Stay strong or how AI can help in elderly care

In this and two upcoming posts we are going to talk about elderly care.

What does it have to do with next generation technology? Well, as aging is one of the most pressing issues that globally affect all societies, the power of data-driven AI methods will be pretty much needed.

In this post our goal is to show the complexity and importance of elderly care, while the next post is about some of the application areas where modern Data Science and Artificial Intelligence can be the secret weapon. In the last post we are getting a bit more technical and talk about some of the smart ideas and technologies that allow for efficient use of AI in the future.

One of the challenges facing the modern world is the problem of the aging population. And it is a misconception that it only concerns developed countries. It is a problem faced by most countries in the world.

The elderly population is defined as people aged 65 and over. [1]

The following graph is a projection of the aging trends for various geographic regions:

Source: ENSZ (2017) World Population Prospects: the 2017 Revision.

Interesting statistics on the aging population are provided by the WHO [2]. Let’s take a look at some selected predictions for next years:

Between the years 2015 and 2050, the proportion of the world’s population over 60 years will increase from 12% to 22% (The number of elderly people will almost double).
By 2020, the number of the elderly (aged 60 years and older) will be greater than the number of children younger than 5 years.
In 2050, 80% of older people will be living in low- and middle-income countries.

So we all need to think about how to alleviate the burden on the healthcare system and our society due to the likely demographic shift.

To see the future now, let us just take a look at Japan as it is home to the oldest society. According to [4] these are the not too bright projections for Japan:

2020: Half of female population will be more than 50 years old. (checked)
2021: A lot of separated employees because of nursing care for their family. (checked)
2024: 33% of the population will be more than 65 years old.
2025: Population-shrinking will start even in Tokyo.
2026: More than 7 million people with dementia.
2027: Blood for transfusion will be scarce.
2030: Big department stores, banks, and retirement homes will close their branches in smaller cities.
2035: 33% of male population and 20% of the female population will live and die single.
2039: Serious lack of crematory.
2040: Half of provincial governments will disappear.

Not surprisingly, Japan is one of the pioneers regarding ICT and smart solutions for health and elderly care. As an example, due to cultural conflicts [5], they have been the first to use service robots in hospitals and other institutes, instead of employing foreign workforces.

Source: https://foreignpolicy.com/2017/03/01/japan-prefers-robot-bears-to-foreign-nurses/

Before talking about how AI will save the world – ok, it is a bit of exaggeration- let us see what are the most fundamental consequences and what changes in infrastructure, policy and services can be foreseen.

The ever growing rate of those who might need extra help in various activities will shift focus in policy making and social priorities. For one, the elderly will remain valuable consumers, yet their needs are different from those who are still active (changing infrastructure and business environment). For two, as the proportion of the active population decreases, personal care cannot be provided for everyone (shortage in skilled workforce). For three, services – in particular healthcare – will be more expensive as the active payers’ proportion decreases.

The missing labor power is a general problem and better process optimization and automation will mitigate the issue to some extent. For skilled labor, AI with learning capabilities can provide some leverage. However, for elderly care, there are very specific requirements that present a real challenge for the strategy makers for tomorrow’s technology.

So let us see what are the most frequent issues for elderly care.

Source: https://www.ncoa.org/article/the-top-10-most-common-chronic-conditions-in-older-adults

Aging affects both our physical and mental well being. According to the WHO report [2] the most common health conditions of the body associated with aging include: hearing loss, cataracts and refractive errors, back and neck pain, osteoarthritis, chronic obstructive pulmonary disease, diabetes. The most outstanding mental or psychological issues are loneliness, exclusion, depression and dementia. These conditions often interact and worsen each other. Feeling of exclusion resulting from the low level of physical activity will deepen depression. The positive side is that if we can compensate for a particular loss (like helping in locomotion) we can actually induce improvement in many other conditions.

However, due to many unknown factors (like climate change or breakthroughs in medicine), some of these conditions may become less important, while new ones emerge.

Now let us see what specific requirements must be met if we want to successfully create new AI based technologies:

Probably the most important requirement is to make humane solutions. Human caregivers should provide more psychological support (communication, empathy, availability/presence), while technology should be used for the rest (diagnosis, monitoring, etc, [3]) -> Human-centered solutions
The majority of the people over 65 may have more than one of the conditions listed above. So general solutions just fail. -> Highly flexible, customizable solutions
Next generation technologies are increasingly difficult to adopt for not only the subjects, but also for the caregivers -> User acceptance, special user support
Centralized health care institutes have limited capacity or are not available to everyone -> decentralized, distributed, localized solutions
If hardware is involved, maintenance can also be an issue -> low cost, yet robust and reliable solutions are needed
People as well as health conditions are going to evolve -> highly adaptive and evolving solutions are needed

Clearly, AI is much needed when it comes to adaptive solutions, flexibility and learning. Also, we talk about large scale challenges where we need to understand the general as well as the unique factors that define each and every case. And here comes Data Science to our rescue!

You can read about some of our exciting projects in the following posts.

References

[1] ‘OECD Data – Demography – Elderly population’. Accessed: Jun. 28, 2022. [Online]. Available: http://data.oecd.org/pop/elderly-population.htm

[2] ‘WHO Fact Sheet – Aging and Health’. [Online]. Available: https://www.who.int/news-room/fact-sheets/detail/ageing-and-health

[3] ‘[EC] Market study on telemedicine’. 2018. [Online]. Available: https://health.ec.europa.eu/system/files/2019-08/2018_provision_marketstudy_telemedicine_en_0.pdf

[4] Kawai, M. , Mirai no Nenpyo (=Future Chronologic Table), Tokyo: Kodansha. 2017

[5] https://foreignpolicy.com/2017/03/01/japan-prefers-robot-bears-to-foreign-nurses/