Cloud Analytics for Health Information

Our client develops data analytics software that enables healthcare organizations to evaluate community health metrics and provides data analysis capabilities to healthcare providers. However, their platform lacked functionality that would enable users to perform comparative analysis against external data sources, limiting the value for their customers. Additionally, the organization's existing on premise infrastructure required their technical team to perform daily data cube reconstruction, a time intensive process that became increasingly difficult as product adoption grew and customer demand increased. The client selected CiTechT to enhance their latest product iteration with the objective of giving healthcare providers a clearer way to benchmark performance against aggregated peer metrics. Furthermore, the enhanced data collection and analysis capabilities would enable state and local government agencies to access relevant benchmarking information to support evidence based community health policy decisions.

CiTechT designed and delivered a cloud based solution for the product platform. Our technical team implemented Microsoft Azure cloud infrastructure and integrated Microsoft Data Platform technologies, including HDInsight, to create a secure, HIPAA compliant data lake architecture. This cloud native solution integrated with the client's existing technology ecosystem, enabling the organization to execute complex data processing and analytics workloads in the cloud while maintaining the flexibility to incorporate on premise data sources when business requirements required it. The solution delivered on demand reporting capabilities while supporting healthcare data protection requirements.

1. Conducted comprehensive assessment of existing on premise infrastructure and data processing workflows
2. Designed secure, HIPAA compliant cloud architecture leveraging Microsoft Azure platform
3. Implemented Microsoft Data Platform tools including HDInsight for cloud data processing
4. Created secure data lake infrastructure supporting large scale data aggregation and analytics
5. Developed integration framework connecting cloud analytics with existing technology stack
6. Established hybrid architecture connecting cloud and on premise data sources
7. Implemented real time data processing capabilities replacing daily data cube rebuilds
8. Configured secure data pipelines for external data source integration and comparison
9. Deployed comprehensive monitoring and performance optimization systems
10. Conducted extensive testing and validation to ensure HIPAA compliance and data security