Assisting nurses in care documentation: from automated sentence classification to coherent document structures with subject headings

Embracing Artificial Intelligence: Revolutionizing Nursing Documentation for a Better Future

Nursing documentation stands as a critical aspect of healthcare delivery, ensuring comprehensive patient records and facilitating communication among healthcare providers. However, traditional documentation methods are often time-consuming and prone to errors, diverting nurses' attention from direct patient care. This editorial explores the transformative potential of artificial intelligence (AI) in revolutionizing nursing documentation processes. By leveraging AI-driven technologies, such as natural language processing and machine learning, healthcare organizations can automate data entry, extract key clinical information, and generate personalized care plans, thereby streamlining workflows and improving documentation accuracy. This editorial also examines various AI-powered software applications and platforms that facilitate nursing documentation, highlighting their benefits in terms of efficiency, accuracy, and clinical decision support. Furthermore, it discusses considerations such as privacy, security, and the need for nurse training to effectively integrate AI into nursing practice. By embracing AI in nursing documentation, healthcare organizations can empower nurses to devote more time to patient care while enhancing the quality and safety of healthcare delivery.

Charting the Future: The Role of AI in Transforming Nursing Documentation

This editorial delves into the integration of artificial intelligence (AI) into nursing documentation, emphasizing its potential to streamline workflows, reduce human error, and enhance patient care. AI technologies, notably natural language processing and decision support systems, present opportunities to automate tedious documentation tasks and enhance record accuracy. However, their adoption raises ethical considerations, such as privacy, bias, and accountability. Striking a balance between technological advancements and ethical imperatives is pivotal to harnessing the benefits of AI while safeguarding patient safety and upholding professional integrity in nursing practice. Advocating for ongoing evaluation, regulation, and education is crucial to ensure the responsible integration of AI into nursing documentation. This approach aims to improve patient outcomes and maintain the high standards of the nursing profession.

Research Trends in Artificial Intelligence-Associated Nursing Activities Based on a Review of Academic Studies Published From 2001 to 2020

The present study referred to the technology-based learning model to conduct a systematic review of the dimensions of nursing activities, research samples, research methods, roles of artificial intelligence, applied artificial intelligence algorithms, evaluation measure of algorithms, and research foci. Based on the Preferred Reporting Items for Systematic Reviews and Meta-Analyses procedure, this study obtained and analyzed a total of 102 high-quality artificial intelligence-associated nursing activities studies published from 2001 to 2020 in the Web of Science database. The results showed: (1) In terms of nursing activities, nursing management was explored the most, followed by nursing assessment; (2) quantitative methods were most frequently adopted in artificial intelligence-associated nursing activities studies to investigate issues related to patients, followed by nursing staff; (3) the most adopted roles of artificial intelligence in artificial intelligence-associated nursing activities studies were profiling and prediction, followed by assessment and evaluation; (4) artificial intelligence-associated nursing activities studies frequently mixed applied artificial intelligence algorithms and evaluation measure of algorithms; (5) in the dimension of research foci, most studies mainly paid attention to the design or evaluation of the artificial intelligence systems/instruments, followed by investigating the correlation and affect issues. Based on the findings, several recommendations are raised as a reference for future researchers, educators, and policy makers.

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

AIM

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

BACKGROUND

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

EVALUATION

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

KEY ISSUES

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

IMPLICATIONS FOR NURSING MANAGEMENT

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

Year 2020 (with COVID): Observation of Scientific Literature on Clinical Natural Language Processing

Objectives: To analyze the content of publications within the medical NLP domain in 2020.

Methods: Automatic and manual preselection of publications to be reviewed, and selection of the best NLP papers of the year. Analysis of the important issues.

Results: Three best papers have been selected in 2020. We also propose an analysis of the content of the NLP publications in 2020, all topics included.

Conclusion: The two main issues addressed in 2020 are related to the investigation of COVID-related questions and to the further adaptation and use of transformer models. Besides, the trends from the past years continue, such as diversification of languages processed and use of information from social networks

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.