Challenges and Ethical Considerations in Implementing Assistive Technologies in Healthcare
Abstract
:1. Introduction
2. Objective
- What are the most popular assistive technology methods used in hospitals and other healthcare settings for patient profiling and treatment?
- What are the challenges faced by healthcare professionals in implementing assistive technologies for patient profiling and treatment, and what ethical considerations surround the use of such technologies in healthcare settings?
- How does the introduction of care robots and socially assistive technologies affect the workload and job satisfaction of healthcare professionals?
- What factors influence the successful adoption and long-term use of assistive technology in community health settings, and what are the barriers to widespread implementation?
3. Methodology
4. Results
4.1. Popular Assistive Technology Methods in Healthcare Settings
4.2. Challenges in Implementation of Assistive Technologies by Healthcare Professionals
4.3. Ethical Considerations
4.4. Effects on Workload and Job Satisfaction
4.5. Barriers and Facilitators for Patients and Professionals
5. Discussion
6. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
References
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General Search Strings | (“Assistive technologies” AND “Hospital” AND “Effectiveness”) |
For Patient Profiling | (“Assistive technologies” AND “Hospital” AND “Patient profiling”) OR (“Assistive technologies” AND “Hospital” AND “Diagnostic tools”) OR (“Assistive technologies” AND “Hospital” AND “Patient monitoring systems”) |
For Treatment | (“Assistive technologies” AND “Hospital” AND “Treatment”) OR (“Assistive technologies” AND “Hospital” AND “Rehabilitation devices”) OR (“Assistive technologies” AND “Hospital” AND “Robotic surgery”) OR (“Assistive technologies” AND “Hospital” AND “Therapeutic devices”) |
For Outcome and Impact | (“Assistive technologies” AND “Hospital” AND “Patient outcomes”) OR (“Assistive technologies” AND “Hospital” AND “Clinical outcomes”) OR (“Assistive technologies” AND “Hospital” AND “Healthcare efficiency”) OR (“Assistive technologies” AND “Hospital” AND “Clinical workflow”) OR (“Assistive technologies” AND “Hospital” AND “Treatment efficacy”) OR (“Assistive technologies” AND “Hospital” AND “Patient care”) |
Reference | Type of Intervention | Type of Assistive Technology | Setting | Sample | Participants’ Age | Diagnosis (For Patients) | Expertise (For Professionals) | |
---|---|---|---|---|---|---|---|---|
1 | [16] | Quantitave data and questionnaires | “ARNA” robot | Hospital room simulation suite | 24 | NA | - | Nursing students |
2 | [27] | Qualitative interview | Point-of-care ultrasound (POCUS) | Online interview | 16 | 36–65 years | - | Physicians, paramedics, etc. |
3 | [34] | Survey-based assessment (questionnaires) | Robots | Metropolitan area in the western United States | 499 | 18–44 years 45–64 years 65–98 years (avg. 38.7 years) | - | General population (non-experts) |
4 | [28] | Surveys and focus group discussions (qualitative data) | “Alexa Echo Show 8” Voice activated device (smart speaker) | Participants’ homes (UK) | 51 (44 patients and 7 informal carers) | 50–90 years | Diabetes (both Type 1 and Type 2), dementia, Parkinson’s disease, asthma, Behçet’s disease, Cushing’s syndrome, phenylketonuria, liver disorders, low mood, depression, anxiety, dyslexia, cognitive impairment, severe visual impairment, chronic knee pain, and trauma | Informal carers |
5 | [35] | Focus group discussion (qualitative data) | Physically assistive robots | Care homes | 7 | NA | - | Professional carers for older people in care homes |
6 | [17] | 10-week intervention program | “Pepper” robot | Care home | 11 (6 older adult residents and 5 caregivers) | 80–94 years | Elderly individuals in need of visual and mobility assistance | Caregivers and former manager |
7 | [19] | Intervention sessions divided into 100 individual parts (one executed each day) | “PHAROS” (PHysical Assistant RObot System) | Controlled lab environment | 7 | NA | Elderly individuals in need for physical exercise assistance | - |
8 | [36] | Mixed-methods approach (qualitative and quantitative data) | Mobility assistive technologies (wheelchairs and components, assistive robotics, human–machine interfaces, smart device applications) | Online survey | 161 | 18–65+ years | - | Providers of mobility-assistive technologies (clinicians, engineers, assistive technology professionals, occupational therapists, physical therapists, nurses, physicians, rehabilitation engineers, and technicians) |
9 | [29] | Randomized clinical trial (RCT) (eHealth intervention group and control group) | “ElderTree” eHealth platform (interactive website) | Home-based intervention | 390 | <65 years | Having at least one health risk factor in the preceding 12 months (including one or more falls Receipt of home health services Skilled nursing facility stay Emergency room visit Hospital admission Sustained sadness or depression) | - |
10 | [37] | Randomised controlled trial (intervention group and control group) | Assistive technology and telecare (ATT) (safety devices, reminder/prompting devices, monitoring devices, communication devices | Home-based setting (UK) | 495 | 65–80 years 80+ years | Dementia or cognitive difficulties sufficient to suggest dementia, participants with a high risk of safety concerns or with a history of wandering were also included | - |
11 | [38] | Mixed-method design (qualitative and quantitative data) | “Zora” robot | Nursing care organizations | 245 elderly residents 62 professionals | NA (for elderly residents) 16–62 years (for professionals) | Elderly individuals with psychogeriatric problems (e.g., dementia). Some were also in day care or had somatic or psychiatric conditions | Activity counselors, nurses, trainees, policy makers, physiotherapists, and volunteers |
12 | [21] | Data collected through focus groups and individual interviews (qualitative) | “Silbot” humanoid robot, “Hyodol” doll-shaped care robot for emotional support, “Aria” cylindrical-shaped smart speaker, “Care Call” weekly interactive call service using AI-based technology | Community health centers | 18 | 25–59 years | - | Nurses with work experience in caring for older adults |
13 | [22] | Mixed-methods design (qualitative and quantitative data) | “Matilda” robot | Residential aged care facilities | Qualitative study (Study 1): observations of 13 carers, 15 in-depth interviews, and 3 focus groups with carers. Quantitative study (Study 2): 302 carers | Qualitative study: 35–60 years Quantitative study: 20–60 years | - | Carers with varying levels of experience in aged care |
14 | [39] | Mixed-methods approach (qualitative and quantitative data) | Various digital technologies | Online survey | 578 | 18–74 years | - | Diverse group of participants (non-experts) |
15 | [30] | Quantitative approach | “Touch Talker” digital text-to-speech system | Controlled environment | 12 (6 visually impaired individuals and 6 blindfolded individuals with normal vision) | 21–38 years (for visually impaired individuals) 15–20 years (for blindfolded individuals with normal vision) | Varying degrees of visual impairment | - |
16 | [31] | Quantitative approach | High-tech assistive technologies designed for people with visual impairments (e.g., “BrainPort Vision Pro”, “The vOICe”) | Online survey | 25 | 21–68 years | Individuals with visual impairments | - |
17 | [23] | Quantitative approach | “Huggable” social robot | Pediatric inpatient hospital setting | 54 | 3–10 years | Hospitalized children with a range of diagnoses (including leukemia, other cancers etc.) | - |
18 | [20] | Quantitative and qualitative approach | “PHAROS 2.0” (PHysical Assistant RObot System Improved) | Care home | 8 | 60–90 years | Elderly residents with varying levels of physical capability | - |
19 | [40] | Randomized controlled trial (RCT) (quantitative and qualitative approach) | Various assistive technologies (for mobility and daily activities) | Home settings | 90 dyads (a care recipient and their family caregiver) | 75 years average (for care recipients) 65 years (for caregivers) | Limitations in mobility or daily activities (osteoarthritis, cardiorespiratory conditions, neurological disorders etc.) | Family members or friends who provided unpaid assistance |
20 | [41] | Quantitative and qualitative approach | Assistive technology (tablets, smartphones, computers, wearable devices, and augmentative communication) | Online setting | 96 | - | - | Parents, guardians, caregivers, teachers, therapists of individuals with ASD/ID |
21 | [32] | Quantitative approach | Microsoft Band 2 smartwatch for collection of physiological data | Controlled experimental setting | 19 (11 with Parkinson’s disease and 8 healthy control subjects) | 48–78 years | Mild to moderate idiopathic Parkinson’s Disease | - |
22 | [24] | Qualitative approach | “AMiCUS 2.0” system, a robotic arm controlled by head motion using inertial sensors placed on the user’s head | Controlled experimental environment | 1 | 58 years | Progressed multiple sclerosis and tetraplegia, with severe head motion limitations and additional symptoms such as fatigue, attention deficit, and visual impairment | - |
23 | [25] | Preliminary evaluation | “Mini” desktop social robot | Nursing home | 20 (10 elders, 7 caregivers, and 3 relatives) | NA | Elderly individuals, with potential cognitive impairments | Caregivers and relatives |
24 | [18] | Qualitative case study | “Pepper” humanoid robot and “CPGE” application | Psychiatric hospitals and geriatric health facilities | 9 | NA | Schizophrenia and/or dementia | - |
25 | [42] | Quantitative approach | Social robots (SRs) in rehabilitation and assistance | Online survey | 323 | 23–58 years | - | Physiotherapists in training or working in the field |
26 | [43] | Qualitative phenomenological study | Wide range of devices (e.g., talking clocks, electronic medication dispensers, robotic vacuum cleaners, smart gas meters, audio books, etc.) | Interviewing at participant’s own home or at the researcher’s office, or over the telephone | 23 | 42–91 years | - | Informal carers (family members, friends, or neighbors) of persons with dementia |
27 | [26] | Collaborative research methodology (qualitative approach) | “RoboTSS” robotic system designed to support clinical teams | Hospital | 7 | 28–44 years | - | Nurses and anesthesiologist |
28 | [33] | Quantitative approach | Ambient assisted living (AAL) systems | Online questionnaire | 174 | 19–68 years | - | Professional caregivers (in geriatric care, nursing care, and care/support of people with disabilities) |
29 | [44] | Qualitative approach | Intelligent assistive technologies (IATs) (AI, robotics, and wearable computing for healthcare) | In person and online interviews | 20 | NA | - | Professionals with expertise in gerontology, geriatrics, general practice, neurology, neuropsychology, nursing, nursing home management, and psychiatry |
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Gkiolnta, E.; Roy, D.; Fragulis, G.F. Challenges and Ethical Considerations in Implementing Assistive Technologies in Healthcare. Technologies 2025, 13, 48. https://doi.org/10.3390/technologies13020048
Gkiolnta E, Roy D, Fragulis GF. Challenges and Ethical Considerations in Implementing Assistive Technologies in Healthcare. Technologies. 2025; 13(2):48. https://doi.org/10.3390/technologies13020048
Chicago/Turabian StyleGkiolnta, Eleni, Debopriyo Roy, and George F. Fragulis. 2025. "Challenges and Ethical Considerations in Implementing Assistive Technologies in Healthcare" Technologies 13, no. 2: 48. https://doi.org/10.3390/technologies13020048
APA StyleGkiolnta, E., Roy, D., & Fragulis, G. F. (2025). Challenges and Ethical Considerations in Implementing Assistive Technologies in Healthcare. Technologies, 13(2), 48. https://doi.org/10.3390/technologies13020048