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position determination, the movement detection, the
embedded triage guidance together with the user in-
terface of the bracelet as well as the central situation
overview still have to be designed.
The integration of machine learning to forecast
possible triage status changes is an interesting re-
search topic. In both prehospital and clinical settings
data based triage models show promising results. A
prehospital triage forecasting model based on time
series data can only be developed after it was deter-
mined which vital data can be measured continuously
during a prehospital triage.
The effectiveness of an electronic triage system
can only be validated with emergency drills or even
better with real emergency situations. Especially
our assumption that first responders without spe-
cific triage training are enabled to perform a semi-
automated prehospital triage has to be verified this
way. Additionally, in the same manner, the validity
of the proposed blood pressure measurement method
must be confirmed.
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