Source to Concept Mapping
Mapping local codes to standard concepts, which is the substantive work and determines whether your data compares with other institutions.
Custom Software
Hire OMOP Engineers
OMOP engineers convert clinical data into the OMOP Common Data Model so it can support research and analysis alongside data from other institutions. They handle source-to-concept mapping, vocabulary work, ETL into the model, and the quality assessment that determines whether converted data supports the studies built on it.
The model’s value is that analysis written once runs across institutions. That only holds if mapping is done properly, and mapping is where the effort concentrates. Poorly mapped data conforms structurally and produces study results that do not compare with anyone else’s. Our hire dedicated developers hub covers adjacent roles.
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What OMOP Engineers Build
Work spans vocabulary mapping, ETL construction, and quality assessment against the model’s conventions. The work below reflects that, drawing on extraction practices in our healthcare integration services.
ETL Into the Model
Building transformation from source systems into the model’s tables with the conventions the model specifies rather than approximations of them.
Vocabulary Management
Loading and maintaining the vocabulary with version control, since concept mappings change and analysis depends on knowing which version was used.
Data Quality Assessment
Running the quality checks the community has developed, since structural conformance does not establish that the data supports analysis.
Incremental Refresh Design
Building ongoing refresh that captures amendments, since research databases become stale and full reconversion is impractical at scale.
Study Support and Query Assistance
Supporting researchers using the converted data, since questions about what mapped where determine whether study results are interpretable.
Model and Research Context This Role Requires
Conversion is a research data activity with its own conventions and community standards. Understanding what the model represents determines whether converted data is usable. The context below spans the healthcare work you assign.
01
Mapping Quality Determines Comparability
Structural conformance is straightforward. Whether your mapped data compares with another institution’s depends entirely on mapping decisions.
02
Unmapped Source Codes Disappear
Local codes without concept mappings are lost or dropped to non-standard concepts. Analysis then misses conditions your patients actually had.
03
The Model Does Not Represent Everything
Some source data has no model representation. Deciding what is dropped, extended, or stored elsewhere is a documented choice rather than an omission.
04
Vocabulary Versions Affect Results
Concept mappings change between vocabulary releases. Study reproducibility depends on recording which version was used.
05
Quality Assessment Is Community Standard
The community has developed quality checks. Running them is expected practice rather than optional, and results indicate whether data supports analysis.
06
Conversion Serves Research, Not Operations
The model suits observational research. It is not an operational data store, and using it as one produces a system unsuited to either purpose.
Technical Skills This Work Requires
The differentiating skills are vocabulary work and mapping judgment rather than ETL tooling. The competencies below reflect that, with verification consistent with our quality assurance approach.
Vocabulary and Concept Mapping
Working with the standardized vocabularies to map local codes, including handling for codes with no clean standard equivalent.
Model Convention Implementation
Building ETL that follows the model’s conventions for eras, visit construction, and derived tables rather than approximating them.
Source Data Profiling
Examining what source data contains before mapping, since documented meanings and actual content diverge in every conversion.
Quality Check Execution and Interpretation
Running community quality tools and interpreting results, since failures indicate mapping problems rather than only structural issues.
Incremental Refresh Engineering
Building ongoing conversion capturing amendments and new data, since research databases require currency without full reconversion.
Documentation of Mapping Decisions
Recording what was mapped where and what was dropped, since researchers need that to interpret results and other institutions need it to compare.
How We Evaluate OMOP Engineers
The distinguishing question is what they could not map. Engineers reporting complete mapping either had unusually clean sources or dropped codes without documenting it. Our assessment centers on mapping rigor and quality assessment. Our delivery process includes review points where you can reassess fit.
Unmapped Code Handling
We ask what they could not map and what happened to it. Engineers reporting complete mapping likely dropped codes without documenting the loss.
Quality Check Results
We ask what quality assessment revealed. Engineers who did not run community checks have conversions whose analytical usability is unestablished.
Convention Adherence
We ask how they built derived tables and eras. Engineers approximating conventions produced data that does not compare with other institutions.
Vocabulary Version Management
We ask how vocabulary versions were tracked. Studies run against unrecorded versions cannot be reproduced or compared reliably.
Refresh Design
We ask how the database stayed current. Engineers performing full reconversion each time built processes that stop being run.
Verified Conversion Experience
We describe which conversions each engineer performed and at what scale. We do not claim research credentials for engineers who lack them.
Engagement Options for Conversion Work
Engagements should assess source data before scoping, since mapping effort depends on how much local coding exists. Structures below reflect that, and our engagement models accommodate project or ongoing arrangements.
Source Profiling and Mapping Assessment
Examining source coding to estimate mapping effort, since institutions with heavy local coding face substantially more work than those using standards.
A Single Engineer for Conversion
Suits converting a defined source scope with vocabulary mapping, ETL, quality assessment, and documentation.
Engineer With Clinical Informatics Input
Mapping decisions encode clinical judgment. Engagements including informatics produce mappings that reflect what codes actually meant locally.
Augmenting Your Research Data Team
Where you own the conversion, staff augmentation adds capacity within your existing mapping conventions and quality standards.
Full Team for Research Data Programs
A dedicated healthcare development team suits programs spanning extraction, conversion, quality, and the analytical environment researchers use.
Fixed-Scope Conversion Delivery
Where source scope is defined, a fixed-scope build delivers conversion with mapping documentation, quality results, and refresh capability.
Tell Us How Much Local Coding You Have
Share your source systems and how much uses local rather than standard codes. That determines mapping effort more than data volume does.
Data Handling, Mapping Integrity, and Boundaries
Conversion moves clinical data into research environments. We build to HIPAA-aligned practices where HIPAA applies; software cannot be HIPAA certified. Research use requires appropriate authorization and review, which belongs to your research governance rather than to engineering.
01
Research Authorization Precedes Conversion
Converting clinical data for research use requires appropriate institutional authorization and review, confirmed before engineering proceeds.
02
Mapping Decisions Documented
What mapped where and what was dropped is recorded, since researchers cannot interpret results without knowing what the data represents.
03
Unmapped Data Reported, Not Hidden
Codes without standard equivalents are reported rather than silently dropped, since missing conditions produce studies that understate prevalence.
04
Vocabulary Version Recorded
Each conversion records which vocabulary version was used, since concept mappings change and reproducibility depends on that record.
05
Sensitive Data Handling
Behavioral health and similar data requires additional restriction in research environments. We built CHIPSS, a behavioral health system, where such controls were foundational.
06
Conversions We Would Not Deliver
We would not deliver conversions with undocumented mapping loss, unrun quality assessment, or approximated conventions presented as model-conformant.
Cost to Hire OMOP Engineers and Convert
Cost tracks local coding prevalence and source complexity rather than data volume. Institutions using mostly standard codes convert faster than those with extensive local vocabularies. We publish no figures on conversion timelines, because those depend on your source coding.
MVP or Single Module
$40,000 to $80,000
Conversion of a bounded source scope with vocabulary mapping, ETL, quality assessment, documentation, and initial refresh capability.
Full Platform Build
$80,000 to $200,000
Full conversion across source systems with comprehensive mapping, incremental refresh, quality infrastructure, and researcher support tooling.
Enterprise Deployment
Starting at $200,000
Multi-site conversion with source variation, coordinated mapping, governance documentation, and analytical environment integration.
Discovery Phase Scoping
Discovery is paid and time-boxed. It produces a source coding profile, mapping effort estimate, quality expectations, and an itemized fixed-scope estimate.
Cost Drivers to Expect
Local coding prevalence, source system count, data volume and history depth, mapping review requirements, refresh frequency, and quality remediation scope.
Ongoing Support Costs
Vocabularies update and sources change. Budget for refresh operation, vocabulary version migration, mapping maintenance, and quality reassessment.
Third-party licensing, cloud infrastructure, data subscriptions, and hardware are separate from engineering cost and itemised clearly.
Why Convert With Taction
Two questions matter. Whether mapping loss is documented, and whether quality assessment is run. Taction Software has built healthcare software since 2013, more than twelve years, with over 200 healthcare projects delivered and ISO 27001 certification. Leadership brings more than twenty years of personal experience in the field, which is separate from company age.
We Built the Systems Data Comes From
We built Voyant Health, an EHR platform, which means we understand how local coding accumulates and what source fields actually contain.
Sensitive Data Handling Experience
We built CHIPSS, a behavioral health system, where research use of such data required restrictions general conversion does not address.
Experience Under Regulatory Registration
We built Revive Ease and PainKare, both FDA-registered applications. That work informs how we document transformation decisions.
We Document What Did Not Map
Unmapped codes are reported rather than silently dropped, since researchers interpreting results need to know what is missing.
We Run Quality Assessment
Community quality checks are executed and results reported, since structural conformance does not establish analytical usability.
We Will Say Mapping Effort Is Larger Than Expected
Institutions with heavy local coding face substantial mapping work. We state that from source profiling rather than after the engagement begins.
FAQs
Frequently Asked Questions
We profile your source coding to estimate mapping effort, confirm research authorization, then present engineers with conversion experience for approval.
Bounded conversion runs $40,000 to $80,000, full conversion $80,000 to $200,000, and multi-site programs start at $200,000. Infrastructure and vocabulary licensing are itemized separately.
Our delivery history includes the Voyant Health EHR platform, the CHIPSS behavioral health system, and the FDA-registered applications Revive Ease and PainKare, within more than 200 healthcare projects delivered since 2013.
Because structural conversion is straightforward while deciding what each local code means as a standard concept requires clinical judgment and determines comparability.
They are reported and handled according to documented decisions rather than silently dropped, since missing conditions produce studies that understate prevalence.
ETL moves data between systems generally. Conversion targets a specific research model where vocabulary mapping and community conventions determine usability.
Share your source systems, how much uses local rather than standard codes, your research authorization status, your refresh needs, and the engagement model you have in mind. We will profile sources before estimating. We do not promise instant matching or guaranteed availability.