자연주의적 탐구조사(Naturalistic Inquiry)

Background: Artificial intelligence (AI) is set to transform the health system, yet little research to date has explored its influence on nurses—the largest group of health professionals. Furthermore, there has been little discussion on how AI will influence the experience of person-centered compassionate care for patients, families, and caregivers.
Objective: This review aims to summarize the extant literature on the emerging trends in health technologies powered by AI and their implications on the following domains of nursing: administration, clinical practice, policy, and research. This review summarizes the findings from 3 research questions, examining how these emerging trends might influence the roles and functions of nurses and compassionate nursing care over the next 10 years and beyond.
Methods: Using an established scoping review methodology, MEDLINE, CINAHL, EMBASE, PsycINFO, Cochrane Database of Systematic Reviews, Cochrane Central, Education Resources Information Center, Scopus, Web of Science, and ProQuest databases were searched. In addition to the electronic database searches, a targeted website search was performed to access relevant gray literature. Abstracts and full-text studies were independently screened by 2 reviewers using prespecified inclusion and exclusion criteria. Included articles focused on nursing and digital health technologies that incorporate AI. Data were charted using structured forms and narratively summarized.
Results: A total of 131 articles were retrieved from the scoping review for the 3 research questions that were the focus of this manuscript (118 from database sources and 13 from targeted websites). Emerging AI technologies discussed in the review included predictive analytics, smart homes, virtual health care assistants, and robots. The results indicated that AI has already begun to influence nursing roles, workflows, and the nurse-patient relationship. In general, robots are not viewed as replacements for nurses. There is a consensus that health technologies powered by AI may have the potential to enhance nursing practice. Consequently, nurses must proactively define how person-centered compassionate care will be preserved in the age of AI.
Conclusions: Nurses have a shared responsibility to influence decisions related to the integration of AI into the health system and to ensure that this change is introduced in a way that is ethical and aligns with core nursing values such as compassionate care. Furthermore, nurses must advocate for patient and nursing involvement in all aspects of the design, implementation, and evaluation of these technologies.

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Predicted Influences of Artificial Intelligence on the Domains of Nursing: Scoping Review

We provide an outlook on the definitions, laboratory research, and applications of social robots, with an aim to understand what makes a robot social—in the eyes of science and the general public. Social robots demonstrate their potential when deployed within contexts appropriate to their form and functions. Some examples include companions for the elderly and cognitively impaired individuals, robots within educational settings, and as tools to support cognitive and behavioural change interventions. Science fiction has inspired us to conceive of a future with autonomous robots helping with every aspect of our daily lives, although the robots we are familiar with through film and literature remain a vision of the distant future. While there are still miles to go before robots become a regular feature within our social spaces, rapid progress in social robotics research, aided by the social sciences, is helping to move us closer to this reality.

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What Makes a Robot Social? A Review of Social Robots from Science Fiction to a Home or Hospital Near You

As social robots are increasingly introduced into health interventions, one potential area where they might prove valuable is in supporting people’s psychological health through conversation. Given the importance of self-disclosure for psychological health, this study assessed the viability of using social robots for eliciting rich disclosures that identify needs and emotional states in human interaction partners. Three within-subject experiments were conducted with participants interacting with another person, a humanoid social robot, and a disembodied conversational agent (voice assistant). We performed a number of objective evaluations of disclosures to these three agents via speech content and voice analyses and also probed participants’ subjective evaluations of their disclosures to three agents. Our findings suggest that participants overall disclose more to humans than artificial agents, that agents’ embodiment influences disclosure quantity and quality, and that people are generally aware of differences in their personal disclosures to three agents studied here. Together, the findings set the stage for further investigation into the psychological underpinnings of self-disclosures to artificial agents and their potential role in eliciting disclosures as part of mental and physical health interventions.

Tell me more! Assessing interactions with social robots from speech

Embodied conversational agents (ECAs) are increasingly used in healthcare and other settings to improve self-management and provide companionship. Their ability to form close relationships with people is important for enhancing effectiveness and engagement. Several studies have looked at enhancing relationships with ECAs through design features focused on behaviours, appearance, or language. However, this evidence is yet to be systematically synthesized. This systematic review evaluates the effect of different design features on relationship quality with ECAs. A systematic search was conducted on electronic databases EMBASE, PsychInfo, PubMed, MEDLINE, Cochrane Library, SCOPUS, and Web of Science in January–February 2019. 43 studies were included for review that evaluated the effect of a design feature on relationship quality and social perceptions or behaviours towards an ECA. Results synthesize effective design features and lay a scientific framework for improving relationships with ECAs in healthcare and other applications. Risk of bias for included studies was generally low, however there were some limitations in the research quality pertaining to outcome measurement and the reporting of statistics. Further research is needed to understand how to make ECAs effective and engaging for all consumers.

The Effect of Design Features on Relationship Quality with Embodied Conversational Agents: A Systematic Review

Objectives This research is part of an international project to design and test a home-based healthcare robot to help older adults with mild cognitive impairment (MCI) or early dementia. The aim was to investigate the perceived usefulness of different daily-care activities for the robot, developed from previous research on needs. Design Qualitative descriptive analysis using semistructured interviews. Two studies were conducted. In the first study, participants watched videos of a prototype robot performing daily-care activities; in the second study, participants interacted with the robot itself. Setting Interviews were conducted at a university and a retirement village. Participants In study 1, participants were nine experts in aged care and nine older adults living in an aged care facility. In study 2, participants were 10 experts in aged care. Results The themes that emerged included aspects of the robot’s interactions, potential benefits, the appearance, actions and humanness of the robot, ways to improve its functionality and technical issues. Overall, the activities were perceived as useful, especially the reminders and safety checks, with possible benefits of companionship, reassurance and reduced caregiver burden. Suggestions included personalising the robot to each individual, simplifying the language and adding more activities. Technical issues still need to be fixed. Conclusion This study adds to knowledge about healthcare robots for people with MCI by developing and testing a new robot with daily-care activities including safety checks. The robot was seen to be potentially useful but needs to be tested with people with MCI.

https://bmjopen.bmj.com/content/bmjopen/9/9/e031937.full.pdf

Developing assistive robots for people with mild cognitive impairment and mild dementia: a qualitative study with older adults and experts in aged care

Limited research has explored preoperative anxiety through qualitative methodologies. This study aimed to identify specific factors that contribute to preoperative anxiety. A total of 17 patients awaiting general, plastic reconstructive and hand, orthopaedic, or ear, nose, and throat/otorhinolaryngology surgery were interviewed about their concerns in a public hospital. Thematic analysis identified five main sources of anxiety: surgical procedures, surgical complications, symptoms, recovery process, and organisation and delivery of care. These themes support current knowledge and identify wider concerns around the hospital environment and recovery. This study may inform the development of future interventions aimed at reducing preoperative anxiety.

Components of preoperative anxiety: A qualitative study.

Background: For robots to be effectively used in health applications, they need to display appropriate social behaviors A fundamental requirement in all social interactions is the ability to engage, maintain, and demonstrate attention Attentional behaviors include leaning forward, self-disclosure, and changes in voice pitch Objective: This study aimed to examine the effect of robot attentional behaviors on user perceptions and behaviors in a simulated health care interaction Methods: A parallel randomized controlled trial with a 1:1:1:1 allocation ratio was conducted We randomized participants to 1 of 4 experimental conditions before engaging in a scripted face-to-face interaction with a fully automated medical receptionist robot Experimental conditions included a self-disclosure condition, voice pitch change condition, forward lean condition, and neutral condition Participants completed paper-based postinteraction measures relating to engagement, perceived robot attention, and perceived robot empathy We video recorded interactions and coded for participant attentional behaviors Results: A total of 181 participants were recruited from the University of Auckland Participants who interacted with the robot in the forward lean and self-disclosure conditions found the robot to be significantly more stimulating than those who interacted with the robot in the voice pitch or neutral conditions (P=03) Participants in the forward lean, self-disclosure, and neutral conditions found the robot to be significantly more interesting than those in the voice pitch condition (PPP=01), whereas significantly more participants in the forward lean condition leant toward the robot during the interaction (P<001) Conclusions: The use of self-disclosure and forward lean by a health care robot can increase human engagement and attentional behaviors Voice pitch changes did not increase attention or engagement The small effects with regard to participant perceptions are potentially because of the limitations in self-report measures or a lack of comparison for most participants who had never interacted with a robot before Further research could explore the use of self-disclosure and forward lean using a within-subjects design and in real health care settings

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The Effect of Robot Attentional Behaviors on User Perceptions and Behaviors in a Simulated Health Care Interaction: Randomized Controlled Trial

Abstract Robots are now starting to be developed and used as receptionists in health applications. In this regard, it is important that robots’ behavioural skills are developed and researched so that people have appropriate and comfortable interactions. Smiling and use of first name are two more important social communication skills used during human interactions. While smiling and use of first name are often employed by robots in human interactions, the effect of these behaviours on perceptions of receptionist robots has not yet been experimentally investigated. This study explored the effects of robot smiling and robot use of the participant’s first name on perceptions of robot friendliness, mind, and personality, as well as attitudes and smiling behaviour. Forty participants interacted with a medical receptionist robot four times, in a two by two repeated measures design. Both smiling and use of first name had significant positive effects on participants’ perceptions of robot personality. Robot smiling also showed significant effects on participants’ overall attitudes towards robots, ratings of robot friendliness, and perceptions of the robot’s mind, and increased the frequency of participants’ own smiling. There were no significant interaction effects. Robot smiling in particular can enhance user perceptions of robots and increase reciprocal smiling.

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Smiling and use of first-name by a healthcare receptionist robot: Effects on user perceptions, attitudes, and behaviours

A growing shortfall exists between the number of older individuals who require healthcare support and the number of qualified healthcare professionals who can provide this. Robots offer the potential to provide healthcare support to patients both at home and in healthcare settings. However, in order for robots to be successfully implemented in these environments, they need to behave in ways that are appropriate and acceptable to human users. One way to identify appropriate social behaviours for healthcare robots is to model their behaviour on interactions between healthcare professionals and patients. This literature review aimed to inform healthcare robotics research by highlighting communication behaviours that are important within the context of healthcare. The review focussed on relevant research in human clinical interactions, followed by a review of similar factors in social robotics research. Three databases were searched for terms relating to healthcare professional communication behaviours associated with patient outcomes. The results identified key communication behaviours that can convey clinical empathy, including humour, self-disclosure, facial expressions, eye gaze, body posture, and gestures. A further search was conducted to identify research examining these key behaviours within the context of social and healthcare robotics. Research into these factors in human–robot interaction in healthcare is limited to date, and this review provides a useful guide for future research.

Deborah L. Johanson

Papers

Developing assistive robots for people with mild cognitive impairment and mild dementia: a qualitative study with older adults and experts in aged care

Components of preoperative anxiety: A qualitative study.

The Effect of Robot Attentional Behaviors on User Perceptions and Behaviors in a Simulated Health Care Interaction: Randomized Controlled Trial

Smiling and use of first-name by a healthcare receptionist robot: Effects on user perceptions, attitudes, and behaviours

Improving Interactions with Healthcare Robots: A Review of Communication Behaviours in Social and Healthcare Contexts