Industry · Healthcare
Healthcare data you can trust.
We do the hard work between systems: data integration, population-health analytics, quality reporting and the operational workflows around them. We work around your clinical platforms, not instead of them.
Where healthcare data breaks down.
The clinical systems usually work. The trouble is in the data and workflows between them.
Quality, finance and operations each have a different number for the same measure.
Analysts spend days matching patients, providers and claims by hand before every report.
ACO, MIPS and HEDIS-related submissions turn into a fire drill every period.
Patients and providers that don't line up across clinical, claims and attribution data.
Every new report or cohort means another extract and another spreadsheet.
It's hard to show where a number came from and how it was calculated.
What we do in healthcare.
Engineering, data and operations work around the systems you already run.
Quality and performance reporting
- ACO quality and performance reporting
- MIPS reporting support
- HEDIS-related measure environments
- Validation of measure results against source data
Population-health analytics
- Attribution and patient cohorts
- Risk and utilization views
- Care-gap and outreach dashboards
- Reporting warehouses and marts
Data integration and quality
- Clinical, claims, attribution and operational data
- Pipelines with patient and provider matching
- Data quality rules and automated reconciliation
- Lineage from source to every reported number
Revenue cycle and operations
- Revenue cycle data and reporting
- Workflow automation for operational teams
- Healthcare application development and enhancements
- Ongoing support and continuous improvement
How we fix a reporting problem.
The same four steps, whether it's one measure set or the whole reporting estate.
- TraceWeeks 1 to 2
We trace each reported number back to its sources and document every transformation and manual step.
Outcome: A source-to-report map and a list of where numbers diverge
- FixNext
Data quality rules, matching logic and reconciliation checks, agreed with your clinical and quality teams.
Outcome: Numbers that reconcile, or documented reasons where they differ
- AutomateThen
Pipelines and refresh schedules replace manual extracts, with checks that flag problems before reports go out.
Outcome: Reports on time without the fire drill
- OperateOngoing
Monitoring, specification changes from one period to the next, and new reporting needs, handled as a service.
Outcome: Reporting kept current as specifications change
Privacy and security first.
Healthcare work is scoped around your systems, data access and obligations before anything starts.
We don't claim certifications or compliance status we haven't independently earned. We'll walk your security and compliance teams through our controls and complete your vendor assessment.
- PHI scoped up front. We confirm whether protected health information is involved, what our role is, and which safeguards apply, before work begins
- Data location agreed. Where data may be stored and who may access it, including whether offshore access is allowed, is agreed in writing and built into the environment
- Minimum necessary data. Least-privilege access for named individuals only
- De-identified or synthetic data for development and testing wherever possible
- Audit trails of access and changes
AI in healthcare
AI where governance allows. Humans where it matters.
We use AI in healthcare only where the data governance supports it and a person stays responsible for the result.
- Answering questions over governed reporting data
- Drafting summaries for human review
- Suggesting care gaps and data issues to investigate
- Speeding up data mapping and reconciliation work
- Human review of anything clinical or financial
- PHI kept inside agreed boundaries
- Evaluations before release
- Audit trails of what the AI produced
Track record.
Healthcare engineering is built on trust earned over years, not months.
7years
Enterprise partnership · healthcare
An engineering partner to a leading US health IT company since 2019
A dedicated Vanna team inside the client's engineering organization, across platform engineering, integrations, quality and support. Priorities changed; the core team didn't. Reference available on request.
Read the storyQuestions healthcare leaders ask.
Asked by quality, analytics and IT leaders before we start.
Do you replace our EHR or analytics platform?
No. We work around the clinical and analytics platforms you already run, on the data, reporting, integration and workflow layers between them.
Will our data leave the US?
Only if you agree to it. Where data is stored and who can access it, including whether offshore access is allowed, is agreed in writing and built into the environment design. Many engagements use de-identified or synthetic data for development.
Can you work with our existing tools?
Yes. We work with the warehouses, integration tools and BI platforms you already use, rather than asking you to replace them.
How do you handle measure specification changes?
Specification changes are planned as part of the service: we review what's changed, update the logic, and validate results against prior periods before reports go out.
Are you an NCQA-certified measure vendor?
No. We engineer the data pipelines, validation and reporting around your certified measure engine or vendor and your existing submission process.
Can we start small?
Yes. The Data & Reporting Check takes two to three weeks and focuses on the reports causing the most pain.
Start with a Data & Reporting Check.
Two to three weeks, focused on the reports causing the most pain. You keep the findings either way.
- A source-to-report map for your priority measures
- Where numbers diverge, and why
- Data quality and matching gaps
- Manual steps that can be automated
- A prioritized fix plan