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      Informatics Competencies of Students in a Doctor of Nursing Practice Program: A Descriptive Study

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          Abstract

          Objectives

          Health systems that apply artificial intelligence (AI) are transforming the roles of healthcare providers, including those of Doctor of Nursing Practice (DNP) providers. These professionals are required to utilize informatics knowledge and skills to deliver quality care, necessitating a high level of informatics competencies, which should be developed through well-structured courses. The purpose of this study is to assess the informatics competency scale scores of DNP students and to provide recommendations for enhancing the informatics curriculum.

          Methods

          An online informatics course was offered to students enrolled in a Bachelor of Science in Nursing to DNP program, and their informatics competency, which includes three subscales, was evaluated. Online survey data were collected from Fall 2021 to Fall 2022 using the “Self-Assessment of Informatics Competency Scale for Health Professionals.”

          Results

          An analysis of 127 student responses revealed that students demonstrated competence in overall informatics competency and in one subscale: “applied computer skills (clinical informatics).” They showed proficiency in the “basic computer skills” and the “role” subscales. However, they reported lower competency in managing data and integrating standard terminology into their practice.

          Conclusions

          The findings offer detailed insights into the current informatics competencies of DNP students and can inform informatics educators on how to enhance their courses. As healthcare institutions increasingly depend on AI applications, it is imperative for informatics educators to include AI-related content in their curricula.

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          Most cited references21

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          The role of artificial intelligence in healthcare: a structured literature review

          Background/Introduction Artificial intelligence (AI) in the healthcare sector is receiving attention from researchers and health professionals. Few previous studies have investigated this topic from a multi-disciplinary perspective, including accounting, business and management, decision sciences and health professions. Methods The structured literature review with its reliable and replicable research protocol allowed the researchers to extract 288 peer-reviewed papers from Scopus. The authors used qualitative and quantitative variables to analyse authors, journals, keywords, and collaboration networks among researchers. Additionally, the paper benefited from the Bibliometrix R software package. Results The investigation showed that the literature in this field is emerging. It focuses on health services management, predictive medicine, patient data and diagnostics, and clinical decision-making. The United States, China, and the United Kingdom contributed the highest number of studies. Keyword analysis revealed that AI can support physicians in making a diagnosis, predicting the spread of diseases and customising treatment paths. Conclusions The literature reveals several AI applications for health services and a stream of research that has not fully been covered. For instance, AI projects require skills and data quality awareness for data-intensive analysis and knowledge-based management. Insights can help researchers and health professionals understand and address future research on AI in the healthcare field.
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            Artificial Intelligence Education and Tools for Medical and Health Informatics Students: Systematic Review

            Background The use of artificial intelligence (AI) in medicine will generate numerous application possibilities to improve patient care, provide real-time data analytics, and enable continuous patient monitoring. Clinicians and health informaticians should become familiar with machine learning and deep learning. Additionally, they should have a strong background in data analytics and data visualization to use, evaluate, and develop AI applications in clinical practice. Objective The main objective of this study was to evaluate the current state of AI training and the use of AI tools to enhance the learning experience. Methods A comprehensive systematic review was conducted to analyze the use of AI in medical and health informatics education, and to evaluate existing AI training practices. PRISMA-P (Preferred Reporting Items for Systematic Reviews and Meta-Analysis Protocols) guidelines were followed. The studies that focused on the use of AI tools to enhance medical education and the studies that investigated teaching AI as a new competency were categorized separately to evaluate recent developments. Results This systematic review revealed that recent publications recommend the integration of AI training into medical and health informatics curricula. Conclusions To the best of our knowledge, this is the first systematic review exploring the current state of AI education in both medicine and health informatics. Since AI curricula have not been standardized and competencies have not been determined, a framework for specialized AI training in medical and health informatics education is proposed.
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              Artificial intelligence in nursing: Priorities and opportunities from an international invitational think-tank of the Nursing and Artificial Intelligence Leadership Collaborative

              Aim To develop a consensus paper on the central points of an international invitational think-tank on nursing and artificial intelligence (AI). Methods We established the Nursing and Artificial Intelligence Leadership (NAIL) Collaborative, comprising interdisciplinary experts in AI development, biomedical ethics, AI in primary care, AI legal aspects, philosophy of AI in health, nursing practice, implementation science, leaders in health informatics practice and international health informatics groups, a representative of patients and the public, and the Chair of the ITU/WHO Focus Group on Artificial Intelligence for Health. The NAIL Collaborative convened at a 3-day invitational think tank in autumn 2019. Activities included a pre-event survey, expert presentations and working sessions to identify priority areas for action, opportunities and recommendations to address these. In this paper, we summarize the key discussion points and notes from the aforementioned activities. Implications for nursing Nursing's limited current engagement with discourses on AI and health posts a risk that the profession is not part of the conversations that have potentially significant impacts on nursing practice. Conclusion There are numerous gaps and a timely need for the nursing profession to be among the leaders and drivers of conversations around AI in health systems. Impact We outline crucial gaps where focused effort is required for nursing to take a leadership role in shaping AI use in health systems. Three priorities were identified that need to be addressed in the near future: (a) Nurses must understand the relationship between the data they collect and AI technologies they use; (b) Nurses need to be meaningfully involved in all stages of AI: from development to implementation; and (c) There is a substantial untapped and an unexplored potential for nursing to contribute to the development of AI technologies for global health and humanitarian efforts.
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                Author and article information

                Journal
                Healthc Inform Res
                Healthc Inform Res
                Healthcare Informatics Research
                Korean Society of Medical Informatics
                2093-3681
                2093-369X
                April 2024
                30 April 2024
                : 30
                : 2
                : 147-153
                Affiliations
                School of Nursing, College of Health and Human Services, University of North Carolina at Wilmington, Wilmington, NC, USA
                Author notes
                Corresponding Author: Jeeyae Choi, School of Nursing, College of Health and Human Services, University of North Carolina at Wilmington, 601 S. College Rd. Wilmington, NC 28403-5995, USA. Tel: +1-910-962-2487, E-mail: choij@ 123456uncw.edu ( https://orcid.org/0000-0001-7287-6384)
                Author information
                https://orcid.org/0000-0001-7287-6384
                https://orcid.org/0000-0002-5623-9366
                https://orcid.org/0000-0003-4654-9747
                Article
                hir-2024-30-2-147
                10.4258/hir.2024.30.2.147
                11098765
                38755105
                7f87e466-24f6-469f-b48c-ea2fd32e7afa
                © 2024 The Korean Society of Medical Informatics

                This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License ( http://creativecommons.org/licenses/by-nc/4.0/) which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.properly cited.

                History
                : 20 October 2023
                : 18 March 2024
                : 4 April 2024
                Categories
                Original Article

                Bioinformatics & Computational biology
                artificial intelligence,nursing informatics,nursing graduate education,advanced practice nursing,students,nursing

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