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Carvalho RLR, Ponce D, Marcolino MS. Artificial intelligence in nursing care: The gap between research and the real world. Intensive Crit Care Nurs 2024; 84:103747. [PMID: 38879953 DOI: 10.1016/j.iccn.2024.103747] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/18/2024]
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Luo C, Mao B, Wu Y, He Y. The research hotspots and theme trends of artificial intelligence in nurse education: A bibliometric analysis from 1994 to 2023. NURSE EDUCATION TODAY 2024; 141:106321. [PMID: 39084073 DOI: 10.1016/j.nedt.2024.106321] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/06/2023] [Revised: 07/09/2024] [Accepted: 07/22/2024] [Indexed: 08/02/2024]
Abstract
OBJECTIVES To explore research hotspots and theme trends in artificial intelligence in nurse education using bibliometric analysis. DESIGN Bibliometric analysis. DATA SOURCES Literature from the Web of Science Core Collection from the time of construction to October 31, 2023 was searched. REVIEW METHODS Analyses of countries, authors, institutions, journals, and keywords were conducted using Bibliometrix (based on R language), CiteSpace, the online analysis platform (bibliometric), Vosviewer, and Pajek. RESULTS A total of 135 articles with a straight upward trend over the last three years were retrieved. By fitting the curve R2 = 0.6022 (R2 > 0.4), we predicted that the number of annual articles is projected to grow in the coming years. The United States (n = 38), the National University of Singapore (n = 16), Professor Jun Ota (n = 8), and Nurse Education Today (n = 14) are the countries, institutions, authors, and journals that contributed to the most publications, respectively. Collaborative network analysis revealed that 32 institutional and 64 author collaborative teams were established. We identified ten high-frequency keywords and nine clusters. We categorized the research hotspots of artificial intelligence in nurse education into three areas: (1) Artificial intelligence-enhanced simulation robots, (2) machine learning and data mining, and (3) large language models based on natural language processing and deep learning. By analyzing the temporal and spatial evolution of keywords and burst detection, we found that future research trends may include (1) expanding and deepening the application of AI technology, (2) assessment of behavioral intent and educational outcomes, and (3) moral and ethical considerations. CONCLUSIONS Future research should be conducted on technology applications, behavioral intent, ethical policy, international cooperation, interdisciplinary cooperation, and sustainability to promote the continued development and innovation of AI in nurse education.
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Affiliation(s)
- Chuhong Luo
- School of Medicine, Hunan Normal University, Changsha, Hunan, People's Republic of China
| | - Bin Mao
- School of Medicine, Hunan Normal University, Changsha, Hunan, People's Republic of China
| | - Ying Wu
- School of Medicine, Hunan Normal University, Changsha, Hunan, People's Republic of China
| | - Ying He
- School of Medicine, Hunan Normal University, Changsha, Hunan, People's Republic of China.
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Rony MKK, Numan SM, Akter K, Tushar H, Debnath M, Johra FT, Akter F, Mondal S, Das M, Uddin MJ, Begum J, Parvin MR. Nurses' perspectives on privacy and ethical concerns regarding artificial intelligence adoption in healthcare. Heliyon 2024; 10:e36702. [PMID: 39281626 PMCID: PMC11400963 DOI: 10.1016/j.heliyon.2024.e36702] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/14/2024] [Revised: 08/08/2024] [Accepted: 08/20/2024] [Indexed: 09/18/2024] Open
Abstract
Background With the increasing integration of artificial intelligence (AI) technologies into healthcare systems, there is a growing emphasis on privacy and ethical considerations. Nurses, as frontline healthcare professionals, are pivotal in-patient care and offer valuable insights into the ethical implications of AI adoption. Objectives This study aimed to explore nurses' perspectives on privacy and ethical concerns associated with the implementation of AI in healthcare settings. Methods We employed Van Manen's hermeneutic phenomenology as the qualitative research approach. Data were collected through purposive sampling from the December 7, 2023 to the January 15, 2024, with interviews conducted in Bengali. Thematic analysis was utilized following member checking and an audit trail. Results Six themes emerged from the research findings: Ethical dimensions of AI integration, highlighting complexities in incorporating AI ethically; Privacy challenges in healthcare AI, revealing concerns about data security and confidentiality; Balancing innovation and ethical practice, indicating a need to reconcile technological advancements with ethical considerations; Human touch vs. technological progress, underscoring tensions between automation and personalized care; Patient-centered care in the AI era, emphasizing the importance of maintaining focus on patients amidst technological advancements; and Ethical preparedness and education, suggesting a need for enhanced training and education on ethical AI use in healthcare. Conclusions The findings underscore the importance of addressing privacy and ethical concerns in AI healthcare development. Nurses advocate for patient-centered approaches and collaborate with policymakers and tech developers to ensure responsible AI adoption. Further research is imperative for mitigating ethical challenges and promoting ethical AI in healthcare practice.
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Affiliation(s)
| | - Sharker Md Numan
- School of Science and Technology, Bangladesh Open University, Gazipur, Bangladesh
| | - Khadiza Akter
- Master of Public Health, Daffodil International University, Dhaka, Bangladesh
| | - Hasanuzzaman Tushar
- Department of Business Administration, International University of Business Agriculture and Technology, Dhaka, Bangladesh
| | - Mitun Debnath
- Master of Public Health, National Institute of Preventive and Social Medicine, Dhaka, Bangladesh
| | - Fateha Tuj Johra
- Masters in Disaster Management, University of Dhaka, Dhaka, Bangladesh
| | - Fazila Akter
- Dhaka Nursing College, Affiliated with the University of Dhaka, Bangladesh
| | - Sujit Mondal
- Master of Science in Nursing, National Institute of Advanced Nursing Education and Research Mugda, Dhaka, Bangladesh
| | - Mousumi Das
- Master of Public Health, Leading University, Sylhet, Bangladesh
| | - Muhammad Join Uddin
- Master of Public Health, RTM Al-Kabir Technical University, Sylhet, Bangladesh
| | - Jeni Begum
- Master of Public Health, Leading University, Sylhet, Bangladesh
| | - Mst Rina Parvin
- School of Medical Sciences, Shahjalal University of Science and Technology, Bangladesh
- Bangladesh Army (AFNS Officer), Combined Military Hospital, Dhaka, Bangladesh
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Akutay S, Kaçmaz HY, Kahraman H. The effect of artificial intelligence supported case analysis on nursing students' case management performance and satisfaction: A randomized controlled trial. Nurse Educ Pract 2024; 80:104142. [PMID: 39299058 DOI: 10.1016/j.nepr.2024.104142] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/13/2024] [Revised: 08/26/2024] [Accepted: 09/10/2024] [Indexed: 09/22/2024]
Abstract
BACKGROUND Rapid developments in artificial intelligence have begun to necessitate changes and transformations in nursing education. OBJECTIVE This study aimed to evaluate the impact of an artificial intelligence-supported case created in the in-class case analysis lecture for nursing students on students' case management performance and satisfaction. DESIGN This study was a randomized controlled trial. METHOD The study involved 188 third-year nursing students randomly assigned to the AI group (n=94) or the control group (n=94). An information form, case evaluation form, knowledge test and Mentimeter application were used to assess the students' case management performance and nursing diagnoses. The level of satisfaction with the case analysis lecture was evaluated using the VAS scale. RESULTS The case management performance scores of the students in the artificial intelligence group were significantly higher than those of the control group (p<0.05). There was no statistically significant difference in satisfaction levels between the artificial intelligence (AI) group and the control group (p>0.05). CONCLUSIONS The study's results indicated that AI-supported cases improved students' case management performance and were as effective as instructor-led cases regarding satisfaction with the case analysis lecture, focus and interest in the case. The integration of artificial intelligence into traditional nursing education curricula is recommended. CLINICAL TRIALS REGISTRATION NUMBER https://register. CLINICALTRIALS gov; (NCT06443983).
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Affiliation(s)
- Seda Akutay
- Department of Surgical Nursing, Erciyes University, Faculty of Health Sciences, Kayseri, Turkey.
| | - Hatice Yüceler Kaçmaz
- Department of Surgical Nursing, Erciyes University, Faculty of Health Sciences, Kayseri, Turkey.
| | - Hilal Kahraman
- Department of Surgical Nursing, Erciyes University, Faculty of Health Sciences, Kayseri, Turkey.
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Ibuki T, Ibuki A, Nakazawa E. Possibilities and ethical issues of entrusting nursing tasks to robots and artificial intelligence. Nurs Ethics 2024; 31:1010-1020. [PMID: 37306294 PMCID: PMC11437727 DOI: 10.1177/09697330221149094] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/13/2023]
Abstract
In recent years, research in robotics and artificial intelligence (AI) has made rapid progress. It is expected that robots and AI will play a part in the field of nursing and their role might broaden in the future. However, there are areas of nursing practice that cannot or should not be entrusted to robots and AI, because nursing is a highly humane practice, and therefore, there would, perhaps, be some practices that should not be replicated by robots or AI. Therefore, this paper focuses on several ethical concepts (advocacy, accountability, cooperation, and caring) that are considered important in nursing practice, and examines whether it is possible to implement these ethical concepts in robots and AI by analyzing the concepts and the current state of robotics and AI technology. Advocacy: Among the components of advocacy, safeguarding and apprising can be more easily implemented, while elements that require emotional communication with patients, such as valuing and mediating, are difficult to implement. Accountability: Robotic nurses with explainable AI have a certain level of accountability. However, the concept of explanation has problems of infinite regression and attribution of responsibility. Cooperation: If robot nurses are recognized as members of a community, they require the same cooperation as human nurses. Caring: More difficulties are expected in care-receiving than in caregiving. However, the concept of caring itself is ambiguous and should be explored further. Accordingly, our analysis suggests that, although some difficulties can be expected in each of these concepts, it cannot be said that it is impossible to implement them in robots and AI. However, even if it were possible to implement these functions in the future, further study is needed to determine whether such robots or AI should be used for nursing care. In such discussions, it will be necessary to involve not only ethicists and nurses but also an array of society members.
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Affiliation(s)
- Tomohide Ibuki
- Faculty of Science and Technology, Tokyo University of Science, Shinjuku-ku, Japan
| | - Ai Ibuki
- Faculty of Nursing, Kyoritsu Women's University, Chiyoda-ku, Japan
| | - Eisuke Nakazawa
- Faculty of Medicine, The University of Tokyo, Bunkyo-ku, Japan
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Wangpitipanit S, Lininger J, Anderson N. Exploring the deep learning of artificial intelligence in nursing: a concept analysis with Walker and Avant's approach. BMC Nurs 2024; 23:529. [PMID: 39090714 PMCID: PMC11295627 DOI: 10.1186/s12912-024-02170-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/14/2024] [Accepted: 07/11/2024] [Indexed: 08/04/2024] Open
Abstract
BACKGROUND In recent years, increased attention has been given to using deep learning (DL) of artificial intelligence (AI) in healthcare to address nursing challenges. The adoption of new technologies in nursing needs to be improved, and AI in nursing is still in its early stages. However, the current literature needs more clarity, which affects clinical practice, research, and theory development. This study aimed to clarify the meaning of deep learning and identify the defining attributes of artificial intelligence within nursing. METHODS We conducted a concept analysis of the deep learning of AI in nursing care using Walker and Avant's 8-step approach. Our search strategy employed Boolean techniques and MeSH terms across databases, including BMC, CINAHL, ClinicalKey for Nursing, Embase, Ovid, Scopus, SpringerLink and Spinger Nature, ProQuest, PubMed, and Web of Science. By focusing on relevant keywords in titles and abstracts from articles published between 2018 and 2024, we initially found 571 sources. RESULTS Thirty-seven articles that met the inclusion criteria were analyzed in this study. The attributes of evidence included four themes: focus and immersion, coding and understanding, arranging layers and algorithms, and implementing within the process of use cases to modify recommendations. Antecedents, unclear systems and communication, insufficient data management knowledge and support, and compound challenges can lead to suffering and risky caregiving tasks. Applying deep learning techniques enables nurses to simulate scenarios, predict outcomes, and plan care more precisely. Embracing deep learning equipment allows nurses to make better decisions. It empowers them with enhanced knowledge while ensuring adequate support and resources essential for caregiver and patient well-being. Access to necessary equipment is vital for high-quality home healthcare. CONCLUSION This study provides a clearer understanding of the use of deep learning in nursing and its implications for nursing practice. Future research should focus on exploring the impact of deep learning on healthcare operations management through quantitative and qualitative studies. Additionally, developing a framework to guide the integration of deep learning into nursing practice is recommended to facilitate its adoption and implementation.
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Affiliation(s)
- Supichaya Wangpitipanit
- Visiting Assistant Professor, Division of Health Informatics, Department of Public Health Sciences, UC Davis School of Medicine, University of California, Davis, USA, Division of Community Health Nursing, Ramathibodi School of Nursing, Faculty of Medicine Ramathibodi Hospital, Mahidol University, Bangkok, Thailand
| | - Jiraporn Lininger
- Division of Community Health Nursing, Ramathibodi School of Nursing, Faculty of Medicine Ramathibodi Hospital, Mahidol University, Bangkok, Thailand.
| | - Nick Anderson
- Division of Health Informatics, Department of Public Health Sciences, UC Davis School of Medicine, University of California, Davis, USA
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Flenady T, Connor J, Byrne AL, Massey D, Le Lagadec MD. The impact of mandated use early warning system tools on the development of nurses' higher-order thinking: A systematic review. J Clin Nurs 2024; 33:3381-3398. [PMID: 38661093 DOI: 10.1111/jocn.17178] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/29/2023] [Revised: 03/17/2024] [Accepted: 04/07/2024] [Indexed: 04/26/2024]
Abstract
AIM Ascertain the impact of mandated use of early warning systems (EWSs) on the development of registered nurses' higher-order thinking. DESIGN A systematic literature review was conducted, following Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines and checklist (Page et al., 2021). DATA SOURCES CINAHL, Medline, Embase, PyscInfo. REVIEW METHODS Eligible articles were quality appraised using the MMAT tool. Data extraction was conducted independently by four reviewers. Three investigators thematically analysed the data. RESULTS Our review found that EWSs can support or suppress the development of nurses' higher-order thinking. EWS supports the development of higher-order thinking in two ways; by confirming nurses' subjective clinical assessment of patients and/or by providing a rationale for the escalation of care. Of note, more experienced nurses expressed their view that junior nurses are inhibited from developing effective higher-order thinking due to reliance on the tool. CONCLUSION EWSs facilitate early identification of clinical deterioration in hospitalised patients. The impact of EWSs on the development of nurses' higher-order thinking is under-explored. We found that EWSs can support and suppress nurses' higher-order thinking. EWS as a supportive factor reinforces the development of nurses' heuristics, the mental shortcuts experienced clinicians call on when interpreting their subjective clinical assessment of patients. Conversely, EWS as a suppressive factor inhibits the development of nurses' higher-order thinking and heuristics, restricting the development of muscle memory regarding similar presentations they may encounter in the future. Clinicians' ability to refine and expand on their catalogue of heuristics is important as it endorses the future provision of safe and effective care for patients who present with similar physiological signs and symptoms. IMPACT This research impacts health services and education providers as EWS and nurses' development of higher-order thinking skills are essential aspects of delivering safe, quality care. NO PATIENT OR PUBLIC CONTRIBUTION This is a systematic review, and therefore, comprises no contribution from patients or the public.
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Affiliation(s)
- Tracy Flenady
- Central Queensland University, Rockhampton, Queensland, Australia
| | - Justine Connor
- Central Queensland University, Rockhampton, Queensland, Australia
| | - Amy-Louise Byrne
- Central Queensland University, Rockhampton, Queensland, Australia
| | - Deb Massey
- Edith Cowen University, Joondalup, Western Australia, Australia
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Lukkien DRM, Stolwijk NE, Ipakchian Askari S, Hofstede BM, Nap HH, Boon WPC, Peine A, Moors EHM, Minkman MMN. AI-Assisted Decision-Making in Long-Term Care: Qualitative Study on Prerequisites for Responsible Innovation. JMIR Nurs 2024; 7:e55962. [PMID: 39052315 PMCID: PMC11310645 DOI: 10.2196/55962] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/01/2024] [Revised: 04/16/2024] [Accepted: 05/24/2024] [Indexed: 07/27/2024] Open
Abstract
BACKGROUND Although the use of artificial intelligence (AI)-based technologies, such as AI-based decision support systems (AI-DSSs), can help sustain and improve the quality and efficiency of care, their deployment creates ethical and social challenges. In recent years, a growing prevalence of high-level guidelines and frameworks for responsible AI innovation has been observed. However, few studies have specified the responsible embedding of AI-based technologies, such as AI-DSSs, in specific contexts, such as the nursing process in long-term care (LTC) for older adults. OBJECTIVE Prerequisites for responsible AI-assisted decision-making in nursing practice were explored from the perspectives of nurses and other professional stakeholders in LTC. METHODS Semistructured interviews were conducted with 24 care professionals in Dutch LTC, including nurses, care coordinators, data specialists, and care centralists. A total of 2 imaginary scenarios about AI-DSSs were developed beforehand and used to enable participants articulate their expectations regarding the opportunities and risks of AI-assisted decision-making. In addition, 6 high-level principles for responsible AI were used as probing themes to evoke further consideration of the risks associated with using AI-DSSs in LTC. Furthermore, the participants were asked to brainstorm possible strategies and actions in the design, implementation, and use of AI-DSSs to address or mitigate these risks. A thematic analysis was performed to identify the opportunities and risks of AI-assisted decision-making in nursing practice and the associated prerequisites for responsible innovation in this area. RESULTS The stance of care professionals on the use of AI-DSSs is not a matter of purely positive or negative expectations but rather a nuanced interplay of positive and negative elements that lead to a weighed perception of the prerequisites for responsible AI-assisted decision-making. Both opportunities and risks were identified in relation to the early identification of care needs, guidance in devising care strategies, shared decision-making, and the workload of and work experience of caregivers. To optimally balance the opportunities and risks of AI-assisted decision-making, seven categories of prerequisites for responsible AI-assisted decision-making in nursing practice were identified: (1) regular deliberation on data collection; (2) a balanced proactive nature of AI-DSSs; (3) incremental advancements aligned with trust and experience; (4) customization for all user groups, including clients and caregivers; (5) measures to counteract bias and narrow perspectives; (6) human-centric learning loops; and (7) the routinization of using AI-DSSs. CONCLUSIONS The opportunities of AI-assisted decision-making in nursing practice could turn into drawbacks depending on the specific shaping of the design and deployment of AI-DSSs. Therefore, we recommend considering the responsible use of AI-DSSs as a balancing act. Moreover, considering the interrelatedness of the identified prerequisites, we call for various actors, including developers and users of AI-DSSs, to cohesively address the different factors important to the responsible embedding of AI-DSSs in practice.
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Affiliation(s)
- Dirk R M Lukkien
- Vilans Centre of Expertise of Long Term Care, Utrecht, Netherlands
- Copernicus Institute of Sustainable Development, Utrecht University, Utrecht, Netherlands
| | | | - Sima Ipakchian Askari
- Vilans Centre of Expertise of Long Term Care, Utrecht, Netherlands
- Human Technology Interaction, Eindhoven University of Technology, Eindhoven, Netherlands
| | - Bob M Hofstede
- Vilans Centre of Expertise of Long Term Care, Utrecht, Netherlands
- Human Technology Interaction, Eindhoven University of Technology, Eindhoven, Netherlands
| | - Henk Herman Nap
- Vilans Centre of Expertise of Long Term Care, Utrecht, Netherlands
- Human Technology Interaction, Eindhoven University of Technology, Eindhoven, Netherlands
| | - Wouter P C Boon
- Copernicus Institute of Sustainable Development, Utrecht University, Utrecht, Netherlands
| | - Alexander Peine
- Faculty of Humanities, Open University of The Netherlands, Heerlen, Netherlands
| | - Ellen H M Moors
- Copernicus Institute of Sustainable Development, Utrecht University, Utrecht, Netherlands
| | - Mirella M N Minkman
- Vilans Centre of Expertise of Long Term Care, Utrecht, Netherlands
- TIAS School for Business and Society, Tilburg University, Tilburg, Netherlands
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Kilci Erciyas Ş, Cirban Ekrem E, Keten Edis E. Relationship Between Individual Innovativeness Levels and Attitudes Toward Artificial Intelligence Among Nursing and Midwifery Students. Comput Inform Nurs 2024:00024665-990000000-00212. [PMID: 39023377 DOI: 10.1097/cin.0000000000001170] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 07/20/2024]
Abstract
The aim of this study is to explore the connection between individual innovativeness levels and attitudes toward artificial intelligence among nursing and midwifery students. Data were collected from 500 nursing and midwifery students studying at a university in Türkiye. The data gathered between November and December 2023 involved a Personal Information Form, the Individual Innovation Scale, and the General Attitudes toward Artificial Intelligence Scale. Data analysis used descriptive statistics, independent-samples t test, analysis of variance, Bonferroni test, and logistic regression models. Students' average Individual Innovativeness Scale score was 59.47 ± 7.23. Consequently, it was determined that students' individual innovativeness levels were inadequate, placing them in the questioning group. Students demonstrated positive attitudes toward artificial intelligence, with General Attitudes toward Artificial Intelligence Scale-positive scores at a good level (42.67 ± 7.10) and negative attitudes at an average level (24.08 ± 5.81). A significant, positive relationship was found between Individual Innovation Scale and General Attitudes toward Artificial Intelligence Scale total scores (P < .001). The individual innovation level of students proved to be a significant predictor of attitudes toward artificial intelligence (P < .001). Students' individual innovativeness levels positively influence their attitudes toward artificial intelligence. However, it was identified that students' individual innovativeness levels are not sufficient and require improvement.
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Affiliation(s)
- Şeyma Kilci Erciyas
- Author Affiliations: Department of Nursing, Faculty of Health Sciences, Amasya University, Türkiye (Mrs Erciyas and Mrs Edis); and Department of Nursing, Faculty of Health Sciences, Bartin University, Bartin, Türkiye (Mrs Ekrem)
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Özçevik Subaşi D, Akça Sümengen A, Semerci R, Şimşek E, Çakır GN, Temizsoy E. Paediatric nurses' perspectives on artificial intelligence applications: A cross-sectional study of concerns, literacy levels and attitudes. J Adv Nurs 2024. [PMID: 39003632 DOI: 10.1111/jan.16335] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/20/2024] [Revised: 06/24/2024] [Accepted: 07/02/2024] [Indexed: 07/15/2024]
Abstract
AIMS This study aimed to explore the correlation between artificial intelligence (AI) literacy, AI anxiety and AI attitudes among paediatric nurses, as well as identify the influencing factors on paediatric nurses' AI attitudes. DESIGN A descriptive, correlational and cross-sectional research. METHODS This study was conducted between January and February 2024 with 170 nurses actively working in paediatric clinics in Turkey. The data collection tools included the Nurse Information Form, the General Attitudes Towards Artificial Intelligence Scale (GAAIS), the Artificial Intelligence Literacy Scale (AILS) and the Artificial Intelligence Anxiety Scale (AIAS). To determine the associations between the variables, the data was analysed using IBM SPSS 28, which included linear regression and Pearson correlation analysis. RESULTS The study indicated significant positive correlations between paediatric nurses' age and their AIAS scores (r = .226; p < .01) and significant negative correlations between paediatric nurses' age and their AILS (r = -.192; p < .05) and GAAIS scores (r = -.152; p < .05). The GAAIS was significantly predictive (p < .000) and accounted for 50% of the variation in AIAS and AILS scores. CONCLUSION Paediatric nurses' attitudes towards AI significantly predicted AI literacy and AI anxiety. The relationship between the age of the paediatric nurses and the anxiety, AI literacy and attitudes towards AI was demonstrated. Healthcare and educational institutions should create customized training programs and awareness-raising activities for older nurses, as there are noticeable variations in the attitudes of paediatric nurses towards AI based on their age. IMPLICATIONS FOR PROFESSION AND/OR PATIENT CARE Providing in-service AI training can help healthcare organizations improve paediatric nurses' attitudes towards AI, increase their AI literacy and reduce their anxiety. This training has the potential to impact their attitudes positively and reduce their anxiety. REPORTING METHOD The study results were critically reported using STROBE criteria. PATIENT OR PUBLIC CONTRIBUTION No patient or public contribution.
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Affiliation(s)
| | - Aylin Akça Sümengen
- Capstone College of Nursing, The University of Alabama, Tuscaloosa, Alabama, USA
| | - Remziye Semerci
- Department of Pediatric Nursing, School of Nursing, Koç University, Istanbul, Turkey
| | - Enes Şimşek
- Department of Pediatric Nursing, School of Nursing, Koç University, Istanbul, Turkey
| | - Gökçe Naz Çakır
- Department of Nursing, Faculty of Health Science, Yeditepe University, Istanbul, Turkey
| | - Ebru Temizsoy
- Department of Nursing, Faculty of Health Sciences, Istanbul Bilgi University, Istanbul, Turkey
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11
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da Rosa NG, Vaz TA, Lucena ADF. Nursing workload: use of artificial intelligence to develop a classifier model. Rev Lat Am Enfermagem 2024; 32:e4239. [PMID: 38985046 PMCID: PMC11251687 DOI: 10.1590/1518-8345.7131.4239] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/03/2023] [Accepted: 03/13/2024] [Indexed: 07/11/2024] Open
Abstract
OBJECTIVE to describe the development of a predictive nursing workload classifier model, using artificial intelligence. METHOD retrospective observational study, using secondary sources of electronic patient records, using machine learning. The convenience sample consisted of 43,871 assessments carried out by clinical nurses using the Perroca Patient Classification System, which served as the gold standard, and clinical data from the electronic medical records of 11,774 patients, which constituted the variables. In order to organize the data and carry out the analysis, the Dataiku® data science platform was used. Data analysis occurred in an exploratory, descriptive and predictive manner. The study was approved by the Ethics and Research Committee of the institution where the study was carried out. RESULTS the use of artificial intelligence enabled the development of the nursing workload assessment classifier model, identifying the variables that most contributed to its prediction. The algorithm correctly classified 72% of the variables and the area under the Receiver Operating Characteristic curve was 82%. CONCLUSION a predictive model was developed, demonstrating that it is possible to train algorithms with data from the patient's electronic medical record to predict the nursing workload and that artificial intelligence tools can be effective in automating this activity.
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Affiliation(s)
- Ninon Girardon da Rosa
- Universidade Federal do Rio Grande do Sul, Escola de Enfermagem, Porto Alegre, RS, Brazil
- Hospital de Clínicas de Porto Alegre, Diretoria de Enfermagem, Porto Alegre, RS, Brazil
| | - Tiago Andres Vaz
- University Medical Center Utrecht, Data Science and Bioestatistic, Utrecht, Netherlands
| | - Amália de Fátima Lucena
- Universidade Federal do Rio Grande do Sul, Escola de Enfermagem, Porto Alegre, RS, Brazil
- Hospital de Clínicas de Porto Alegre, Comissão do Processo de Enfermagem, Porto Alegre, RS, Brazil
- Scholarship holder at the Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq), Brazil
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Karacan E. Evaluating the Quality of Postpartum Hemorrhage Nursing Care Plans Generated by Artificial Intelligence Models. J Nurs Care Qual 2024; 39:206-211. [PMID: 38701406 DOI: 10.1097/ncq.0000000000000766] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/05/2024]
Abstract
BACKGROUND With the rapidly advancing technological landscape of health care, evaluating the potential use of artificial intelligence (AI) models to prepare nursing care plans is of great importance. PURPOSE The purpose of this study was to evaluate the quality of nursing care plans created by AI for the management of postpartum hemorrhage (PPH). METHODS This cross-sectional exploratory study involved creating a scenario for an imaginary patient with PPH. Information was put into 3 AI platforms (GPT-4, LaMDA, Med-PaLM) on consecutive days without prior conversation. Care plans were evaluated using the Grading of Recommendations Assessment, Development, and Evaluation (GRADE) scale. RESULTS Med-PaLM exhibited superior quality in developing the care plan compared with LaMDA ( Z = 4.354; P = .000) and GPT-4 ( Z = 3.126; P = .029). CONCLUSIONS Our findings suggest that despite the strong performance of Med-PaLM, AI, in its current state, is unsuitable for use with real patients.
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Affiliation(s)
- Emine Karacan
- Dortyol Vocational School of Health Services, Iskenderun Technical University, Hatay, Turkey
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Wang Y, Fu W, Zhang Y, Wang D, Gu Y, Wang W, Xu H, Ge X, Ye C, Fang J, Su L, Wang J, He W, Zhang X, Feng R. Constructing and implementing a performance evaluation indicator set for artificial intelligence decision support systems in pediatric outpatient clinics: an observational study. Sci Rep 2024; 14:14482. [PMID: 38914707 PMCID: PMC11196575 DOI: 10.1038/s41598-024-64893-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/01/2023] [Accepted: 06/13/2024] [Indexed: 06/26/2024] Open
Abstract
Artificial intelligence (AI) decision support systems in pediatric healthcare have a complex application background. As an AI decision support system (AI-DSS) can be costly, once applied, it is crucial to focus on its performance, interpret its success, and then monitor and update it to ensure ongoing success consistently. Therefore, a set of evaluation indicators was explicitly developed for AI-DSS in pediatric healthcare, enabling continuous and systematic performance monitoring. The study unfolded in two stages. The first stage encompassed establishing the evaluation indicator set through a literature review, a focus group interview, and expert consultation using the Delphi method. In the second stage, weight analysis was conducted. Subjective weights were calculated based on expert opinions through analytic hierarchy process, while objective weights were determined using the entropy weight method. Subsequently, subject and object weights were synthesized to form the combined weight. In the two rounds of expert consultation, the authority coefficients were 0.834 and 0.846, Kendall's coordination coefficient was 0.135 in Round 1 and 0.312 in Round 2. The final evaluation indicator set has three first-class indicators, fifteen second-class indicators, and forty-seven third-class indicators. Indicator I-1(Organizational performance) carries the highest weight, followed by Indicator I-2(Societal performance) and Indicator I-3(User experience performance) in the objective and combined weights. Conversely, 'Societal performance' holds the most weight among the subjective weights, followed by 'Organizational performance' and 'User experience performance'. In this study, a comprehensive and specialized set of evaluation indicators for the AI-DSS in the pediatric outpatient clinic was established, and then implemented. Continuous evaluation still requires long-term data collection to optimize the weight proportions of the established indicators.
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Affiliation(s)
- Yingwen Wang
- Nursing Department, Children's Hospital of Fudan University, Shanghai, 201102, China
| | - Weijia Fu
- Medical Information Center, Children's Hospital of Fudan University, Shanghai, 201102, China
| | - Yuejie Zhang
- School of Computer Science, Fudan University, Shanghai, 200438, China
| | - Daoyang Wang
- School of Public, Health Fudan University, Shanghai, 200032, China
| | - Ying Gu
- Nursing Department, Children's Hospital of Fudan University, Shanghai, 201102, China
| | - Weibing Wang
- School of Public, Health Fudan University, Shanghai, 200032, China
| | - Hong Xu
- Nephrology Department, Children's Hospital of Fudan University, Shanghai, 201102, China
| | - Xiaoling Ge
- Statistical and Data Management Center, Children's Hospital of Fudan University, Shanghai, 201102, China
| | - Chengjie Ye
- Medical Information Center, Children's Hospital of Fudan University, Shanghai, 201102, China
| | - Jinwu Fang
- School of Public, Health Fudan University, Shanghai, 200032, China
| | - Ling Su
- Statistical and Data Management Center, Children's Hospital of Fudan University, Shanghai, 201102, China
| | - Jiayu Wang
- National Health Commission Key Laboratory of Neonatal Diseases (Fudan University), Children's Hospital of Fudan University, Shanghai, 201102, China
| | - Wen He
- Respiratory Department, Children's Hospital of Fudan University, Shanghai, 201102, China
| | - Xiaobo Zhang
- Respiratory Department, Children's Hospital of Fudan University, Shanghai, 201102, China.
| | - Rui Feng
- School of Computer Science, Fudan University, Shanghai, 200438, China.
- School of Computer Science, Fudan University, 2005 Songhu Road, Shanghai, 200438, China.
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14
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Kuziemsky CE, Chrimes D, Minshall S, Mannerow M, Lau F. AI Quality Standards in Health Care: Rapid Umbrella Review. J Med Internet Res 2024; 26:e54705. [PMID: 38776538 PMCID: PMC11153979 DOI: 10.2196/54705] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/19/2023] [Revised: 04/03/2024] [Accepted: 04/04/2024] [Indexed: 05/25/2024] Open
Abstract
BACKGROUND In recent years, there has been an upwelling of artificial intelligence (AI) studies in the health care literature. During this period, there has been an increasing number of proposed standards to evaluate the quality of health care AI studies. OBJECTIVE This rapid umbrella review examines the use of AI quality standards in a sample of health care AI systematic review articles published over a 36-month period. METHODS We used a modified version of the Joanna Briggs Institute umbrella review method. Our rapid approach was informed by the practical guide by Tricco and colleagues for conducting rapid reviews. Our search was focused on the MEDLINE database supplemented with Google Scholar. The inclusion criteria were English-language systematic reviews regardless of review type, with mention of AI and health in the abstract, published during a 36-month period. For the synthesis, we summarized the AI quality standards used and issues noted in these reviews drawing on a set of published health care AI standards, harmonized the terms used, and offered guidance to improve the quality of future health care AI studies. RESULTS We selected 33 review articles published between 2020 and 2022 in our synthesis. The reviews covered a wide range of objectives, topics, settings, designs, and results. Over 60 AI approaches across different domains were identified with varying levels of detail spanning different AI life cycle stages, making comparisons difficult. Health care AI quality standards were applied in only 39% (13/33) of the reviews and in 14% (25/178) of the original studies from the reviews examined, mostly to appraise their methodological or reporting quality. Only a handful mentioned the transparency, explainability, trustworthiness, ethics, and privacy aspects. A total of 23 AI quality standard-related issues were identified in the reviews. There was a recognized need to standardize the planning, conduct, and reporting of health care AI studies and address their broader societal, ethical, and regulatory implications. CONCLUSIONS Despite the growing number of AI standards to assess the quality of health care AI studies, they are seldom applied in practice. With increasing desire to adopt AI in different health topics, domains, and settings, practitioners and researchers must stay abreast of and adapt to the evolving landscape of health care AI quality standards and apply these standards to improve the quality of their AI studies.
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Affiliation(s)
| | - Dillon Chrimes
- School of Health Information Science, University of Victoria, Victoria, BC, Canada
| | - Simon Minshall
- School of Health Information Science, University of Victoria, Victoria, BC, Canada
| | | | - Francis Lau
- School of Health Information Science, University of Victoria, Victoria, BC, Canada
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15
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Georgantes ER, Gunturkun F, McGreevy TJ, Lough ME. Machine learning evaluation of inequities and disparities associated with nurse sensitive indicator safety events. J Nurs Scholarsh 2024. [PMID: 38773783 DOI: 10.1111/jnu.12983] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/02/2024] [Revised: 04/23/2024] [Accepted: 05/01/2024] [Indexed: 05/24/2024]
Abstract
PURPOSE To use machine learning to examine health equity and clinical outcomes in patients who experienced a nurse sensitive indicator (NSI) event, defined as a fall, a hospital-acquired pressure injury (HAPI) or a hospital-acquired infection (HAI). DESIGN This was a retrospective observational study from a single academic hospital over six calendar years (2016-2021). Machine learning was used to examine patients with an NSI compared to those without. METHODS Inclusion criteria: all adult inpatient admissions (2016-2021). Three approaches were used to analyze the NSI group compared to the No-NSI group. In the univariate analysis, descriptive statistics, and absolute standardized differences (ASDs) were employed to compare the demographics and clinical variables of patients who experienced a NSI and those who did not experience any NSIs. For the multivariate analysis, a light grading boosting machine (LightGBM) model was utilized to comprehensively examine the relationships associated with the development of an NSI. Lastly, a simulation study was conducted to quantify the strength of associations obtained from the machine learning model. RESULTS From 163,507 admissions, 4643 (2.8%) were associated with at least one NSI. The mean, standard deviation (SD) age was 59.5 (18.2) years, males comprised 82,397 (50.4%). Non-Hispanic White 84,760 (51.8%), non-Hispanic Black 8703 (5.3%), non-Hispanic Asian 23,368 (14.3%), non-Hispanic Other 14,284 (8.7%), and Hispanic 30,271 (18.5%). Race and ethnicity alone were not associated with occurrence of an NSI. The NSI group had a statistically significant longer length of stay (LOS), longer intensive care unit (ICU) LOS, and was more likely to have an emergency admission compared to the group without an NSI. The simulation study results demonstrated that likelihood of NSI was higher in patients admitted under the major diagnostic categories (MDC) associated with circulatory, digestive, kidney/urinary tract, nervous, and infectious and parasitic disease diagnoses. CONCLUSION In this study, race/ethnicity was not associated with the risk of an NSI event. The risk of an NSI event was associated with emergency admission, longer LOS, longer ICU-LOS and certain MDCs (circulatory, digestive, kidney/urinary, nervous, infectious, and parasitic diagnoses). CLINICAL RELEVANCE Machine learning methodologies provide a new mechanism to investigate NSI events through the lens of health equity/disparity. Understanding which patients are at higher risk for adverse outcomes can help hospitals improve nursing care and prevent NSI injury and harm.
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Affiliation(s)
- Erika R Georgantes
- Nursing Quality Management Coordinator, Nursing Quality, Stanford Health Care, Stanford, California, USA
| | - Fatma Gunturkun
- Quantitative Sciences Unit, Stanford University, Stanford, California, USA
| | - T J McGreevy
- Quality Analytics, Stanford Health Care, Stanford, California, USA
| | - Mary E Lough
- Center for Evidence Based Practice and Implementation Science, Stanford Health Care, Stanford, California, USA
- Stanford School of Medicine, Stanford University, Stanford, California, USA
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16
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Wang Z, Tan X, Xue Y, Xiao C, Yue K, Lin K, Wang C, Zhou Q, Zhang J. Smart diabetic foot ulcer scoring system. Sci Rep 2024; 14:11588. [PMID: 38773207 PMCID: PMC11109117 DOI: 10.1038/s41598-024-62076-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/27/2023] [Accepted: 05/13/2024] [Indexed: 05/23/2024] Open
Abstract
Current assessment methods for diabetic foot ulcers (DFUs) lack objectivity and consistency, posing a significant risk to diabetes patients, including the potential for amputations, highlighting the urgent need for improved diagnostic tools and care standards in the field. To address this issue, the objective of this study was to develop and evaluate the Smart Diabetic Foot Ulcer Scoring System, ScoreDFUNet, which incorporates artificial intelligence (AI) and image analysis techniques, aiming to enhance the precision and consistency of diabetic foot ulcer assessment. ScoreDFUNet demonstrates precise categorization of DFU images into "ulcer," "infection," "normal," and "gangrene" areas, achieving a noteworthy accuracy rate of 95.34% on the test set, with elevated levels of precision, recall, and F1 scores. Comparative evaluations with dermatologists affirm that our algorithm consistently surpasses the performance of junior and mid-level dermatologists, closely matching the assessments of senior dermatologists, and rigorous analyses including Bland-Altman plots and significance testing validate the robustness and reliability of our algorithm. This innovative AI system presents a valuable tool for healthcare professionals and can significantly improve the care standards in the field of diabetic foot ulcer assessment.
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Affiliation(s)
- Zheng Wang
- School of Computer Science, Hunan First Normal University, Changsha, 410205, China
- Department of Dermatology, Shenzhen People's Hospital, The Second Clinical Medical College, Jinan University, The First Affiliated Hospital, Southern University of Science and Technology, Shenzhen, 518020, Guangdong, China
| | - Xinyu Tan
- School of Computer Science, Hunan First Normal University, Changsha, 410205, China
| | - Yang Xue
- School of Computer Science, Hunan First Normal University, Changsha, 410205, China
| | - Chen Xiao
- Department of Dermatology, Shenzhen People's Hospital, The Second Clinical Medical College, Jinan University, The First Affiliated Hospital, Southern University of Science and Technology, Shenzhen, 518020, Guangdong, China
- Candidate Branch of National Clinical Research Center for Skin Diseases, Shenzhen, 518020, Guangdong, China
| | - Kejuan Yue
- School of Computer Science, Hunan First Normal University, Changsha, 410205, China
| | - Kaibin Lin
- School of Computer Science, Hunan First Normal University, Changsha, 410205, China
| | - Chong Wang
- Department of Dermatology, Shenzhen People's Hospital, The Second Clinical Medical College, Jinan University, The First Affiliated Hospital, Southern University of Science and Technology, Shenzhen, 518020, Guangdong, China.
- Candidate Branch of National Clinical Research Center for Skin Diseases, Shenzhen, 518020, Guangdong, China.
| | - Qiuhong Zhou
- Department of Clinical Nursing, Xiangya Hospital, Central South University, Changsha, 410008, China.
- Foot Prevention and Treatment Center, Xiangya Hospital, Central South University, Changsha, 410008, China.
| | - Jianglin Zhang
- Department of Dermatology, Shenzhen People's Hospital, The Second Clinical Medical College, Jinan University, The First Affiliated Hospital, Southern University of Science and Technology, Shenzhen, 518020, Guangdong, China.
- Candidate Branch of National Clinical Research Center for Skin Diseases, Shenzhen, 518020, Guangdong, China.
- Department of Geriatrics, Shenzhen People's Hospital, The Second Clinical Medical College, Jinan University, The First Affiliated Hospital, Southern University of Science and Technology, Shenzhen, 518020, Guangdong, China.
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17
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Katwaroo AR, Adesh VS, Lowtan A, Umakanthan S. The diagnostic, therapeutic, and ethical impact of artificial intelligence in modern medicine. Postgrad Med J 2024; 100:289-296. [PMID: 38159301 DOI: 10.1093/postmj/qgad135] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/27/2023] [Accepted: 12/02/2023] [Indexed: 01/03/2024]
Abstract
In the evolution of modern medicine, artificial intelligence (AI) has been proven to provide an integral aspect of revolutionizing clinical diagnosis, drug discovery, and patient care. With the potential to scrutinize colossal amounts of medical data, radiological and histological images, and genomic data in healthcare institutions, AI-powered systems can recognize, determine, and associate patterns and provide impactful insights that would be strenuous and challenging for clinicians to detect during their daily clinical practice. The outcome of AI-mediated search offers more accurate, personalized patient diagnoses, guides in research for new drug therapies, and provides a more effective multidisciplinary treatment plan that can be implemented for patients with chronic diseases. Among the many promising applications of AI in modern medicine, medical imaging stands out distinctly as an area with tremendous potential. AI-powered algorithms can now accurately and sensitively identify cancer cells and other lesions in medical images with greater accuracy and sensitivity. This allows for earlier diagnosis and treatment, which can significantly impact patient outcomes. This review provides a comprehensive insight into diagnostic, therapeutic, and ethical issues with the advent of AI in modern medicine.
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Affiliation(s)
- Arun Rabindra Katwaroo
- Department of Medicine, Trinidad Institute of Medical Technology, St Augustine, Trinidad and Tobago
| | | | - Amrita Lowtan
- Department of Preclinical Sciences, Faculty of Medical Sciences, The University of the West Indies, St. Augustine, Trinidad and Tobago
| | - Srikanth Umakanthan
- Department of Paraclinical Sciences, Faculty of Medical Sciences, The University of the West Indies, St. Augustine, Trinidad and Tobago
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18
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Ruksakulpiwat S, Thorngthip S, Niyomyart A, Benjasirisan C, Phianhasin L, Aldossary H, Ahmed BH, Samai T. A Systematic Review of the Application of Artificial Intelligence in Nursing Care: Where are We, and What's Next? J Multidiscip Healthc 2024; 17:1603-1616. [PMID: 38628616 PMCID: PMC11020344 DOI: 10.2147/jmdh.s459946] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/16/2024] [Accepted: 03/05/2024] [Indexed: 04/19/2024] Open
Abstract
Background Integrating Artificial Intelligence (AI) into healthcare has transformed the landscape of patient care and healthcare delivery. Despite this, there remains a notable gap in the existing literature synthesizing the comprehensive understanding of AI's utilization in nursing care. Objective This systematic review aims to synthesize the available evidence to comprehensively understand the application of AI in nursing care. Methods Studies published between January 2019 and December 2023, identified through CINAHL Plus with Full Text, Web of Science, PubMed, and Medline, were included in this review. The Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines guided the identification, screening, exclusion, and inclusion of articles. The convergent integrated analysis framework, as proposed by the Joanna Briggs Institute, was employed to synthesize data from the included studies for theme generation. Results A total of 337 records were identified from databases. Among them, 35 duplicates were removed, and 302 records underwent eligibility screening. After applying inclusion and exclusion criteria, eleven studies were deemed eligible and included in this review. Through data synthesis of these studies, six themes pertaining to the use of AI in nursing care were identified: 1) Risk Identification, 2) Health Assessment, 3) Patient Classification, 4) Research Development, 5) Improved Care Delivery and Medical Records, and 6) Developing a Nursing Care Plan. Conclusion This systematic review contributes valuable insights into the multifaceted applications of AI in nursing care. Through the synthesis of data from the included studies, six distinct themes emerged. These findings not only consolidate the current knowledge base but also underscore the diverse ways in which AI is shaping and improving nursing care practices.
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Affiliation(s)
- Suebsarn Ruksakulpiwat
- Department of Medical Nursing, Faculty of Nursing, Mahidol University, Bangkok, Thailand
| | - Sutthinee Thorngthip
- Department of Nursing Siriraj Hospital, Faculty of Medicine Siriraj Hospital, Mahidol University, Bangkok, Thailand
| | - Atsadaporn Niyomyart
- Ramathibodi School of Nursing, Faculty of Medicine Ramathibodi Hospital, Mahidol University, Bangkok, Thailand
| | | | - Lalipat Phianhasin
- Department of Medical Nursing, Faculty of Nursing, Mahidol University, Bangkok, Thailand
| | - Heba Aldossary
- Department of Nursing, Prince Sultan Military College of Health Sciences, Dammam, Saudi Arabia
| | - Bootan Hasan Ahmed
- Frances Payne Bolton School of Nursing, Case Western Reserve University, Cleveland, OH, USA
| | - Thanistha Samai
- Department of Public Health Nursing, Faculty of Nursing, Mahidol University, Nakhon Pathom, Thailand
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19
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Zhang R, Ge Y, Xia L, Cheng Y. Bibliometric Analysis of Development Trends and Research Hotspots in the Study of Data Mining in Nursing Based on CiteSpace. J Multidiscip Healthc 2024; 17:1561-1575. [PMID: 38617080 PMCID: PMC11016257 DOI: 10.2147/jmdh.s459079] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/11/2024] [Accepted: 04/04/2024] [Indexed: 04/16/2024] Open
Abstract
Backgrounds With the advent of the big data era, hospital information systems and mobile care systems, among others, generate massive amounts of medical data. Data mining, as a powerful information processing technology, can discover non-obvious information by processing large-scale data and analyzing them in multiple dimensions. How to find the effective information hidden in the database and apply it to nursing clinical practice has received more and more attention from nursing researchers. Aim To look over the articles on data mining in nursing, compiled research status, identified hotspots, highlighted research trends, and offer recommendations for how data mining technology might be used in the nursing area going forward. Methods Data mining in nursing publications published between 2002 and 2023 were taken from the Web of Science Core Collection. CiteSpace was utilized for reviewing the number of articles, countries/regions, institutions, journals, authors, and keywords. Results According to the findings, the pace of data mining in nursing progress is not encouraging. Nursing data mining research is dominated by the United States and China. However, no consistent core group of writers or organizations has emerged in the field of nursing data mining. Studies on data mining in nursing have been increasingly gradually conducted in the 21st century, but the overall number is not large. Institution of Columbia University, journal of Cin-computers Informatics Nursing, author Diana J Wilkie, Muhammad Kamran Lodhi, Yingwei Yao are most influential in nursing data mining research. Nursing data mining researchers are currently focusing on electronic health records, text mining, machine learning, and natural language processing. Future research themes in data mining in nursing most include nursing informatics and clinical care quality enhancement. Conclusion Research data shows that data mining gives more perspectives for the growth of the nursing discipline and encourages the discipline's development, but it also introduces a slew of new issues that need researchers to address.
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Affiliation(s)
- Rui Zhang
- Department of Nursing, Huadong Hospital Affiliated to Fudan University, Shanghai, 200040, People’s Republic of China
- Department of Nursing, Fudan University, Shanghai, 200433, People’s Republic of China
| | - Yingying Ge
- Yijiangmen Community Health Service Center, Nanjing, 210009, People’s Republic of China
| | - Lu Xia
- Day Surgery Unit, Huadong Hospital Affiliated to Fudan University, Shanghai, 200040, People’s Republic of China
| | - Yun Cheng
- School of Medicine, The Chinese University of Hong Kong, Shenzhen, 518172, People’s Republic of China
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20
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Rippon MG, Fleming L, Chen T, Rogers AA, Ousey K. Artificial intelligence in wound care: diagnosis, assessment and treatment of hard-to-heal wounds: a narrative review. J Wound Care 2024; 33:229-242. [PMID: 38573907 DOI: 10.12968/jowc.2024.33.4.229] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/06/2024]
Abstract
OBJECTIVE The effective assessment of wounds, both acute and hard-to-heal, is an important component in the delivery by wound care practitioners of efficacious wound care for patients. Improved wound diagnosis, optimising wound treatment regimens, and enhanced prevention of wounds aid in providing patients with a better quality of life (QoL). There is significant potential for the use of artificial intelligence (AI) in health-related areas such as wound care. However, AI-based systems remain to be developed to a point where they can be used clinically to deliver high-quality wound care. We have carried out a narrative review of the development and use of AI in the diagnosis, assessment and treatment of hard-to-heal wounds. We retrieved 145 articles from several online databases and other online resources, and 81 of them were included in this narrative review. Our review shows that AI application in wound care offers benefits in the assessment/diagnosis, monitoring and treatment of acute and hard-to-heal wounds. As well as offering patients the potential of improved QoL, AI may also enable better use of healthcare resources.
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Affiliation(s)
- Mark G Rippon
- University of Huddersfield, Huddersfield, UK
- Daneriver Consultancy Ltd, Holmes Chapel, UK
| | - Leigh Fleming
- School of Computing and Engineering, University of Huddersfield, Huddersfield, UK
| | - Tianhua Chen
- School of Computing and Engineering, University of Huddersfield, Huddersfield, UK
| | | | - Karen Ousey
- University of Huddersfield Department of Nursing and Midwifery, Huddersfield, UK
- Adjunct Professor, School of Nursing, Faculty of Health at the Queensland University of Technology, Australia
- Visiting Professor, Royal College of Surgeons in Ireland, Dublin, Ireland
- Chair, International Wound Infection Institute
- President Elect, International Skin Tear Advisory Panel
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21
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Zhang G, Liu X, Zeng Y. Advancements in oncology nursing: Embracing technology-driven innovations. Asia Pac J Oncol Nurs 2024; 11:100399. [PMID: 38465238 PMCID: PMC10920149 DOI: 10.1016/j.apjon.2024.100399] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/29/2024] [Accepted: 01/31/2024] [Indexed: 03/12/2024] Open
Affiliation(s)
- Guolong Zhang
- Respiratory Intervention Center, The First Affiliated Hospital of Guangzhou Medical University, Guangzhou, China
| | - Xuanhui Liu
- Department of Industrial Design, Hangzhou City University, Hangzhou, China
| | - Yingchun Zeng
- School of Medicine, Hangzhou City University, Hangzhou, China
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22
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Wolf-Ostermann K, Rothgang H. [Digital technologies in nursing-what can they achieve?]. Bundesgesundheitsblatt Gesundheitsforschung Gesundheitsschutz 2024; 67:324-331. [PMID: 38326568 DOI: 10.1007/s00103-024-03843-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/01/2023] [Accepted: 01/26/2024] [Indexed: 02/09/2024]
Abstract
Digital care technologies are becoming increasingly important in long-term care. They encompass all technologies that change processes and products by means of networking and/or sensor technology and include artificial intelligence, that is, processes, methods, and algorithms for learning by means of data and enabling meaningful decisions based on this. Their application ranges from the promotion of professional collaboration, control and management, knowledge acquisition and transfer, interaction and relationships to physical caregiving.Digital care technologies have the potential to simultaneously increase the quality of care and improve working conditions in care. However, there are obstacles to this at various levels: The development of these technologies is often driven by technical possibilities, resulting in products that do not provide any concrete benefits in routine nursing care. During implementation, only the operator is trained; however, there is no organizational development for the systematic integration of these technologies into routine work. In addition, there is a lack of high-quality evaluations showing evidence of the actual benefits to routine work in order to attract potential users to these technologies. Finally, there is no sustainable financing, especially for the maintenance of these technologies.Successful digitization in long-term care therefore requires that technology developers and users, as well as policymakers and scientists, jointly overcome these obstacles. This implies that caregivers are involved in the development process from the outset (co-creation) but also that spaces are created where the effect of digital care technologies can be evaluated in routine caregiving.
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Affiliation(s)
- Karin Wolf-Ostermann
- Institut für Public Health und Pflegeforschung, Universität Bremen, Grazer Str. 4, 28359, Bremen, Deutschland.
- Leibniz-WissenschaftsCampus Digital Public Health, Bremen, Deutschland.
| | - Heinz Rothgang
- Leibniz-WissenschaftsCampus Digital Public Health, Bremen, Deutschland
- SOCIUM Forschungszentrum Ungleichheit und Sozialpolitik, Universität Bremen, Bremen, Deutschland
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23
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Zhou E, Shen Q, Hou Y. Integrating artificial intelligence into the modernization of traditional Chinese medicine industry: a review. Front Pharmacol 2024; 15:1181183. [PMID: 38464717 PMCID: PMC10921893 DOI: 10.3389/fphar.2024.1181183] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/07/2023] [Accepted: 02/08/2024] [Indexed: 03/12/2024] Open
Abstract
Traditional Chinese medicine (TCM) is the practical experience and summary of the Chinese nation for thousands of years. It shows great potential in treating various chronic diseases, complex diseases and major infectious diseases, and has gradually attracted the attention of people all over the world. However, due to the complexity of prescription and action mechanism of TCM, the development of TCM industry is still in a relatively conservative stage. With the rise of artificial intelligence technology in various fields, many scholars began to apply artificial intelligence technology to traditional Chinese medicine industry and made remarkable progress. This paper comprehensively summarizes the important role of artificial intelligence in the development of traditional Chinese medicine industry from various aspects, including new drug discovery, data mining, quality standardization and industry technology of traditional Chinese medicine. The limitations of artificial intelligence in these applications are also emphasized, including the lack of pharmacological research, database quality problems and the challenges brought by human-computer interaction. Nevertheless, the development of artificial intelligence has brought new opportunities and innovations to the modernization of traditional Chinese medicine. Integrating artificial intelligence technology into the comprehensive application of Chinese medicine industry is expected to overcome the major problems faced by traditional Chinese medicine industry and further promote the modernization of the whole traditional Chinese medicine industry.
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Affiliation(s)
- E. Zhou
- Yuhu District Healthcare Security Administration, Xiangtan, China
| | - Qin Shen
- Department of Respiratory Medicine, Hunan Provincial People’s Hospital (The First Affiliated Hospital of Hunan Normal University), Changsha, China
| | - Yang Hou
- Xiangya School of Pharmaceutical Sciences, Central South University, Changsha, China
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24
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Giddings R, Joseph A, Callender T, Janes SM, van der Schaar M, Sheringham J, Navani N. Factors influencing clinician and patient interaction with machine learning-based risk prediction models: a systematic review. Lancet Digit Health 2024; 6:e131-e144. [PMID: 38278615 DOI: 10.1016/s2589-7500(23)00241-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/23/2023] [Revised: 10/20/2023] [Accepted: 11/14/2023] [Indexed: 01/28/2024]
Abstract
Machine learning (ML)-based risk prediction models hold the potential to support the health-care setting in several ways; however, use of such models is scarce. We aimed to review health-care professional (HCP) and patient perceptions of ML risk prediction models in published literature, to inform future risk prediction model development. Following database and citation searches, we identified 41 articles suitable for inclusion. Article quality varied with qualitative studies performing strongest. Overall, perceptions of ML risk prediction models were positive. HCPs and patients considered that models have the potential to add benefit in the health-care setting. However, reservations remain; for example, concerns regarding data quality for model development and fears of unintended consequences following ML model use. We identified that public views regarding these models might be more negative than HCPs and that concerns (eg, extra demands on workload) were not always borne out in practice. Conclusions are tempered by the low number of patient and public studies, the absence of participant ethnic diversity, and variation in article quality. We identified gaps in knowledge (particularly views from under-represented groups) and optimum methods for model explanation and alerts, which require future research.
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Affiliation(s)
- Rebecca Giddings
- Lungs for Living Research Centre, UCL Respiratory, University College London, London, UK.
| | - Anabel Joseph
- Lungs for Living Research Centre, UCL Respiratory, University College London, London, UK
| | - Thomas Callender
- Lungs for Living Research Centre, UCL Respiratory, University College London, London, UK
| | - Sam M Janes
- Lungs for Living Research Centre, UCL Respiratory, University College London, London, UK
| | - Mihaela van der Schaar
- Department of Applied Mathematics and Theoretical Physics, University of Cambridge, Cambridge, UK; The Alan Turing Institute, London, UK
| | - Jessica Sheringham
- Department of Applied Health Research, University College London, London, UK
| | - Neal Navani
- Lungs for Living Research Centre, UCL Respiratory, University College London, London, UK
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O'Connor S, Vercell A, Wong D, Yorke J, Fallatah FA, Cave L, Anny Chen LY. The application and use of artificial intelligence in cancer nursing: A systematic review. Eur J Oncol Nurs 2024; 68:102510. [PMID: 38310664 DOI: 10.1016/j.ejon.2024.102510] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/05/2023] [Revised: 01/07/2024] [Accepted: 01/10/2024] [Indexed: 02/06/2024]
Abstract
PURPOSE Artificial Intelligence is being applied in oncology to improve patient and service outcomes. Yet, there is a limited understanding of how these advanced computational techniques are employed in cancer nursing to inform clinical practice. This review aimed to identify and synthesise evidence on artificial intelligence in cancer nursing. METHODS CINAHL, MEDLINE, PsycINFO, and PubMed were searched using key terms between January 2010 and December 2022. Titles, abstracts, and then full texts were screened against eligibility criteria, resulting in twenty studies being included. Critical appraisal was undertaken, and relevant data extracted and analysed. The Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines were followed. RESULTS Artificial intelligence was used in numerous areas including breast, colorectal, liver, and ovarian cancer care among others. Algorithms were trained and tested on primary and secondary datasets to build predictive models of health problems related to cancer. Studies reported this led to improvements in the accuracy of predicting health outcomes or identifying variables that improved outcome prediction. While nurses led most studies, few deployed an artificial intelligence based digital tool with cancer nurses in a real-world setting as studies largely focused on developing and validating predictive models. CONCLUSION Electronic cancer nursing datasets should be established to enable artificial intelligence techniques to be tested and if effective implemented in digital prediction and other AI-based tools. Cancer nurses need more education on machine learning and natural language processing, so they can lead and contribute to artificial intelligence developments in oncology.
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Affiliation(s)
- Siobhan O'Connor
- Florence Nightingale Faculty of Nursing, Midwifery and Palliative Care, King's College London, London, United Kingdom.
| | - Amy Vercell
- Florence Nightingale Faculty of Nursing, Midwifery and Palliative Care, King's College London, London, United Kingdom; The Christie NHS Foundation Trust, Wilmslow Rd, Manchester, M20 4BX, United Kingdom.
| | - David Wong
- Leeds Institute for Health Informatics, University of Leeds, Leeds, United Kingdom.
| | - Janelle Yorke
- Florence Nightingale Faculty of Nursing, Midwifery and Palliative Care, King's College London, London, United Kingdom; The Christie NHS Foundation Trust, Wilmslow Rd, Manchester, M20 4BX, United Kingdom.
| | - Fatmah Abdulsamad Fallatah
- Department of Nursing Affairs, King Faisal Specialist Hospital and Research Centre, Riyadh, Saudi Arabia.
| | - Louise Cave
- NHS Transformation Directorate, NHS England, England, United Kingdom.
| | - Lu-Yen Anny Chen
- Institute of Clinical Nursing, College of Nursing, National Yang Ming Chiao Tung University, Taipei, Taiwan.
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Gosak L, Pruinelli L, Topaz M, Štiglic G. The ChatGPT effect and transforming nursing education with generative AI: Discussion paper. Nurse Educ Pract 2024; 75:103888. [PMID: 38219503 DOI: 10.1016/j.nepr.2024.103888] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/05/2023] [Revised: 12/10/2023] [Accepted: 12/23/2023] [Indexed: 01/16/2024]
Abstract
AIM The aim of this study is to present the possibilities of nurse education in the use of the Chat Generative Pre-training Transformer (ChatGPT) tool to support the documentation process. BACKGROUND The success of the nursing process is based on the accuracy of nursing diagnoses, which also determine nursing interventions and nursing outcomes. Educating nurses in the use of artificial intelligence in the nursing process can significantly reduce the time nurses spend on documentation. DESIGN Discussion paper. METHODS We used a case study from Train4Health in the field of preventive care to demonstrate the potential of using Generative Pre-training Transformer (ChatGPT) to educate nurses in documenting the nursing process using generative artificial intelligence. Based on the case study, we entered a description of the patient's condition into Generative Pre-training Transformer (ChatGPT) and asked questions about nursing diagnoses, nursing interventions and nursing outcomes. We further synthesized these results. RESULTS In the process of educating nurses about the nursing process and nursing diagnosis, Generative Pre-training Transformer (ChatGPT) can present potential patient problems to nurses and guide them through the process from taking a medical history, setting nursing diagnoses and planning goals and interventions. Generative Pre-training Transformer (ChatGPT) returned appropriate nursing diagnoses, but these were not in line with the North American Nursing Diagnosis Association - International (NANDA-I) classification as requested. Of all the nursing diagnoses provided, only one was consistent with the most recent version of the North American Nursing Diagnosis Association - International (NANDA-I). Generative Pre-training Transformer (ChatGPT) is still not specific enough for nursing diagnoses, resulting in incorrect answers in several cases. CONCLUSIONS Using Generative Pre-training Transformer (ChatGPT) to educate nurses and support the documentation process is time-efficient, but it still requires a certain level of human critical-thinking and fact-checking.
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Affiliation(s)
- Lucija Gosak
- Faculty of Health Sciences, University of Maribor, Maribor 2000, Slovenia.
| | - Lisiane Pruinelli
- College of Nursing and College of Medicine, University of Florida, Gainesville, FL, USA.
| | - Maxim Topaz
- Columbia University School of Nursing, New York City, NY, USA.
| | - Gregor Štiglic
- Faculty of Health Sciences, University of Maribor, Maribor 2000, Slovenia; Faculty of Electrical Engineering and Computer Science, University of Maribor, Maribor 2000, Slovenia; Usher Institute, University of Edinburgh, Edinburgh EH8 9YL, UK.
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Fazakarley CA, Breen M, Thompson B, Leeson P, Williamson V. Beliefs, experiences and concerns of using artificial intelligence in healthcare: A qualitative synthesis. Digit Health 2024; 10:20552076241230075. [PMID: 38347935 PMCID: PMC10860471 DOI: 10.1177/20552076241230075] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 01/16/2024] [Indexed: 02/15/2024] Open
Abstract
Objective Artificial intelligence (AI) is a developing field in the context of healthcare. As this technology continues to be implemented in patient care, there is a growing need to understand the thoughts and experiences of stakeholders in this area to ensure that future AI development and implementation is successful. The aim of this study was to conduct a literature search of qualitative studies exploring the opinions of stakeholders such as clinicians, patients, and technology experts in order to establish the most common themes and ideas that have been presented in this research. Methods A literature search was conducted of existing qualitative research on stakeholder beliefs about the use of AI use in healthcare. Twenty-one papers were selected and analysed resulting in the development of four key themes relating to patient care, patient-doctor relationships, lack of education and resources, and the need for regulations. Results Overall, patients and healthcare workers are open to the use of AI in care and appear positive about potential benefits. However, concerns were raised relating to the lack of empathy in interactions of AI tools, and potential risks that may arise from the data collection needed for AI use and development. Stakeholders in the healthcare, technology, and business sectors all stressed that there was a lack of appropriate education, funding, and guidelines surrounding AI, and these concerns needed to be addressed to ensure future implementation is safe and suitable for patient care. Conclusion Ultimately, the results found in this study highlighted that there was a need for communication between stakeholder in order for these concerns to be addressed, mitigate potential risks, and maximise benefits for patients and clinicians alike. The results also identified a need for further qualitative research in this area to further understand stakeholder experiences as AI use continues to develop.
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Affiliation(s)
| | | | | | - Paul Leeson
- RDM Division of Cardiovascular Medicine, University of Oxford, John Radcliffe Hospital, Oxford, UK
| | - Victoria Williamson
- King's Centre for Military Health Research, King's College London, London, UK
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Johnson EA, Dudding KM, Carrington JM. When to err is inhuman: An examination of the influence of artificial intelligence-driven nursing care on patient safety. Nurs Inq 2024; 31:e12583. [PMID: 37459179 DOI: 10.1111/nin.12583] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/14/2023] [Revised: 07/05/2023] [Accepted: 07/09/2023] [Indexed: 01/18/2024]
Abstract
Artificial intelligence, as a nonhuman entity, is increasingly used to inform, direct, or supplant nursing care and clinical decision-making. The boundaries between human- and nonhuman-driven nursing care are blurred with the advent of sensors, wearables, camera devices, and humanoid robots at such an accelerated pace that the critical evaluation of its influence on patient safety has not been fully assessed. Since the pivotal release of To Err is Human, patient safety is being challenged by the dynamic healthcare environment like never before, with nursing at a critical juncture to steer the course of artificial intelligence integration in clinical decision-making. This paper presents an overview of artificial intelligence and its application in healthcare and highlights the implications which affect nursing as a profession, including perspectives on nursing education and training recommendations. The legal and policy challenges which emerge when artificial intelligence influences the risk of clinical errors and safety issues are discussed.
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Affiliation(s)
- Elizabeth A Johnson
- Mark & Robyn Jones College of Nursing, Montana State University, Bozeman, Montana, USA
| | - Katherine M Dudding
- Department of Family, Community, and Health Systems, UAB School of Nursing, The University of Alabama at Birmingham, Birmingham, Alabama, USA
| | - Jane M Carrington
- Department of Family, Community and Health System Science, University of Florida College of Nursing, Gainesville, Florida, USA
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Luo Y, Weng H, Yang L, Ding Z, Wang Q. College Students' Employability, Cognition, and Demands for ChatGPT in the AI Era Among Chinese Nursing Students: Web-Based Survey. JMIR Form Res 2023; 7:e50413. [PMID: 38133923 PMCID: PMC10770778 DOI: 10.2196/50413] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/29/2023] [Revised: 08/31/2023] [Accepted: 11/22/2023] [Indexed: 12/23/2023] Open
Abstract
BACKGROUND With the rapid development of artificial intelligence (AI) and the widespread use of ChatGPT, nursing students' artificial intelligence quotient (AIQ), employability, cognition, and demand for ChatGPT are worthy of attention. OBJECTIVE We aimed to investigate Chinese nursing students' AIQ and employability status as well as their cognition and demand for the latest AI tool-ChatGPT. This study was conducted to guide future initiatives in nursing intelligence education and to improve the employability of nursing students. METHODS We used a cross-sectional survey to understand nursing college students' AIQ, employability, cognition, and demand for ChatGPT. Using correlation analysis and multiple hierarchical regression analysis, we explored the relevant factors in the employability of nursing college students. RESULTS In this study, out of 1788 students, 1453 (81.30%) had not used ChatGPT, and 1170 (65.40%) had never heard of ChatGPT before this survey. College students' employability scores were positively correlated with AIQ, self-regulation ability, and their home location and negatively correlated with school level. Additionally, men scored higher on college students' employability compared to women. Furthermore, 76.5% of the variance was explained by the multiple hierarchical regression model for predicting college students' employability scores. CONCLUSIONS Chinese nursing students have limited familiarity and experience with ChatGPT, while their AIQ remains intermediate. Thus, educators should pay more attention to cultivating nursing students' AIQ and self-regulation ability to enhance their employability. Employability, especially for female students, those from rural backgrounds, and students in key colleges, deserves more attention in future educational efforts.
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Affiliation(s)
- Yuanyuan Luo
- Clinical Nursing Teaching and Research Section, The Second Xiangya Hospital of Central South University, Changsha, China
- Xiangya School of Nursing, Central South University, Changsha, Hunan, China
| | - Huiting Weng
- Clinical Nursing Teaching and Research Section, The Second Xiangya Hospital of Central South University, Changsha, China
| | - Li Yang
- Clinical Nursing Teaching and Research Section, The Second Xiangya Hospital of Central South University, Changsha, China
| | - Ziwei Ding
- Clinical Nursing Teaching and Research Section, The Second Xiangya Hospital of Central South University, Changsha, China
| | - Qin Wang
- Clinical Nursing Teaching and Research Section, The Second Xiangya Hospital of Central South University, Changsha, China
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30
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Marra AR, Nori P, Langford BJ, Kobayashi T, Bearman G. Brave new world: Leveraging artificial intelligence for advancing healthcare epidemiology, infection prevention, and antimicrobial stewardship. Infect Control Hosp Epidemiol 2023; 44:1909-1912. [PMID: 37395009 DOI: 10.1017/ice.2023.122] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 07/04/2023]
Affiliation(s)
- Alexandre R Marra
- Hospital Israelita Albert Einstein, São Paulo, Brazil
- Department of Internal Medicine, University of Iowa Carver College of Medicine, Iowa City, Iowa, United States
| | - Priya Nori
- Division of Infectious Diseases, Department of Medicine, Montefiore Health System, Albert Einstein College of Medicine, Bronx, New York, United States
| | - Bradley J Langford
- Dalla Lana School of Public Health, University of Toronto, Toronto, Canada
- Hotel Dieu Shaver Health and Rehabilitation Centre, St. Catharines, Canada
| | - Takaaki Kobayashi
- Department of Internal Medicine, University of Iowa Carver College of Medicine, Iowa City, Iowa, United States
| | - Gonzalo Bearman
- Division of Infectious Diseases, Virginia Commonwealth University Health, Virginia Commonwealth University, Richmond, Virginia, United States
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31
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Labrague LJ, Aguilar-Rosales R, Yboa BC, Sabio JB. Factors influencing student nurses' readiness to adopt artificial intelligence (AI) in their studies and their perceived barriers to accessing AI technology: A cross-sectional study. NURSE EDUCATION TODAY 2023; 130:105945. [PMID: 37625351 DOI: 10.1016/j.nedt.2023.105945] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/27/2023] [Revised: 07/27/2023] [Accepted: 08/17/2023] [Indexed: 08/27/2023]
Abstract
BACKGROUND With the projected significant increase in the use of AI in nursing education, it becomes vital for nurse faculty to adequately equip student nurses with the necessary competences to effectively utilize AI in their studies. Ensuring that student nurses are prepared and ready to embrace AI technology is imperative for their successful integration into the healthcare workforce. OBJECTIVE This study aimed to examine student nurses' readiness to embrace AI technology, explore associated factors, and identify perceived barriers to accessing AI technology. DESIGN Cross sectional study. SETTINGS One public-owned nursing school in the Philippines. PARTICIPANTS Three hundred twenty-three student nurses. METHODS Data were collected using structured questionnaires. Descriptive statistics and multivariable analysis were performed to analyze the data. RESULTS The results revealed that student nurses demonstrated moderate readiness to embrace AI in their studies (M = 2.906, SD = 0.692) and perceived moderate barriers to accessing AI technology (M = 2.336, SD = 0.719). Factors associated with students' readiness to embrace AI included self-rated technological proficiency (β = 0.170, p = 0.014), understanding of AI-powered technologies (β = 0.260, p < 0.001), and perceived AI use in nursing practice (β = 0.153, p = 0.022). The study also identified potential barriers to accessing AI technology, such as lack of computer skills to navigate AI, lack of AI knowledge and awareness, and time constraints. CONCLUSION The findings of this study provided valuable insights into the factors influencing student nurses' attitudes towards AI and shed light on their perceived barriers to accessing AI technology. By enhancing technological proficiency, increasing AI understanding, and providing practical experiences, nurse faculty can better prepare future nurses to effectively navigate the AI-driven healthcare environment and contribute to improved patient care outcomes.
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Affiliation(s)
| | | | - Begonia C Yboa
- College of Nursing and Health Sciences, Samar State University, Philippines
| | - Jeanette B Sabio
- College of Nursing and Health Sciences, Samar State University, Philippines
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32
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Zhong NN, Wang HQ, Huang XY, Li ZZ, Cao LM, Huo FY, Liu B, Bu LL. Enhancing head and neck tumor management with artificial intelligence: Integration and perspectives. Semin Cancer Biol 2023; 95:52-74. [PMID: 37473825 DOI: 10.1016/j.semcancer.2023.07.002] [Citation(s) in RCA: 9] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/29/2023] [Revised: 07/11/2023] [Accepted: 07/15/2023] [Indexed: 07/22/2023]
Abstract
Head and neck tumors (HNTs) constitute a multifaceted ensemble of pathologies that primarily involve regions such as the oral cavity, pharynx, and nasal cavity. The intricate anatomical structure of these regions poses considerable challenges to efficacious treatment strategies. Despite the availability of myriad treatment modalities, the overall therapeutic efficacy for HNTs continues to remain subdued. In recent years, the deployment of artificial intelligence (AI) in healthcare practices has garnered noteworthy attention. AI modalities, inclusive of machine learning (ML), neural networks (NNs), and deep learning (DL), when amalgamated into the holistic management of HNTs, promise to augment the precision, safety, and efficacy of treatment regimens. The integration of AI within HNT management is intricately intertwined with domains such as medical imaging, bioinformatics, and medical robotics. This article intends to scrutinize the cutting-edge advancements and prospective applications of AI in the realm of HNTs, elucidating AI's indispensable role in prevention, diagnosis, treatment, prognostication, research, and inter-sectoral integration. The overarching objective is to stimulate scholarly discourse and invigorate insights among medical practitioners and researchers to propel further exploration, thereby facilitating superior therapeutic alternatives for patients.
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Affiliation(s)
- Nian-Nian Zhong
- State Key Laboratory of Oral & Maxillofacial Reconstruction and Regeneration, Key Laboratory of Oral Biomedicine Ministry of Education, Hubei Key Laboratory of Stomatology, School & Hospital of Stomatology, Wuhan University, Wuhan 430079, China
| | - Han-Qi Wang
- State Key Laboratory of Oral & Maxillofacial Reconstruction and Regeneration, Key Laboratory of Oral Biomedicine Ministry of Education, Hubei Key Laboratory of Stomatology, School & Hospital of Stomatology, Wuhan University, Wuhan 430079, China
| | - Xin-Yue Huang
- State Key Laboratory of Oral & Maxillofacial Reconstruction and Regeneration, Key Laboratory of Oral Biomedicine Ministry of Education, Hubei Key Laboratory of Stomatology, School & Hospital of Stomatology, Wuhan University, Wuhan 430079, China
| | - Zi-Zhan Li
- State Key Laboratory of Oral & Maxillofacial Reconstruction and Regeneration, Key Laboratory of Oral Biomedicine Ministry of Education, Hubei Key Laboratory of Stomatology, School & Hospital of Stomatology, Wuhan University, Wuhan 430079, China
| | - Lei-Ming Cao
- State Key Laboratory of Oral & Maxillofacial Reconstruction and Regeneration, Key Laboratory of Oral Biomedicine Ministry of Education, Hubei Key Laboratory of Stomatology, School & Hospital of Stomatology, Wuhan University, Wuhan 430079, China
| | - Fang-Yi Huo
- State Key Laboratory of Oral & Maxillofacial Reconstruction and Regeneration, Key Laboratory of Oral Biomedicine Ministry of Education, Hubei Key Laboratory of Stomatology, School & Hospital of Stomatology, Wuhan University, Wuhan 430079, China
| | - Bing Liu
- State Key Laboratory of Oral & Maxillofacial Reconstruction and Regeneration, Key Laboratory of Oral Biomedicine Ministry of Education, Hubei Key Laboratory of Stomatology, School & Hospital of Stomatology, Wuhan University, Wuhan 430079, China; Department of Oral & Maxillofacial - Head Neck Oncology, School & Hospital of Stomatology, Wuhan University, Wuhan 430079, China.
| | - Lin-Lin Bu
- State Key Laboratory of Oral & Maxillofacial Reconstruction and Regeneration, Key Laboratory of Oral Biomedicine Ministry of Education, Hubei Key Laboratory of Stomatology, School & Hospital of Stomatology, Wuhan University, Wuhan 430079, China; Department of Oral & Maxillofacial - Head Neck Oncology, School & Hospital of Stomatology, Wuhan University, Wuhan 430079, China.
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Vitorino LM, Yoshinari GH. Artificial intelligence as an ally in Brazilian nursing: challenges, opportunities and professional responsibility. Rev Bras Enferm 2023; 76:e760301. [PMID: 37792851 PMCID: PMC10550101 DOI: 10.1590/0034-7167.2023760301] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/06/2023] Open
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Lim DYZ, Ke YH, Sng GGR, Tung JYM, Chai JX, Abdullah HR. Large language models in anaesthesiology: use of ChatGPT for American Society of Anesthesiologists physical status classification. Br J Anaesth 2023; 131:e73-e75. [PMID: 37474421 DOI: 10.1016/j.bja.2023.06.052] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/16/2023] [Revised: 06/02/2023] [Accepted: 06/09/2023] [Indexed: 07/22/2023] Open
Affiliation(s)
- Daniel Y Z Lim
- Department of Gastroenterology and Hepatology, Singapore General Hospital, Singapore; Duke-NUS Medical School, Singapore
| | - Yu He Ke
- Department of Anaesthesiology and Perioperative Medicine, Singapore General Hospital, Singapore
| | - Gerald G R Sng
- Department of Endocrinology, Singapore General Hospital, Singapore
| | - Joshua Y M Tung
- Department of Urology, Singapore General Hospital, Singapore
| | - Jia X Chai
- Department of Anaesthesiology and Perioperative Medicine, Singapore General Hospital, Singapore
| | - Hairil R Abdullah
- Duke-NUS Medical School, Singapore; Department of Anaesthesiology and Perioperative Medicine, Singapore General Hospital, Singapore.
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Jung S. Challenges for future directions for artificial intelligence integrated nursing simulation education. KOREAN JOURNAL OF WOMEN HEALTH NURSING 2023; 29:239-242. [PMID: 37813667 PMCID: PMC10565529 DOI: 10.4069/kjwhn.2023.09.06.1] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/27/2023] [Revised: 09/02/2023] [Accepted: 09/06/2023] [Indexed: 10/11/2023] Open
Abstract
Artificial intelligence (AI) has tremendous potential to change the way we train future health professionals. Although AI can provide improved realism, engagement, and personalization in nursing simulations, it is also important to address any issues associated with the technology, teaching methods, and ethical considerations of AI. In nursing simulation education, AI does not replace the valuable role of nurse educators but can enhance the educational effectiveness of simulation by promoting interdisciplinary collaboration, faculty development, and learner self-direction. We should continue to explore, innovate, and adapt our teaching methods to provide nursing students with the best possible education.
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Affiliation(s)
- Sunyoung Jung
- College of Nursing and Research Institute of Nursing Science, Daegu Catholic University, Daegu, Korea
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36
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van der Meijden S, Arbous M, Geerts B. Possibilities and challenges for artificial intelligence and machine learning in perioperative care. BJA Educ 2023; 23:288-294. [PMID: 37465235 PMCID: PMC10350557 DOI: 10.1016/j.bjae.2023.04.003] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 04/21/2023] [Indexed: 07/20/2023] Open
Affiliation(s)
- S.L. van der Meijden
- Healthplus.ai-R&D B.V., Amsterdam, The Netherlands
- Intensive Care Unit, Leiden University Medical Centre, Leiden, The Netherlands
| | - M.S. Arbous
- Intensive Care Unit, Leiden University Medical Centre, Leiden, The Netherlands
| | - B.F. Geerts
- Healthplus.ai-R&D B.V., Amsterdam, The Netherlands
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37
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Lukić A, Kudelić N, Antičević V, Lazić-Mosler E, Glunčić V, Hren D, Lukić IK. First-year nursing students' attitudes towards artificial intelligence: Cross-sectional multi-center study. Nurse Educ Pract 2023; 71:103735. [PMID: 37541081 DOI: 10.1016/j.nepr.2023.103735] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/05/2023] [Revised: 07/25/2023] [Accepted: 07/27/2023] [Indexed: 08/06/2023]
Abstract
AIM To assess the attitudes of nursing students toward artificial intelligence. BACKGROUND Possible applications of artificial intelligence-powered systems in nursing cover all aspects of nursing care, from patient care to risk management. Although the final acceptance of artificial intelligence in practice will depend on positive 'nurses' attitudes toward artificial intelligence, those attitudes have gained little attention so far. DESIGN A cross-sectional multicenter study. METHODS The study was performed at nursing schools of four Croatian universities, surveying a total of 336 first-year nursing students (response rate 69.7%) enrolled in 2021. A validated instrument, the General Attitudes towards Artificial Intelligence Scale, consisting of 20 Likert-type items, was chosen for the study. Where applicable, the items were contextualized for nursing. Four sub-scales were identified based on the outcomes of the factor analysis. RESULTS The average attitude score was (mean ± standard deviation) 64.5 ± 11.7, out of a maximum of 100, which was significantly higher than the neutral score of 60.0 (p < 0.001). The attitude towards AI did not differ across the universities and was not associated with students' age. Male students scored slightly higher than their female colleagues. Scores on subscales "Benefits of artificial intelligence in nursing", "Willingness to use artificial intelligence in nursing practice", and "Dangers of artificial intelligence" were favorable of artificial intelligence-based solutions. However, scores on the subscale "Practical advantages of artificial intelligence" were somewhat unfavorable. CONCLUSIONS First-year nursing students had slightly positive attitudes towards artificial intelligence in nursing, which should make it easier for the new generations of nurses to embrace and implement artificial intelligence systems. Reservations about artificial intelligence in daily nursing practice indicate that nursing students might benefit from education focused specifically on applications of artificial intelligence in nursing.
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Affiliation(s)
- Anita Lukić
- Varaždin General Hospital, Varaždin, Croatia; University of Applied Sciences, Bjelovar, Croatia; University North, Koprivnica, Croatia
| | - Nenad Kudelić
- Varaždin General Hospital, Varaždin, Croatia; University North, Koprivnica, Croatia
| | - Vesna Antičević
- University Department of Health Studies, University of Split, Split, Croatia
| | - Elvira Lazić-Mosler
- Department of Nursing, Catholic University of Croatia, Zagreb, Croatia; School of Medicine, Catholic University of Croatia, Zagreb, Croatia
| | - Vicko Glunčić
- Department of Anesthesiology, Mount Sinai Hospital, Chicago, IL, USA
| | - Darko Hren
- Department of Psychology, Faculty of Humanities and Social Sciences, University of Split, Split, Croatia
| | - Ivan K Lukić
- University of Applied Sciences, Bjelovar, Croatia; School of Medicine, Catholic University of Croatia, Zagreb, Croatia.
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Fournaise A, Lauridsen JT, Nissen SK, Gudex C, Bech M, Mejldal A, Wiil UK, Rasmussen JB, Kidholm K, Matzen L, Espersen K, Andersen-Ranberg K. Structured decision support to prevent hospitalisations of community-dwelling older adults in Denmark (PATINA): an open-label, stepped-wedge, cluster-randomised controlled trial. THE LANCET HEALTHY LONGEVITY 2023; 4:e132-e142. [PMID: 37003272 DOI: 10.1016/s2666-7568(23)00023-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/11/2022] [Revised: 02/17/2023] [Accepted: 02/17/2023] [Indexed: 03/30/2023]
Abstract
BACKGROUND Ageing populations and health-care staff shortages encourage efforts in primary care to recognise and prevent health deterioration and acute hospitalisation in community-dwelling older adults. The PATINA algorithm and decision-support tool alerts home-based-care nurses to older adults at risk of hospitalisation. The study aim was to test whether use of the PATINA tool was associated with changes in health-care use. METHODS An open-label, stepped-wedge, cluster-randomised controlled trial was done in three Danish municipalities, covering 20 area teams providing home-based care to around 7000 recipients. During a period of 12 months, area teams were randomly assigned to an intervention crossover for older adults (aged 65 years or older) who received care at home. The primary outcome was hospitalisation within 30 days of identification by the algorithm as being at risk of hospitalisation. Secondary outcomes were hospital readmission and other hospital contacts, outpatient contacts, contact with primary care physicians (PCPs), temporary care, and death, within 30 days of identification. This study was registered at ClinicalTrials.gov (NTC04398797). FINDINGS In total, 2464 older adults participated in the study: 1216 (49·4%) in the control phase and 1248 (50·6%) in the intervention phase. In the control phase, 102 individuals were hospitalised within 30 days during 33 943 days of risk (incidence 0·09 per 30 days), compared with 118 individuals within 34 843 days of risk (0·10 per 30 days) during the intervention phase. The intervention was not associated with a reduction in the number of first hospitalisations within 30 days (incidence rate ratio [IRR] 1·10 [90% CI 0·90-1·40]; p=0·28). Furthermore it was not associated with reduced rates of other hospital contacts (IRR 1·10 [95% CI 0·90-1·40]; p=0·28), outpatient contacts (1·10 [0·88-1·40]; p=0·42), or mortality (0·82 [0·58-1·20]; p=0·25). The intervention was associated with a 59% reduction in readmissions within 30 days of hospital discharge (IRR 0·41 [95% CI 0·24-0·68]; p=0·0007), a 140% increase in contacts with PCPs (2·40 [1·18-3·20]; p<0·0001), and a 150% increase in use of temporary care (2·50 [1·40-4·70]; p=0·0027). INTERPRETATION Despite having no effect on the primary outcome, the PATINA tool showed other benefits for older adults receiving home-based care. Such algorithms have the potential to shift health-care use from secondary to primary care but need to be tested in other home-based care settings. Implementation of algorithms in clinical practice should be informed by analysis of cost-effectiveness and potential harms as well as the benefits. FUNDING Innovation Fund Denmark and Region of Southern Denmark. TRANSLATIONS For the Danish, French and German translations of the abstract see Supplementary Materials section.
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Gellert GA. Medical Scribes: Symptom or Cause of Impeded Evolution of a Transformative Artificial Intelligence in the Electronic Health Record? PERSPECTIVES IN HEALTH INFORMATION MANAGEMENT 2023; 20:1d. [PMID: 37215336 PMCID: PMC9860472] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Subscribe] [Scholar Register] [Indexed: 05/24/2023]
Abstract
Studies have quantified various specific benefits related to the use of medical scribes, finding physician workflow and productivity improvements, with some demonstrating marginal value or detrimental impact. However, this evidence base misses a critical underlying issue with the expanding number of physicians using medical scribes routinely. There are an estimated 28,000-33,000 peer reviewed biomedical journals worldwide, currently publishing an estimated 1.8-2 million scientific articles every year. Over a typical physician's career from the 11-13 years of undergraduate through medical school and specialty/residency training as well as 34-36 practice/care delivery years beyond (to age 65), this yields 84-94+ million peer reviewed journal articles that are published in the global medical literature and to be potentially consumed/ considered over a roughly 47-year career. Clinical trial results in various stages of peer review, with 409,000 clinical trials registered in 2022, augment this massive volume of new clinical and bioscience information that clinicians might utilize to advance their care delivery by over 19 million bioscientific reports over a lifetime of training and care delivery. Inclusive of clinical trial reports and peer reviewed journal articles, a physician might derive clinical care value from an expanding career-long evidence base of 103-113+ million scientific communications. Even if only 0.1 percent of the global output of biomedical science has clinical relevance to a highly specialized physician, the narrowed career-long total remains a staggering 103,000 journal publications and clinical trial reports. For physicians with a more general and diverse clinical focus such as family medicine, emergency medicine physicians, and hospitalists, if 1 percent of newly published evidence-based literature is pertinent, the total career-long estimate is over 1 million journal articles and clinical trials to be reviewed and clinically integrated. As a result, a challenging issue created by the increasing role of medical scribes is not just evaluating their value (or lack thereof) for practicing physicians in their workflows and productivity. Rather it concerns the impact that medical scribes may be having by decoupling physicians from the iterative technological and cognitive progression of the electronic health record (EHR) and its evolving artificial intelligence (AI), which can facilitate the integration of the year-over-year proliferation of clinically pertinent new scientific evidence into a physician's practice of medicine. This commentary addresses the challenge to the evolution of the AI of the EHR posed by physicians' increasing use of and reliance upon medical scribes, and highlights how medical scribes may also, inadvertently, isolate and insulate physicians from their essential role in continuous refinement and advancement of EHR AI. Consideration is given to the broader challenge of inadequate focus and resources needed across sectors to drive the evolution of AI in the EHR, and associated health informatics research, as a US national priority.
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Pailaha AD. The Impact and Issues of Artificial Intelligence in Nursing Science and Healthcare Settings. SAGE Open Nurs 2023; 9:23779608231196847. [PMID: 37691725 PMCID: PMC10492460 DOI: 10.1177/23779608231196847] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/07/2023] [Revised: 04/26/2023] [Accepted: 08/06/2023] [Indexed: 09/12/2023] Open
Abstract
Research and development of artificial intelligence (AI)-based technologies systems in healthcare has increased over the past decade, highlighting the strong potential of AI to improve the quality of nursing care. To meet the new demands for nursing care, it is necessary that AI be integrated into nursing science and healthcare setting, especially in nursing care. The current challenge is to transform this expanded set of technology into clinical benefits for patients, through more advanced, accurate, practical, effective, efficient, and economical and personalized care. Along with the potential positive outcomes, AI technology also has unintended consequences that have the potential to negatively impact and adversely affect the nursing profession and the primary purpose of nursing practice in healthcare system. This aimed to explore and discuss the impact of applying AI in nursing science and healthcare system to provide approximate nursing care. Some of the impacts that can be evaluated and seen today in the context of using AI technology systems in the scope of nursing and healthcare are expanding access to quality medical care, improving medical records, and improving the quality of services. The use of AI technology systems also has some issues, such as bias and algorithms, which are drawbacks that need to be considered when evaluating the accuracy of the displayed results.
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Affiliation(s)
- Aprianto Daniel Pailaha
- Inpatient Department Nurse, General Ward Nursing, Department of Nursing, Siloam Hospitals Agora, Jakarta, Indonesia
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Shi J, Wei S, Gao Y, Mei F, Tian J, Zhao Y, Li Z. Global output on artificial intelligence in the field of nursing: A bibliometric analysis and science mapping. J Nurs Scholarsh 2022. [DOI: 10.1111/jnu.12852] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/22/2022] [Revised: 09/26/2022] [Accepted: 11/07/2022] [Indexed: 12/23/2022]
Affiliation(s)
- Jiyuan Shi
- School of Nursing, Chinese Academy of Medical Sciences and Peking Union Medical College Beijing China
| | - Shuaifang Wei
- School of Nursing, Chinese Academy of Medical Sciences and Peking Union Medical College Beijing China
| | - Ya Gao
- Evidence‐Based Medicine Center, School of Basic Medical Sciences Lanzhou University Lanzhou China
| | - Fan Mei
- Chinese Evidence‐Based Medicine Center and Cochrane China Center, West China Hospital Sichuan University Chengdu China
| | - Jinhui Tian
- Evidence‐Based Medicine Center, School of Basic Medical Sciences Lanzhou University Lanzhou China
| | - Yang Zhao
- School of Nursing Southern Medical University Guangzhou China
| | - Zheng Li
- School of Nursing, Chinese Academy of Medical Sciences and Peking Union Medical College Beijing China
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Chang CY, Jen HJ, Su WS. Trends in artificial intelligence in nursing: Impacts on nursing management. J Nurs Manag 2022; 30:3644-3653. [PMID: 35970485 DOI: 10.1111/jonm.13770] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/25/2022] [Revised: 07/19/2022] [Accepted: 08/11/2022] [Indexed: 12/30/2022]
Abstract
OBJECTIVE To investigate the academic use of artificial intelligence (AI) in nursing. BACKGROUND A bibliometric analysis combined with the VOSviewer software quantification method has been utilized for a literature analysis. In recent years, this approach has attracted the interest of scholars in various research fields. Thus far, there is no publication using bibliometric analysis combined with the VOSviewer software to analyse the applications of AI in nursing. METHOD A bibliometric analysis methodology was used to search for relevant articles published between 1984 and March 2022. Six databases, Embase, Scopus, PubMed, CINAHL, WoS and MEDLINE, were included to identify relevant studies, and data such as the year of publication, journals, country, institutional source, field and keywords were analysed. RESULTS Most relevant articles were published from institutions in the United States. The League of European Research Universities has published most research studies that use AI and nursing. Scholars have mainly focused on nursing, medical informatics, computer science AI, healthcare sciences services and physics particles fields. Commonly used keywords were machine learning, care, AI, natural language processing, prediction and nurse. CONCLUSION Research articles were mainly published in Nurse Education Today. Research topics such as AI-assisted medical recording and medical decision making were also identified. According to this study, AI in nursing has the potential to attract more attention from researchers and nursing managers. Additional high-quality research beyond the scope of medical education, as well as on cross-domain collaboration, is warranted to explore the acceptability and effective implementation of AI technologies. IMPLICATIONS FOR NURSING MANAGEMENT This study provides scholars and nursing managers with structured information regarding the use of AI in nursing based on scientific and technological developments across different fields and institutions. The application of AI can improve nursing management, nursing quality, safety management and team communication, as well as encourage future international collaboration.
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Affiliation(s)
- Ching-Yi Chang
- School of Nursing, College of Nursing, Taipei Medical University, Taipei, Taiwan.,Department of Nursing, Taipei Medical University-Shuang Ho Hospital, New Taipei, Taiwan
| | - Hsiu-Ju Jen
- School of Nursing, College of Nursing, Taipei Medical University, Taipei, Taiwan.,Department of Nursing, Taipei Medical University-Shuang Ho Hospital, New Taipei, Taiwan
| | - Wen-Song Su
- Department of Dentistry, Tri-Service General Hospital and Department of Dentistry, Taoyuan Armed Forces General Hospital, Taoyuan City, Taiwan, ROC
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Gosak L, Martinović K, Lorber M, Stiglic G. Artificial intelligence based prediction models for individuals at risk of multiple diabetic complications: A systematic review of the literature. J Nurs Manag 2022; 30:3765-3776. [PMID: 36329678 PMCID: PMC10100477 DOI: 10.1111/jonm.13894] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/08/2022] [Revised: 10/03/2022] [Accepted: 10/27/2022] [Indexed: 11/06/2022]
Abstract
AIM The aim of this review is to examine the effectiveness of artificial intelligence in predicting multimorbid diabetes-related complications. BACKGROUND In diabetic patients, several complications are often present, which have a significant impact on the quality of life; therefore, it is crucial to predict the level of risk for diabetes and its complications. EVALUATION International databases PubMed, CINAHL, MEDLINE and Scopus were searched using the terms artificial intelligence, diabetes mellitus and prediction of complications to identify studies on the effectiveness of artificial intelligence for predicting multimorbid diabetes-related complications. The results were organized by outcomes to allow more efficient comparison. KEY ISSUES Based on the inclusion/exclusion criteria, 11 articles were included in the final analysis. The most frequently predicted complications were diabetic neuropathy (n = 7). Authors included from two to a maximum of 14 complications. The most commonly used prediction models were penalized regression, random forest and Naïve Bayes model neural network. CONCLUSION The use of artificial intelligence can predict the risks of diabetes complications with greater precision based on available multidimensional datasets and provides an important tool for nurses working in preventive health care. IMPLICATIONS FOR NURSING MANAGEMENT Using artificial intelligence contributes to a better quality of care, better autonomy of patients in diabetes management and reduction of complications, costs of medical care and mortality.
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Affiliation(s)
- Lucija Gosak
- Faculty of Health Sciences, University of Maribor, Maribor, Slovenia
| | - Kristina Martinović
- Faculty of Health Sciences, University of Maribor, Maribor, Slovenia.,Faculty of Health Sciences, University of Primorska, Izola, Slovenia
| | - Mateja Lorber
- Faculty of Health Sciences, University of Maribor, Maribor, Slovenia
| | - Gregor Stiglic
- Faculty of Health Sciences, University of Maribor, Maribor, Slovenia.,Faculty of Electrical Engineering and Computer Science, University of Maribor, Maribor, Slovenia.,Usher Institute, University of Edinburgh, Edinburgh, UK
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Chen Y, Moreira P, Liu WW, Monachino M, Nguyen TLH, Wang A. Is there a gap between artificial intelligence applications and priorities in health care and nursing management? J Nurs Manag 2022; 30:3736-3742. [PMID: 36216773 PMCID: PMC10092524 DOI: 10.1111/jonm.13851] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/16/2022] [Revised: 09/02/2022] [Accepted: 10/02/2022] [Indexed: 12/30/2022]
Abstract
AIM The article aims to outline a contrast between three priorities for nursing management proposed a decade ago and key features of the following 10 years of developments on artificial intelligence for health care and nursing management. This analysis intends to contribute to update the international debate on bridging the essence of health care and nursing management priorities and the focus of artificial intelligence developers. BACKGROUND Artificial intelligence research promises innovative approaches to supporting nurses' clinical decision-making and to conduct tasks not related to patient interaction, including administrative activities and patient records. Yet, even though there has been an increase in international research and development of artificial intelligence applications for nursing care during the past 10 years, it is unclear to what extent the priorities of nursing management have been embedded in the devised artificial intelligence solutions. EVALUATION Starting from three priorities for nursing management identified in 2011 in a special issue of the Journal Nursing Management, we went on to identify recent evidence concerning 10 years of artificial intelligence applications developed to support health care management and nursing activities since then. KEY ISSUE The article discusses to what extent priorities in health care and nursing management may have to be revised while adopting artificial intelligence applications or, alternatively, to what extent the direction of artificial intelligence developments may need to be revised to contribute to long acknowledged priorities of nursing management. CONCLUSION We have identified a conceptual gap between both sets of ideas and provide a discussion on the need to bridge that gap, while admitting that there may have been recent field developments still unreported in scientific literature. IMPLICATIONS FOR NURSING MANAGEMENT Artificial intelligence developers and health care nursing managers need to be more engaged in coordinating the future development of artificial intelligence applications with a renewed set of nursing management priorities.
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Affiliation(s)
- Yanjiao Chen
- Research Center on Social Work and Social Governance in Henan Province, Henan Normal University, Sociology Department, Xinxiang, China
| | - Paulo Moreira
- Shandong Provincial Qianfoshan Hospital, Jinan, Shandong, China.,Departamento de Ciencias da Gestao (Gestao em Saude), Atlantica Instituto Universitario, Oeiras, Portugal
| | - Wei-Wei Liu
- School of Social Work, Henan Normal University, Xinxiang, China
| | | | - Thi Le Ha Nguyen
- VNU University of Medicine and Pharmacy, Vietnam National University, Hanoi, Vietnam
| | - Aihua Wang
- Obstetrics Department, Kunming Maternal and Child Hospital, Kunming, China
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O'Connor S, Gasteiger N, Stanmore E, Wong DC, Lee JJ. Artificial intelligence for falls management in older adult care: A scoping review of nurses' role. J Nurs Manag 2022; 30:3787-3801. [PMID: 36197748 PMCID: PMC10092211 DOI: 10.1111/jonm.13853] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/02/2022] [Revised: 08/29/2022] [Accepted: 09/30/2022] [Indexed: 12/30/2022]
Abstract
AIM This study aims to synthesize evidence on nurses' involvement in artificial intelligence research for managing falls in older adults. BACKGROUND Artificial intelligence techniques are used to analyse health datasets to aid clinical decision making, patient care and service delivery but nurses' involvement in this area of research for managing falls in older adults remains unknown. EVALUATION A scoping review was conducted. CINAHL, the Cochrane Library, Embase, MEDLI and PubMed were searched. Results were screened against inclusion criteria. Relevant data were extracted, and studies summarized using a descriptive approach. KEY ISSUES The evidence shows many artificial intelligence techniques, particularly machine learning, are used to identify falls risk factors and build predictive models that could help prevent falls in older adults, with nurses leading and participating in this research. CONCLUSION Further rigorous experimental research is needed to determine the effectiveness of algorithms in predicting aspects of falls in older adults and how to implement artificial intelligence tools in gerontological nursing practice. IMPLICATIONS FOR NURSING MANAGEMENT Nurses should pursue interdisciplinary collaborations and educational opportunities in artificial intelligence, so they can actively contribute to research on falls management. Nurses should facilitate the collection of digital falls datasets to support this emerging research agenda and the care of older adults.
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Affiliation(s)
- Siobhan O'Connor
- Division of Nursing, Midwifery and Social Work, School of Health Sciences, The University of Manchester, Manchester, UK
| | - Norina Gasteiger
- Division of Nursing, Midwifery and Social Work, School of Health Sciences, The University of Manchester, Manchester, UK.,Division of Informatics, Imaging and Data Sciences, The University of Manchester, Manchester, UK
| | - Emma Stanmore
- Division of Nursing, Midwifery and Social Work, School of Health Sciences, The University of Manchester, Manchester, UK
| | - David C Wong
- Division of Informatics, Imaging and Data Sciences, The University of Manchester, Manchester, UK
| | - Jung Jae Lee
- School of Nursing, The University of Hong Kong, Pokfulam, Hong Kong
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O'Connor S. Teaching artificial intelligence to nursing and midwifery students. Nurse Educ Pract 2022; 64:103451. [PMID: 36166951 DOI: 10.1016/j.nepr.2022.103451] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/05/2023]
Affiliation(s)
- Siobhán O'Connor
- School of Health Sciences, The University of Manchester, United Kingdom.
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Pan LC, Wu XR, Lu Y, Zhang HQ, Zhou YL, Liu X, Liu SL, Yan QY. Artificial intelligence empowered Digital Health Technologies in Cancer Survivorship Care: a scoping review. Asia Pac J Oncol Nurs 2022; 9:100127. [PMID: 36176267 PMCID: PMC9513729 DOI: 10.1016/j.apjon.2022.100127] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/26/2022] [Accepted: 07/29/2022] [Indexed: 12/03/2022] Open
Abstract
Objective The objectives of this systematic review are to describe features and specific application scenarios for current cancer survivorship care services of Artificial intelligence (AI)-driven digital health technologies (DHTs) and to explore the acceptance and briefly evaluate its feasibility in the application process. Methods Search for literatures published from 2010 to 2022 on sites MEDLINE, IEEE-Xplor, PubMed, Embase, Cochrane Central Register of Controlled Trials and Scopus systematically. The types of literatures include original research, descriptive study, randomized controlled trial, pilot study, and feasible or acceptable study. The literatures above described current status and effectiveness of digital medical technologies based on AI and used in cancer survivorship care services. Additionally, we use QuADS quality assessment tool to evaluate the quality of literatures included in this review. Results 43 studies that met the inclusion criteria were analyzed and qualitatively synthesized. The current status and results related to the application of AI-driven DHTs in cancer survivorship care were reviewed. Most of these studies were designed specifically for breast cancer survivors’ care and focused on the areas of recurrence or secondary cancer prediction, clinical decision support, cancer survivability prediction, population or treatment stratified, anti-cancer treatment-induced adverse reaction prediction, and so on. Applying AI-based DHTs to cancer survivors actually has shown some positive outcomes, including increased motivation of patient-reported outcomes (PROs), reduce fatigue and pain levels, improved quality of life, and physical function. However, current research mostly explored the technology development and formation (testing) phases, with limited-scale population, and single-center trial. Therefore, it is not suitable to draw conclusions that the effectiveness of AI-based DHTs in supportive cancer care, as most of applications are still in the early stage of development and feasibility testing. Conclusions While digital therapies are promising in the care of cancer patients, more high-quality studies are still needed in the future to demonstrate the effectiveness of digital therapies in cancer care. Studies should explore how to develop uniform standards for measuring patient-related outcomes, ensure the scientific validity of research methods, and emphasize patient and health practitioner involvement in the development and use of technology.
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Affiliation(s)
- Lu-Chen Pan
- Department of Nursing, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430022, China
| | - Xiao-Ru Wu
- School of Nursing, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430030, China
| | - Ying Lu
- Department of Nursing, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430022, China
- School of Nursing, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430030, China
| | - Han-Qing Zhang
- Health Science Center, Yangtze University, Jinzhou 434023, China
| | - Yao-Ling Zhou
- Department of Nursing, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430022, China
- School of Nursing, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430030, China
| | - Xue Liu
- School of Nursing, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430030, China
| | - Sheng-Lin Liu
- Department of Medical Engineering, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430022, China
- Corresponding authors.
| | - Qiao-Yuan Yan
- Department of Nursing, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430022, China
- Corresponding authors.
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O'Connor S, Yan Y, Thilo FJS, Felzmann H, Dowding D, Lee JJ. Artificial intelligence in nursing and midwifery: A systematic review. J Clin Nurs 2022. [PMID: 35908207 DOI: 10.1111/jocn.16478] [Citation(s) in RCA: 29] [Impact Index Per Article: 14.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/28/2021] [Revised: 07/04/2022] [Accepted: 07/18/2022] [Indexed: 12/11/2022]
Abstract
BACKGROUND Artificial Intelligence (AI) techniques are being applied in nursing and midwifery to improve decision-making, patient care and service delivery. However, an understanding of the real-world applications of AI across all domains of both professions is limited. OBJECTIVES To synthesise literature on AI in nursing and midwifery. METHODS CINAHL, Embase, PubMed and Scopus were searched using relevant terms. Titles, abstracts and full texts were screened against eligibility criteria. Data were extracted, analysed, and findings were presented in a descriptive summary. The PRISMA checklist guided the review conduct and reporting. RESULTS One hundred and forty articles were included. Nurses' and midwives' involvement in AI varied, with some taking an active role in testing, using or evaluating AI-based technologies; however, many studies did not include either profession. AI was mainly applied in clinical practice to direct patient care (n = 115, 82.14%), with fewer studies focusing on administration and management (n = 21, 15.00%), or education (n = 4, 2.85%). Benefits reported were primarily potential as most studies trained and tested AI algorithms. Only a handful (n = 8, 7.14%) reported actual benefits when AI techniques were applied in real-world settings. Risks and limitations included poor quality datasets that could introduce bias, the need for clinical interpretation of AI-based results, privacy and trust issues, and inadequate AI expertise among the professions. CONCLUSION Digital health datasets should be put in place to support the testing, use, and evaluation of AI in nursing and midwifery. Curricula need to be developed to educate the professions about AI, so they can lead and participate in these digital initiatives in healthcare. RELEVANCE FOR CLINICAL PRACTICE Adult, paediatric, mental health and learning disability nurses, along with midwives should have a more active role in rigorous, interdisciplinary research evaluating AI-based technologies in professional practice to determine their clinical efficacy as well as their ethical, legal and social implications in healthcare.
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Affiliation(s)
- Siobhán O'Connor
- Division of Nursing, Midwifery and Social Work, School of Health Sciences, The University of Manchester, Manchester, UK
| | - Yongyang Yan
- School of Nursing, The University of Hong Kong, Pokfulam, Hong Kong
| | - Friederike J S Thilo
- Applied Research and Development in Nursing, Department of Health Professions, Bern University of Applied Sciences, Bern, Switzerland
| | - Heike Felzmann
- School of Humanities, National University of Ireland Galway, Galway, Ireland
| | - Dawn Dowding
- Division of Nursing, Midwifery and Social Work, School of Health Sciences, The University of Manchester, Manchester, UK
| | - Jung Jae Lee
- School of Nursing, The University of Hong Kong, Pokfulam, Hong Kong
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