Nurses' Anxiety level toward Partnering with Artificial Intelligence in Providing Nursing Care: Pre&Post Training Session

2021 ◽  
Vol 12 (4) ◽  
pp. 1386-1396
Author(s):  
Walaa Nasreldin Othman ◽  
Mostafa Mohamed Zanaty ◽  
Shaimaa Mohamed Elghareeb
Author(s):  
Zi Qi Pamela Ng ◽  
Li Ying Janice Ling ◽  
Han Shi Jocelyn Chew ◽  
Ying Lau

Author(s):  
Kathrin Seibert ◽  
Dominik Domhoff ◽  
Dominik Bruch ◽  
Matthias Schulte-Althoff ◽  
Daniel Fürstenau ◽  
...  

2020 ◽  
Author(s):  
Kathrin Seibert ◽  
Dominik Domhoff ◽  
Dominik Bruch ◽  
Matthias Schulte-Althoff ◽  
Daniel Fürstenau ◽  
...  

BACKGROUND Artificial intelligence (AI) holds the promise to support nurses’ clinical decision making in complex care situations or to conduct tasks that are remote from direct patient interaction such as documentation processes. There has been an increase in research and development of AI applications for nursing care, but a persistent lack of an extensive overview covering the evidence-base for promising application scenarios. OBJECTIVE The paper synthesizes literature on application scenarios for AI in nursing care settings, as well as highlighting adjacent aspects in the ethical, legal and social discourses surrounding the application of AI in nursing care. METHODS Following a rapid review design, databases PubMed, CINAHL, ACM Digital Library, IEEE Xplore, DBLP, and AIS Library, as well as the libraries of leading conferences were searched in June 2020. Publications of quantitative and qualitative original research, systematic reviews, or discussion papers and essays on ethical, legal, and social implications were eligible for inclusion. Based on predetermined selection criteria, eligible studies were analyzed. RESULTS Titles and abstracts of 6,818 publications and 699 fulltexts were screened and 285 publications have been included. Hospitals were the most prominent setting, followed by independent living-at-home, whereas less application scenarios for nursing homes or homecare were identified. Most studies employed machine learning algorithms while expert or hybrid systems were entailed in less than every tenth publication. Application context focused on image and signal processing with tracking, monitoring or classification of activity and health followed by care coordination and communication as well as fall detection was the main purpose of AI applications. Few studies reported effects for clinical or organizational outcomes of AI applications, lacking particularly in data gathered outside of laboratory conditions. Aside from technological requirements, reporting on requirements captures more overarching topics such as data privacy, safety or technology acceptance. Ethical, legal and social implications reflected the discourse on technology use in health care, but have gone mostly undiscussed in detail. CONCLUSIONS The results highlight potential for the application of AI systems in different care settings. With regard to the lack of findings on effectiveness and application of AI systems in real-world scenarios, future research should reflect on a more nursing care specific perspective on objectives, outcomes and benefits. We find an advancement in the technological-societal discourse, surrounding the ethical and legal implications of AI applications in nursing care, to be a practical and needed next step for similar research groups. Further, we outline the need for a greater participation among stakeholders. CLINICALTRIAL not applicable


2021 ◽  
Vol 13 (1) ◽  
Author(s):  
Frances M. Russell ◽  
Robert R. Ehrman ◽  
Allen Barton ◽  
Elisa Sarmiento ◽  
Jakob E. Ottenhoff ◽  
...  

Abstract Background The goal of this study was to assess the ability of machine artificial intelligence (AI) to quantitatively assess lung ultrasound (LUS) B-line presence using images obtained by learners novice to LUS in patients with acute heart failure (AHF), compared to expert interpretation. Methods This was a prospective, multicenter observational study conducted at two urban academic institutions. Learners novice to LUS completed a 30-min training session on lung image acquisition which included lecture and hands-on patient scanning. Learners independently acquired images on patients with suspected AHF. Automatic B-line quantification was obtained offline after completion of the study. Machine AI counted the maximum number of B-lines visualized during a clip. The criterion standard for B-line counts was semi-quantitative analysis by a blinded point-of-care LUS expert reviewer. Image quality was blindly determined by an expert reviewer. A second expert reviewer blindly determined B-line counts and image quality. Intraclass correlation was used to determine agreement between machine AI and expert, and expert to expert. Results Fifty-one novice learners completed 87 scans on 29 patients. We analyzed data from 611 lung zones. The overall intraclass correlation for agreement between novice learner images post-processed with AI technology and expert review was 0.56 (confidence interval [CI] 0.51–0.62), and 0.82 (CI 0.73–0.91) between experts. Median image quality was 4 (on a 5-point scale), and correlation between experts for quality assessment was 0.65 (CI 0.48–0.82). Conclusion After a short training session, novice learners were able to obtain high-quality images. When the AI deep learning algorithm was applied to those images, it quantified B-lines with moderate-to-fair correlation as compared to semi-quantitative analysis by expert review. This data shows promise, but further development is needed before widespread clinical use.


2021 ◽  
Author(s):  
Hirokazu Ito ◽  
Tetsuya Tanioka ◽  
Michael Joseph S. Diño ◽  
Irvin L. Ong ◽  
Rozzano C. Locsin

Robots in healthcare are being developed rapidly, as they offer wide-ranging medical applications and care solutions. However, it is quite challenging to develop high-quality, patient-centered, communication-efficient robots. This can be attributed to a multitude of barriers such as technology maturity, diverse healthcare practices, and humanizing innovations. In order to engineer an ideal Humanoid-Nurse Robots (HNRs), a profound integration of artificial intelligence (AI) and information system like nursing assessment databases for a better nursing care delivery model is required. As a specialized nursing database in psychiatric hospitals, the Psychiatric Nursing Assessment Classification System and Care Planning System (PsyNACS©) has been developed by Ito et al., to augment quality and safe nursing care delivery of psychiatric health services. This chapter describes the nursing landscape in Japan, PsyNACS© as a specialized nursing database, the HNRs of the future, and the future artificial brain for HNRs linking PsyNACS© with AI through deep learning and Natural Language Processing (NLP).


2019 ◽  
Vol 9 (2) ◽  
pp. 95-102
Author(s):  
Abdul Wakhid ◽  
Suwanti Suwanti

Efek samping dari hemodialisa ini berupa perubahan psikologis sehingga terjadi kecemasan. Kecemasan pasien GGK merupakan respon pasien GGK terhadap situasi yang dialami yang mengancam dan merupakan hal normal yang terjadi yang disertai perkembangan, perubahan, pengalaman baru, serta dalam menemukan identitas diri dan hidupnya. Tujuan penelitian ini yakni untuk mengetahui gambaran tingkat kecemasan pasien Gagal Ginjal Kronis yang menjalani hemodialisa di Kabupaten Semarang. Penelitian ini menggunakan desain deskriptif, dengan pendekatan survei. Sampel dalam penelitian ini sejumlah 88 responden,jumlah populasi 124 responden dan menggunakan tekhnikconvenience sampling. Instrumen yang digunakan yakni kuesioner HRSA untuk kategori tingkat kecemasan. Analisis data yang digunakan adalah analisis univariat. Hasil penelitian ditemukan bahwa sebagian besar responden mengalami tingkat kecemasan berat berat sejumlah 30 responden (34,1%).Hasil penelitian ini diharapkan dapat dijadikan sebagai sumber informasi dalam memberikan asuhan keperawatan yang komprehensif dalam hal penanganan masalah psikologis yaitu kecemasan pada pasien yang timbul akibat penyakit kronik.   Kata kunci : Gagal ginjal kronik, Kecemasan   DESCRIPTION OF THE ANXIETY LEVEL OF PATIENTS UNDERGOING HEMODIALYSIS   ABSTRACT The side effects of hemodialysis are psychological changes resulting in anxiety. Anxiety in CRF patients is the response of CRF patients to a situation that is threatening and is a normal thing that happens that is accompanied by developments, changes, new experiences, and in finding their identity and life. The purpose of this study is to describe the anxiety level of patients with chronic kidney failure who undergo hemodialysis in Semarang Regency. This study uses descriptive design, with a survey approach. The sample in this study amounted to 88 respondents, the total population was 124 respondents and used the sampling sampling technique. The instrument used was the HRSA questionnaire for the anxiety level category. The data analysis used is univariate analysis. The results of the study found that most respondents experienced a severe level of anxiety of 30 respondents (34.1%) .The results of this study are expected to be used as a source of information in providing comprehensive nursing care in terms of handling psychological problems, namely anxiety in patients arising from disease chronic.   Keywords: Chronic renal failure, anxiety


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