RAIR generates new insights using a strongly-typed graph database which enables it to model specific scenarios or healthcare domains based on high-level, logical and object-oriented principles.
Healthcare data is rich in detail and highly interrelated. RAIR structures its real-world data, curated from the data lake, as fully formed networks: these "data graphs" enable us to express the data in ways which closer reflect real-world relationships.
We can also apply state-of-the-art AI and network statistics to interact with the data and to discover hidden patterns and associations which might form the basis for new products, better processes/pathways and improved patient outcomes. Novel new insights, gleaned from the data, may also fuel RAIR's internal and partnered development programmes.
RAIR's graphs already contain hundreds of entities and 10's of millions of relationships and the repository is constantly growing and forming new links.
Our graph database has its own intuitive query language that makes it easy to query millions of de-identified patient records. It also has the flexibility to operate using common ontologies like SNOMED, ICD-10 and LOINC or even those parameters defined by you.
We can also apply a range of statistics and advanced network analytics to these cohorts to uncover patterns and associations or even notify Doctors of the presence of an eligible clinical trial participant in their clinic.
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