Our mission is to increase access to imaging for the population, help radiology leaders achieve operational excellence, and deliver on the promise of patient-centered healthcare. To achieve our mission, we are building the digital twin of radiology operations so you can finally ask questions of your data, measure performance in real-time, and test different operational scenarios in-silico.
Our prediction? Radiology operations will be radically reshaped in the next 5 years.
Hours of scanner time made freed up with Digital Twin optimization
Exams processed with Quantivly harmonization engine (TM)
Medical imaging hardware is expensive – up to $4M per MRI. Yet it’s so difficult to know how these machines perform in the clinical workflow. For example:
– How many exams start on time?
– How much time is wasted between patients? between images?
– How long do imaging protocols truly take?
We call this the Operational Black Hole.
We built a software platform that extracts, cleans, and harmonizes the information from imaging device data and scheduling data, and builds a unified ontology. It is vendor agnostic, fully queryable, and extendable. You can finally replace gut feeling with data and start understanding
– Why is scanner 3 always running behind ?
– Why are some technologists more agile than others ?
– How does imaging duration compares to slot size?
Unlocking data and analytics is just the first step. Beyond a retrospective analysis, our simulation engine will allow you to test different operational scenarios in software, so you can find the right solution for your department – without disrupting your workflow or compromising patient care.
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.
Leaders can be anywhere in an organization. We built Quantivly for all data-driven leaders that want to understand and transform their radiology operations with data, wherever they are.
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.
We work with leading institutions to transform their radiology operations and achieve operational excellence.
The platform was key in reducing sedation by 20%, having an impact not only on the bottom line but also on the patient experience and health“
Rick Robertson, MD
Former Radiologist-in-Chief, Boston Children's Hospital
Being able to construct our own queries is VERY unique compared to other vendors. Along with the ability to parse MRI properly and a (true) vendor agnostic platform. This can be helpful.
Director of Medical Physics
I love the idea of digital simulations
Zhen Wang, MD
Section head, Abdominal imaging
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Javier Villanueva-Meyer, MD
Assistant Professor Vice Chair, Quality and Technology - UCSF
test new row
test new row