How to Integrate Genetic Test Orders With Epic and Cerner Without Creating Duplicate Orders

How Do Labs Integrate Genetic Test Orders With Epic or Cerner?

Clinical genomics labs integrate genetic test ordering with Epic or Cerner by connecting a governed test catalogue, EHR order-entry workflow, structured requisition and document collection, LIS or LIMS intake, and result-delivery process through HL7 and FHIR-based interfaces.

The integration should do more than transmit an order. It should validate required information, identify duplicate or conflicting orders, route exceptions to the right staff member, preserve an auditable data trail, and return the final report to the EHR in a format clinicians can use.

This matters because genomic testing involves more contextual information than many routine lab orders. A test order may require clinical information that determines which assay is appropriate, whether a specimen is usable, what report should be produced, and which internal workflow should process the case.

Why Standard Lab Interfaces Break Down in Genomics

A basic lab interface often assumes that an order contains a standard set of data: patient demographics, provider details, specimen information, and a discrete test code.

Genetic and genomic testing workflows are more complex.

A single test can have different versions, gene content, methodologies, specimen requirements, turnaround expectations, clinical indications, and documentation requirements. A hereditary cancer panel, pharmacogenomics test, rare-disease exome, tumor-normal sequencing test, or reproductive-health assay may each need a different order workflow.

The result is equally complex. It may include a PDF report, variant-level findings, an interpretation, recommendations, addenda, and potentially revised interpretations over time.

Many genomic programs still return results as unstructured documents. This makes it difficult for clinicians to search, reuse, or apply the data in decision support and downstream care workflows. The PennChart Genomics Initiative describes the value of moving beyond standalone PDF reports toward discrete, computable genomic data connected to the EHR record.

The goal is not to make every genetic result fully structured on day one. The goal is to build an order-to-report workflow where the lab knows:

• What test was actually ordered
• Why it was ordered
• Which required information was received
• What exception or correction occurred
• Which version of the test was performed
• Which report and structured data elements were returned to the EHR

The Five Capabilities a Genomics Lab Needs

NonStop's genomics solutions cover clinical and bioinformatics workflows, including LIMS interoperability and structured requisition intake. Its Clinical Genomics Platform is designed around test order management, sample tracking, reporting, and EHR integration.

1. A governed genetic test catalogue

A test catalogue should be more than a website page or a static menu in an EHR.

It should act as the operational source of truth for what the lab can perform and what each test requires. Every orderable test needs a controlled definition that remains consistent across the EHR, requisition workflow, LIS, reporting environment, and billing-facing data processes.

A governed catalogue can include:

• Test name and internal test identifier
• Test version and effective date
• Intended use and clinical indication
• Gene list or panel configuration where relevant
• Accepted specimen types and collection rules
• Required clinical fields and documents
• Consent requirements where applicable
• Turnaround-time expectations
• Report template or reporting pathway
• Downstream system mappings
• Test owner and change-approval process

Without this governance, the same test can appear under different names in Epic, Cerner, an intake form, the LIS, and a report template. That creates avoidable confusion, especially when a test changes or a new version is introduced.

2. Required-data validation at the point of order

The best time to find a missing clinical detail is before the order reaches laboratory operations.

A genetic-test ordering workflow should be able to identify missing or invalid information at the point where the clinician, clinic staff member, or ordering portal user can still correct it. If the lab waits until sample accessioning, the result is often a delayed sample, multiple follow-ups, and fragmented documentation.

Validation logic may check for:

• Patient identifiers and demographics
• Ordering-provider information
• Test selection and test version
• Diagnosis or clinical indication
• Specimen and collection details
• Required attachments or clinical notes
• Consent information, if required by the lab's workflow
• Conflicting or invalid field values
• Missing signatures or incomplete requisition sections

SmartReq is designed to support structured requisition intake, extraction, validation, and audit logging so that missing information can be identified before it creates downstream workflow or billing rework.

The objective is not to reject every imperfect order automatically. It is to create a visible workflow: accept valid orders, flag incomplete ones, and route each exception to a clear owner.

3. Duplicate-order detection

Duplicate orders are not always identical. They can appear as:

• The same test ordered twice through different channels
• An EHR order and a faxed requisition for the same patient
• A reordered test that uses an updated test name
• Two related panels ordered before the lab has reviewed the first
• An order re-entered after a clinic assumes an interface message failed
• A sample received without a visible corresponding order

A duplicate-order rule should not treat every repeat test as an error. Some retesting is clinically appropriate. Instead, it should give lab staff the context needed to evaluate the order:

• Is there an active order for the same patient?
• Was the same test completed recently?
• Is this a new test version, a reflex order, or a replacement specimen?
• Does the new order have a different clinical indication?
• Has the clinic been notified of the potentially duplicate request?

This is where order management becomes a laboratory-operations capability, not merely a message-routing exercise.

4. Reliable EHR-to-LIS data mapping

An integration can only be reliable if the lab knows which system owns each data element.

The specific architecture will vary by lab. What matters is that data ownership is deliberate.

A robust Epic or Cerner integration also needs rules for what happens when a message fails, data is missing, a patient record cannot be matched, or a test selected in the EHR is no longer available. Without those controls, staff end up correcting records manually in multiple systems, and no one can be certain which version is correct.

NonStop builds clinical genomics platforms and integrations spanning order intake, EHR connectivity, sample tracking, bioinformatics workflows, variant interpretation, and reporting.

5. Results that return to the correct clinical workflow

The integration should complete the loop.

A final report needs to return to the correct patient chart, encounter, ordering provider, and clinical context. Ideally, the workflow also makes it easy to distinguish preliminary, amended, and final reports.

For genomics programs that need more than a document attachment, structured results can support searchable data, clinical decision support, quality reporting, research workflows, and future reanalysis. PennChart Genomics Initiative shows how discrete genomic data integrated with the EHR can support operational and clinical use cases that are difficult to achieve with unstructured PDFs alone.

Not every lab needs the same level of structured-result implementation immediately. But every lab needs clear rules for:

• Report status and version
• Result delivery confirmations
• Amendments and addenda
• Failed delivery messages
• Patient and provider matching
• Preservation of the link between the result, original order, and performed test

A Practical Epic or Cerner Integration Workflow

A reliable implementation usually follows seven steps.

A consistent starting point for genomic orders can make testing easier to navigate for clinicians and help programs manage integrated and nonintegrated tests through a standardized workflow. ACCC's genomic-data EHR integration example describes this type of implementation approach.

Build vs. Buy: What Is the Right Approach?

The right approach depends on the complexity of the lab's test menu, integration landscape, internal IT capacity, and workflow maturity.

For many genomics labs, the practical option is a configurable foundation plus custom engineering. NonStop's Forward Deployed Engineers model combines reusable genomics-platform components, including SmartReq, Intergenix, StrixFlow, Varion, GENVAR, and ReportStudio, with implementation work tailored to the lab's existing systems and workflow requirements.

Epic and Cerner Integration Checklist for Genetic Labs

Before starting an integration project, ask:

• Do we have one approved source of truth for our test catalogue?
• Can clinicians select the correct test without relying on vague names or free-text instructions?
• Does each test enforce the clinical, provider, specimen, and document data the lab requires?
• Can we detect potential duplicate orders before sample accessioning?
• Are patient, provider, test, and specimen identifiers consistently mapped between the EHR and LIS?
• Do incomplete or invalid orders enter a visible, owned exception workflow?
• Can staff trace a final report to the original order and associated documents?
• Do we know what happens when an HL7 or FHIR message fails?
• Are report amendments, addenda, and corrected results handled consistently?
• Who owns future changes to test definitions, mappings, and workflow rules?

If several answers are unclear, the lab does not simply need "an Epic integration" or "a Cerner interface." It needs a defined genomic order-management operating model.

Frequently Asked Questions