Common Data Framework for Healthcare

Unify healthcare data for sharper analytics and an AI-ready future

Fragmented data, rising cost of care, and AI adoption challenges are real, slowing down healthcare innovation. Built on the Lakehouse framework, KANINI’s Common Data Framework powers leading common data models such as TUVA and OMOP, to break down silos, standardize healthcare data, and ensure compliance. This translates to trusted insights and meaningful decisions for better care outcomes.

common data framework

Accelerate Transformation with a Framework that Delivers

Our Common Data Framework is designed to serve every stakeholder in healthcare – from providers and payers to health tech innovators. It enables cross-payer/provider compatibility and delivers business-ready insights to power clinical decisions, population health strategies, value-based care initiatives, financial risk management, and more.

One Framework. Infinite Possibilities.

KANINI’s Common Data Framework stands out, and here’s why:

Designed to Power Leading Common Data Models Faster and Smarter

Supports TUVA, FHIR, OMOP, and all other well-established healthcare frameworks.

Designed to Power Leading Common Data Models Faster and Smarter

Built to Adapt and

Ready to Scale

The flexible architecture enables you to adopt one or multiple common data models based on your compliance, operational, and analytics needs.

Built to Adapt and Ready to Scale

Delivers Infinite Insights through a Trusted Data Foundation

The framework creates a single, trusted data foundation to meet the insight needs of every stakeholder across the healthcare ecosystem.

Built for Real-World Healthcare Data Complexity. Powered for AI.

Less than half of AI initiatives successfully make it from pilot to production, largely due to weak, non-scalable data foundations. To capitalize on revolutionary technologies like GenAI, healthcare organizations need a strong, scalable, and interoperable data ecosystem.

Our Common Data Framework solves this by supporting the ingestion and transformation of massive, heterogeneous healthcare data into industry-aligned, actionable formats. It breaks down data silos, powers advanced analytics, and creates real impact with meaningful insights across cloud – whether you are on AWS, Azure, or GCP. 

From Ingestion to Insights – Lakehouse architecture with the Medallion approach:

One Framework. Infinite Possibilities.

Break down data silos, power advanced analytics, and create real impact with meaningful insights across cloud – whether you are on AWS, Azure, or GCP.

Key Differentiators

CDM wireframe
CDM wireframe

Use Case Highlights

A unified data platform to manage large volumes of Machine-Readable Files (MRFs)

Enabled a leading health tech company to manage TiC (Transparency in Coverage) data mandates.

  • Unified 73k+ MRFs into a common data model
  • Automated ingestion, metadata management, and high scalability.

A next-gen lakehouse platform transforming data accessibility and analytics

Empowered faster, data-driven care delivery for a leading provider of in-home services.

  • 12B raw records refined into ~2B business-ready insights
  • Built on Azure Synapse Delta Lake with RBAC-based access controls, autoscaling, and other features.

A modern enterprise data reporting framework powering scalable analytics

Delivered scalable analytics across claims, financial, and enrollment data for a leading payer organization.

  • Consolidated 322M records into 67M insights on plan performance and risk metrics
  • Enabled performance dashboards on Power BI, actuarial forecasts, and plan optimization.

Why Healthcare Leaders Choose KANINI’s Common Data Framework

Our framework is built to ensure end-to-end data governance, enabling high data quality and robust data security controls.

  • Adheres to HL7/FHIR healthcare standards for consistency and interoperability.
  • Safeguards patient confidentiality and data integrity.

Absolutely.

  • Supports TUVA, OMOP, FHIR, and other regulatory frameworks:
    • TUVA Model – Combines claims, EHR, FHIR, ADT, and lab data to deliver high-quality healthcare analytics.
    • OMOP – Enables standardized, comparable observational health data for advanced research and analytics.
    • FHIR – Facilitates secure, fast interoperability across healthcare systems.
  • This ensures alignment with evolving healthcare standards while exchanging and analyzing data.

Yes, interoperability is built in by design.

  • Allows ross-payer and provider compatibility across diverse data formats.
  • Enables seamless consolidation and management of complex healthcare data.

KANINI’s Common Data Framework is designed for flexibility.

  • Schema flexibility supports both custom and standardized models.
  • Driven by real-world requirements and evolving business needs.

We bring deep healthcare industry knowledge and technical strength through our industry partnerships with Microsoft, Databricks, Snowflake, and other leading technology platforms.

  • Deep healthcare domain expertise and understanding of provider and payer data.
  • Proven technical capabilities and trusted industry partnerships.
  • Tailored approach to address the unique data challenges of every healthcare organization.

The common data framework is designed for speed and scalability.

  • Quick implementation while maintaining flexibility and scalability.
  • Fast onboarding of new data sources to accommodate evolving compliance and business needs.
  • Accelerated time-to-value.

 Yes, it’s a hybrid and customizable framework.

  • Supports bespoke customizations aligned with client-specific workflows.
  • Accommodates regional compliance requirements.
  • Easily extendable to support evolving business and regulatory demands.

Discover how KANINI builds scalable, compliant, and AI-ready data ecosystems in healthcare.

Discover the analysis results and our recommendations that helped the healthcare organization maximize its ServiceNow ROI.

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Discover the analysis results and our recommendations that helped the healthcare organization maximize its ServiceNow ROI.

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