Solutions · Pharmacology
A multimodal clinical model that brings together everything known about a patient — genes, history, laboratory results, imaging and current medicines — to help clinicians choose the right treatment.
Prescribing decisions depend on more information than any clinician can hold in mind at once: comorbidities, previous reactions, laboratory trends, imaging findings, the patient’s genetic profile and a constantly changing drug evidence base. Adverse drug reactions and harmful interactions remain a significant, often preventable cause of harm.
Our model combines five types of data — genetics, medical history, laboratory results, imaging and drug databases — into a single view of the patient, so recommendations reflect the whole clinical picture.
For a given diagnosis, the system suggests suitable medicines, doses and treatment plans, with the reasoning and evidence behind each suggestion visible to the clinician.
A separate model runs in parallel to check every proposed plan for drug–drug, drug–gene and drug–condition interactions. Keeping it independent means the safety check is never skipped, even under load.
The model is further trained on local clinical data, so it reflects medicines available in Moldova, national treatment protocols and real outcomes.
Read the evidence: Pharmacogenomics in clinical practice.
FAQ
No. It is clinical decision support that works alongside existing prescribing systems. Prescriptions are issued by clinicians through the normal channels.
The model works with whatever data is available. Genetic information makes recommendations more precise when it exists, but is not required.
Further reading
A 2023 Lancet trial showed that a 12-gene pharmacogenomic panel cut clinically relevant adverse drug reactions by about 30%. Here is what the evidence shows, how Estonia returns PGx results to its population, and what implementation takes.
Four European countries have built national genomics programmes at scale, each with a different goal. Comparing what they sequenced, how they linked it to health records and how they returned results gives Moldova a practical blueprint for screening 1% of its population.
Population-level risk prediction has saved lives when it was validated, calibrated and tied to a clear response. It has also misled health systems when it was not. Here is what the evidence shows.
Talk to our team about specification, supply, deployment and support — from a single hospital department to a national programme.