There are many approaches to improve radiology operations, but traditional methods require trial-and-error experimentation that can be costly and disruptive. The digital twin is a model in software that continuously assimiliates new data to capture the entirety of your department’s physical and operational system – including scanners, processes, and people – so that you have a complete view of your operations. Predict the impact of interventions and finally replace a reactive approach with an engineering mindset to radiology operations.
A radiology department is a complex physical system that is continuously evolving. – just like planes, forests, and even the human body – with many interdependencies. Collecting and interpreting the massive amount of siloed, heterogeneous data manually is an impossible task. Instead, a digital twin continuously assimilates new data and updates the model to evolve with the physical system.
“A digital twin is a set of coupled computational models that evolve over time to persistently represent the structure, behavior, and context of a unique physical asset” -The Oden Insitute at UT Austin.
Beyond unlocking data, we are building a simulation engine to evaluate the impact of interventions (e.g., staffing change, operating hour changes, scanner purchase, …) at scale, in-silico, without the need for physical interventions. The world is waking up to the idea that many problems can be solved by unlocking data and applying a proactive engineering mindset instead of the old reactive approaches based on anecdotes and gut feeling. Quantivly aims to be the pioneer in bringing these tools to radiology operations to solve the urgent need of increased and sustained access to medical imaging