NLP-based Sentiment Analysis for a Healthcare Provider
Medical record named entity extraction and sentiment analysis from a large volume of unstructured data that includes electronic & physical records.
Key Metrics
95% accuracy rate with 5000+ documents
60% decrease in time for synthesizing clinical guidelines
100% compliance with healthcare regulations
Reduced FTE for handling exceptions from 3 to 1
The client follows a manual medical record (EHR & physical record) summarization process using a licensed medical staff to identify the disease category, sentiments, and ICD code conversion. Developed a custom user interface to retrieve the health records from EHR vendor endpoints following SFTP. Implemented an NLP engine for analyzing unstructured text, entity extraction and applied OCR mechanism to read pdfs or scanned images. Displayed summary and sentiments in the UI.
Key Features
EDI Retrieval
OCR Extraction
NLP based Summarization
ICD-10 Code Mapping
Record Summary with Sentiments
Key Metrics
95% accuracy rate with 5000+ documents
60% decrease in time for synthesizing clinical guidelines
100% compliance with healthcare regulations
Reduced FTE for handling exceptions from 3 to 1
Case Studies
Mobile App Development • Retail & eCommerce
Intuitive Shopfloor Management Mobile App for a Toy Manufacturer
Developed a cross-platform mobile app to automate and reduce human involvement in monitoring, operating, and scheduling manufacturing machines.
Key Metrics
45% improvement in Production Planning Efficiency
3x output through optimal Resource Scheduling
65% reduction in unplanned downtimes
100% paperless manufacturing
The app was built for remote monitoring and controlling plastic molding machines. The need for physical involvement was eliminated using automated workflows, beacons, and QR code scanners. Enabled operators to access their tasks, operate the machine with task-specific code and update the progress via the app. Integrated with peripheral production systems, combined multiple data sources, REST APIs using AWS AppSync GraphQL mutations. Created in-app analytics for tracking and optimization.
key Features
NFC & Barcode Scanning
Real-time Data Sync
Internationalization
Notifications & Alerts
Inspection & Audit Trail
Key Metrics
45% improvement in Production Planning Efficiency
3x output through optimal Resource Scheduling
65% reduction in unplanned downtimes
100% paperless manufacturing
AI & ML Automation • Manufacturing
AWS Sagemaker based Computer Vision Solution for a Manufacturer
Developed object detection at the Edge using AWS consumable production-ready services like Sagemaker, Groundtruth, IoT Greengrass, S3, and Lambda.
Key Metrics
80% efforts reduced in data annotation & labeling
65% time saved in ML Modelling
54% lower total cost of ownership
One-click deployment to the cloud
Client is required to submit the old physical devices for the new inventory fulfillment request. They wanted to replace this time-consuming process with computer vision. Configured Raspberry Pi device and made it interact with IoT Greengrass Core using Lambda. Captured images, annotated using Groudtuth, and loaded into S3 buckets. Built ML model using AWS Sagemaker built-in algorithm XGBoost. Deployed model back on Greengrass device using Lambda for object detection. Established ETL for database transformation and enabled Quick Sight insights.
key Features
Raspberry Pi Device Configuration
Video to Frames Conversion
Auto Annotation & Data Labelling
AWS Built-in-Algorithms
Image Classification Model on Edge
Key Metrics
80% efforts reduced in data annotation & labeling
65% time saved in ML Modelling
54% lower total cost of ownership
One-click deployment to the cloud
Data Management • Health Care
Member Data Management and Extraction from Payer Systems
Managed Facets administrative system from backend and created procedures to extract and manage data
Key Metrics
72% reduction in Data extraction time
98% data validation accuracy achieved
24 hour support for data warehouse management
20% reduction in manual effort through job automation
To meet the CMS compliance and data analytics requirements – member, provider, and claims data need to be processed from multiple tables. We performed data masking, anonymization, cross-referencing, duplicate removal, and business level validations at various levels. Next, we converted the data into the required format, including .txt, EDI, JSON, and other related formats. Finally, we created a data warehouse to store the data and data marts to create analytics on top of the data.
key Features
Rearchitect The System Design with Modern Tech Stack
Migration of Millions of Healthcare Records into Azure
Automated Data Validations
Data Warehouse Management
Custom File Formatting
Key Metrics
72% reduction in Data extraction time
98% data validation accuracy achieved
24 hour support for data warehouse management
20% reduction in manual effort through job automation
Hey, we are still working on those cases, they will be up soon
Please check out our other case studies in the meanwhile.
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