Nurse practitioners' involvement and experience with AI-based health technologies: A systematic review

Raymond, Louis; Castonguay, Alexandre; Doyon, Odette; Paré, Guy

doi:10.1016/j.apnr.2022.151604

Cited by 10 publications

(9 citation statements)

References 28 publications

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“…Our findings established that expectancy (performance expectancy and effort expectancy) influenced healthcare workers’ adoption intention through the mediation of social influence. The perceived utility and ease of use of AI-assisted diagnosis and treatment by healthcare workers would trigger positive attitudes among those around them [ 75 , 91 ]. When people engage in social interactions, healthcare workers are more likely to believe that adopting AI-assisted diagnosis and treatment is useful and effortless and thus would like to accept it.…”

Section: Discussionmentioning

confidence: 99%

Promoting Healthcare Workers’ Adoption Intention of Artificial-Intelligence-Assisted Diagnosis and Treatment: The Chain Mediation of Social Influence and Human–Computer Trust

Cheng

2022

IJERPH

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Artificial intelligence (AI)-assisted diagnosis and treatment could expand the medical scenarios and augment work efficiency and accuracy. However, factors influencing healthcare workers’ adoption intention of AI-assisted diagnosis and treatment are not well-understood. This study conducted a cross-sectional study of 343 dental healthcare workers from tertiary hospitals and secondary hospitals in Anhui Province. The obtained data were analyzed using structural equation modeling. The results showed that performance expectancy and effort expectancy were both positively related to healthcare workers’ adoption intention of AI-assisted diagnosis and treatment. Social influence and human–computer trust, respectively, mediated the relationship between expectancy (performance expectancy and effort expectancy) and healthcare workers’ adoption intention of AI-assisted diagnosis and treatment. Furthermore, social influence and human–computer trust played a chain mediation role between expectancy and healthcare workers’ adoption intention of AI-assisted diagnosis and treatment. Our study provided novel insights into the path mechanism of healthcare workers’ adoption intention of AI-assisted diagnosis and treatment.

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Section: Discussionmentioning

confidence: 99%

Promoting Healthcare Workers’ Adoption Intention of Artificial-Intelligence-Assisted Diagnosis and Treatment: The Chain Mediation of Social Influence and Human–Computer Trust

Cheng

2022

IJERPH

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“…With regard to the organizational context of surveillance, nurse staffing and nursing resources have been associated to patient outcomes and safety (Griffiths et al, 2018; West et al, 2009), as well as the healthcare organization's culture and safety climate (Peet et al, 2019; Thornton Bacon et al, 2021). As to the technological context, nurses' surveillance interventions may be enabled by their use of health information technology (IT)‐based systems such as clinical information systems (CIS; Islam et al, 2018), clinical decision support systems (CDSS; Mebrahtu et al, 2021), artificial intelligence (AI)‐based systems (Raymond et al, 2022) and early warning systems (EWS) in particular (Lee et al, 2020). And apart from their professional and regulatory context, the environmental context of nurses' surveillance interventions has been characterized by their adherence to practice guidelines (Pella et al, 2018) and standards (Gorski et al, 2021), and by their membership in practice communities (Terry et al, 2020).…”

Section: Conceptual and Empirical Backgroundmentioning

confidence: 99%

Surveillance and patient safety in nursing research: A bibliometric analysis from 1993 to 2023

Doyon

Raymond

2023

Journal of Advanced Nursing

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AimsTo identify and characterize the thematic foci, structure and evolution of nursing research on surveillance and patient safety.DesignBibliometric analysis.MethodsBibliometric methods were employed to analyse 1145 articles, using Bibliometrix and VOSviewer software.Data SourceThe Scopus bibliographic database was searched on April 7, 2023.ResultsA keyword co‐occurrence analysis found the most frequently occurring keywords to be: patient safety, nursing, nurses, adverse events, monitoring, critical care, quality improvement, vital signs, safety, alarm fatigue, education, nursing care, surveillance, clinical alarms, failure to rescue, evidence‐based practice, acute care, clinical deterioration, communication, intensive care. Network mapping, clustering and time‐tracking of the keywords revealed the focal themes, structure and evolution of the research field.ConclusionBy assessing critical areas of the nursing research field, this study extends and enriches the current discourse on surveillance and patient safety for nursing researchers and practitioners. Critical challenges still have to be met by nurses, however, including the failure to rescue deteriorating patients. Further knowledge and understanding of surveillance and patient safety must be successfully translated from research to practice.Implications for the ProfessionThis study highlights the gaps in nursing knowledge with regard to surveillance and patient safety and encourages nursing professionals to turn to evidence‐based surveillance practices.ImpactIn addressing the problem of surveillance and its effect on patient safety, this study found that, in most clinical care settings, preventing failures to rescue and adverse patient outcomes still remains a challenge for the nursing profession. This study should have an impact on nursing academics' future research themes and on nursing professionals' future clinical practices.Reporting MethodRelevant EQUATOR guidelines have been adhered to by employing recognized bibliometric reporting methods.

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“…Previous systematic reviews have explored specific aspects of digital tool integration in health care, offering valuable insights into topics such as mobile health, EHRs, and AI-based technologies [44][45][46]. These reviews have effectively highlighted the impacts of digital tools on HCP interactions, communication, and documentation, contributing to a better understanding of the advantages of digital tools in health care and their negative impacts on clinician well-being and burnout [15,[47][48][49][50]. Another review provides comprehensive insights into the positive experiences, facilitators, challenges, barriers, and suggestions for the enhancement of digital care visits [51].…”

Section: Objectivementioning

confidence: 99%

Experience of Health Care Professionals Using Digital Tools in the Hospital: Qualitative Systematic Review

Wosny,

Strasser,

Hastings

2023

JMIR Hum Factors

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Background The digitalization of health care has many potential benefits, but it may also negatively impact health care professionals’ well-being. Burnout can, in part, result from inefficient work processes related to the suboptimal implementation and use of health information technologies. Although strategies to reduce stress and mitigate clinician burnout typically involve individual-based interventions, emerging evidence suggests that improving the experience of using health information technologies can have a notable impact. Objective The aim of this systematic review was to collect evidence of the benefits and challenges associated with the use of digital tools in hospital settings with a particular focus on the experiences of health care professionals using these tools. Methods We conducted a systematic literature review following the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines to explore the experience of health care professionals with digital tools in hospital settings. Using a rigorous selection process to ensure the methodological quality and validity of the study results, we included qualitative studies with distinct data that described the experiences of physicians and nurses. A panel of 3 independent researchers performed iterative data analysis and identified thematic constructs. Results Of the 1175 unique primary studies, we identified 17 (1.45%) publications that focused on health care professionals’ experiences with various digital tools in their day-to-day practice. Of the 17 studies, 10 (59%) focused on clinical decision support tools, followed by 6 (35%) studies focusing on electronic health records and 1 (6%) on a remote patient-monitoring tool. We propose a theoretical framework for understanding the complex interplay between the use of digital tools, experience, and outcomes. We identified 6 constructs that encompass the positive and negative experiences of health care professionals when using digital tools, along with moderators and outcomes. Positive experiences included feeling confident, responsible, and satisfied, whereas negative experiences included frustration, feeling overwhelmed, and feeling frightened. Positive moderators that may reinforce the use of digital tools included sufficient training and adequate workflow integration, whereas negative moderators comprised unfavorable social structures and the lack of training. Positive outcomes included improved patient care and increased workflow efficiency, whereas negative outcomes included increased workload, increased safety risks, and issues with information quality. Conclusions Although positive and negative outcomes and moderators that may affect the use of digital tools were commonly reported, the experiences of health care professionals, such as their thoughts and emotions, were less frequently discussed. On the basis of this finding, this study highlights the need for further research specifically targeting experiences as an important mediator of clinician well-being. It also emphasizes the importance of considering differences in the nature of specific tools as well as the profession and role of individual users. Trial Registration PROSPERO CRD42023393883; https://tinyurl.com/2htpzzxj

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Nurse practitioners' involvement and experience with AI-based health technologies: A systematic review

Cited by 10 publications

References 28 publications

Promoting Healthcare Workers’ Adoption Intention of Artificial-Intelligence-Assisted Diagnosis and Treatment: The Chain Mediation of Social Influence and Human–Computer Trust

Promoting Healthcare Workers’ Adoption Intention of Artificial-Intelligence-Assisted Diagnosis and Treatment: The Chain Mediation of Social Influence and Human–Computer Trust

Surveillance and patient safety in nursing research: A bibliometric analysis from 1993 to 2023

Experience of Health Care Professionals Using Digital Tools in the Hospital: Qualitative Systematic Review

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