Laboratory data quality oversight
Operational reporting is unreliable when test codes, units, and status values differ across sources.
The challenge behind the workflow.
Operational reporting is unreliable when test codes, units, and status values differ across sources.
Connect operational and research data with clear purpose, provenance, and access. Help teams prepare reliable evidence for service planning, quality review, research, and administration while keeping clinical judgment with qualified professionals.
Data to bring together
- Laboratory records
- Test reference catalogs
- Quality control metadata
Source systems are examples of the data required. Confirm connector availability, permitted access, refresh timing, and the authoritative owner before implementation.
How Genedata supports the work.
Configure one connected workflow, then reuse its mappings, definitions, and review process as the business grows.
- 01
Connect and prepare
Normalize permitted units and test references while retaining source values.
Build the preparation as a reusable visual workflow or reviewed code, with explicit source mappings and validation checks. - 02
Investigate and validate
Surface missing units, inconsistent status transitions, and unmatched codes.
Keep shared definitions and source versions with the analysis so another team can reproduce the result. - 03
Publish and review
Publish a data-quality queue with responsible laboratory data owners.
Make the output available to the right people with appropriate access, ownership, and review evidence.
A laboratory data quality workspace
Make the next run easier.
Shared reference checks reduce recurring manual cleanup of laboratory extracts.
Start from one approved example. Save the agreed mappings and definitions, publish the reviewed output, and let the next team follow the same evidence instead of rebuilding the preparation.
Measure your own improvement
Unresolved laboratory data mapping exceptions
Record a baseline before the pilot and compare like-for-like work afterward. Results depend on source quality, configuration, and adoption.Review and responsibility
Qualified laboratory professionals validate interpretations and approved transformations.
AI with professional judgment.
Use AI to organize evidence and draft summaries for review. Clinical interpretation, patient care, and research eligibility decisions remain with qualified professionals.
Try the workflow with your team.
Use representative, approved sample data. Agree the expected output and a review owner before extending the workflow to a live process.