How a Healthcare Claims Company Modernized Its EDI and ETL Platform

‍Overview

A healthcare claims processing company who manages complex reimbursement for hospitals and health systems, depends on healthcare EDI files, such as 837/835. They processed large volumes daily and needed accurate transformations, fast throughput, and audit trails.

Their previous platform could not keep pace. It struggled with new client requirements and growing file complexity. As a result, the company decided to modernize both parsing and downstream data processing, engaging v4c.ai to redesign the platform around a scalable lakehouse architecture on Databricks.

‍

The Challenge

Legacy Parser and Pipelines

The company used a custom C# parser built more than a decade earlier. It extracted required data from 837/835 transactions. The system suffered from excessive built-in custom logic, which led to error-prone processing. When it failed, operators often had to re-run the processes manually or patch partial outputs.

A third-party Ember-based parser was tested, but covered only about 20% of the field mapping needed for healthcare claims and remittance use cases. To get full coverage, they would have needed to write a significant amount of  custom code on top of it, reintroducing long-term maintenance and scalability challenges.

‍

Tight Coupling and Low Observability

Downstream pipelines were built using C#, SSIS, and SQL stored procedures. The components were tightly coupled, so changes in the parser logic often required edits in orchestration and transformation logic.

There was limited logging and no end-to-end data lineage. When a claim failed downstream, engineers had difficulty tracing it to a specific field extraction or validation step.

Audit and compliance needs only emphasized this shortcoming. Healthcare claims involve strict rules, and auditors expect traceable processing, not silent failures.

‍

Rising Workloads and Complexity

Healthcare transactions have become more complex over time. Clients asked for richer mappings, expanded field coverage, and new rules for remittance analysis. The legacy stack couldn’t scale, and adding new formats slowed delivery.

The company needed:

• A healthcare-compatible EDI parser
• Better data quality and error handling
• Clear audit trails
• A scalable processing architecture
• Near real-time visibility for operations

The existing environment could not provide that without major rework. Rather than incrementally extending the legacy stack, the organization partnered with v4c.ai to rebuild the EDI and ETL workflows using Apache Spark within Databricks, aligning parsing, transformation, and analytics in a single governed environment.

‍

The Solution

Refactored Healthcare EDI Architecture

The team successfully re-architected the 837 processing flow by strictly decoupling data extraction from business logic. This move from a rigid Ember-based implementation to a modular Spark-based system eliminated silent data loss and established a reusable framework for future clients.

By implementing the new processing model on Databricks, extraction and transformation logic were redesigned as distributed Spark workflows rather than application-bound parsing routines.

Key Architectural Shifts:

• Lossless Extraction: The ‘What’
  Instead of selective parsing, the new engine captures the full fidelity of the source EDI. It deterministically persists every segment, element, and sub-element before any mapping occurs, ensuring zero data loss.
  
  This extraction layer now runs as a scalable Spark job within Databricks, allowing horizontal scaling as claim volumes increase while preserving full source traceability.

• Rules-Driven Mapping: The ‘How’
  Hard-coded logic was replaced by an external configuration table. Using native Spark SQL, mapping is now declarative rather than code-driven. The benefit is logic changes or new requirements being able to be handled by updating rules, not rewriting code.

The pipeline has four stages: extraction, mapping, validation, and extensibility. It captures raw EDI, applies rules, validates output, and allows new loop IDs and fields without major refactoring.

1. Extraction → Parses and persist the complete EDI structure (lossless)
2. Mapping → Applies rules from Rules Table to transformation logic
3. Validation → Compare mapped outputs against source-of-truth tables for validation
4. Extensibility → Allows adding new loop IDs and fields without major code refactoring

Within the Databricks lakehouse architecture, these stages align with bronze, silver, and gold data layers, separating raw ingestion, governed transformation, and analytics-ready outputs.

This refactor transformed a fragile, one-off parser into a scalable, high-performance product. It guarantees data correctness, simplifies onboarding for new healthcare clients, and drastically reduces maintenance effort.

Data Quality, Observability, and Auditing

The team added data quality rules at multiple points. These checks flagged malformed claims, missing provider data, inconsistent control numbers, and remittance mismatches. They added observability hooks for pipeline start/stop events, data volumes, validation errors, and dead letter queues. They added basic audit trails showing what data was received, how it changed, and why certain rows failed validation. Because ingestion, transformation, and validation now operated within the unified Databricks lakehouse, lineage is preserved across layers, enabling engineers to trace any failed claim back to its originating EDI segment and applied transformation rule.

Lakehouse ELT Architecture

Legacy SSIS and C# workflows were re-engineered into a Databricks lakehouse architecture.

Key elements included:

• Cloud jobs for pipeline orchestration
• Delta Live Tables for declarative transformations
• Bronze → Silver → Gold Delta tables for progressive data refinement

The bronze layer held raw EDI payloads. The silver layer applied mapping and validation. The gold layer held normalized claims and remittances for downstream analytics.

Consolidating ingestion, transformation, and analytics within Databricks replaced fragmented tooling with a single governed data platform, making it easier to debug, replay, and scale.

Performance Optimization with Photon

As part of this EDI modernization initiative, the team optimized large-scale 837 processing using Databricks Lakehouse with Photon enabled. During scale testing, multiple gigabytes of simulated EDI volume were generated to validate stable, horizontally scalable performance under high load.

Parsing and mapping workflows that traditionally required days in legacy environments were completed within hours. Photon improved transformation performance through optimized Spark SQL execution, delivering measurable runtime gains and establishing a clear path toward a scalable, cloud-native EDI processing platform.

‍

The Solution

The team successfully re-architected the 837 processing flow by strictly decoupling data extraction from business logic. This move from a rigid Ember-based implementation to a modular Spark-based system eliminated silent data loss and established a reusable framework for future clients.

The modernization project changed both engineering operations and business outcomes.

Engineering Outcomes:

• 60% reduction in ETL maintenance (fewer ad-hoc fixes and fewer SSIS jobs)
• 80–90% fewer lines of custom parsing code (due to modular parsing and declarative ELT)
• Better observability made failed claims easier to diagnose
• Parser became a reusable internal asset for future clients

Claims Processing Outcomes

• Faster ingestion and transformation of 837/835 files
• More reliable remittance matching
• Stronger data quality and validation checks
• Better auditability for compliance and payer disputes

Analytics and Operations Outcomes

• Near real-time visibility into claims processing
• New metrics available to operations teams
• Ability to trace rejected claims back to specific fields or validation steps
• Foundation for future automation efforts

‍

Why It Matters

Healthcare EDI formats are strict, detailed, and unforgiving. Missing key fields or mis-mapped segments can delay payments, create disputes, or trigger audit findings. Legacy ETL systems often treat EDI as opaque text, rather than structured data with schemas and rules.

This project made EDI parsing a first-class engineering capability. It introduced schema evolution, observability, lineage, and quality checks, tools that modern data teams expect but many healthcare players still lack. Through its collaboration with v4c.ai and the adoption of a Databricks lakehouse architecture, the company transformed EDI processing into a scalable, governed data platform capability rather than a fragile application component.  It also made the parsing logic reusable, which cuts the time needed to onboard new clients with similar EDI needs.

‍

Looking Ahead

With the core parser and lakehouse in place, the company is exploring:

• Additional EDI transaction sets beyond 837/835
• Machine-readable audit logs for regulatory reporting
• Remittance analytics for denial prediction
• Operational dashboards for payer performance
• Automated replay pipelines for failed batches
• Tools for client-specific extension without core changes

These capabilities move the company from reactive claims processing to proactive revenue cycle intelligence.

‍