- INDUSTRY: Healthcare
- Vertical: Computer Vision, AI
- Location: Florida, USA
- Completed Date: 30-12-2024
Executive Summary
A healthcare organization aimed to enhance its pharmaceutical processes for pills detection and identification. This initiative involved developing an AI-powered solution to automate and improve the accuracy of pills identification, leveraging image recognition and machine learning technologies. This case study explores the challenges, the implemented solution, the technologies used, and the resulting benefits, focusing on the transformative potential of AI in healthcare operations.
Vision
The organization, a key player in healthcare services, sought to optimize its prescription management by replacing manual processes with an automated, AI-driven system. The goal was to improve efficiency, reduce errors, and gain actionable insights through data visualization and analytics. This project represented a critical step towards modernizing operations and embracing advanced technologies in healthcare.
Challenge
The organization faced several challenges with its existing prescription identification process::
Manual Inefficiencies: The manual process was time-consuming and labor-intensive.-
Potential Errors: Manual identification was prone to human error, leading to potential inaccuracies in prescription fulfillment.
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Lack of Data Insights: Limited ability to track and analyze prescription data for performance and improvement.
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Scalability Issues: The existing process was not easily scalable to handle increasing volumes of prescriptions.
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Data Security: Ensuring the security and privacy of sensitive patient data was paramount.
Solution
OktaBytes team helped the organization develop a comprehensive Payer Portal with the following features:
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Real-time Inference Integration: Seamless integration with existing systems for immediate Pill detection using private Endpoint.
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Model Management: Efficient handling of NDC (National Drug Code) allocations and model retraining.
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Smart Inference Routing: Smart routing mechanism allows routing of inference requests to the correct AI model.
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Automated Data Collection: Data pipelines to collect metrics on NDC performance and AI error rates.
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Alerting System: Notifications for system errors or performance issues.
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User Interface (UI): User-friendly interfaces for model management, routing management, business rules management, performance monitoring, and alert management.
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Data Visualization: Integration with data visualization tools to generate insights from collected data.
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Robust Data Security: Implemented encryption at rest and in transit, access controls, and audit trails to ensure data security and compliance with healthcare regulations.
Tech Stack
React
Python
PostgreSQL
SQL Server
AWS Services
Lambda
API Gateway
S3
RDS
CloudFront
Rekognition
EC2
QuickSight
Site 2 Site VPN
Impact
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Improved Efficiency: Automation reduced manual effort and processing time.
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Enhanced Accuracy: AI-driven identification minimized human error and improved prescription accuracy.
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Data-Driven Insights: Data visualization provided actionable insights for performance monitoring and optimization.
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Scalability: The cloud-based solution easily scaled to handle increasing prescription volumes.
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Reduced Operational Costs: Automation and efficiency improvements led to cost savings.
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Enhanced Data Security: Robust security measures ensured the confidentiality, integrity, and availability of sensitive patient data.
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Future-Proofing: The solution laid the foundation for future AI applications and data-driven advancements in healthcare operations.
Conclusion
The project successfully transformed the organization's pharmaceutical decision-making process by implementing an AI-powered prescription identification solution. By leveraging AWS cloud services, advanced AI technologies, and robust backend and frontend development, the organization achieved significant improvements in efficiency, accuracy, and data insights. This case study highlights the potential of AI to revolutionize healthcare operations and pave the way for future innovation.
