In the Netherlands about 0.85% of the population has an intellectual disability. This means that they have an IQ lower than 70. The life expectancy of people with intellectual disabilities is lower than that of the average population. One of the causes is that people with disabilities do not always receive the care they need, for example because signals are missed.
Within the project Sterker op eigen benen (‘Stronger on your own two feet’) led by Radboud University, PhD student Joep Tummers of the Information Technology (INF) group is building a platform that makes responsible and safe use of data from care and medical systems to identify illness and other problems in a timely manner.
'People with a mental disability often live in care institutions. They have to deal with a multitude of care providers: counsellors for daily care, specialised doctors, dentists, general practitioners. All these care providers report on the care provided and the condition of the client in their own digital systems. All that information is therefore scattered across various information systems that all know their own language and format', says Joep Tummers.
The systems don't communicate with each other, which is a pity, Tummers observes, because all those files are an unprecedented potential source of information to improve care for people with intellectual disabilities - if we could combine that data to discover patterns in them. ‘We know, for example, that the length of the daily reports can be a predictor of the likelihood of incidents. As healthcare providers' daily reports become longer, it often turns out that an aggressive episode is imminent. And changes in eating patterns or people who become introverted can be a signal for illness. It is important that diseases are diagnosed in time. It is well known that people with intellectual disabilities are less likely to come to hospital for oncological care', says Tummers.
Machine learning, artificial intelligence, natural language processing and text mining are key terms in Tummers' work: 'The crux is to move from data to information and then to knowledge. Anonymity and confidentiality of the data are, of course, of the utmost importance', he emphasises. In our group we do a lot in the field of agricultural data. The technologies and methodologies are not very different whether you're talking about people or cucumbers. But when it comes to people's privacy, of course, it's a completely different story from that of cucumbers.'
Big data platform
The ultimate goal of the project is to create a big data platform where healthcare professionals, interested parties, researchers and people with intellectual disabilities can address all kinds of questions related to illness and behaviour of people with intellectual disabilities. These can range from the question of whether there is a link between eating peanut butter and aggression in people with Down's syndrome to the research question of whether electronic patient records allow early detection of breast cancer', says Tummers. Research questions are submitted via the easy-to-read and accessible Web platform Crowdience which was launched at the end of 2019.
Tummers says that there is a need in the healthcare sector for a platform such as this. It proved to be necessary at the time of the Corona lockdown: healthcare institutions wanted to know whether there were more or fewer incidents in institutions during the lockdown as a result of the Coronavirus outbreak. ‘Within two weeks, we were able to provide feedback that the number of incidents during the lockdown initially decreased by 20% and then gradually returned to the old level. On the basis of these results, the doctors and healthcare providers can make an analysis and draw lessons. In this way, we use information technology to help improve the quality of life and care for people with intellectual disabilities.'