Healthcare Analytics:
Data Lakehouse & GenAI Pipeline

The Challenge

A regional healthcare provider had acquired several smaller clinics over the years, leaving their master patient data fragmented across dozens of siloed MySQL databases and on-premise Excel sheets. Data scientists spent 80% of their time just finding and cleaning data rather than building models.

They wanted to implement predictive models to anticipate patient no-shows and supply chain shortages, but lacked the centralized, reliable data infrastructure and model deployment pipelines needed to support enterprise-grade AI.

The Solution Architecture

Cloudepok designed an end-to-end modern data stack emphasizing governance and security.

• Data Lakehouse Architecture: We implemented Databricks on AWS. Using AWS Glue and Airflow, we orchestrated robust pipelines to extract, clean, and land data continuously into a Delta Lake, supporting both BI reporting and ML workloads.
• Strict Data Governance: As this involved Protected Health Information (PHI), we implemented strict Role-Based Access Controls (RBAC) and automated PII redaction rules utilizing AWS Macie before data hit the analytics zone.
• Automated MLOps Pipeline: We deployed an MLflow registry to track experiments. Once a predictive model was validated, GitHub Actions automated its deployment as a containerized REST API in Amazon SageMaker.
• GenAI Query Assistant: For non-technical administrative staff, we built an internal Retrieval-Augmented Generation (RAG) agent using LangChain and enterprise GenAI models, allowing them to ask natural language queries against their secure data warehouse.

The Business Impact

The new platform turned hidden, siloed data into their most valuable operational asset.

• 2.5 Petabytes Consolidated

  Successfully unified over a decade of fragmented clinical history into a single, query-able, HIPAA-compliant platform.

• 3x Faster ML Iterate cycles

  With clean data and MLOps tooling, data science teams slashed the time taking a model from prototype to production from months to weeks.

• 18% Reduction in Supply Waste

  The first predictive models deployed immediately generated ROI by accurately forecasting specific pediatric medical supply needs across their 40 clinics.