Patient record matching review
Care organizations encounter inconsistent patient identifiers across approved clinical and administrative sources.
The challenge behind the workflow.
Care organizations encounter inconsistent patient identifiers across approved clinical and administrative 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
- Patient registries
- Encounter references
- Identity correction records
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
Standardize approved identifiers while preserving original values and source context.
Build the preparation as a reusable visual workflow or reviewed code, with explicit source mappings and validation checks. - 02
Investigate and validate
Generate potential duplicate and conflicting-record cases for specialist review.
Keep shared definitions and source versions with the analysis so another team can reproduce the result. - 03
Publish and review
Publish a restricted reconciliation queue with a documented correction trail.
Make the output available to the right people with appropriate access, ownership, and review evidence.
A patient identity review queue
Make the next run easier.
Shared matching preparation reduces repetitive manual comparisons across systems.
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
Time resolving patient record discrepancies
Record a baseline before the pilot and compare like-for-like work afterward. Results depend on source quality, configuration, and adoption.Review and responsibility
Authorized specialists confirm identity; do not merge clinical records automatically.
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.