We are told that technology follows a linear progress and that the benefits to our health and lives will follow suit. In the healthcare sector, this narrative promises a causation between an increase in digitalisation and an increase in positive health outcomes, particularly for elderly populations. A narrative that is pertinent considering the growing awareness on the impact of increased life expectancy on health systems across the world. In fact, research indicates that the rate of the global population over 65 is likely to increase from 10% in 2022 to 16% by 2050 (Chen et al, 2023). Others, such as the World Health Organisation (2022) report that whereas in 2020 there were 1 billion people in the world aged 60 years or over, the figure will raise to 2.1 billion by 2050 (WHO, 2022). This article will discuss the emergence of digital health technologies in ageing population care, first by exploring how elderly care has evolved within our societies, then by exploring the risks associated with these new technologies, and finally by highlighting a number of solutions for improvement.
Our societies, and the modes of social production and interaction within them, are constantly evolving and are shaped by economic and cultural circumstances. This includes the design and provision of healthcare services for ageing populations. It is important to contemplate the way that medical care is delivered and how changes can impact populations differently. Human relationships and face-to-face care have been the mediums to deliver medical support for centuries, and our society was also structured in a way that saw older generations supported by immediate family members. In a society where multi-generational households were the norm, regular medical observation or the ability to ensure medicines routines were regularly upheld was rendered easier by the presence of children or grandchildren. In today’s society, where economic pressures and societal norms have evolved, the quality of care for elders has been negatively impacted. In the UK for example, the NHS waiting-list backlog crisis and the severe reduction in the availability of GP appointments, are intertwined with a growing number of elderly people living by themselves or in care homes. The precarity of the situation is exacerbated by the fact that there has been a decrease in the caregiver workforce (Chen et al, 2023) and an increase in the costs of care homes, with many of these boasting poor reputations due to the for-profit-incentive that drives the companies behind the homes (Cabin et al, 2014).
Figure 1: Article from Cabin et al evaluating the cost and quality of care homes, available at: https://www.healthaffairs.org/doi/10.1377/hlthaff.2014.0307
It is within the cracks of the system that digital medical technologies have emerged, promoted by Artificial Intelligence (AI) enthusiasts and med-tech companies as the panacea to the issues proliferating within elderly healthcare provisions. The development of technology is seen by many experts as the undisputed pathway to a better future, and healthcare in particular is seen as the great benefactor of the ‘arc of progress’. Certainly, the appeal for digital technologies is compounded by the fact that costs related to medical care are increasing across the globe. In fact the OECD (2020) reports that countries spend an average of 1.5% of their gross domestic product (GDP) on long-term care services (OECD, 2020). Some countries such as Sweden and the Netherlands are spending close to 3.5%, with the United Kingdom, France, and Germany spending between 2 and 2.5% of GDP.
As a relatively nascent field, digital technologies in the medical field are constantly evolving and new software and hardware appliances are regularly emerging out of the R&D departments of med-tech companies and governments alike. Whereas the benefits of digitalisation are clear, enabling rapid data-gathering and analysis, alongside enabling consistent (and constant) health-monitoring, it is also imperative to critically assess the underlying structures of these technologies. Before attending to this, it is important to briefly outline some of the benefits related to these emerging technologies.
One of these emerging technologies are ‘wearable technologies’, which, according to Chen et al. (2023) can be grouped into four categories: wearable physical sensors, wearable chemical sensors, hybrid and multi-parameter wearable sensing platforms and non-wearable sensors. These ‘wearable’ devices promise to greatly assist the ability of healthcare professionals to overcome economic, space and time pressures by enabling them to gain continuous access to the health status of older adults (Chen, 2023.) Moreover, the ‘wearables’ will support older adults, and their carers, to remotely track their ever-evolving health conditions and ongoing treatments without causing severe disruption to their daily lives. Indeed, devices such as smart watches remotely linked to their healthcare provider will ensure that any emergencies or deterioration can be effectively responded to. As Chen et al. (2023) highlight: [wearable technologies] can generate instantaneous alarms in cases of emergencies, such as stroke, seizure or fall, to allow timely medical interventions. Such tools are also expected to reduce geographical inequalities by providing older adults living in rural areas with improved access to healthcare services.
The rapid proliferation of wearable sensors has occurred amidst an expansion of ‘telehealth’ platforms, which has the potential to turn private homes into ‘smart’ homes. In practice, this means that existing household appliances including kitchens, bathrooms and toilets can be integrated into a large digital system platform as individual data-gathering tools that supporters argue will enable tailored ‘geriatric healthcare needs’ (Chen et al., 2023). This will include the monitoring and data-sharing of a patient’s movements, consumption and the result of this consumption, via analysis of urine and faeces. In other words: Such efforts will lead to smart homes, with an extended surveillance and communication system, that will help older residents live healthily and independently in their own familiar environment (Chen et al., 2023).
Familiar accessories will also become integrated in this ‘health-conscious’ digitalisation of households, where home devices such as the Amazon Echo or Google Home will be able to provide “continuous monitoring and help to detect emergency situations (for example, falls), providing personalised medication reminders and alerts as well as social and cognitive stimulation” (Chen et al, 2023). Supporters of this new technology, argue that with the advent of ‘telemonitoring’, older adults will be able to “ stay safely and comfortably in their home setting, under constant medical supervision through video-based services” (Chen et al., 2023).
This proliferation of digital healthcare will without doubt enable to provide a continuous and longitudinal assessment to patients that would otherwise be impossible, whilst also alleviating pressure on healthcare professionals operating in over-stretched and underfunded systems. Nonetheless, novel technologies induce novel threats, and it is vital for the sector to be aware and gain an understanding of how new processes can have nefarious impacts for patients, particularly those in ageing populations. Shedding a light on these new threats is an essential pillar of ensuring our healthcare systems are providing the support that patients need. Below is a quick overview of some of the most pertinent threats.
Emerging technologies based on algorithms and data run the risk of reifying or solidifying existing biases that have a negative impact on patient outcomes. Van Kolfschooten (2023) explores the concept of ‘ageism’, which refers “to biases, stereotypes, negative attitudes, and discrimination toward older people based upon chronological age” (Van Kolfschooten, 2023). This pre-existing bias has the potential to turn into ‘digital ageism’ which refers to the aged-related biases that may be enmeshed in the algorithms and technologies that power the digital technologies of the modern healthcare system.
This concern for ‘ageism’, as highlighted by Van Kolfschooten (2023), has been echoed by the World Health Organisation (WHO), who published a report in 2021 that warned about:
"The increasing practice of ageism in healthcare in general, and in medical AI systems in particular. Ageism persists especially across healthcare settings, where older adults are commonly stereotyped as physically weak, incompetent, dependent, incapable of autonomous decision-making, or indispensable".
Concerns regarding pre-existing biases in the healthcare system are exacerbated by the danger of incorporating these existing practices of discrimination into opaque AI systems whose outputs will, rather than alleviate, only help to reinforce contemporary health inequalities. As argued by Van Kolfschooten (2023), “unconscious (or implicit) age-related biases are widely displayed in both individual behaviours (e.g., by health professionals) and in systematic barriers (e.g., in the design of healthcare systems)”.
With this in mind, the unchecked proliferation of digital medical technologies could lead to what Van Kolfschooten (2003) calls the ‘AI Cycle of Health Inequality’. According to this model, biases and discrimination can be reinforced by AI systems in three stages: (i) data, (ii) modelling, and (iii) application stage. This model provides a useful visualisation of how easily existing health care inequalities can be integrated in digital technologies, dispelling the objectivity myth of pure mathematical calculations.
Figure 2: Article from Kolfschooten et al evaluating the intersection of health inequity and digital ageism https://pubmed.ncbi.nlm.nih.gov/38075950/
So far, we have explored the dangers inherent to the production of digital technologies, but these novel accessories themselves can also lead to novel syndromes. This is a topic extensively explored by Straw et al. (2023), who have coined the term ‘biotech syndromes’ to give a formulaic expression to the phenomena of “illnesses that arise at the intersection of human physiology and digital technology”. As explored in their paper, digital technology is ‘redefining our expression of symptoms, the observable signs of pathology, and the range of possible diseases that may occur” (Straw et al., 2023). Indeed, the risk of these technologies to induce adverse effects are numerous: Psychiatrists are responding to unusual psychological sequelae resulting from digital technologies (e.g., digital hoarding), ophthalmologists are observing advanced rates of myopia related to screen-use, and neurologists are witnessing new neuropsychiatric manifestations from errors in implanted Deep Brain Stimulators (DBS).
The route to consecrating health disparities and towards ever increasing ‘biotech syndromes’ is not inevitable. However, without the appropriate concern and diligence, it is the likely destination. To ensure that the ‘arc of progress’ is one in which all members of society, particularly those currently experiencing healthcare inequalities, we need to ensure that healthcare professionals “have the research knowledge, clinical training or investigations required to provide all the answers when the technology goes wrong" and that “the patient perspective takes precedent, and that their subjective experience is not treated secondary to device interrogations” (Straw et al., 2023).
It is true that increasing the efficiency of these devices in terms of their security capacity, data-analytic performance, and by upskilling senior citizens in digital technology can help alleviate some of the issues raised in this article. Nonetheless, it will be vital to invest in raising awareness on the inherent biases within algorithmic models and on the associated malfunctions of devices, in both the medical workforce and the general public. Only by both demystifying the impact of progress and acknowledging the existence of human error can we ensure that the advent of digital technology is one that benefits all individuals regardless of socio-economic or demographic circumstance.
References
Cabin, William, et al. "For-profit Medicare home health agencies’ costs appear higher and quality appears lower compared to nonprofit agencies." Health Affairs 33.8 (2014): 1460-1465.
Chen, C., Ding, S. & Wang, J. Digital health for aging populations. Nat Med 29, 1623–1630 (2023). https://doi.org/10.1038/s41591-023-02391-8 OECD, Spending on long-term care, 2020, available at: https://www.oecd.org/health/ Straw, I., Rees, G. & Nachev, P. 21st century medicine and emerging biotechnological syndromes: a cross-disciplinary systematic review of novel patient presentations in the age of technology. BMC Digit Health 1, 41 (2023). https://doi.org/10.1186/s44247-023-00044-x
Van Kolfschooten H. The AI cycle of health inequity and digital ageism: mitigating biases through the EU regulatory framework on medical devices. J Law Biosci. 2023 Dec 7;10(2):lsad031. doi: 10.1093/jlb/lsad031. PMID: 38075950; PMCID: PMC10709664.
World Health Organisation, 2022, report, Ageing and Health. Available at: https://www.who.int/news-room/fact-sheets/detail/ageing-and-health
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