Our data harmonization engine cleans and harmonizes data from image metadata and the Radiology Information System (RIS) to build an ontology that goes beyond DICOM and HL7 to extract concepts that truly describe radiology operations.

Our unified data layer is augmented with Machine Learning to add new query-able descriptors of derived from image data and metadata.

Our unified data layer is fully query-able via SQL and GraphQL so you can ask any questions of your data. Your data is always accessible and can be integrated and exported to other systems via API.

Perform simulations in-silico to predict the impact of interventions. Receive insights via custom alerts from our smart recommendation engine.

Extend our ontology and unlock new research opportunities by adding custom descriptors that are fully query-able.

Use data-driven strategies for capital and operational budget planning; Measure the ROI of investments (e.g. new scanner, new software, increased staff).

Measure and compare the quality, efficiency, and adoption of protocols; similar. Unlock new clinical research by combining clinical and operational data.

Manage protocols and detect deviations. Measure and compare quality, duration, and repeats and slice the data any way you like *by scanner, age, protocol, etc). Unlock new research; Build quality and safety dashboards and reports.

Access real-time protocol information. Measure and compare technologist agility to identify training opportunities and measure the success of clinical applications training.

Data liquidty will fuel the next era of radiology innovation. Curate training, testing, validation data sets and unlock new AI research with our fully query-able and extensible ontology.