Your Epic data, out of the queue.

Epic holds your most valuable clinical, financial, and operational data. Getting to it means going through Clarity, Caboodle, Chronicles, and CSV exports. Databasin was co-created at Washington University School of Medicine to change that, at AMC scale, in production.

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Where Databasin Was Born
"No commercial solution existed at AMC scale. Epic data pipelines were failing and research operations were at risk. So we built one."
Co-created at Washington University School of Medicine's Institute for Informatics, Data Science & Biostatistics (I2DB)
HIMSS '23·'24·'25
Featured by Microsoft and Databricks
75 +
Connectors including all Epic layers
80 %
Average cost reduction vs. comparable platforms
The Problems We Solve

Epic is not the problem. Getting data out of it shouldn't require a specialist queue.

01
Fragmented data across three distinct environments
Chronicles, Clarity, and Caboodle each have different access patterns, refresh schedules, and schema complexity. Most organizations treat them as one problem and end up maintaining three fragile pipelines.
02
Every analytics request requires an Epic specialist
Clarity schema knowledge is rare, and it stays concentrated in a few people. Researchers wait months for IT capacity. Operational leaders can't act on data they can't access. What blocks the work isn't interest. It's expertise gatekeeping.
03
Pipelines that break when Epic updates
Every schema change, nightly refresh failure, and Clarity version upgrade becomes an incident. Research coordinators get called at 6am. Data gaps bake into reports before anyone notices.
04
PHI governance ruling out standard cloud platforms
HIPAA requirements and institutional governance policies eliminate most commercial platforms before evaluation begins. The answer is always "we'd need to deploy in our own environment." In practice that means it never happens.
05
Metric definitions that differ between departments
Length of stay. Readmission. RVUs. Every AMC has multiple definitions floating across individual reports. When the CFO's number doesn't match the CMO's number, someone built the calculation twice in different places.
06
AI initiatives stalled at the data layer
Clinical AI projects require clean, governed, labeled data. Plenty of organizations can describe their AI use case in detail and still can't get it into production, because the foundation isn't ready: ingestion, governance, semantic layer.
How We Solve Each One

The problem. The Databasin fix.

Area
The Reality at Most AMCs
The Databasin Fix
Data Access Epic data locked in specialist queues
Clarity queries require deep schema knowledge that takes years to develop. Researchers and operational leaders submit tickets and wait months. The bottleneck is structural, not a staffing problem.
Natural language queries against governed gold data mean a researcher can ask "show me sepsis encounters in the last 12 months by unit" and get a governed answer without a Clarity expert or a ticket.
Pipeline Stability Pipelines that break on Epic updates
It takes one schema change, one tenant upgrade, or one Clarity version bump to take the pipeline down. Research coordinators get called at 6am. Data gaps bake into dashboards silently.
Schema versioning at the bronze layer catches upstream changes and logs them before they propagate, so pipelines adapt instead of breaking. Bronze is the raw ingestion layer of Databasin's medallion architecture: bronze for raw data, silver for transformed, gold for governed analytics.
Governance Conflicting metric definitions
One readmission definition in the CFO's dashboard, another in the CMO's report, a third in the research database. Leadership stops trusting data and defaults to gut instinct.
Silver-layer business rules encode institutional definitions centrally. That means one readmission calculation and one LOS formula, applied at transformation time and enforced by the platform rather than by individual analysts.
Compliance PHI governance blocking deployment
Standard cloud platforms require PHI to leave your environment. HIPAA posture and institutional governance policies eliminate most options before evaluation. The result is years of "we're evaluating options."
Private install in your own Azure tenant keeps PHI inside your governance perimeter. You keep your own LLM, your own endpoints, your own security posture. It is HIPAA-ready by architecture, not by compliance policy alone.
Two Buyers. One Platform.

What matters most depends on who you are.

The CRIO and the CFO have different problems. Databasin solves both.

For the CRIO / Research Informatics
Research operations that don't depend on Epic experts
You know the data is in Epic. Getting it into a research-ready format requires a Clarity specialist, an IT queue, and three weeks. Databasin was built at WashU Medicine's I2DB precisely because this bottleneck was killing research operations.
  • Epic connector handles Chronicles, Clarity, and Caboodle through one pipeline — not three
  • Schema changes absorbed at bronze, so research pipelines survive tenant updates
  • REDCap, FHIR, and research databases in the same governed environment as Epic data
  • Natural language queries for research teams who don't know Clarity table names
See how it works
For the VP Finance / CFO
Financial and operational reporting that everyone agrees on
Your finance team builds models from Epic exports, Workday data, and manually reconciled spreadsheets. The CFO's revenue number doesn't match the CMO's. Month-end close requires a team of analysts just to reconcile the discrepancy.
  • Workday and Epic financial data in one governed lake house, with intercompany reconciliation running automatically
  • One definition of charges, payments, and adjustments that the platform enforces so individuals don't have to
  • Board and investor reporting from a single trusted gold mart the platform assembles, instead of a scramble the night before
  • Operational dashboards that leaders actually trust and use
See how it works →
Proven in Production

Live in production. Not a proof of concept.

Databasin runs at WashU Medicine, KU Medical Center, and St. Francis Medical Center today. In each one it is the production data infrastructure for clinical research and operations, not a pilot.

75 +
Connectors including all major Epic layers, Azure, Databricks, and AI APIs
80 %
Average cost reduction vs. comparable lake house platforms
3×
Featured at HIMSS by Microsoft and Databricks in 2023, 2024, and 2025
"
The institutional origin of Databasin isn't a marketing story — it's the reason the product actually works. We built it because we needed it, in an environment where failure had real consequences.
Jake Gower — Co-Founder & CEO, Databasin
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Your Epic data.
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