Our Healthcare Data Services
What is AI-Ready Data
Healthcare Data We Work With
We have direct experience with the data types that make healthcare analytics complex:
EHR and EMR data (Epic, Cerner, Meditech, legacy custom systems)
HL7 and FHIR interoperability feeds
Claims and billing data (837, 835, 270/271 transaction sets)
Lab and pathology results
Pharmacy and medication data
ADT (admit, discharge, transfer) feeds
Clinical quality measures and HEDIS data sets
Operational and financial system data (scheduling, billing, GL)
This list reflects the source systems we have built pipelines from in production healthcare environments.
Our Process
Healthcare data projects follow a consistent structure at Smart Data. The phases are not rigid. They adjust to where your organization is starting from. But the sequence is deliberate.
1–2 weeks • Low commitment • High clarity
We assess your current data environment: source systems, data quality, existing pipelines, and analytics tools in use. We identify the gaps between what you have and what a production analytics environment requires. We deliver a target architecture document and a scoped implementation plan.
Phase 2: Data Pipeline Design and Build
4–8 weeks • Working solution • Measurable outcome
We build the extraction, transformation, and loading pipelines that move healthcare data from source systems into a centralized data environment. This includes data model design, dbt transformation development, quality validation rules, and initial testing against your actual data.
Phase 3: Analytics Layer and Dashboards
2–4 weeks • Working solution • Measurable outcome
We build the reporting layer on top of the data model: Power BI dashboards, report suites, and any self-service analytics configuration your teams need. We connect the BI layer to the governed data model, not directly to source systems.
Phase 4: Ongoing Support and Optimization
Ongoing • Expand what works • Embed into operations
Production data environments require ongoing maintenance as source systems change, data volumes grow, and reporting needs evolve. We offer managed services arrangements for organizations that want a long-term delivery partner rather than a one-time build.
Tech Stack
The technology stack we use for healthcare analytics reflects the current enterprise standard. The same platforms that large health systems and payers use, sized and configured for mid-market organizations.
Most competing healthcare analytics firms specialize in one platform. We build the full stack because healthcare data problems cross platform boundaries.
Azure Data Factory
for pipeline orchestration and ETL across healthcare source systems
Snowflake
for cloud data warehousing with healthcare-grade security configurations
Microsoft Fabric
for unified analytics and interoperability with Microsoft ecosystems
dbt
for data transformation, testing, and documentation of clinical and operational data models
Power BI
for reporting, dashboards, and self-service analytics
Azure Private Link and Microsoft Entra ID
for network security and access governance
What This Looks Like in Practice
A mid-sized Medicaid managed care organization came to us with a data environment built on a combination of legacy SQL Server databases, manual Excel extracts, and a reporting tool that their analytics team spent most of their time maintaining rather than using.
Their immediate need was a claims analysis capability that could support population health management. Their underlying problem was that no one trusted the numbers. Different reports from different teams produced different population counts, and no one could explain why.
We conducted a data quality assessment first. That assessment surfaced significant inconsistencies in how member eligibility, claims adjudication status, and provider attribution were recorded across source systems. The $16 million in recoverable Medicaid revenue that surfaced during that work was a direct result of data quality problems that had been accumulating for years.
We rebuilt the data pipeline on Azure Data Factory, established a Snowflake-based data warehouse with a dbt transformation layer, and connected Power BI for reporting. The analytics team went from spending most of their time maintaining extracts to building analyses. The claims data had a single source of truth.
The AI program they wanted came after that foundation was in place.
