Healthcare data analytics consulting
Healthcare analytics fails when the consultant treats the EHR like a generic transactional system and the HIPAA boundary like an afterthought. DI Squared builds healthcare data analytics that fit how providers, payers, and life sciences organizations actually work, with encounter detail, payer mix, and quality measures all reconciled to the same source. We bring the architecture, the governance, and the clinical fluency to make the analytics useful.
Snowflake, Databricks, dbt, Fivetran fluent
HQ Atlanta, serving US, Canada, Europe
- We work with hospitals and health systems, payers, and life sciences organizations.
- Common platforms: Snowflake or Databricks for the data layer; Qlik, Power BI, or Tableau for clinician-facing analytics.
- HIPAA-aligned architecture, with PHI segregation patterns built into the warehouse design.
- Discover, Map, Navigate, Adjust framework applied across every engagement.
What healthcare data analytics actually requires
Healthcare data lives across an EHR, ancillary clinical systems, a claims feed, a revenue cycle system, sometimes a separate ambulatory platform, and increasingly a population health or care management tool layered on top. None of these systems were designed to be the analytics platform. Healthcare data analytics consulting, done well, starts by accepting that reality. We build a healthcare-fluent data layer (typically Snowflake or Databricks) that integrates encounter detail, claims, and operational data into a model your clinical, operational, and finance teams can all use, with PHI governed where it belongs. From there, the analytics layer (Qlik, Power BI, or Tableau) serves the audiences that need it, with row-level security and audit logging that hold up under a compliance review.
- Healthcare and life sciences served continuously since 2008
- HIPAA-aligned architecture by design
- Vendor-neutral on EHR; we have integrated Epic, Cerner/Oracle Health, Meditech, athenahealth, and others
Healthcare analytics use cases we deliver
Healthcare analytics is not one program; it is several adjacent ones with different consumers and different cadences. Our engagements typically cover one or more of the following.
Encounter and utilization analytics.
Inpatient, outpatient, ED, and ambulatory volume, case mix, LOS, and readmission trends.
Revenue cycle and payer mix.
Net revenue by service line, payer, and contract; denial trends; days in A/R; underpayment recovery analytics.
Quality and regulatory reporting.
CMS quality programs, HEDIS measures, state-mandated reporting, and accreditation evidence packages.
Clinical operations.
Throughput, capacity, staffing analytics, OR utilization, and care variation reporting.
Population health and value-based care.
Attributed populations, risk stratification, care gap analytics, and contract performance.
Life sciences commercial analytics.
Specialty pharmacy data, HCP and HCO segmentation, field force effectiveness, and patient access analytics where data sharing agreements allow.
Architecture that respects the HIPAA perimeter
A healthcare data platform has to do three things at once: integrate clinical and financial data in a model people will trust, segregate PHI so access can be granted and audited at the row level, and stay performant enough that a clinician will actually open the dashboard between cases. We design healthcare warehouses around a clear PHI boundary, with de-identified marts available to broader audiences and identified data restricted to the people whose role requires it. Where the client uses Snowflake, we lean on row access policies, dynamic data masking, and object tagging tied to the governance catalog. Where Databricks is the platform, we use Unity Catalog with equivalent controls. The point is not the vendor; it is that a clinician analyst and a compliance officer can both see the system and agree that it is appropriate.
Discover, Map, Navigate, Adjust in healthcare
Discover.
We assess the EHR, ancillary clinical systems, the revenue cycle platform, and any existing warehouse or BI footprint. We look at master patient indexing, provider master data, payer and contract reference data, and the governance and security perimeter. We talk to clinical and operational leaders about which analytics they trust today and which they have stopped using.
Map.
We document a healthcare-specific target state: the data domains, the warehouse model, the PHI handling pattern, the BI footprint, the governance approach, and the roadmap that sequences the work. The document is one a CMIO, a CFO, and a CIO can all sign off on.
Navigate.
We partner through delivery: pipeline and warehouse build, semantic model, BI implementation, and rollout to clinical and operational consumers. Where the client has internal data engineering or analytics staff, we work alongside them and transfer ownership deliberately.
Adjust.
As payer contracts shift, quality programs change, and service lines evolve, we help the analytics keep pace. Health systems do not get to stand still, and neither does the data platform.
Healthcare technology stacks we work with
We are vendor-neutral, but fluent in the healthcare and life sciences platforms that matter. Our clients commonly rely on Epic Clarity and Caboodle, Oracle Health (Cerner), Meditech, athenahealth, Veeva, and other clinical, operational, and commercial systems. We integrate these data sources to create a trusted foundation for analytics, reporting, and AI.
A typical engagement may use Snowflake or Databricks as the data platform, dbt for transformation, and Fivetran or client-specific HL7 and FHIR integrations for data ingestion. For governance, we work with platforms such as Collibra, while Qlik, Power BI, and Tableau provide the analytics layer.
We are pragmatic about existing technology investments. If your organization has standardized on Epic and Power BI, Oracle Health and Tableau, or Veeva and Qlik, we build on that foundation rather than recommend starting over. Our focus is maximizing value from the systems you already use while creating a scalable data platform for future initiatives.
Healthcare data analytics consulting: common questions
Q: Can DI Squared work directly with Epic Clarity or Caboodle?
A: Yes. We have integrated Epic-derived data (Clarity, Caboodle, and the Reporting Workbench layer) into Snowflake, Databricks, and on-premises data warehouses for client teams. We respect Epic’s licensing and the contractual constraints around their data; the integration work always runs alongside the client’s Epic relationship, not around it.
Q: Do you handle PHI inside the engagement?
A: Where the engagement requires it and the client’s BAA and security review allow it, our consultants work with PHI under the same controls that govern your internal staff. Many engagements are scoped so that the senior architecture work happens against de-identified samples and the implementation work happens inside your environment under your access controls.
Q: How does healthcare analytics consulting differ from generic BI consulting?
A: The data model is different, the consumers are different, the regulatory perimeter is different, and the operational cadence is different. A clinician will not adopt a dashboard that does not respect how care is actually delivered. A compliance officer will not approve a platform that does not respect PHI boundaries. Healthcare-specific consultants have already absorbed those constraints.
Q: What does a healthcare data analytics engagement typically cost?
A: Engagement size depends on scope, but most strategy engagements run six to ten weeks and most implementation engagements run six to twelve months. We scope each engagement against the client’s actual environment and budget, and we will not quote a price before we have done enough discovery to give a number we will stand behind.
Build healthcare analytics that clinicians and CFOs both trust
Talk with a DI Squared healthcare strategist about your EHR footprint, your reporting backlog, and the analytics roadmap your board has been asking about.