2021 Team


Emergency rooms are hectic places were all kinds of patients come in to find some relief from what is worrying them. Some of them walk in with a simple stomach discomfort, while others need specialized medical attention. The issue comes when those patients with low-risk symptoms or conditions make inadequate use of the ER, causing it to saturate and depriving those who need fast medical attention from getting it.

This is extremally common in pediatric patients, since their parents don ́t hesitate in bringing them into the ER instead of booking an appointment with the General Practitioner when a minimum symptom arises . Some studies show that in an investigation done in 5 hospital in Madrid, 40% of the patients were cataloged as "frequenters" meaning that they visit the ER from 2–9 times a year. Another relevant piece if information from this study is that the frequenter patient tends to visit the ER in the afternoon shift for an acute illness of low complexity for which they have not previously consulted their health center.

This is an important issue to consider, because there is a lack of global resources in the framework of primary care with special emphasis on the shortage of paediatricians. Solving this issue will help stop the ER from collapsing, therefore having medical staff give better and faster care to those who really need it in an emergency.

The proposed idea is innovative in multiple ways. On the one hand its innovative form, a pacifier. Most babies are used to use pacifiers, so it will not be a problem to use it. Mainly during the nights when parents want to have their children extra-controlled. The second fact is the number of variables that it is capable of measuring through its tiny sensors: temperature, respiratory rate, oxygen saturation, glucose, dehydration, heart rate and ph. Measuring up to 4 variables more than the other systems. The pacifier is connected to a mobile application in which, through the data collected and stored, the system create patterns due to machine learning and detect specific anomalies for each user, having 100% personalized control of the baby.

Team Lead
Carmen Arquero Domínguez

Universidad Europea de Madrid

Lead Mentors
Shivang Dave
Camila Maciel de Oliveira
Germán Gonzalez Serrano