Use of Robots in Critical Care: Systematic Review

Health Care Social Robots in the Age of Generative AI: Protocol for a Scoping Review

BACKGROUND

Social robots (SR), sensorimotor machines designed to interact with humans, can help to respond to the increasing demands in the health care sector. To ensure the successful use of this technology, acceptance is paramount. Generative artificial intelligence (AI) is an emerging technology with the potential to enhance the functionality of SR and promote user acceptance by further improving human-robot interaction.

OBJECTIVE

We present a protocol for a scoping review of the literature on the implementation of generative AI in SR in the health care sector. The aim of this scoping review is to map out the intersection of SR and generative AI in the health care sector; to explore if generative AI is applied in SR in the health care sector; to outline which models of generative AI and SR are used for these implementations; and to explore whether user acceptance is reported as an outcome following these implementations. This scoping review supports future research by providing an overview of the state of connectedness of 2 emerging technologies and by mapping out research gaps.

METHODS

We follow the methodological framework developed by Arksey and O'Malley and the recommendations by the Joanna Briggs Institute. Our protocol was drafted using the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-analyses extension for Scoping Reviews). We will conduct a systematic literature search of the online databases MEDLINE, Embase, CINAHL (Cumulative Index to Nursing and Allied Health Literature), Web of Science, and IEEE Xplore, aiming to retrieve relevant data items via tabular data charting from references meeting specific inclusion criteria which are studies published from 2010 onwards, set in the health care sector, focusing on SR with physical bodies and implemented generative AI. There are no restrictions on study types. Results will be categorized, clustered, and summarized using tables, graphs, visual representations, and narratives.

RESULTS

After conducting a preliminary search and deduplication in the second quarter of 2024, we retrieved 3176 preliminary results. This scoping review will be supplemented with the next methodological steps, including retrieving the results in a reference management tool as well as screening titles, abstracts, and full text regarding specific inclusion criteria. The completion of these steps is scheduled for the second quarter of 2025. Limitations based on the heterogeneity of the included studies and the general breadth of a scoping review compared to a systematic review are to be expected. To reduce bias, we adopted a system of dual reviews and thorough documentation of the study selection.

CONCLUSIONS

The conducted preliminary search implies that there are a sufficient number of heterogeneous references to complete this scoping review. To our knowledge, this is the first scoping review on generative AI in health care SR.

INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID)

PRR1-10.2196/63017.

Evaluation of nurses' perspectives on the design and use of assistant nurse robots in obstetrics and neonatal care: a mixed-method study

BACKGROUND

This study aims to evaluate nurses' perspectives on the design of nurse assistant robots that can be utilized in obstetrics and neonatal units. The research examines the potential of these robots in enhancing the quality of patient care, reducing workload, and standardizing care processes from the nurses' perspective.

METHODS

The study was conducted with 52 nurses working in obstetrics and neonatal units of hospitals. Conjoint analysis was used to evaluate preferences for the features of nurse assistant robots while qualitative data were obtained through semi-structured questions. The Artificial Intelligence Anxiety Scale was used to measure nurses' concerns.

RESULTS

Quantitative analysis results indicate that nurses prioritize features such as sterilization, data transfer, alarm systems, precision, and autonomous navigation in nurse assistant robots. Qualitative analysis findings reveal positive perceptions regarding the robots' potential to reduce error rates, enhance patient safety, and alleviate workload. However, concerns about technological dependency, sterilization issues, and potential job displacement were also expressed. Furthermore, technological/systematic issues and lack of communication/empathy were identified as disadvantages of nurse assistant robots. Considering the sensitive nature of obstetrics and neonatal units, it was suggested that these robots should primarily focus on vital sign monitoring and material preparation tasks. The findings from the Artificial Intelligence Anxiety Scale indicate that participants exhibit moderate-to-high levels of general anxiety (87.6). Specifically, the Socio-Technical Blindness and Job Transition subscales scored higher compared to other dimensions (r = -0.35, p < 0.01).

CONCLUSIONS

The findings emphasize that features such as sterilization, data transfer, safety sensors, and user-friendly guidance systems should be prioritized in the design of nurse assistant robots. Moreover, experience and training were found to positively influence technological adaptation. The results provide valuable insights into the design and integration of nurse assistant robots into healthcare services. This study offers both theoretical and practical guidance for the development of nurse assistant robots.

CLINICAL TRIAL NUMBER

Not applicable.

Clinicians' Perceptions and Potential Applications of Robotics for Task Automation in Critical Care: Qualitative Study

BACKGROUND

Interest in integrating robotics within intensive care units (ICUs) has been propelled by technological advancements, workforce challenges, and heightened clinical demands, including during the COVID-19 pandemic. The integration of robotics in ICUs could potentially enhance patient care and operational efficiency amid existing challenges faced by health care professionals, including high workload and decision-making complexities.

OBJECTIVE

This qualitative study aimed to explore ICU clinicians' perceptions of robotic technology and to identify the types of tasks that might benefit from robotic assistance. We focused on the degree of acceptance, perceived challenges, and potential applications for improving patient care in 5 Southeastern US hospitals between January and August 2023.

METHODS

A qualitative study through semistructured interviews and questionnaires was conducted with 15 ICU clinicians (7 nurses, 6 physicians, and 2 advanced practice providers) from 5 hospitals in the Southeast United States. Directed content analysis was used to categorize and interpret participants' statements, with statistical tests used to examine any role-based differences in how they viewed robotic integration.

RESULTS

Among the 15 participants, 73% (11/15) were female, with an average of 6.4 (SD 6.3) years of ICU experience. We identified 78 distinct tasks potentially suitable for robotic assistance, of which 50 (64%) involved direct patient care (eg, repositioning patients and assisting with simple procedures), 19 (24%) concerned indirect patient care (eg, delivering supplies and cleaning), 6 (8%) addressed administrative tasks (eg, answering call lights), and 3 (4%) were classified as mixed direct and indirect (eg, sitting with a patient to keep them calm). Most participants supported the automation of routine, noncritical tasks (eg, responding to nurse calls and measuring glucose levels), viewing this strategy as a way to alleviate workload and enhance efficiency. Conversely, high-complexity tasks requiring nuanced clinical judgment (eg, ventilator settings) were deemed unsuitable for full automation. Statistical analysis revealed no significant difference in how nurses, physicians, and advanced practice providers perceived these tasks (P=.22).

CONCLUSIONS

Our findings indicate a significant opportunity to use robotic systems to perform noncomplex tasks in ICUs, thereby potentially improving efficiency and reducing staff burden. Clinicians largely view robots as supportive tools rather than substitutes for human expertise. However, concerns persist regarding privacy, patient safety, and the loss of human touch, particularly for tasks requiring high-level clinical decision-making. Future research should involve broader, more diverse clinician samples and investigate the long-term impact of robotic assistance on patient outcomes while also incorporating patient perspectives to ensure ethical, patient-centered adoption of robotic technology.

Assistive technologies in healthcare: utilization and healthcare workers perceptions in Germany

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.

Satisfaction analysis of 5G remote ultrasound robot for diagnostics based on a structural equation model

Objectives

With the increasing application of 5G remote ultrasound robots in healthcare, robust methods are in critical demand to assess participant satisfaction and identify its influencing factors. At present, there is limited empirical research on multi-parametric and multidimensional satisfaction evaluation of participants with 5G remote ultrasound robot examination. Previous studies have demonstrated that structural equation modeling (SEM) effectively integrates various statistical techniques to examine the relationships among multiple variables. Therefore, this study aimed to evaluate the satisfaction of participants with 5G remote ultrasound robot examination and its influencing factors using SEM.

Methods

Between April and June 2022, 213 participants from Wuhan Automobile Manufacturing Company underwent remote ultrasound examinations using the MGIUS-R3 remote ultrasound robot system. After these examinations, the participants evaluated the performance of the 5G remote ultrasound robot based on their personal experiences and emotional responses. They completed a satisfaction survey using a self-developed questionnaire, which included 19 items across five dimensions: examination efficiency, examination perception, communication perception, value perception, and examination willingness. A SEM was established to assess the satisfaction of participants with the 5G remote ultrasound robot examinations and the influencing factors.

Results

A total of 201 valid questionnaires were collected. The overall satisfaction of participants with the 5G remote ultrasound robot examination was 45.43 ± 11.60, with 169 participants (84%) expressing satisfaction. In the path hypothesis relationship test, the dimensions of examination efficiency, examination perception, communication perception, and value perception had positive effects on satisfaction, with standardized path coefficients of 0.168, 0.170, 0.175, and 0.191. Satisfaction had a direct positive effect on examination willingness, with a standardized path coefficient of 0.260. Significant differences were observed across different educational levels in the dimensions of examination perception, communication perception, value perception, and examination willingness. Participants with different body mass indices also showed significant differences in examination perception; all p-values were less than 0.05.

Conclusion

In this study, value perception was identified as the most significant factor influencing satisfaction. It could be improved by enhancing participants' understanding of the accuracy and safety of 5G remote ultrasound robot examinations. This enhances satisfaction and the willingness to undergo examinations. Such improvements not only facilitate the widespread adoption of this technology but also promote the development of telemedicine services.

Care Beyond Cure: Humanizing the Intensive Care Unit Journey

How to cite this article: Chacko B. Care Beyond Cure: Humanizing the Intensive Care Unit Journey. Indian J Crit Care Med 2024;28(10):901-902.

The Potential of Artificial Intelligence in Unveiling Healthcare's Future

This article examines the transformative potential of artificial intelligence (AI) in shaping the future of healthcare. It highlights AI's capacity to revolutionize various medical fields, including diagnostics, personalized treatment, drug discovery, telemedicine, and patient care management. Key areas explored include AI's roles in cancer screening, reproductive health, cardiology, outpatient care, laboratory diagnosis, language translation, neuroscience, robotic surgery, radiology, personal healthcare, patient engagement, AI-assisted rehabilitation with exoskeleton robots, and administrative efficiency. The article also addresses challenges to AI adoption, such as privacy concerns, ethical issues, cost barriers, and decision-making authority in patient care. By overcoming these challenges and building trust, AI is positioned to become a critical driver in advancing healthcare, improving outcomes, and meeting the future needs of patients and providers.

A systematic review of collaborative robots for nurses: where are we now, and where is the evidence?

Introduction: Robots present an opportunity to enhance healthcare delivery. Rather than targeting complete automation and nurse replacement, collaborative robots, or "cobots", might be designed to allow nurses to focus on high-value caregiving. While many institutions are now investing in these platforms, there is little publicly available data on how cobots are being developed, implemented, and evaluated to determine if and how they support nursing practice in the real world. Methods: This systematic review investigates the current state of cobotic technologies designed to assist nurses in hospital settings, their intended applications, and impacts on nurses and patient care. A comprehensive database search identified 28 relevant peer-reviewed articles published since 2018 which involve real studies with robotic platforms in simulated or actual clinical contexts. Results: Few cobots were explicitly designed to reduce nursing workload through administrative or logistical assistance. Most included studies were designed as patient-centered rather than nurse-centered, but included assistance for tasks like medication delivery, vital monitoring, and social interaction. Most applications emerged from India, with limited evidence from the United States despite commercial availability of nurse-assistive cobots. Robots ranged from proof-of-concept to commercially deployed systems. Discussion: This review highlights the need for further published studies on cobotic development and evaluation. A larger body of evidence is needed to recognize current limitations and pragmatic opportunities to assist nurses and patients using state-of-the-art robotics. Human-centered design can assist in discovering the right opportunities for cobotic assistance. Committed research-practice partnerships and human-centered design are needed to guide the technical development of nurse-centered cobotic solutions.

Advances in the Application of AI Robots in Critical Care: Scoping Review

BACKGROUND

In recent epochs, the field of critical medicine has experienced significant advancements due to the integration of artificial intelligence (AI). Specifically, AI robots have evolved from theoretical concepts to being actively implemented in clinical trials and applications. The intensive care unit (ICU), known for its reliance on a vast amount of medical information, presents a promising avenue for the deployment of robotic AI, anticipated to bring substantial improvements to patient care.

OBJECTIVE

This review aims to comprehensively summarize the current state of AI robots in the field of critical care by searching for previous studies, developments, and applications of AI robots related to ICU wards. In addition, it seeks to address the ethical challenges arising from their use, including concerns related to safety, patient privacy, responsibility delineation, and cost-benefit analysis.

METHODS

Following the scoping review framework proposed by Arksey and O'Malley and the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines, we conducted a scoping review to delineate the breadth of research in this field of AI robots in ICU and reported the findings. The literature search was carried out on May 1, 2023, across 3 databases: PubMed, Embase, and the IEEE Xplore Digital Library. Eligible publications were initially screened based on their titles and abstracts. Publications that passed the preliminary screening underwent a comprehensive review. Various research characteristics were extracted, summarized, and analyzed from the final publications.

RESULTS

Of the 5908 publications screened, 77 (1.3%) underwent a full review. These studies collectively spanned 21 ICU robotics projects, encompassing their system development and testing, clinical trials, and approval processes. Upon an expert-reviewed classification framework, these were categorized into 5 main types: therapeutic assistance robots, nursing assistance robots, rehabilitation assistance robots, telepresence robots, and logistics and disinfection robots. Most of these are already widely deployed and commercialized in ICUs, although a select few remain under testing. All robotic systems and tools are engineered to deliver more personalized, convenient, and intelligent medical services to patients in the ICU, concurrently aiming to reduce the substantial workload on ICU medical staff and promote therapeutic and care procedures. This review further explored the prevailing challenges, particularly focusing on ethical and safety concerns, proposing viable solutions or methodologies, and illustrating the prospective capabilities and potential of AI-driven robotic technologies in the ICU environment. Ultimately, we foresee a pivotal role for robots in a future scenario of a fully automated continuum from admission to discharge within the ICU.

CONCLUSIONS

This review highlights the potential of AI robots to transform ICU care by improving patient treatment, support, and rehabilitation processes. However, it also recognizes the ethical complexities and operational challenges that come with their implementation, offering possible solutions for future development and optimization.

Human resource management (HRM) strategies of medical staff during the COVID-19 pandemic

Healthcare workers are at the forefront of fight against COVID-19 and the managers of medical centers should develop coping strategies for the challenges caused by COVID-19, especially for health human resources in order to improve the performance of healthcare organizations. Hence, the purpose of this study is to investigate the human resource management strategies of medical staff during the COVID-19 to help them cope with the new strains of COVID-19 or epidemics of viral diseases that may occur in the future. In this study, a search was performed in the international Web of Science electronic database, using keywords such as human resource management and COVID-19. As a result, a total of 1884 articles published between January 1st, 2020 and October 22nd, 2021 were extracted. After screening the articles based on inclusion and exclusion criteria, 24 articles were selected to enter the study. Then, a scientometric analysis was performed on the content of selected articles and the results were presented in the form of tables and conceptual models. In total, 9 strategies were extracted from the selected articles including development of organizational culture, staff screening, policy-making, infection control training and monitoring the implementation of learned materials, patient management, human resource management, psychological and motivational support, communication and coordination, and digital health services. Employing comprehensive strategies to maintain the health of healthcare workers during the COVID-19 can play an effective role in reducing burnout, improving productivity and employee satisfaction, and in increasing the resilience of healthcare workers. It also has a positive effect on the patient's safety. Revision and reengineering of human resource management strategies in health and treatment organizations according to different cultures and contexts require research and investment in creative and innovative strategies.

Improving environmental sustainability of intensive care units: A mini-review

The carbon footprint of healthcare is significantly impacted by intensive care units, which has implications for climate change and planetary health. Considering this, it is crucial to implement widespread efforts to promote environmental sustainability in these units. A literature search for publications relevant to environmental sustainability of intensive care units was done using PubMed. This mini-review seeks to equip intensive care unit practitioners and managers with the knowledge necessary to measure and mitigate the carbon cost of healthcare for critically ill patients. It will also provide an overview of the current progress in this field and its future direction.