Designing systems that replace coordination with execution

A static PGx report tells a clinician what a patient's genotype means today — but the questions that change prescribing are forward-looking. What happens if we add this antidepressant to that antifungal? Does the current regimen convert a normal metabolizer into a functional poor metabolizer? Answering that by hand, across disconnected references, is slow and inconsistent — and it is exactly where dosing errors hide.

Test the regimen before the patient does.

Clinicians add, remove, or swap drugs in the report and SignalAI re-scores the full regimen in real time — modeling CYP-mediated phenoconversion, drug–drug–gene interactions, and genotype-adjusted exposure against the patient's called diplotypes. Every result is evidence-linked and surfaced as a recommendation, never an order; the medical director reviews and signs, and the clinician owns the prescribing decision.

Inside the report, the simulator lets clinicians:

• Model add, remove, and swap scenarios

• Detect inhibitor-driven phenoconversion instantly

• Flag drug–drug–gene interaction risk

• Surface genotype-aware doses and alternatives

Every scenario is scored against the same curated knowledge base — CPIC and FDA labeling, DPWG guidance, and DailyMed — so two clinicians running the same regimen see the same phenoconversion call and the same ranked alternatives. Recommendations carry their source and evidence level inline, keeping the medical director's review fast and defensible rather than a matter of interpretation.

The simulator lives inside the report your lab already issues under its own brand — no separate tool, no re-keying. Called genotypes flow straight from your pipeline, and the same signals reach the clinician through the report, the FHIR export, and the CDS Hooks surface, so a scenario modeled in the report matches what fires at the point of prescribing.

As medication lists grow more complex, the manual review burden that once scaled with each added drug stays flat — the engine absorbs the combinatorics. Guideline updates re-score prior scenarios automatically, so the answer a clinician gets in six months reflects current evidence without your team re-checking a single case by hand.

The lab ships a report that answers the prescriber's real question — “is this regimen safe for this patient?” — and clinicians make faster, more consistent decisions with the genotype already in the room.