“The major challenge for us in AI is, as much as possible, to free up time for healthcare professionals so that they can focus on their patients and the tasks that require their care expertise. Our platform combines historical and real-time data so that the built-in decision support systems can detect disease symptoms and alert staff. That way, they do not need to keep an eye on everything themselves. Instead, the get digital tools that help them in their work. This increases the quality of care and reduces the stress on the staff.
When we develop the decision support systems, it is a challenge that health data is so heterogeneous. This means that it takes time to clean, annotate and combine data from different sources. It is often complicated to develop predictive models for different types of diagnoses, so we work together with clinical experts and researchers in the field. The ethical factors and data security are also influencing factors since patient integrity must always be preserved across the entire process.
An additional aspect is how we work with the design of the actual user interfaces. When, where and how should you inform the user if the system estimates there is a certain percentage of risk for a certain diagnosis? These are important issues for which we constantly have to find a balance between different things such as patient safety, risk of ‘information overload’ for the staff and prioritization of healthcare resources.“
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