Health Systems
Deliver explainable medication actions directly in clinical workflow.
Continuous-learning medication intelligence
PGxAI unifies interaction risk, genomic response, and condition context with real‑world outcomes (RWE) to deliver explainable therapy actions inside the EHR and continuously improve safely inside the hospital boundary.
Why this matters now
- Polypharmacy and comorbidity make medication response multi‑factorial. Genetics alone is not enough.
- Static CDS rules create alert fatigue and inconsistent actionability.
- Guidelines change slowly. Local patient populations and practices change faster.
- Health systems already generate the data, but lack a governed learning loop to improve decisions.
What you get
Unified response reasoning
Interaction risk, genomic response, and condition context are considered together to produce a single, coherent therapy action.
Explainable actions, not noise
Clear recommendation, confidence, and rationale designed to reduce low‑value interrupts.
Closed-loop RWE improvement
Outcome measurement + evaluation + versioned updates are safely deployed under governance.
Enterprise integration
EHR‑native delivery through standard pathways, phased rollout, and auditability.
High-impact scenarios (examples)
- Polypharmacy risk prioritization (DDI) with patient-specific context
- Anticoagulation and bleeding risk management (DDI + DGI + renal DCI)
- Psychiatry medication response and adverse effect risk (multi-signal)
- Infectious disease dosing and monitoring constraints (DCI + labs + interactions)
- Transplant / immunosuppression monitoring and interactions (DDI + DCI)
Keep this list generic and non-claimy. Do not add numeric outcomes unless validated.
Implementation that scales
- Pilot a targeted set of high-impact workflows
- Expand across service lines with governance
- Continuous optimization via the RWE learning loop
See the learning loop in action
Request access to walk through point‑of‑care therapy actions, explainability, and the RWE governance pipeline.