Case Study | Healthcare

Enhancing Claims Intelligence with AI-Driven Analytics

Transforming claims processing into a scalable, near real-time system that delivers accurate insights and reduces operational delays.

Overview

A trusted leader in life sciences needed to modernize legacy, weeks-long claims processing across hundreds of millions of rows. 10Pearls delivered a cloud-native data platform on AWS with distributed processing and AI-driven entity resolution to accelerate analytics and improve accuracy.

Reduced processing time
from weeks to hours

Improved data accuracy
with AI entity mapping

Challenge

The client’s legacy on-prem systems were processing massive healthcare claims datasets in multi-week cycles. The process was resource-intensive, error-prone, and provided limited insight into practitioner and prescriber behaviors. Fragmented HCP identity data made it difficult to perform accurate analytics or target communications effectively. These inefficiencies hindered reporting accuracy, delayed client deliverables, and increased operational costs.

Solution

10Pearls rebuilt the client’s claims intelligence system into a distributed AWS platform capable of processing hundreds of millions of records in hours rather than weeks. Apache Spark on Amazon EMR enabled large-scale data processing and machine learning-driven entity resolution to unify fragmented practitioner records. Amazon S3 and Amazon Glacier provided secure storage and archiving, while Amazon Redshift powered high-performance analytics and Amazon QuickSight delivered interactive dashboards. Event-driven automation with AWS Lambda and Amazon ECS, monitored by Amazon CloudWatch, streamlined ingestion and orchestration. This modernization accelerated processing, improved accuracy in practitioner mapping, reduced operational overhead, and provided a scalable foundation for ongoing AI-driven analytics and reporting.

Customer

Life sciences leader

Services

Artificial Intelligence
Business Process Optimization
Custom Software Development
Machine Learning

tech stack

AWS
  • Amazon ECS
  • Amazon EMR
  • Amazon Glacier
  • Amazon QuickSight
  • Amazon Redshift
  • Amazon S3
  • AWS Lambda
  • Amazon CloudWatch
    • PostgreSQL Python

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