Improve efficiency with digital triage you can trust

Red Robin is the name of the technology included in our medical device registration (MDR). We have developed Red Robin to be exceptionally good at gathering patient history, assessing urgency, and suggesting differential diagnoses. All flows before and after this - such as patient intake and identification solutions, summaries to medical records or referral groups for onward patient navigation - can be integrated into or from other systems. The end-to-end solution incorporating in/out flows and Red Robin are what we refer to as Visiba Triage.

Since Red Robin is a medical device with a predefined "intended use," it is essential to understand where the product starts and ends, especially as it often functions as an integrated part of a larger whole. 

Red Robin uses machine learning, which is a form of AI, to make decisions based on the input information and statistical probability. AI is capable of processing large volumes of data at a much faster speed and with greater consistency than the human brain. The medical core consists of a probability-based network built from scratch by our medical AI team, along with algorithms that can draw clinical conclusions based on this.

The biggest advantage of the probability-based network is that it is much more dynamic than the traditional tree structure. This makes the results more accurate and makes it easier to further improve the results over time. Compared to black-box models, the advantage is that our model is explainable and robust. Because it is not self-learning, bias will not be inadvertently introduced.

The benefit of a probability-based network is that outputs generated by the model, such as differential diagnosis and triage scores, are fully explainable. There is no black box logic. This allows clinicians and engineers to identify and rectify issues swiftly/ effectively. 

Patient safety is our highest priority - which is why we always have a clinician in the loop when using the outcomes of Red Robin to navigate patients to the most appropriate point of care. As you would expect, our clinical product team continually monitor the performance of the model and have clear processes around the use of clinical feedback to further refine it.