Machine Learning Model Speeds Up Healthcare Record Classification
An OCR and NLP model that automatically classifies diagnosis letters into discharge, follow-up, and referral categories.
At a Glance
Manually processing thousands of diagnosis letters slowed patient record management and required a large classification team. Maruti Techlabs developed an OCR and NLP solution to automate classification and integrate with HIMS for faster, more accurate records.
Industry
Healthcare
Function
Data Management & Automation
Solution
AI/ML-Based Document Classification
Engagement
OCR & NLP Model Development
The Client
Our client is one of the largest healthcare service providers in the UK, responsible for the smooth operation and data management of thousands of hospitals, care units, diagnostic clinics, and pharmacies.
The Challenges
Manual Classification Couldn't Keep Pace
The high volume of diagnosis letters required a sizable team to analyze and classify documents into the client's central Hospital Information Management System, making the process time-consuming and resource-intensive.
Thousands of diagnosis letters generated every day
Doctors across the client's network generate hundreds to thousands of discharge, referral, and follow-up letters daily, and each one had to be read and categorized manually before it could be trusted as part of a patient's record.
Manual classification required a large, costly team
The sheer volume of letters meant the client needed a sizable team just to keep up with analysis and classification, making the process time-consuming and resource-intensive for the client's data teams.
Manual entry carried a high risk of error
Feeding and categorizing data into the Hospital Information Management System by hand carried a high risk of errors and discrepancies, a serious concern for patient records.
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The Solution
OCR-Based Text Extraction From Scanned Letters
Physical diagnosis letters were stored as scanned digital files. An OCR model was trained to recognize and process the text, converting unstructured letters into data the classification model could read and interpret.
Physical letters were stored as scanned digital files, and an OCR model was trained to recognize and process the text, converting an unstructured pile of letters into a structured, readable dataset.
Physical letters were stored as scanned digital files, and an OCR model was trained to recognize and process the text, converting an unstructured pile of letters into a structured, readable dataset.
The Journey
Letters To Labels
Business Outcomes
Faster, More Accurate, and Far Less Manual
Processing Time Dropped by 87%
Automated letter classification dramatically reduced the turnaround time compared to the previous manual process.
Accuracy Climbed to 93%
Automated classification significantly improved patient data management accuracy compared to the manual process.
Headcount Needs Fell by 83%
Classification staffing dropped from around 12 people to just 2 overseeing the automated process.
Records Update and Visualize Automatically
HIMS integration simplified data maintenance and enabled faster visualization of classified letters for decision-makers.
Conclusion
The solution transformed manual letter processing into a faster, more accurate, and largely automated workflow, reducing effort while improving patient record management.
Tech Stack
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Frequently Asked Questions
These three categories were the ones the client's data teams were already manually sorting letters into for the Hospital Information Management System, so training the model against the same three labels meant its output could plug directly into the existing records workflow without redefining how staff worked.
The NLP layer interpreted words and sentences as vectors and matched them against context-specific phrases doctors commonly used, like 'see you in 3 months' or 'completely discharged', rather than requiring exact wording, which is what let it generalize across different doctors' writing styles.
The model started at an 80% confidence score at deployment because it was learning from real, messy letter data rather than a clean training set. Six months of supervised training, correcting mislabeled letters as they came in, is what pushed that confidence up to 85% without needing a full retrain.
The model handles most letter classification automatically, but the remaining 2 people per hospital oversee edge cases, verify low-confidence classifications, and manage exceptions the model flags, keeping a human check in place for patient records rather than removing it entirely.
Once integrated, classified letters updated the Hospital Information Management System automatically instead of waiting on manual data entry, which is what enabled easier data maintenance and faster visualization for decision-making that data teams saw after go-live.