19.02.2020

CleverHealth Network wants to start new projects – Pfizer latest partner to join the ecosystem

CleverHealth Network develops better care by utilising health and well-being data and the latest digital solutions. New product and service innovations are created in collaboration with the HUS healthcare experts and top businesses. The Ecosystem of digital health care innovations, which was started two years ago, is currently running four collaboration projects and new projects are being prepared.

• The strength of the Ecosystem is particularly in combining universities and businesses. Traditionally, universities are good at innovating but businesses are the ones to put the ideas into practice. But then the Ecosystem gives the opportunity for businesses to take part in the innovation process and the universities get to contemplate how the final product or service works in real life, says the HUS Development Director Visa Honkanen.

By taking part in the CleverHealth Network Ecosystem, Pfizer brings in the skills and knowledge of the innovative pharmaceutical industry and the connections of its international organisation. Pfizer collaborates with various partners in order to utilise health records to enable new breakthroughs in health care. Pfizer supports the realisation of the Finnish health care industry’s research and innovation growth strategy by attracting investments into Finland and by taking Finnish innovations to the international market.

The CleverHealth Network offers an excellent opportunity for creating new world-class solutions based on health care data processing. Pfizer brings their own skills in data processing and health care solution development into the Network.

• We at Pfizer see the CleverHealth Network as a significant investment into the future and greatly appreciate the opportunity to be involved in developing innovative health care solutions for the good of the patients, says Päivi Kerkola, Managing Director at Pfizer Finland.

The Ecosystem project results improve care and create cost savings

There are currently four ongoing development projects at the CleverHealth Network, the largest of which has three sub-projects. All projects develop digital solutions for utilising health data in various ways for well-defined clinical challenges, for both short and long-term medical conditions.

For example, gestational diabetes only inflicts a certain group of people for a limited time, but it may increase the chances of the mother getting type 2 diabetes and increases the health risks of the child. A solution for one stage can therefore have long-term effects. CleverHealth Network’s eMOM GDM project is developing a mobile application that measures and records the glucose levels, physical activity and nutriments of the mother in real time.

• The application helps mothers understand the impact of diet and activity on the glucose levels and therefore the health of a newborn. Additionally, with the help of the artificial intelligence on the application, the values fed into the programme can be examined and the health care personnel can react if needed, says Seppo Heinonen, Director of Gynecology and Obstetrics.

Artificial intelligence makes the doctors’ work easier and enables a better doctor-patient encounter

Preventing gestational diabetes turning later into type 2 diabetes significantly reduces the burden on the individual and the society. Other illnesses, particularly rare diseases, benefit similarly from early diagnosis and rapid treatment.

• According to statistics, six per cent of Finns have a rare disease. Diagnosing these diseases takes a lot of time, which is why the treatment is started relatively late, when it is also more demanding. About 18 per cent of the budget of the Hospital District of Helsinki and Uusimaa is spent on the treatment of rare diseases, says Mikko Seppänen, the Chief Physician and Director of Rare Diseases at HUS.

The eCare for Me project is developing a faster and more efficient treatment for rare diseases, acute leukaemia and home dialysis patients. The artificial intelligence applied in this project is able to combine the analyses of patient paths, compare laboratory and scanning results and control complex causes and effects, which help develop the treatment.

When the doctor’s time is not wasted in browsing databases in order to make a diagnosis, or when the patient discovers the decline of their condition via the application, the health care resources are saved, the efficiency of patient care is increased, and both the health care staff’s work and the patient’s daily life is made easier.