read, tag, and understand.

Step 2: Enable Natural Language Processing

NLP implementation is finding the proper language model for translating the story to such vectors while maintaining a standard link between the 2 distinct entities. With deep-learning-powered language processing, state-of-the-art pre-trained language models are accessible to perform these jobs.

We were able to deliver indexed text for dynamic end-user engagement and funnel language embeddings to power our ML models training and inference once we constructed our data pipeline to appropriately extract and stream text.

Our Deep Learning models were able to determine if specific portions or sentences in the case file were relevant to the medical treatments under consideration as a result of this. The system then routed relevant data to other parties.

Step 3: Write a Case Summary

Natural language processing to summarize dense material is a two-step method, pragmatically speaking. The first step is to compile useful information. The second step is to rewrite the material that has been retrieved into a logical story. Because the source material for this project was so lengthy, AIRA ran many tests to get the best results possible.

Pre-Extraction

First, depending on the established priorities, our technology combed through the original case file and retrieved the 500 most critical sentences.

Extraction

Our technology subsequently decreased the word count even lower during the extraction step. It chose ten of the 500 sentences to serve as the shortest possible summary. In this situation, we set the system to prioritize capturing all of the information contained in the source material, even if it meant repeating information.

Generation

After the machine had condensed the case file to a single page, we used Natural Language Generation technologies to recreate those ten sentences into a completely summarized, entirely comprehensive story.

The Result

Thousands of hours have already been saved by our system for this organization. Its physicians can now immediately comprehend case elements by automatically arranging and summarizing case file information, allowing them to make informed, medically correct, and timely decisions.

The stakes for healthcare firms are enormous. The ramifications might be severe if our system misses a critical aspect of a patient’s case. This medical organization could serve more cases, faster, and at a lower cost by entrusting AIRA with the development of this mission-critical technology.

Testimonial

Every patient may have hundreds of interactions or episodes in a single day because we deal with so much data in our system. As a result, reconciling all of the information is a difficult task. We’ve been able to accomplish things that would have been impossible for humans to accomplish without the help of RPA. Where other vendors failed, we’ve had success with AIRA. We can do it swiftly and consistently with AI Computer Vision, and we won’t have to worry about updating the automation regularly.”

About AIRA

Artificially Intelligent Robotic Automation  

AIRA is an AI and ML-powered digital agent that is designed specially to overtake and automate your everyday tasks and business processes through robotic automation. AIRA offers effective and scalable integration with digital technologies and legacy systems to free your workforce from labor-intensive/low output tasks. It also allows you to craft your automated workflow to build your personalized low code/no-code automation suite.