A network of internet-enabled devices is far better than a single IT device. Combined with artificial intelligence (AI) and an IT system with carefully selected, well integrated elements, a unique network helps people with dementia improve their lives.
In Surrey, UK, more than 6000 people have a formal diagnosis of dementia, although it is estimated that around 16,801 people have the condition. Four hundred of these patients have a better chance of living a quality life in their homes. NHS runs an IT project in the region called Technology Integrated Health Management (TIHM) for dementia and has four goals we totally identify with:
- to improve the lives of people with dementia;
- to support people with dementia to stay safe and well in their own homes;
- to reduce hospital and care home admissions; and
- to relieve the stress on carers.
The houses of people participating in this project is filled with sensors, wearables, monitors and other devices, which gathered in one internet of things (IoT) network, monitors their health at home.
In this project NHS partnered up with many institutions; one of them is the University of Surrey. Scientists from the university developed technology that can identify and help reduce the occurrence of urinary tract infections (UTIs), the most common cause of hospital visits among older people. IT specialists from the university’s Centre for Vision, Speech and Signal Processing (CVSSP) used machine learning algorithms to identify early symptoms of urinary tract infections, based on the signals already found. The goal is to determine the health problems of people living with dementia before they require a hospital visit – thus helping clinicians and carers.
This machine learning system gathers data from the sensors installed in the patient’s home. The algorithms were able to detect a rise in body temperature and nighttime activity in a patient, successfully leading to a diagnosis of urinary tract infection. Data streamed from devices in the patient’s home, including monitoring sensors and devices tracking vital signs, were analyzed by machine learning techniques and health problems were flagged on a digital dashboard to be followed up by the clinical team.
Early results are promising. Scientists are hoping that this trial will be as successful as their first program: that trial showed a significant statistical reduction in neuropsychiatric symptoms in the 400 participants involved in the project, and as a direct result the program was awarded an extra £1 million from NHS England.
What convinced me, that this program is on the right track, was the relaxed faces of relatives and people living with dementia involved in the program. You don’t have to take my word for it, see it for yourself here. You can read more about the program here.
This pilot has a lot in common with in our recent Horizon2020 project, ICT4Life. The integrated platform of ICT4Life was connecting patients and carers, as well as providing an integrated package to set up the home environment with smart monitoring technology to detect early symptoms of changes in the patient’s condition, non-standard behavior or any other warning signs. Early intervention can prevent unnecessary hospitalization or deterioration of the patient’s health, while accumulated data can also assist healthcare professionals to research and analyze them further, in order to improve future treatment.
Technology can be used for monitoring in a non-intrusive and reliable way, also helping family members to focus on the human aspects of care. Connecting this facility with the concept of integrated care, dementia patients may have a safer alternative if they prefer to stay in their home.
Author: Eva Lajko