The CleverHealth Network ecosystem is buzzing with activity as the ongoing six co-development projects aim to develop new treatments and solutions to address clearly defined clinical challenges. The product and service innovations under development will use extensive and high-quality health care data of the Helsinki University Hospital (HUS). The projects are characterized by close-knit, multidisciplinary cooperation between HUS clinicians, research institutes and leading health technology and ICT companies.
Recent results of the ongoing projects were presented at the virtual Result Highlights event on the 15th of May 2020. The event was targeted at the members of the CleverHealth Network and facilitated by Spinverse. CleverHealth Network has taken the brave challenge to develop new world-class healthcare solutions for critical medical problems widely utilising the health and wellbeing data. Spinverse expert team facilitates the intensive and highly multi-disciplinary joint R&D work and boosts the work from well-defined research problems towards business with real impact.
”The current projects are doing pioneering joint development work by top clinicians, data scientists and experts from many companies, putting data to work for better healthcare solutions. In addition to good results, key learnings from these first projects are important takeaways for the preparation of new co-innovation projects,” says Markku Heino, Principal Consultant and Ecosystem Leader at Spinverse. He continues: “The exceptional times due to Covid-19 have naturally affected the practical work during this spring, but also highlighted the importance of these new digital solutions, in particular related to remote care. On the other hand, we have had several active virtual events like this one, which have showed the real cooperation spirit and commitment of the ecosystem partners.”
The Remote monitoring of gestational diabetes project aims to improve the treatment and monitoring of gestational diabetes by developing a mobile application for measuring the mother’s glucose levels, physical activity, nutrition, pulse and daily weight and storing it in the cloud in real time. This helps the mothers to understand how these affect the blood glucose levels and weight gain, and thus the course of pregnancy and health of the new-born baby.
“Development of the mobile application for pregnant women has progressed well, and this also applies to the user interface for healthcare professionals. Recently, we have been focusing on the seamless transfer of data and presenting information in a meaningful way. This work utilizes the broad-based expertise of the entire project network,” says Ville Väärälä, Business Development Director at Fujitsu.
The significance of brain disorders and their treatment will increase as the population ages all around the world. The Head area imaging analytics project develops a tool for physicians that aims for more exact cerebral hemorrhage diagnostics and better care for patients. An AI-based algorithm to provide extreme accuracy in the detection of cerebral hemorrages in the subarachnoid space has now been developed in the first phase of the project.
The eCare for Me project comprises of three research themes focused on early disease detection (proof-of-concept: Rare diseases), optimal diagnostics and treatment (Acute leukemia), and facilitation of early and advanced home care (Home dialysis). The project on Diagnostics for rare diseases has now completed the first forecasting models based on AI to find and detect rare diseases and is progressing to the prospective phase, where the prediction models are tested in practice.
The ecosystem research on Treatment of acute leukemia makes use of machine learning and neural networks to automate the diagnostics for malignant diseases and to identify individual treatments for the patients. It utilizes clinical data reserves from the HUS Data Lake and in-depth profiling data on basic disease mechanisms collected from academic research. A comprehensive, standardised and GDPR-compatible PoC modelling environment has already been established in the HUS Data Lake and it is extensively used by the researchers and partners.
The Development of home dialysis project in turn aims to develop an application that will help pre and home dialysis patients to monitor their health on a daily basis and to provide AI based models for dialysis prescriptions and identification of risks. Together, this offers a better quality of life and more efficient care of the dialysis patients. The project is currently making preparations for a digital research environment to monitor the research protocol, and all patient and personnel instructions are ready, waiting for the moment to start the practical research phase when it is again safe for research patients and staff in terms of the COVID-19 situation.
The Child with diabetes project aims to increase the safety of type 1 diabetic patients by creating an open source API solution to facilitate their daily life. The solution is using only permission-based data, given by the child or their family. The project has developed a secured and consent-based flow of data from the home to the patient information system so that the doctor and patient/family can see the same information at the same time. According to the project participants, the cooperation between partners and patients’ families has been extremely productive and rewarding.
Photo: Antti Kirves
Read more on the CleverHealth project