Our Technology

Our Innovation

Sets Us Apart

Our innovative, machine learning based clinical informatics system allows us to deliver industry leading products and services to patients, physicians, and hospitals.

The Navya Difference

At the heart of Navya’s clinical informatics system is the Navya Ontology, an innovative, standardized way of storing the relationships between different pieces of clinical information. Navya stores clinical trial data, international guidelines, and previously treated patients’ treatment pathways, decisions, and outcomes. By storing information in an intelligent way, the Navya Ontology allows the Evidence and Experience engines to make inferences and replicate expert oncologists’ intuition when recommending a course of treatment.

Evidence Engine

Navya’s Evidence Engine enables a comprehensive and dynamic search for published clinical evidence relevant to a particular patient’s case. Because of our focus on oncology solutions, our database offers a highly structured index of papers relevant to treatment assessments. Navya’s machine learning algorithms enable treatment applicability analyses for each query to rank the applicability of different treatments to a patient’s case.

Keyword searches for medical information or on PubMed/MEDLINE are time consuming, and don’t provide patient-specific results. Each paper must be read to fully understand its results and applicability to the patient, if any. Navya’s experience engine saves physicians critical time by connecting them to the information they need.

Experience Engine

Navya’s Experience Engine allows the clinical informatics system to make treatment decisions for patients for whom there are no matching clinical trials. The Experience Engine indexes thousands of previously treated patients’ treatment pathways, decisions, and outcomes into the Navya ontology. It models the decision for a patient on the cluster of patients that have similar diagnoses and have already received treatment. The Experience Engine allows Navya to make inferences and predict tumor board decisions for patients with complex or unique cases not represented adequately in literature.