Implementing a Robust Strategy for Healthcare Big Data

Big Data in healthcare is growing at an unprecedented rate and the enormous volume of data from disparate sources makes it challenging for healthcare organizations to derive actionable insights. 

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CitiusTech Big Data Practice helps healthcare organizations develop and execute their Big Data strategy and manage growing volumes, velocity and variety of data from disparate sources. The practice leverages best-in-class Big Data technologies to effectively store, process and query large healthcare datasets, and support Big Data use cases such as creating a single source of truth to manage structured, semi-structured and unstructured data, generating real-time clinical alerts, notifications and events for automated and streaming analytics, Machine Learning, statistical modeling, predictive analytics and NLP algorithms for mining healthcare data. The Big Data practice also enables healthcare organizations to build custom Big Data solutions that can be integrated with existing applications, helping them accelerate their Big Data initiative.

 

Big Data: Key Highlights

  • Expertise with Hadoop distributions (Hortonworks and Cloudera), NoSQL databases, Azure Databricks, AWS EMR and Google BigQuery
  • Strategic partnership with Microsoft Azure and AWS for healthcare Big Data tools 
  • CitiusTech H-Scale platform for pre-built Big Data accelerators – data quality measurement, ETL, HL7 / FHIR / claims / CCDA parsing, NLP (for unstructured data)
  • Strong expertise in healthcare interoperability, BI / DWH and Data Science models 

 

Big Data: Key Offerings

Consultation and POCs 

  • Recommendations around Big Data tools and platforms for data management and analytics
  • POCs to test new solutions / technologies  

Infra and Architecture

  • Big Data environment setup and maintenance for development
  • QA, staging and production, integration, security and hardening
   

Data Management

  • Data ingestion, data indexing and searching for easy access
  • Data lake creation, data normalization, standardization and processing

Data Analytics 

  • Analytics on cleansed and combined data (real-time and batch)
  • Machine Learning, predictive modeling, etc. using open source and commercial software
  • Visualization using Tableau, Cognos, Qlik, etc.