Baby FM

Users and doctors are not provided with any often-crucial information, especially in hospital conditions, when early fever detection can save $2,550-5,600 in medications alone and help to fight Hospital Acquired Infections (Oxford Academic Journal) which kill more people than HIV, breast cancer, and auto accidents combined in the US, with over 2M cases and 99K deaths (CDC). HAIs increase healthcare costs by $40 billion yearly in the US (CDC).

Current solutions lack a connection to the doctor, and telemedicine channels can lead to medication mistakes (due to incorrect diagnosis), medication dosing errors (every eight minutes for babies under 6 months old in the US), prolonged illness, and can cost billions of dollars in medical and recovery costs.

• Low level of automation of data collection and big-data analysis
• Unlocking predictive modeling for preventive medicine
• Modernized triage patients-care with AI chatbots (pre-screening symptoms)
• AI support for telehealth consultations and decision support (real-time patient data analyses)

With a shortage of 1 million medical staff across the EU, there is a growing need for efficient and effective CFM solutions.

BABYFM, a sensor-data-AI-based continuous temperature monitoring system, offers alarm, notification, and reporting features for home and hospital environments. As the first of its kind on the market, it helps parents and patients track metabolic response through CFM, and sends analytics to doctors for improved productivity, management, and digitized patient records. 

BABYFM's AI analyzes real-time temperature data, identifies patterns and trends, and alerts users and healthcare professionals of potential health risks. The AI algorithm is trained on temperature readings to detect deviations from normal ranges and alert of potential issues. 

With ML models, BABYFM enhances data collection and analysis, leading to earlier detection of health problems and improved patient care. The system includes a contact sensor, intelligent battery, and data collection via cellular and Bluetooth. Chatbot and telehealth consultation and scheduling is also available. The AI can distinguish viral from bacterial infections based on temperature trends and analyze other vital signs for improved accuracy.

The system is designed for people living in countries with frequent pandemics, and mass infections, elderly people or parents with babies in rural areas, and people with chronic diseases with frequent inflammations that cause an increase in temperature. Baby FM is a low-cost, fit-for-purpose system with data shareable across information systems, and streamlined for data collectors-doctors. The system is designed to be reusable, washable, easy to maintain, and used as a monitoring tool during a certain period of time when people do not have wifi, electricity, or other smart devices. It is secured for each user-patient and could be set up by doctors depending on the desired outcomes. ThermoFly gives accessible insights for health care providers, and it can be used to optimize the performance of primary health care. With the prediction (machine learning) algorithms, it could balance the pressure on frontline health workers and save lives in different pandemics or mass infections. 

Our team was fully engaged during the beginning of the Covid19 pandemic in the Republic of Serbia to invent solutions that will improve the conditions of the health workers at the front. We have developed the solution Doctors for Doctors, for monitoring the physical and mental health of the doctors during the pandemic and offering the right help in critical situations. The project was supported by the Serbian Government and the Ministry of Innovation and technological development of Serbia. At the same time, team members and co-founders of Baby FM Dr Ivan Soldatovic and Vladimir Jeftovic were engaged in the development of the world's first bracelet for covid prediction AVA (Switzerland). Ivan Soldatovic is a consultant for international companies for clinical trial designs (Roche, Phizer, LaRosche Possay, CoreMedic, etc) and a professor at the Faculty of Medicine. Vladimir Jeftovic is a former employee at Amazon, who was engaged in Alexa voice recognition system development. CEO and co-founder Tamara Papic was engaged in business development for several international companies, and experienced in project management, innovative ecosystem development, etc. The team member prof. Dr. Nenad Jovičić was engaged in international and national IoT solution development and design of custom electronics, as well as Milijan Ćelić who is the team lead for software development was engaged in several projects for USAID, and EBRD, creating the solutions which transfer the knowledge from innovative systems to the industry in Bosnia and Hercegovina. Our team is working and creating solutions from the Balkan area (low- and middle-income countries) and understands the challenges which could be applicable in all other areas around the globe. 

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Our main motivation is to become a Solver and help the Global Challenges program.