It starts with intake, not interpretation

Clinical labs generate genotypes in very different ways. Results might arrive as a sequencing VCF, a CSV result sheet, an Agena MassARRAY panel, or a genotyping array. Each format encodes the same biology differently, and inconsistent inputs are where most reporting errors begin.

SignalPGx normalizes every input into a single structured format. Whether results come in as VCF, CSV, an Agena MassARRAY export, or array data, the pipeline reconciles the calls, resolves star alleles and phenotypes, and stages a clean, validated genotype that is ready for interpretation.

Interpretation graded against the evidence

A genotype on its own tells a clinician very little. The value lies in the gene-drug interactions it implies, and in how strong the evidence behind each one really is. SignalPGx covers 50 pharmacogenes and roughly 950 medications, reconciled from more than 15 curated sources.

Each implication is graded against CPIC guidelines, FDA pharmacogenomic biomarker labeling, DailyMed prescribing data, and DPWG, so that every interpretation is:

• consistent

• reproducible

• auditable

The result is guidance that stays uniform across patients and analysts, with a clear line back to the guideline that supports each recommendation.

SignalAI assists, it never decides

Volume is the hard part of pharmacogenomics. A single patient can be on a dozen medications, and the interaction space grows quickly. SignalAI helps analysts work through that volume without letting anything slip.

SignalAI surfaces relevant gene-drug interactions, flags potential conflicts, and drafts the supporting evidence for review. It never issues a recommendation on its own. Every flag is a prompt for a clinician, not a replacement for one, and the reviewer stays in control of what the report says.

Nothing ships without a medical-director sign-out

Software can assemble a report, but it cannot take clinical responsibility for it. That accountability belongs to a qualified physician.

Every SignalPGx report is reviewed and signed out by the lab's own medical director before it is released. The workflow fits established CLIA laboratory processes, and final prescribing decisions always stay with the ordering clinician. SignalPGx is not a diagnostic test and does not replace the physician.

A branded report that stays current

Once it is signed out, the interpretation becomes a finished report carrying the lab's own name, logo, and layout, rather than a generic template a patient would not recognize.

Pharmacogenomics does not stand still.

Living Reports automatically re-analyze a patient whenever a guideline changes or their medication list is updated, and any report the change affects is routed back for a fresh medical-director review.

report = interpret(genotype)
sign_out(report)

Delivered where the clinician already works

A report that sits in a portal no one opens has little clinical impact. SignalPGx delivers results directly into the systems clinicians already use.

It speaks the standards modern EHRs already support:

1. FHIR R4 and HL7 ORU result feeds

2. CDS Hooks and SMART on FHIR apps

3. a revocable Patient Passport QR link

The Patient Passport is a revocable QR link to a minimal-PHI patient view that a patient can share and the lab can switch off at any time.

Conclusion

The pipeline does more than format data, it turns a genotype into guidance a clinician can trust.

Labs that treat PGx reporting as an evidence-graded, physician-reviewed pipeline, not a one-off data export, deliver more reliable results, launch faster, and give clinicians pharmacogenomic guidance they can act on with confidence. Most SignalPGx labs go live in five to seven days.