Deep Learning is a term that is often associated with the field of artificial intelligence (AI). In the case of Med-Triage, this term took on another meaning. In collaboration with scientists, a triage tool was developed using artificial intelligence, that would determine the urgency of a medical consultation. The AI learned from a pool of several million recorded teleconsultations. Several years of work were required to create a usable tool in this way.

Interestingly (one could also say logically), the humans involved learned far more than the machines. A profound understanding of the problems associated with the combination of telemedicine, medical consultation and digitalization resulted for the medical leaders of the project, deep learning in the field of human field of human intelligence.

The developments of Med-Triage are based on these insights. At its core, a question-and-answer dialogue in the form of digitizable algorithms guides patients to a meaningful emergency triage assessment within a maximum of 10 questions in about 5 minutes.

Also logically, learning in this regard is not complete, which is why both effective quality management and comprehensive PROM (patient related outcome meassurement) remain necessary as an important component of the future product. As long as this learning is not considered complete, physician supervision of med-triage remains equally necessary.