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How AI can offer personalised care to the elderly in their own homes



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Diana Heinrichs, CEO and co-founder of Berlin-based Lindera, has developed the first data science tool for the point of care of elderly patients. Her experience at Microsoft Deutschland on the future of the digital world of work helped her to frame the question – how can older people live longer and independently at home?

Mobility test

We live in a networked society. We always have done. After all, the watering system in Ancient Egypt wasn’t built by the Pharaoh alone but by a network of civil servants. Today we excel in this discipline. We can solve our biggest challenges and conflicts through coordinated networks worldwide. But there’s one exception: the care industry. Here, the entire burden lands on the individual caregiver. He or she is the hero who must fight ageing, loneliness, disease and deliver wellbeing, advice and improved health. Caregivers need to document every single step of their daily work yet there is no data science being used to learn from their knowledge and routines. The elderly care industry has often experienced technical solutions that are a mere ‘copy and paste’ of paper-based processes – with more and more measures to control their work but very limited relief and support for their daily routines. Today, many care services reject new IT solutions because they fear new burdens, quality controls and workloads.

A double whammy

Many of us have moved to the cities, have ‘patchwork’ families, work and travel abroad – thanks to Erasmus and the EU. Today we’re back to recognising the importance of the family. We all want one – but we also want a career, to travel; we want it all. There’s our ageing society on one hand and the new family structures and geographical distances on the other. We all want to care, get involved, be informed – just as long as it doesn’t interfere with our lifestyle too much. At the same time the healthcare industry has seen tremendous changes. Many were told not to apply for an apprenticeship as a caregiver in the 1990s as there weren’t enough training places. The result is that today we see a shocking lack of skilled labour. There simply aren’t enough caregivers available. How can we enhance quality despite these flaws?

More than control and surveillance

In this networked society, we could look for the next Trip Advisor. We could build a knowledge base that looks at the results – the menu in front of you, not the cook in the kitchen. We could gather and connect the wisdom of the crowd. We could use machine learning to optimise the results and make them comparable across different locations. In care, we still fill out forms, document every step and continue political discussions on yet more controlling routines. At IFA, a consumer technology show, you can find plenty of camera systems to monitor seniors and their caregivers. So far, Germany has not seen a data science tool at the point of care that uses cognitive computing for optimisation and integration into the care system.

The age of personalized elder care

Together with our partners from within the industry such as the German Red Cross, we have developed the first data science tool for the point of care. The result is a reliable mobility test for seniors powered by cognitive computing.

Diana Heinrichs

Diana Heinrichs

Every year, falls by the elderly cost more than €2bn in treatment in Germany alone – that’s 7 per cent of all insurance spending for that age group. Imagine if you could know the individual likelihood of why, when and where a person might fall? How many people and how much money could you save? We designed an integrative model combining proven psychological tests with a mathematical analysis of the gait to calculate the individual likelihood of a fall and to then provide tailored recommendations. Our goal is to provide elders something easy to use themselves at home. The test itself is straightforward – it works with a smartphone. With our video app we analyze a senior’s gait as the physical indicator for falls. While it’s easy for the user, it requires complex data analysis in the background. To precisely measure the physical risk factors, we apply skeleton tracking and machine learning.

It’s working well. Insurance companies have licensed the Lindera Tool to better align preventive measures, benchmark diagnosis, secure health services in rural areas, and to reduce costs. For the patient, we can provide a tailored fall prevention plan in plain words that they can easily share with their family, caregiver and doctor.

We want to show how data analytics can connect the whole team around a patient and support their daily work.

And for care services, we measure the effect of assessments and therapies to better allocate resources and save costs. For pharma companies, we want to become a partner that can provide real-world-based evidence to help them market new medications such as antidotes for osteoporosis. Our vision is to provide the first data science tool at the point of care to enhance the quality of care and connect all stakeholders.

About the author

Journalist and editor Kathryn Reilly has worked in consumer, contract and medical writing for more than 20 years.

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