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Background: Globally, the rates at which the aging population and the prevalence of chronic diseases are increasing are substantial. With declining birth rates and a growing percentage of older individuals, the demand for nursing staff is steadily rising. However, the shortage of nursing personnel has been a long-standing issue. In recent years, numerous researchers have advocated for the implementation of nursing robots as a substitute for traditional human labor.
Objective: This study analyzes hospital visitors' attitudes and priorities regarding the functional areas of artificial intelligence (AI) nursing robots based on the Kano model. Building on this analysis, recommendations are provided for the functional optimization of AI nursing robots, aiming to facilitate their adoption in the nursing field.
Methods: Using a random sampling method, 457 hospital visitors were surveyed between December 2023 and March 2024 to compare the differences in demand for AI nursing robot functionalities among the visitors.
Results: A comparative analysis of the Kano attribute quadrant diagrams showed that visitors seeking hospitalization prioritized functional aspects that enhance medical activities. In contrast, visitors attending outpatient examinations focused more on functional points that assist in medical treatment. Additionally, visitors whose purpose was companionship and care emphasized functional aspects that offer psychological and life support to patients.
Conclusions: AI nursing robots serve various functional areas and cater to diverse audience groups. In the future, it is essential to thoroughly consider users' functional needs and implement targeted functional developments to maximize the effectiveness of AI nursing robots.
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http://www.ncbi.nlm.nih.gov/pmc/articles/PMC11612591 | PMC |
http://dx.doi.org/10.2196/59442 | DOI Listing |
Disabil Rehabil Assist Technol
September 2025
Department of Senior Citizen Services, National Tainan Junior College of Nursing, Tainan, Taiwan.
Purpose: This study explored the experiences of long-term care workers in using assistive technologies in dementia-specific care facilities in Taiwan, with a focus on perceived benefits, challenges encountered, and required support strategies.
Methods: A qualitative research design was employed. Ten female care workers from five dementia-specific long-term care institutions, each with at least 1 year of experience using assistive technologies, participated in semi-structured in-depth interviews.
NeuroRehabilitation
September 2025
Centre for Smart Health, School of Nursing, The Hong Kong Polytechnic University, Kowloon, Hong Kong.
BackgroundWith the development of modern biomedical engineering, bio-signal feedback-based robots, such as electromyography (EMG)-based and brain-computer interface (BCI)-based rehabilitation robot, have emerged beyond conventional designs. However, their comparative effectiveness for improving upper limb function in stroke patients remains unassessed.ObjectiveTo evaluate the comparative effectiveness and ranking of the conventional rehabilitation robot and bio-signal feedback-based rehabilitation robot in improving upper limb function in stroke patients.
View Article and Find Full Text PDFJMIR Rehabil Assist Technol
September 2025
Department of Electrical and Computer Systems Engineering, Faculty of Engineering, Monash University, 18 Alliance Lane, Melbourne, 3800, Australia, 61 399055562.
Background: Socially assistive robots (SARs) are robotic technology platforms equipped with sensing (eg, through audio or visual) and acting (eg, speech and movement) capabilities to interact socially with users. SARs are increasingly adopted in physiotherapy to aid patients in their rehabilitation journey by providing feedback, motivation, and encouragement. However, while many studies have explored SAR implementation in physiotherapy, research involving clinical populations remains scarce, and the overall state of SAR deployment is unclear.
View Article and Find Full Text PDFFront Med (Lausanne)
August 2025
Peking University School of Nursing, Beijing, China.
Background: The fields of dysphagia is progressively acknowledging the transformative capacity of artificial intelligence (AI). The implementation of this technology is profoundly impacting research directions, clinical practices, and healthcare systems. However, existing studies remain scattered and predominantly focus on specific techniques or case applications, lacking a systematic synthesis of global research output, influential contributors, collaboration networks, and evolving thematic trends.
View Article and Find Full Text PDFFront Digit Health
August 2025
Department of Internal Medicine, Faculty of Medicine, University Medicine Halle (Saale), Health Service Research Working Group | Acute Care, Martin Luther University Halle-Wittenberg, Halle (Saale), Germany.
Background: Lower back pain (LBP) is one of the most common occupational health issues among healthcare professionals, particularly in long-term care settings. The HAL® Lumbar Type Exoskeleton is a wearable assistive technology designed to reduce strain on the lower back during physically demanding care activities. However, evidence regarding its feasibility, usability, and acceptance in real-world long-term care settings remains limited.
View Article and Find Full Text PDF