Assistive technologies in healthcare: utilization and healthcare workers perceptions in Germany
D Sommer, Eva Lermer, Florian Wahl
- 发表年份
- 2025
- 引用次数
- 8
- 访问权限
- 开放获取
摘要
BACKGROUND: According to the WHO, assistive technology (AT) is defined as the superset of technologies that improve or maintain the functioning of different senses, mobility, self-care, well-being, and inclusion of patients. ATs also include technologies for healthcare workers (HCWs) to reduce workloads and improve efficiency and patient care outcomes. Software ATs for HCWs include communication software, artificial intelligence (AI), text editors, planning tools, decision support systems, and health records. Hardware ATs for HCWs can range from communication devices, sensors, and specialized medical equipment to robots. AIMS: With this indicative study, we explore HCW utilization, perceptions, and adoption barriers of ATs. We emphasize ATs role in enhancing HCWs' efficiency and effectiveness in healthcare delivery. METHODS: A cross-sectional online survey was conducted through August 2024 with HCWs in Bavaria via a network recruiting approach. We used convenience sampling but ensured that only HCWs were part of our study population. Our survey included (i) usage, (ii) usefulness, and (iii) perceptions regarding ATs. The survey comprised 11 close-ended and three open-ended questions, including story stems evaluated by a deductive qualitative template analysis. Our mixed-method evaluation also employed descriptive and bivariate statistics. RESULTS: Three hundred seventy-one HCWs (♂63.9 %, ♀36.1 %) participated in our survey, primarily 133 administrators, 116 nurses, and 34 doctors. More than half of the study participants (58.6 %) reported having advanced technical skills. Regarding usage, communication platforms (82.2 %) and communication devices (86 %) were the most commonly used ATs. Advanced ATs such as body-worn sensors, medical devices with interfaces, identification devices, and robots were underutilized in our sample. ATs were reported to be helpful in all job roles but need improvements in capacity and integration. Key barriers to adoption included outdated infrastructure, interoperability, and a lack of training. CONCLUSION: Our study suggests that HCWs may want to incorporate ATs into their workflows as they see how, in theory, these technologies would improve HCW's efficiency, resulting in better patient care. However, to realize this potential, efforts in ATs integration and accessibility are essential. Given this study's modest sample size and generalizability limitations, further research is needed to explore the adoption, implementation, and impact of ATs in healthcare.
关键词
相关论文
Artificial intelligence: a modern approach
1995
Are we ready for autonomous driving? The KITTI vision benchmark suite
Andreas Geiger, P Lenz, R. Urtasun
2012
TensorFlow: Large-Scale Machine Learning on Heterogeneous Distributed Systems
Martı́n Abadi, Ashish Agarwal, Paul Barham 等 20 位作者
2016
Vision meets robotics: The KITTI dataset
Andreas Geiger, Philip Lenz, Christoph Stiller 等 4 位作者
2013