Sil Aarts

Maastricht University, Living Lab in Ageing and Long-Term Care


Sil Aarts

Since 2018, I am working as an assistant professor at the Living Lab in Ageing and Long-Term Care AWO-L, part of the Maastricht University. Our Living Lab was established by Maastricht University in 1998. To date, Maastricht University, nine large long-term care organisations, Gilde Intermediate Vocational Training Institute, VISTA College (secondary vocational education) and Zuyd University of Applied Sciences participate in this Living Lab. Our mission is to improve by means of scientific research: (1) quality of life for older people and their families; (2) quality of care; and (3) quality of work for those employed in long-term care (Verbeek et al., 2019)

My research line is primarily focused on the use of data accumulated in long-term care for older adults. With the use of innovative, data-driven explorations we can increase our knowledge on quality of care, quality of life and quality of work in long-term care for older adults.


Questions that interest me and that are related to my research line:

  • Which role can data (and the analyses thereof) play in studying quality: i.e. how can these types of data improve the quality of care, quality of life and quality of work in long-term care for older adults?
  • How can we create a learning environment/network regarding ‘data-informed care’ in our long-term care organisations?
  • How can text-mining support and deepen our understanding on quality of care, quality of life and quality of work in long-term care settings? For example by analysing electronic health records.
  • How can we combine data from various sources (e.g. electronic health records, sensors, wearables etc) in order to acquire innovative insights, for example related to challenging behavior of cliënts?

FURTHER INFORMATION

Countries Netherlands;
Topics Community-based LTC; Technology and LTC;
Methods Data science and LTC research; Longitudinal data analysis; Qualitative studies; Questionnaire; Research ethics; Surveys;
Role Research;
Interest Groups Data Science;
Websitehttps://silaarts.netlify.app
ORC.ID0000-0002-3121-412
Twitterhttps://twitter.com/sil_aarts
Research interests

Data-informed care, data (science) (incl. text-mining), R(stats) (learning Python), Quality of Care, Long-term Elderly Care, Nursing Home Care, Statistical Analysis, Qualitative Research, Ethics.

Key publications

Expected start 2024: Hendriks A., Hacking C., Verbeek H. & Aarts S. Data science techniques to gain novel insights into quality of care: a scoping review of long-term care for older adults. Special Issue: Data-Informed Decision Making in Healthcare. Exploration of Digital Health Technologies.(accepted).

2023

Hacking C., Verbeek H., Hamers JPH. & Aarts S. Comparing text mining and manual coding methods: Analysing interview data on quality of care in long-term care for older adults. PLOS ONE. 2023.

Aarts S. Data-informed decision making in long-term care for older adults: what do we need? IPA. 2023.

Hacking C., Verbeek H., Hamers JPH. & Aarts S. The development of an automatic speech recognition model using interview data from long-term care for older adults. Journal of the American Medical Informatics Association. 2023; 30(3).

2022

Golz C. Aarts S., Hacking C., Hahn S. & Zwakhalen SMG. Health professionals’ sentiments towards implemented information technologies in psychiatric hospitals: a text-mining analysis. BMC Health Services Research, 2022: 22(1).

Hacking C., Verbeek H., Hamers JPH., Sion K., & Aarts S. Text mining in long-term care: Exploring the usefulness of artificial intelligence in a nursing home setting. PLOS ONE. 2022; 17(8).