As a leading technology and services company in the healthcare sector, this client specializes in an AI-powered Revenue Cycle Management (RCM) solution. In partnership with Onix, this client successfully migrated and modernized their diversified facility groups on a unified data platform on Databricks and Microsoft Azure.
About the customer
For over 2 decades, this company has been the pioneer in RCM technology. As a solution provider, this company has enabled hospitals and healthcare providers to optimize their patient billing, collections, and financial performance using AI-powered solutions and advanced analytics. With a global presence, this healthcare provider supports financial operations across diversified facilities and groups.
The challenge
This healthcare company sourced its patient data from multiple electronic medical record (EMR) systems, with data that was distributed across MS SQL Server and Snowflake systems. Each of these facility groups maintained a different target data model, thus resulting in data fragmentation and inconsistency.
Here are some of the common challenges faced by the client:
- Lack of a common or unified enterprise data structure.
- Inconsistent definitions of RCM and other clinical entities.
- Duplication of transformation logic across facility groups.
- High maintenance and operational overheads.
- Slow and error-prone onboarding process of new EMR systems.
The objective
The client wanted to modernize and unify their fragmented data ecosystem by migrating their workloads from their legacy SQL Server and Snowflake systems to a centralized Microsoft Azure/ Databricks platform. This was intended to implement a canonical data model and layered data architecture that would standardize the data, enhance scalability, and facilitate faster onboarding of new EMR systems.
The solution
The Onix team engineered this transformation using multiple strategic initiatives, which included the following:
Canonical data model- Design and implementation of this model to represent enterprise-wide healthcare and RCM entities.
- Abstraction of EMR-specific variations into a single, standardized structure.
- Implementation of a trusted data foundation used for analytics, reporting, and downstream consumption.
- Execution of a generic, reusable ingestion framework that’s aligned to the canonical data model.
- Configurable mappings to enable faster onboarding of new EMRs.
- Support for SOAP and REST APIs, file-based feeds, and direct database sources.
Additionally, Onix’s solution included a 4-layered ETL adapter framework comprising:
Ingestion layer for:- Ingesting raw data from EMRs with minimal transformation.
- Preserving the source fidelity for audit and traceability purposes.
- Applying cleansing, validation, and normalization rules.
- Aligning the source data to the canonical data model.
- Enriching the canonical data with business rules, derived attributes, and reference data.
- Preparing the data for advanced analytics and AI-related use cases.
- Publishing the curated, trusted datasets for enterprise-level reporting and consumption.
- Enabling consistent access for downstream applications and analytics teams.
For cloud migration and orchestration of this client, the Onix team:
- Performed a discovery-driven migration planning for MS SQL Server and Snowflake.
- Converted legacy tables, views, and DDLs to Databricks using the Raven tool.
- Orchestrated their legacy workloads using Airflow as the scheduler.
Outcome
By implementing this migration to Databricks, the client addressed each of the following challenges:
- Eliminated all fragmented data models with a single canonical data model.
- Reduced the duplication of ETL logic using standardized adapters.
- Improved overall scalability and performance using distributed processing.
- Simplified EMR onboarding through reusable canonical mappings.
- Improved overall data quality, consistency, and trust.
- Enabled enterprise-wide analytics and AI initiatives.
Business impact
- Unified enterprise data platform powered by MS Azure/ Databricks
- Canonical data model implemented as the system of record
- Faster, seamless onboarding of new EMRs
- Improved operational efficiency and lower maintenance costs
- Accelerated time-to-insight for RCM analytics
Conclusion
To summarize, this healthcare service company transformed its fragmented legacy environment into a standardized, scalable data platform powered by MS Azure and Databricks. With the implementation of a canonical data model and a 4-layered architecture, Onix addressed the client’s integration challenges and streamlined their EMR onboarding. Effectively, Onix helped this client build a strong, resilient foundation for AI and advanced analytics.
