Sensor technology in healthcare – opportunities and challenges (Part 1)

How can digital technologies be used in health care and prevention? Can technology relieve the burden on nursing staff? And what does it take for the technology to be accepted by patients and staff? Researchers at BFH Health are investigating these questions in an Innosuisse project. In a series of articles, they provide an insight into aspects ranging from data use and process design to acceptance.

In the course of the 5th industrial revolution, robotics and artificial intelligence have found their way into our healthcare system. Thanks to these technologies, data-driven decisions can be made and quality in the healthcare sector can be improved. In addition, the cost development of healthcare can be positively influenced (Popov et al., 2022). The integration of new technologies into everyday care is still associated with difficulties (Sorg et al., 2022). On the one hand, this is because nursing professionals are often not yet able to use new technologies in a way that generates added value. On the other hand, they fear losing contact with patients or have concerns about data protection and ethical aspects (e.g. responsibility in diagnostics and decision-making) (Sorg et al., 2022).

The innovation field “Digital Health” of aF&E Nursing at the Bern University of Applied Sciences deals, among other things, with the question of what it takes for new technologies to be successfully introduced and applied in healthcare (technology adoption). Among other things, research is being conducted into which factors influence acceptance, e.g. process design, attitudes or digital skills, and what is needed to be able to generate added value for patients and healthcare professionals with the new technologies.

RAMOS – a research project at the BFH

One project that deals with these very questions is the Innosuisse project “RAMOS” at the BFH. In collaboration with ARTORG, the Centre for Biomedical Research and the industry partner “QUMEA”, we launched this project in summer 2022. Together we are investigating the potential use of “QUMEA Care”, a sensor-based solution for patient monitoring for healthcare professionals, in the everyday care of long-term patients and residents, i.e. residents of a retirement and nursing home and patients of a gerontological psychiatric institution. QUMEA Care is intended to contribute to maintaining the high standard of nursing and medical care despite the increase in multimorbid patients and the lack of qualified staff.

QUMEA Care is a technology that captures and verifies movement data through radar-based monitoring. You can read more about this technology and how it works in our next article “Of sensor data and algorithms”, which will be published in the next few weeks.

Hurdles to overcome when introducing new technologies

Often, the adoption of new technologies happens faster than end-users have time to acquire the necessary skills (Gance-Cleveland et al., 2020). Technologies are still developed without sufficient involvement of health professionals, resulting in a gap between the intention of the manufacturer and the implementation in the care process (Jahnke et al., 2021).

For this reason, our research project RAMOS emphasises the early collaboration of users, i.e. health professionals and residents/patients as well as relatives, us, the research team and the technology manufacturer (Pfannstiel et al., 2018). This enables us to identify the added value of QUMEA Care for the people involved as well as key factors of technology adoption, because the users’ perspective, i.e. requirements and needs for the QUMEA Care technology, will be explored qualitatively as well as quantitatively.

On the one hand, technology acceptance questionnaires will be used, such as the UTAUT (Unified theory of Acceptance and use of technology) questionnaire (Venkatesh et al., 2012), and also physical and mental health questionnaires, e.g. Safety Climate Tool, PSCHO17 and HTF survey (Alsuyayfi & Alanazi, 2022). Secondly, interviews and focus groups will be conducted to fully understand changes in processes, practices, competencies and skills associated with technology use, as well as facilitating and hindering factors. Data collection at multiple points in time will reveal possible changes and interrelationships. The impact of QUMEA Care on patient confidence, safety and well-being will be explored.

This multi-method approach is important because it is known that new technologies can pose problems at different levels, i.e. processes, practices, competences or skills. For example, digital devices can lead to sensory overload due to the alarms they send out (Alsuyayfi & Alanazi, 2022) and to stress reactions due to the noise level (Cohen et al., 2017). However, digital devices can also frighten patients if they feel that the health professional monitoring the technology is unsuitable, untrustworthy or uncaring (Dermody et al., 2021). The wealth of information available on the internet further increases patients’ expectations of technology, as they want to be involved in the decision-making process of the treatment plan (Sorg et al., 2022). Another frequently mentioned issue is privacy, which is especially true for QUMEA Care. Constant monitoring affects privacy and can be perceived as disruptive or lead to the rejection of a technology if the added value cannot be ranked higher than privacy. (Dermody et al., 2021).

Opportunities in the course of digitalisation

Increasing digitisation in healthcare not only challenges, but also opens up new opportunities. Providers are moving away from the ‘health professional knows best attitude’ towards a person-centred approach with increasing levels of patient involvement (Konttila et al., 2019). This can often lead to higher patient satisfaction (Konttila et al., 2019). It is precisely this patient satisfaction that we want to take into account in our project, which is why we have included qualitative measurement methods in the project design.

Through early interventions and better awareness of one’s own health status, health can be improved and at the same time the number of hospitalisations and re-hospitalisations can be reduced, which also brings economic benefits (Konttila et al., 2019). By further developing the algorithms that make QUMEA Care work, the technology should be brought to the point where it can predictively identify when there is a change in health status using the mobility and behavioural data of each individual patient. This should enable timely preventive measures to be taken and maintain independence for longer, as well as promote patient safety (Konttila et al., 2019).

Health professionals benefit from digitalisation, as digital progress enables them to initiate more precise diagnostic, therapeutic and preventive measures (Sorg et al., 2022). However, a reduction in workload per se is rarely achieved at this point in time, and so it is primarily the patients who benefit from increasing digitalisation.

Article series

You can find out how the sensors and algorithms behind QUMEA Care work in Part 2 of the series “Sensor Technology in Healthcare”. This will be published in four weeks.


  1. Alsuyayfi, S., & Alanazi, A. (2022). Impact of clinical alarms on patient safety from nurses’ perspective. Informatics in Medicine Unlocked, 32.
  2. Cohen, C., Kampel, T., & Verloo, H. (2017). Acceptability Among Community Healthcare Nurses of Intelligent Wireless Sensor-system Technology for the Rapid Detection of Health Issues in Home-dwelling Older Adults. Open Nurs J, 11, 54-63.
  3. Dermody, G., Fritz, R., Glass, C., Dunham, M., & Whitehead, L. (2021). Factors influencing community-dwelling older adults’ readiness to adopt smart home technology: A qualitative exploratory study. J Adv Nurs, 77(12), 4847-4861.
  4. Gance-Cleveland, B., McDonald, C. C., & Walker, R. K. (2020). Use of theory to guide development and application of sensor technologies in Nursing. Nurs Outlook, 68(6), 698-710.
  5. Jahnke, I., Riedel, N., Popescu, M., Skubic, M., & Rantz, M. (2021). Social practices of nurse care coordination using sensor technologies – Challenges with an alert system adoption in assisted living communities for older adults. Int J Nurs Sci, 8(3), 289-297.
  6. Konttila, J., Siira, H., Kyngas, H., Lahtinen, M., Elo, S., Kaariainen, M., . . . Mikkonen, K. (2019). Healthcare professionals’ competence in digitalisation: A systematic review. J Clin Nurs, 28(5-6), 745-761.
  7. Pfannstiel, M. A., Krammer, S., & Swoboda, W. (2018). Voice in digitalisation: Systematic and practical involvement of users of health-related technologies. In (pp. 173-186). Springer Fachmedien Wiesbaden GmbH. https://doi. org/10.1007/978-3-658-13642-0_11
  8. Sorg, H., Ehlers, J. P., & Sorg, C. G. G. (2022). Digitalization in Medicine: Are German Medical Students Well Prepared for the Future? Int J Environ Res Public Health, 19(14).
  9. Venkatesh, V., Thong, J. Y. L., & Xu, X. (2012). Consumer Acceptance and Use of Information Technology: Extending the Unified Theory of Acceptance and Use of Technology. In Source: MIS Quarterly (Vol. 36, Issue 1).
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  11. Popov, V. V., Kudryavtseva, E. V., Katiyar, N. K., Shishkin, A., Stepanov, S. I., & Goel, S. (2022). Industry 4.0 and Digitalisation in Healthcare. In Materials (Vol. 15, Issue 6). MDPI.
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AUTHOR: Selina Burch

Selina Burch is a research associate in the BFH School fo Health Professions.

AUTHOR: Tabea Schmid

Tabea Schmid is a research assistant at the Department of Nursing at BFH School of Health Professions.

AUTHOR: Marco Buri

Marco Buri is an IT specialist at BFH School of Health Professions. He is working as a software engineer/architect on the RAMOS project and is developing algorithms for the early detection of health deterioration due to mobility changes, among other things.

AUTHOR: Lena Bruhin

Lena Bruhin is a PHD student at the University of Bern and a member of the BFH's RAMOS project.

AUTHOR: Friederike J. S. Thilo

Prof. Dr Friederike Thilo is Head of Innovation Field "Digital Health", aF&E Nursing, BFH Health. Her research focuses are: Design collaboration human and machine; technology acceptance; need-driven development, testing and evaluation technologies in the context of health/disease; data-based care (artificial intelligence).

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