OMOP Common Data Model
The shared data standard that allows clinical data from different EHR platforms to be queried consistently across sites. Each contributing health system maintains its own OMOP instance — no patient data leaves the site.
Population health data infrastructure · Minneapolis, Minnesota
Building the systems that make underserved populations visible in health data.
Public health agencies, health system research consortia, and community organizations work with us to build the technical infrastructure needed to generate timely, accurate health estimates for people experiencing homelessness, incarceration, and housing instability — populations that existing surveillance systems routinely miss.
This site includes a working implementation of the federated OMOP pipeline used by the Minnesota Electronic Health Record Consortium — here as evidence of what we build, not as the primary message.
11
MNEHRC member health systems
94%
of Minnesota's population covered
70+
health conditions tracked in HTAC
The data gap this work addresses
Health condition prevalence data in the United States is primarily derived from survey methods that carry a 2–3 year lag and lack the geographic resolution needed by local health departments. More critically, these systems systematically exclude the populations carrying the highest health burdens — people who have recently experienced homelessness or incarceration are largely absent from the national data infrastructure that drives resource allocation and policy.
Electronic health records, linked to administrative data from homeless services systems, corrections agencies, Medicaid enrollment files, and immunization registries, offer a path toward timely, geographically granular estimates for these groups. Building that path requires federated data infrastructure — common data models, privacy-preserving record linkage, validated condition codesets, and governed output layers — that most public health agencies and community organizations cannot build independently. That infrastructure is what this practice designs and implements.
What We Do
Technical Services for Population Health Data Infrastructure
We work with public health agencies, health systems, and research organizations to build the data infrastructure needed to study and serve populations that are routinely underrepresented in existing health data systems — including people experiencing homelessness, incarceration, and housing instability.
Work spans the full implementation stack — from initial OMOP CDM configuration and governance design through federated query execution, administrative data linkage, and publication of suppressed, stratified prevalence estimates to data products that program and policy staff can use.
OMOP Data Engineering
Codeset development, ETL pipelines, and data quality processes for health systems operating on the OMOP common data model.
Federated Network Support
Governance frameworks and analytic infrastructure for distributed health data networks where patient-level data stays on-site.
Administrative Data Linkage
Privacy-preserving pipelines connecting EHR data to HMIS, corrections, Medicaid, and immunization records.
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How the work is done
01
Assess
Understand the organization's current data environment, governance constraints, and analytic priorities before scoping technical work.
02
Design
Specify the federated architecture, data use agreement requirements, codeset definitions, and suppression policy appropriate to the jurisdiction.
03
Implement
Build the OMOP infrastructure, PPRL pipeline, administrative data linkage, and federated query execution layer.
04
Publish
Translate suppressed, stratified estimates into governed data products — dashboards, file extracts, and documentation for oversight bodies.
The technical foundation
This practice is built on the OMOP common data model and the federated architecture developed by the Minnesota Electronic Health Record Consortium — a statewide collaboration covering 94% of Minnesota's population across 11 health systems. Using open, internationally adopted standards means implementations are auditable, replicable across jurisdictions, and not dependent on proprietary vendor infrastructure.
Federated Pipeline
Privacy-preserving record linkage, administrative data enrichment, federated cohort queries, and small-cell suppression — the full architecture from OMOP instances at contributing sites to governed aggregate outputs.
Governed Data Products
Suppressed, stratified prevalence estimates published through dashboards, structured file extracts, and documented APIs — designed for use by program staff and oversight bodies, not just analysts.
Implementation
End-to-end pipeline design and deployment — from OMOP CDM configuration and vocabulary mapping at contributing sites through privacy-preserving record linkage, federated query execution, and aggregate results published to governed outputs. Includes handoff documentation so technical and oversight staff are working from a consistent implementation record.
Governance
Data use agreement alignment, scientific review pathway design, suppression and stratification policy, Tribal and FQHC inclusion planning, and documentation structured for oversight bodies and institutional review. Governance is a design input, not a compliance afterthought.
Interoperability
OMOP CDM as the shared data standard across contributing sites, with OHDSI tooling for cohort definition, quality assessment, and federated execution. Service boundaries between contributor-held data, internal aggregate tables, and externally published outputs are explicit in the architecture.
Start a conversation
Whether you are a public health agency assessing a community health needs assessment data gap, a research consortium evaluating OMOP infrastructure, or an organization preparing a grant application with a data infrastructure component — use the contact form to discuss the specifics.