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Automated Healthcare Data Integration Platform | Adroitent

Customer Story · Healthcare Analytics · Data Integration

Automated Healthcare Data Integration Platform for a Leading US-based Healthcare Analytics Provider

The customer is a Nebraska-based healthcare analytics company that serves health information exchanges for direct primary care groups and critical access hospitals, providing population health and value-based care insights.

Adroitent Case FileHealthcare AnalyticsData Integration · Pentaho on AWS2 min read

Automated healthcare data integration platform

Major Challenges

Fragmented integrations

Each hospital had different source systems and integration patterns across RDBMS, NoSQL, cloud, and standalone environments.

Diverse data & cadences

APIs, HL7/FHIR/CCDA, files, and clinical feeds arrived in different formats and on different schedules.

Manual onboarding

Every new hospital required fresh source analysis, mapping, and extraction logic, making onboarding slow and unpredictable.

No unified patient identity

Patient data had to be reliably linked across multiple systems to create a true 360° view.

Inconsistent data

Formats, data types, and business rules varied by source, requiring standardization before analytics.

PHI & compliance

Secure connectivity, HIPAA compliance, and appropriate de-identification were mandatory for every data exchange.

Manual operations

Refreshes depended on people running and watching jobs, with limited logging and exception handling.

The customer transformed hospital onboarding from bespoke, development-heavy integrations into a repeatable model with connect, configure, schedule, and go.

Solution by Adroitent

Adroitent built a metadata-driven, reusable integration platform on Pentaho Data Integration 7.1, deployable in the cloud or on-premise, transforming hospital onboarding from development into configuration.

Reusable ETL templates

Metadata-driven jobs and transformations accelerated onboarding.

Unified data ingestion

APIs, flat files, CCDA, and HL7 standardized through a common pipeline.

AWS-enabled architecture

S3, Lambda, and PostgreSQL powered scalable data movement and warehousing.

Built-in automation & controls

Scheduling, data quality checks, exception handling, and logging enabled reliable operations.

The game changer

Production-proven scalability

A single parent job automated four claims extracts using metadata-driven file locations with no manual intervention.

Metadata-drivenReusable templatesScheduled refreshesBuilt-in quality checks

By the numbers

Claims extracts automated by a single parent job

Data domains unified in one clinical data warehouse

360°

Patient view with a consistent patient identity

Manual intervention in the automated extracts

Business Outcomes

Transformed hospital onboarding: The customer transformed hospital onboarding from bespoke, development-heavy integrations into a repeatable model with connect, configure, schedule, and go. The team could onboard new hospitals faster, run data refreshes on a predictable cadence, and shift the analytics team's focus from integration plumbing to delivering insights and business value. Configurable metadata and scheduled setup replaced development-heavy onboarding.
Scalable growth: Reusable templates enabled new hospitals without proportionally increasing ETL resources.
Predictable data freshness: Automated, scheduled refreshes replaced manual runs with a reliable data cadence.
Unified clinical data warehouse: Claims, eligibility, pharmacy, EMR, and biometrics consolidated in PostgreSQL with a consistent patient identity.
Greater data trust: Built-in quality and consistency checks identified issues before they reached dashboards.
Lower operational effort: Exception handling, logging, and restart procedures simplified monitoring and troubleshooting.

The stack behind itTools & technology

Build

Pentaho Data Integration 7.1PostgreSQLAWS S3AWS Lambda

Sources & standards

HL7FHIRCCDAAPIsFlat files

This engagement was delivered as part of Adroitent's Integration Services practice.

Good to knowFrequently asked questions

What is metadata-driven ETL, and why does it speed up hospital onboarding?

In a metadata-driven design, source locations, mappings and rules live in configuration rather than being hard-coded into each job. Reusable templates read that configuration at run time, so adding a new hospital becomes a matter of configuring and scheduling, not building new extraction logic from scratch.

What are HL7, FHIR and CCDA, and why do they all need handling?

HL7 is a long-established messaging standard for exchanging clinical data. FHIR is its modern, API-friendly successor. CCDA is an XML document format for clinical summaries. Hospitals use a mix of all three, plus flat files and APIs, so a common ingestion pipeline is what keeps downstream data consistent.

How is patient identity unified across different hospital systems?

Records from claims, eligibility, pharmacy, EMR and biometrics sources are linked so that one person is recognized as one patient across systems. That consistent identity is what makes a true 360-degree patient view possible in the clinical data warehouse.

How is PHI protected in an integration pipeline?

Protected health information requires secure connectivity, HIPAA-compliant handling and de-identification where appropriate. In this engagement those controls were part of every data exchange, not an afterthought added once data had landed.

Can a platform like this run in the cloud or on-premise?

Yes. The platform was built to be deployable in either environment. In this engagement it ran on an AWS-enabled architecture using S3, Lambda and PostgreSQL.

Onboard faster without rebuilding every time.

One metadata-driven platform, one repeatable model, and a data team free to focus on insight.

Start a conversation Explore Integration Services

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