Client Overview
A leading global organization in the healthcare and pharmaceutical sector needed a scalable solution to classify thousands of unstructured product descriptions—spanning medical devices, pharmaceuticals, consumables, and supplements—into accurate, predefined categories. This classification was critical to support valuation processes, regulatory compliance, and streamlined operational workflows.
Business Challenge
The client faced a major bottleneck in managing and categorizing large volumes of product data stored in spreadsheets. The descriptions were highly varied, unstandardized, and included a mix of technical, commercial, and medical terminology. Manually processing such a vast dataset was not only time-consuming but also prone to errors and inconsistencies, significantly impacting data reliability and operational efficiency.
Key issues included:
Unstructured and Ambiguous Data :
Product descriptions lacked consistent language and formatting, complicating manual classification.
High Dependency on Manual Sorting :
Human classification was slow, error-prone, and resource-intensive.
Need for Scalable Automation :
The client sought to automate classification across multiple asset types with high accuracy to support downstream analytics.
What Client Needed
To automatically classify unstructured healthcare product descriptions to reduce manual sorting, improve downstream analytics and compliance.
- Accurately categorize unstructured healthcare asset descriptions.
- Reduce manual effort and increase throughput
- Provide consistent tagging for improved reporting, valuation, and informed decision-making
What We Built
DRC Systems, a leading AI/ML development company, designed and delivered a custom Machine Learning classification model, integrated into a web application, to automate the categorization of healthcare and pharmaceutical data with high accuracy.
Solution Features:
-
Model Training & Evaluation
Applied and benchmarked Support Vector Machine (SVM) and Random Forest classifiers using labeled historical data.
-
Natural Language Processing (NLP)
Used NLP models to process, clean, tokenize, and vectorize medical and commercial terms for model training.
-
OCR Integration
Incorporated EasyOCR and Pytesseract for processing image-based product records and extending coverage beyond text-based data.
-
Web Interface for Review & Export
Delivered a simple, intuitive, and highly interactive web application enabling data upload, classification visualization, manual review, and export.
Business Impact
Classification Accuracy
Achieved 89% model accuracy across pharmaceutical data
Operational Efficiency
Reduced manual sorting effort by over 70%
Faster Data Processing
Enabled large-scale classification in minutes
Improved Decision-Making
Delivered clean, categorized data for reporting and analytics
ML Algorithms for Classification
Support Vector Machine (SVM), Random Forest
Text Preprocessing
Python (Pandas, NLTK), Scikitlearn
OCR Integration for Image-Based Data
Pytesseract, EasyOCR
Web Application for Review and Export
Python with Flask
Tech Stack
Conclusion
The solution enabled rapid scaling of product data management across departments and set the foundation for broader AI adoption. By automating the categorization of complex pharmaceutical product descriptions, DRC Systems significantly streamlined operational workflows, minimized manual effort, and enhanced the accuracy and accessibility of enterprise data across departments. The AI-powered accurate classification also helped the client support regulatory and internal compliance efforts.
Turning Tech Challenges into Solutions!
Contact Us Now