Contact UsBlogHome
Get in Touch

Machine Learning Model Accelerates Healthcare Record Processing by 87%

CASE STUDY

Our client, UKHealth, is one of the largest healthcare service providers in the UK. UKHealth manages thousands of hospitals and care units providing healthcare to the people of the UK. Apart from multispeciality hospitals, diagnostic clinics and pharmacies are also managed by UKHealth. They make healthcare services more efficient and accessible. 

Disclaimer - The name UKHealth is a placeholder as there is an NDA signed between both parties.

Challenge

Our client, UKHealth, is responsible for the smooth working and data management of thousands of hospitals and clinics across the UK. 

The doctors working at these hospitals and clinics would generate hundreds and thousands of discharge, referral, and follow-up letters daily for patients across each medical facility. 

To maintain patient records, data teams of UKHealth would have to manually assess, classify, and update the data from these diagnosis letters into specific categories of:
 
Discharge 
○ Follow-ups 
○ Referrals 

Due to the high volume of letters, a sizable team was deployed to analyze these letters and manually classify them into the central Hospital Information Management System (HIMS) of UKHealth. Manually feeding and categorizing the data into the systems was time-consuming and resource-expensive. It also had a high chance of errors/discrepancies. 

Solution

Looking at the massive volume of letters to be read and manually classified, Maruti Techlabs designed a machine learning model that would automatically extract the data from the letters and classify them into the 3 set categories without any human intervention. 

To achieve this, our ML team developed a text extraction and identification model based on a 2-step process: 

1. Text Extraction via OCR (Optical Character Recognition):

The first step was to organize the humongous amount of diagnosis letters and convert them into a structured dataset that our machine learning model could read. 

To accomplish this, we first stored these physical printed letters of diagnosis as scanned digital files. Using OCR, we trained the text extraction model to recognize and process text from these digital files into something that the model could read, tag, and understand. 

For this, the program analyzed the structure of the letters and divided them into separate elements like text, images, tables, etc. Once the characters were singled out, the model then proceeded to the next step, i.e., identification via NLP. 

2. Phrase Identification via NLP (Natural Language Processing):

The next step was to enable the model to make sense of the extracted text and classify the letters correctly. For this, we designed a Natural Language Processing algorithm. 

Using NLP, the model could interpret words and sentences as vectors (numbers representing the meaning of the words) and match them with the corresponding entities. The ML model picked up context-specific phrases like ‘see you in 3 months, ‘completely discharged’, ‘not required to meet again’, etc. 

Based on the above, each letter of diagnosis was automatically labelled under one of the three categories:

○ Discharge 
○ Follow-ups 
○ Referrals 


Our team integrated the entire machine learning model with our client’s central Hospital Information Management System (HIMS) for automatic updating and easy management of patient records. Integration with HIMS led to easy visualization of data for decision-making. 

Confidence Score of the Model: 

Initially, the confidence score was 80% at the time of model deployment. It was then followed by 85% with over 6 months of supervised training. 

Move beyond keyword match.
Identify user intent & context 

via custom NLP models.  

Technology Stack

MTL's tech stack

Result

✔️87% decrease in the processing time of diagnosis letters.

✔️Accuracy increased to 93% in patient data management.

✔️Easy maintenance of data.

✔️Easy visualization of data for decision-making.

✔️On average, a team of 12 people per hospital was involved in letter assessment & classification. Now, these data teams require only 2 people to oversee the complete function.

Let's Talk!

Our Development Process

We follow Agile, Lean, & DevOps best practices to create a superior prototype that brings your users’ ideas to fruition through collaboration & rapid execution. Our top priority is quick reaction time & accessibility. 

We really want to be your extended team, so apart from the regular meetings, you can be sure that each of our team members is one phone call, email, or message away.

Maruti techlabs Development Process

How We Work? 

Text Preprocessing
In the first stage, we begin by collecting data from multiple sources & building a raw text corpus. We eliminate damaged, irrelevant, or incomplete data & normalize useful text, & prepare for further analysis.

Requirement Analysis

Text Parsing & Exploratory Data Analysis
Here, we sift through the raw data & organize it to do a more focused analysis with a smaller dataset. This involves identifying & removing irrelevant sections, extracting coded metadata, & determining the format. By selecting the various intents & entities required for the predetermined tasks, a deep exploratory analysis helps establish a format for representation

UI Design

Text Representation & Transformation 
Now that we have categorized the datasets, we use various visualization techniques to represent the data in a meaningful format to retrieve useful insights. This includes a semantic, syntactic, & pragmatic analysis of the text to get an overview of the interpretable content.

Development

Modelling 
We now approach the most important Natural Language Processing discipline of modelling artificial neural networks (ANN) and training them to automate the learning of complex linguistic and behavioral models. Text mining at this stage helps to funnel down the data and do targeted information retrieval.

Testing

Evaluation & Deployment
At the final stage, we test the NLP model for performance against a number of training parameters. The metrics are observed and corrective measures are taken where necessary. We then deploy the successful model in the execution environment.

Testing

NLP implementation steps are often very specific to the tasks that need to be executed. Our fundamental process relies on various text and data analysis tools to simplify human-machine interactions and enable businesses to deliver next-generation digital experiences that are contextually relevant, highly interactive, and refreshingly human.

Why choose Maruti Techlabs?

Trusted By:

Book a FREE Consultation

Connect with us

© 2022 Maruti TechLabs Pvt Ltd 

Maruti Techlabs is an agile-powered digital product development company and your guide on the digital transformation journey. We are a team of passionate, purpose-led individuals that obsess over creating innovative solutions to address our clients’ challenges and deliver unparalleled value.

Logo

12+
years experience

180+
Members

50+
Enterprise Clients

4.8/5 
NPS on Clutch

AI Readiness Audit

Finding the right AI partner is no easy task.

Building an AI product that delivers on value, is even more challenging.

We help you get started with a slightly different approach. Before we get into the trenches and kickstart development, we take a top-down approach with an AI Readiness Audit.

This involves really validating the idea, through qualitative and quantitative analysis of your datasets, identifying the best fit approach to model development, and putting together an implementation roadmap.

All this before writing a single line of code, and investing heavily into the idea.

Achieve more with less.

Don't take our word for it, take theirs! 

Our Clients Review
Review Everything
Our Happy Clients

More social proof incase you're still on the fence

Let Our Clients Do the Talking