Going live is a milestone, not a finish line. In the weeks after launch, data-quality problems that did not surface during testing start to appear: a medication list that did not fully migrate, allergies that lost their codes, duplicate patient records. A deliberate verification effort catches these issues while they are still cheap to fix. Here is how to approach it.
Why post-go-live verification matters
Migration and configuration are never perfect. Sampling during the project catches obvious problems, but volume and real-world use reveal the rest. Poor data quality is not just an inconvenience, it undermines clinical decision support, reporting, billing, and ultimately patient safety. The goal is to find issues systematically rather than discovering them one frustrated clinician at a time.
Start with patient identity
The most damaging data problem is identity error: duplicate records for one patient, or two patients merged into one. Run duplicate-detection reports early and reconcile aggressively. Accurate patient matching is foundational, everything else downstream depends on the right data being attached to the right person.
Verify the clinical core
Focus first on the data with the highest safety stakes.
- Allergies: Present, coded, and actually triggering interaction checks
- Medications: Active lists intact and mapped to standard terminology
- Problem lists: Carried over and coded (SNOMED CT / ICD-10)
- Immunizations: Complete and reporting to the registry where required
- Documents: Legible and attached to the correct patient and encounter
Check that coded data is usable
Free-text that should be coded, or codes that did not map cleanly, will quietly break decision support and reporting. Spot-check that medications, problems, and allergies are discrete and standardized rather than sitting as unstructured text. The point of structured data is that the system can act on it; verify that it can.
A practical verification checklist
- Run duplicate and merge reports; reconcile identities
- Confirm record counts against the source system
- Audit allergies, medications, and problems on a sample of charts
- Verify documents are attached correctly and readable
- Test that decision support fires on the migrated data
- Confirm key reports and registry submissions produce expected results
Listen to the front lines
Clinicians and staff find data problems faster than any report. Set up an easy channel for them to flag issues, a missing result, a chart that looks wrong, a medication that should be there, and triage what comes in. Patterns in their reports point straight at systemic problems worth fixing.
Keep the legacy system available
During verification you will need to compare against the source of truth. Maintain read-only access to the legacy system through this period, and well beyond it for record-retention obligations, so you can resolve discrepancies and recover anything that did not come across cleanly.
Prioritize by risk
You cannot verify every field on every chart, so triage by clinical risk. Allergies, active medications, and problem lists carry the highest safety stakes and deserve the most thorough checking. Historical documents and inactive data matter less in the first weeks and can be verified on a sampling basis or as issues arise. Spending your limited verification effort where an error would do the most harm is far more effective than trying to check everything shallowly.
Treat it as a defined phase
The temptation after go-live is to declare victory and move on, but data-quality verification deserves to be a named phase of the project with an owner, a checklist, and a deadline. Without that structure, verification becomes "someone will catch it," which is how problems persist for months. Schedule the work, assign it, and track findings to resolution. A short, deliberate verification phase in the weeks after launch is one of the highest-return investments a practice can make, because every error caught early is an error that never reaches a clinical decision or a claim.