CASE STUDY
 

How a Healthcare Research Organization 85,000 Hours Annually

Empowering Translational Healthcare Research through AI and Team Science

The Situation: Empowering Departmental Connection Through Data Innovation  

Our healthcare research client’s executive leadership team envisioned a future that enabled best-in-class translational research by empowering researchers to do their best work. This future state would drive research innovation and enable AI and team science at an entirely new scale, all while improving governance, security, and compliance.

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Key Factors for Improving Healthcare Infrastructure

To empower translational research, several critical factors must be addressed to upgrade and optimize the current informatics and platform engineering. These include:
 

Existing Business Units

 More than 30 different highly-regulated healthcare business units are leveraged by the existing data environment.

 

Organizational Needs

The client lacks a strong cloud infrastructure and platform engineering, presenting a new opportunity to integrate best-in-class transitional research and HIPAA-compliant cloud environments.

Developing Cloud Informatics

We took an iterative approach and developed a cloud data fabric built on Databricks in Azure. This fabric allowed the aforementioned departments to migrate their data assets to the cloud and expand upon their current capabilities.

 

We fostered user development through native Databricks tools, such as Notebooks. Users are now empowered to ingest data from a wide variety of data sources (RDBMS, APIs, file systems, Synapse, Big Query, etc.) without the need to write code for the majority of scenarios.

“We needed a way to show our senior leadership team that marketing was producing results, and that’s hard when your metrics are scattered. This dashboard saved the day.”

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30+
Departments Connected

Enabling more options for analytics tools and flexible data environments

85,000+ Hours Saved

Annually in data science efforts

200+ Models Deployed

Actionable through rapid AI adoption

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