What it is
The EHR Migration No-Data-Loss Checklist is a clinical-data-integrity playbook for moving patient charts from a legacy EHR into a new one without silently losing the information that keeps patients safe. Migration is where EHR projects fail quietly: medications drop during the transfer, allergies fail to map to the new system's code set, and discrete lab values collapse into flat PDF attachments that no longer trend on a flowsheet. The checklist protects against exactly these failures by walking the migration from extraction through validation and legacy archival.
The document's central distinction is between discrete migration and document migration. Discrete migration moves structured, coded data — problem lists, medication lists, allergies, lab results with their values and units — so that the new EHR can act on them, trend them, run decision support against them, and report quality measures from them. Document migration moves rendered documents and scanned attachments as files. Most failed migrations happen because the team assumed discrete data would carry over and instead received PDFs, so the checklist makes you decide, item by item, what must be discrete versus what can remain a document.
Structured around migration phases and a validation-and-downtime-safeguards section, the checklist insists on reconciling source-to-destination record counts, verifying allergy, medication, and problem lists on a sample of high-risk patients, confirming lab values trend correctly on flowsheets rather than appearing as attachments, and ensuring scanned documents link to the correct patient with type and date intact. It is the difference between a migration you can attest to and one you discover problems with months later in the exam room.
What it's used for
Teams use this checklist to govern an EHR migration end to end, from the moment they decide what to move through the post-cutover validation that lets clinicians trust the new chart. It is most valuable when treated as a gate at each phase rather than a one-time read.
- ✓ Deciding what to migrate and how — drawing the line between discrete coded data that must move structured and historical documents that can move as archived files.
- ✓ Mapping code systems between the old and new EHR so allergies, medications, and problems land in the right fields with the right terminology bindings (RxNorm, SNOMED CT, ICD-10) instead of breaking.
- ✓ Reconciling source-to-destination record counts at each phase and forcing every discrepancy to be explained rather than waved through.
- ✓ Validating high-risk clinical data — allergy, medication, and problem lists — on a targeted patient sample before clinicians rely on the new system.
- ✓ Confirming that lab values trend correctly on flowsheets in the new EHR rather than appearing as flat attachments that lose their discrete value.
- ✓ Verifying that scanned documents link to the correct patient with the right document type and date so the chart stays usable.
- ✓ Planning legacy-system archival and read-only access so historical data remains reachable after cutover without keeping the old system fully live.
Who uses it
An EHR migration touches data engineering, clinical safety, and project governance at once, so the checklist is used by a cross-functional team that spans IT and the clinic floor.
Context & good to know
The reason migrations fail quietly rather than loudly is that the new EHR almost always looks fine on day one — the screens render, the demo patients load, and clinicians start charting. The damage surfaces later, one patient at a time, when a clinician discovers an allergy that didn't map, a medication that dropped, or a lab result that won't trend because it came over as a PDF. This is why the checklist is built around validation on a sample of high-risk patients and around confirming that discrete data actually behaves as discrete data, not just that records exist in the new system.
Code-system mapping is the technical heart of a no-data-loss migration. Allergies, medications, and problems are stored against terminologies — RxNorm for drugs, SNOMED CT for problems, ICD-10 for diagnoses, LOINC for labs — and when the source and destination EHRs use different versions or local dictionaries, unmapped codes either silently drop or land in a free-text limbo where decision support can't see them. A migration from a smaller ambulatory EHR like Kareo or AdvancedEHR into a larger enterprise system such as Epic, or a move in the opposite direction, frequently exposes these mismatches, which is why the checklist forces field-by-field mapping with agreed code sets on both ends.
Validation and downtime safeguards close the loop between the technical migration and clinical reality. Source-to-destination record counts must reconcile, and any gap must be explained, not assumed benign. Beyond counts, the checklist pushes you to trace specific high-risk records through the system to confirm lab values trend on flowsheets and scanned documents attach to the correct patient. It also addresses what happens during the cutover window itself — read-only access to the legacy system for history and a plan for any data captured during downtime — so that no chart is unreachable while the switch is underway.