Page 79 - Petelin, Ana. 2024. Ed. Zdravje delovno aktivnih in starejših odraslih | Health of the Working-Age and Older Adults. Zbornik prispevkov z recenzijo | Proceedings. Koper: University of Primorska Press

changes call for reforms and awareness-raising on the concept of value-based
healthcare (VHBC) (Etges et al., 2022).
It is thus important to understand where new technology can increase
the efficiency of health services with the aim of saving lives and improving
quality of life (Reddy et al., 2019; Gopal et al, 2019; Chen and Decary, 2020).
Reddy and colleagues (2019) state that AI has made great progress with the de-
velopment of network algorithms called deep neural networks, natural lan-
guage processing, computer vision and robotics. The use of these techniques
has already been actively implemented in healthcare services. It should be
stressed, though that at present AI cannot fully replace employees in the pro-
vision of healthcare services. However, AI can be utilised to support the pre-
diction, detection and management of health conditions (Harwich and Lay-
cock, 2018). The two authors cited above also state that “these applications are
key to reducing the strain on the healthcare system, improving the quality of
care and patient outcomes, while at the same time reducing costs” (Harwich 79
and Laycock, 2018, p. 9).
In order to fully reap the benefits of AI in healthcare, it is vital to be
aware of the main barriers to implementing this technology. These are main-
ly data access issues, data quality and the certification of AI algorithms (Har-
wich and Laycock, 2018). This means collecting large volumes of the right
types of data in the right formats, increasing their quality, and ensuring se-
cure access to them (Gopal, 2019). The recommendations for data collection
state that for most healthcare related AI strategies, what matters are large vol-
umes of data, their digitisation and machine readability, and the quality of
the data, determined by the quality of the extraction, analysis and further
use. It is important that all AI-related data is collected in one place (Chen and the use of artificial intelligence in the field of health for working-age adults and older adults
Decary, 2020).
For the use of AI in healthcare, in addition to providing experience on
the usefulness of the new technology, it is also important to formally educate
staff on AI to help with their basic understanding and reduce fear of the new
technology (He et al, 2019; Abdullah and Fakieh, 2020; Salah-Pico and Yang,
2022; Chen and Decary, 2020). It is key to the adoption of AI as a support for
problem solving and identifying solutions (Nancy, 2019; Ronquillo et al., 2021),
but it cannot replace the poor usability of AI systems, which make tools diffi-
cult to use and place additional burdens on employees (Reddy et al., 2019). The
trust of healthcare staff in the reliability of AI is also enhanced by the transpar-
ency and interpretation of the results provided by the new tools (Veale, 2018).
Data security must be ensured or, in other words, standards should be devel-
oped to assess safety and effectiveness of AI (Shah et al., 2019; He et al, 2019; Pe-
dro, 2020; Salas-Pico and Yang, 2022).
This article briefly describes the mechanisms that enable AI systems to
generate clinically meaningful outcomes; and then presents how AI can be ap-
plied in the field of health of working-age and older adults.