Key Takeaway
Cognizant’s system enhances medical code extraction, improving accuracy by 30-40% and reducing effort by 30-75%, while accelerating time to market by 40-45%. This model supports compliance with healthcare regulations like HIPAA and GDPR. Additionally, Cognizant focuses on industrial digital twins, utilizing Nvidia Omniverse to transform manufacturing operations through real-time insights and predictive analytics. Their AI infrastructure, offering “GPU as a Service,” ensures efficient deployment across various environments. A notable implementation for a large US healthcare client achieved 2.7x cost efficiency and 1.8x performance improvement in Spark workload processing.
This system utilizes Cognizant’s domain expertise to improve medical code extraction—the process of identifying and categorizing medical conditions and procedures for billing and analysis.
Based on Cognizant’s internal benchmarking, the model has proven effective in:
- Reducing effort by 30-75%
- Enhancing coding accuracy by 30-40%
- Speeding up time to market by 40-45%
The healthcare model is designed to support greater accuracy, minimize errors, and ensure compliance with healthcare data regulations such as HIPAA and GDPR.
Nvidia Omniverse: Accelerating Cognizant’s Smart Manufacturing and Digital Twins
Another key focus is industrial digital twins—virtual replicas of physical systems used for simulation and analysis.
Cognizant’s smart manufacturing and digital twin solutions, powered by Nvidia Omniverse, aim to facilitate digital transformation in manufacturing operations and supply chain management.
These capabilities will help clients improve plant layouts and process simulations with real-time insights and predictive analytics, as the technology allows for the integration of data from various sources, enabling clients to simulate scenarios and identify solutions to operational challenges.
Cognizant’s AI infrastructure, powered by Nvidia, will grant clients access to GPU computing resources through what Cognizant refers to as a “GPU as a Service” model, along with secure and managed infrastructure—ensuring that AI models can be deployed across various environments, including cloud, data centers, or edge computing locations.
In one implementation for a large healthcare client in the US, Cognizant reports that its AI infrastructure achieved a 2.7x improvement in cost efficiency and a 1.8x enhancement in the performance of Spark workloads—a framework for large-scale data processing.
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