Skip to main content
Loading navigation
Back to challenges

Lab Automation Product Catalog Standardizer with FHIR and OMOP Models

Integrating new lab products across clinical networks requires mapping hardware and reagent SKUs into standardized healthcare data schemas. You will build an automated mapping engine using Python, FHIR resources, and OMOP CDM to normalize vendor device payloads, achieving 95% mapping accuracy.

Data ScienceHosted by Vera
Sign in free to start

What you are building

Construct an OMOP/FHIR standardization data pipeline in Python to normalize laboratory automation equipment and reagent catalogs.

How work is evaluated

Evaluation

Evaluates FHIR resource generation validity and OMOP concept mapping accuracy.

Datasets

Public datasets, plus the private datasets you have access to.

How submissions are scored

Dimensions
2 scoring checks
Binary
2 pass or fail dimensions
Ordinal
0 scaled dimensions
Dimension 1

fhir_validity_check

Ensure output conforms to FHIR R4 Device schema

Binary check

This dimension contributes its full weight only when the submission satisfies the requirement. Partial credit is not awarded.

Dimension 2

omop_concept_accuracy

Accuracy rate of matched OMOP concept IDs • target: 0.95 • range: 0-1

Binary check

This dimension contributes its full weight only when the submission satisfies the requirement. Partial credit is not awarded.

Learning goals

  • Map proprietary lab equipment and assay product specs to FHIR Device and SpecimenDefinition resources

  • Standardize lab product terminology using OMOP CDM concept identifiers (LOINC, SNOMED)

  • Validate interoperability schemas against HL7 FHIR validation specifications

Resources

Dataset notes

Lab product catalog dataset containing 300 vendor equipment and reagent entries.

Challenge setup

Evaluates FHIR resource generation validity and OMOP concept mapping accuracy.

Challenge input

JSON object with vendor, product_name, and catalog_number

FHIR Framework

Industry standard for health data exchange.

OMOP CDM

Standardized observational data model.

Evaluated output

JSON FHIR Device resource object with valid resourceType and codeableConcept

Evaluation checks
  • Ensure output conforms to FHIR R4 Device schema
  • Accuracy rate of matched OMOP concept IDs • target: 0.95 • range: 0-1
Configured evaluation evidence
  • Benchmark: InteropData-Bench
  • Omop Concept Accuracy target: 0.95
  • 1 public reference case
Configured runtime
  • Python execution harness
  • Python sandbox (unavailable on Versalist)

Shows the first task, up to three tools, and up to three evidence items.

View technical recipe

Configured tools

Action Space
  • Zed · Required
  • Durable · Optional
  • Mistral Nemo · Optional

Evaluation contract

  • fhir_validity_check · Weight 1
  • omop_concept_accuracy · Weight 1

Metrics

  • omop_concept_accuracy · Target 0.95

Benchmarks

  • InteropData-Bench
  • general

Reference cases

  • 1 public reference case

Workflow tasks

  1. Task 1

    Input: JSON object with vendor, product_name, and catalog_number

    Output: JSON FHIR Device resource object with valid resourceType and codeableConcept

    Execution harness: Python

Evaluation tools

  • FHIR Framework · Healthcare Interoperability Schema
  • OMOP CDM · Common Data Model

Recipe freeze marker

No recipe freeze time is recorded.

Frequently Asked Questions about Lab Automation Product Catalog Standardizer with FHIR and OMOP Models