The role of artificial intelligence in enhancing clinical nursing care: A scoping review

AIM

To present an overview of how artificial intelligence has been used to improve clinical nursing care.

BACKGROUND

Artificial intelligence has been reshaping the healthcare industry but little is known about its applicability in enhancing nursing care.

EVALUATION

A scoping review was conducted. Seven electronic databases (CINAHL, Cochrane Library, EMBASE, IEEE Xplore, PubMed, Scopus, and Web of Science) were searched from 1 January 2010 till 20 December 2020. Grey literature and reference lists of included articles were also searched.

KEY ISSUES

Thirty-seven studies encapsulating the use of artificial intelligence in improving clinical nursing care were included in this review. Six use cases were identified - documentation, formulating nursing diagnoses, formulating nursing care plans, patient monitoring, patient care prediction such as falls prediction (most common) and wound management. Various techniques of machine learning and classification were used for predictive analyses and to improve nurses' preparedness and management of patients' conditions CONCLUSION: This review highlighted the potential of artificial intelligence in improving the quality of nursing care. However, more randomized controlled trials in real-life healthcare settings should be conducted to enhance the rigor of evidence.

IMPLICATIONS FOR NURSING MANAGEMENT

Education in the application of artificial intelligence should be promoted to empower nurses to lead technological transformations and not passively trail behind others.

Association of artificial intelligence use and the retention of elderly caregivers: A cross-sectional study based on empowerment theory

AIM

The purpose of this study is to investigate how the use of artificial intelligence is associated with the retention of elderly caregivers.

BACKGROUND

The turnover of elderly caregivers is high and increasing. Elderly care institutions are beginning to use artificial intelligence to support caregivers in their work, and the use of technology is critical to staff retention. Empowerment of elderly caregivers has been neglected by managers and researchers.

METHODS

This cross-sectional study involved 511 elderly caregivers in 25 elderly institutions. Six validated standardized scales were used for data collection, and the software SPSS and SmartPLS were used for data analysis.

RESULTS

The quality of artificial intelligence has a significant positive effect on empowerment. Artificial intelligence psychological empowerment (β = .355, p < .001) and artificial intelligence structural empowerment (β = .375, p < .001) both had positive effects on retention intention, and the jointly explained variance (R² ) was 42.6%.

CONCLUSIONS

The results show that a significant relationship exists between artificial intelligence empowerment and retention intention. Elderly caregivers with more structural empowerment have higher retention intention.

IMPLICATIONS FOR NURSING MANAGEMENT

Artificial intelligence suppliers need to pay attention to the role of product quality in elderly care services, continuously improve artificial intelligence quality, and strengthen the application and routine maintenance of artificial intelligence technologies. Elderly care institution managers should pay special attention to artificial intelligence structural empowerment (such as artificial intelligence-related education and training, learning and development opportunities, and resource support).

Home telemonitoring for chronic disease management: Perceptions of users and factors influencing adoption

Home telemonitoring has been used as a solution to support the care of individuals living with chronic disease. While effectiveness of telemonitoring have been widely studied, more research is needed to understand the perceptions among patients and clinicians in incorporating telemonitoring into their daily routine and practices. This paper presents an investigation of patients' and clinicians' experiences in a care augmenting telemonitoring service, their perceived impact delivered through the service, and clinicians' perceptions on how the service was introduced in their organizations. This work was embedded in a large multi-site trial of home telemonitoring using a mixed method approach for evaluation. Interviews with clinicians involved in the study were conducted at multiple time points during the trial. Questionnaires were administered to clinicians and patients at the end of the trial. Results showed that both patients and clinicians recognized the benefits of patient empowerment through telemonitoring, and patient-clinician interactions. Results identified the needs of a dedicated telemonitoring clinical care coordinator role, guidelines that translate telemonitoring services into clinical pathways and engagement of different healthcare providers, especially general practitioners, to support the integration of telemonitoring into chronic disease management programs and long-term organizational strategic plans.

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

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.

INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID)

RR2-10.2196/17490.