Citation: Hobensack M, Song J, Scharp D, Bowles KH, Topaz M. Machine learning applied to electronic health record data in home healthcare: A scoping review. Int J Med Inform. 2023 Feb;170:104978. doi: 10.1016/j.ijmedinf.2022.104978. Epub 2022 Dec 30. PMID: 36592572; PMCID: PMC9869861.

This study aimed to synthesize and evaluate the use of machine learning to predict adverse outcomes, such as hospitalization or mortality, in home healthcare using electronic health record data. The study analyzed 20 studies and found that tree-based algorithms were most commonly used, while psychological predictors were frequently excluded. Most of the studies demonstrated high or unclear risk of bias. The authors concluded that machine learning algorithms should be considered for inclusion in clinical decision support systems in home healthcare and that a more comprehensive approach to risk prediction, including psychological and interpersonal characteristics, is needed. To facilitate the widespread adoption of machine learning in this setting, stakeholders should encourage standardization.

Citation: Peltonen LM, Topaz M. Artificial intelligence in health care: Implications for nurse managers. J Nurs Manag. 2022 Nov;30(8):3641-3643. doi: 10.1111/jonm.13858. PMID: 36201227.

This special issue of the Journal of Nursing Management edited by NAIL members Peltonen and Topaz focuses on the use of AI in nursing and healthcare, exploring its implications for nursing management. The articles included in this issue cover a range of topics, including relevant AI technologies for nursing, ethical issues related to artificial intelligence in healthcare, the role of AI in assessing and improving the quality of care, and the use of AI to support nursing education and training. The articles provide valuable insights and evidence on the impact and potential applications of AI in nursing and healthcare, as well as highlighting some of the ethical considerations and challenges that need to be addressed.

Citation: Von Gerich H, Moen H, Block LJ, Chu CH, DeForest H, Hobensack M, Michalowski M, Mitchell J, Nibber R, Olalia MA, Pruinelli L, Ronquillo CE, Topaz M, Peltonen LM. Artificial Intelligence -based technologies in nursing: A scoping literature review of the evidence. Int J Nurs Stud. 2022 Mar;127:104153.

Artificial Intelligence -based technologies in nursing: A scoping literature review of the evidence

Little evidence exists in the extant literature regarding the efficacious application of many artificial intelligence -based health technologies used in healthcare. Hence, this study aimed to synthesise currently available state-of the-art research in artificial intelligence -based technologies applied in nursing practice. A total of 7610 articles published between January 2010 and March 2021 were revealed, with 93 articles included in this review. Based on the findings, authors concluded that contemporary research on applications of artificial intelligence -based technologies in nursing mainly cover the earlier stages of technology development, leaving scarce evidence of the impact of these technologies and implementation aspects into practice. The content of research reported is varied.

Citation: Ronquillo CE, Peltonen LM, Pruinelli L, Chu CH, Bakken S, Beduschi A, Cato K, Hardiker N, Junger A, Michalowski M, Nyrup R, Rahimi S, Reed DN, Salakoski T, Salanterä S, Walton N, Weber P, Wiegand T, Topaz M. Artificial intelligence in nursing: Priorities and opportunities from an international invitational think-tank of the Nursing and Artificial Intelligence Leadership Collaborative. J Adv Nurs. 2021 Sep;77(9):3707-3717. doi: 10.1111/jan.14855. Epub 2021 May 18. PMID: 34003504; PMCID: PMC7612744.

Artificial intelligence in nursing: Priorities and opportunities from an international invitational think-tank of the Nursing and Artificial Intelligence Leadership Collaborative

The aim of this work was to develop a consensus paper on the central points of an international invitational think-tank on nursing and artificial intelligence (AI). 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.

Citation: Lu, S.-C., Brown, R. J., & Michalowski, M. (2021). A Clinical Decision Support System Design Framework for Nursing Practice. ACI open, 5(2), e84-e93.

A Clinical Decision Support System Design Framework for Nursing Practice

As nurses increasingly engage in decision-making for patients, a unique opportunity exists to translate research into practice using clinical decision support systems (CDSSs). While research has shown that CDSS has led to improvements in patient outcomes and nursing workflow, the success rate of CDSS implementation in nursing is low. Further, the majority of CDSS for nursing are not designed to support the care of patients with comorbidity. The aim of the study is to conceptualize an evidence-based CDSS supporting complex patient care for nursing. We conceptualized the CDSS through extracting scientific findings of CDSS design and development. To describe the CDSS, we developed a conceptual framework comprising the key components of the CDSS and the relationships between the components. We instantiated the framework in the context of a hypothetical clinical case. The proposed framework provides a common architecture for CDSS development and bridges CDSS research findings and development.

Citation: Pruinelli L, Michalowski M. Toward an Augmented Nursing-Artificial Intelligence Future. Comput Inform Nurs. 2021 Jun;39(6):296-297. doi: 10.1097/CIN.0000000000000784. PMID: 34081661.

Toward an Augmented Nursing-Artificial Intelligence Future

How can artificial intelligence (AI) augment and elevate what is known and delivered by nurses on a daily basis? Is AI replacing nursing? How do nurses guide the decisions made by AI used in practice? Those are several questions populating nurses' thoughts currently, mostly resulting from a lack of understanding of what AI is and how it can and will impact their work. AI is already embedded in nurses' daily life as algorithms (ie, sepsis monitoring systems) and smart systems (eg, smart beds and intravenous pumps), just to cite a couple of examples of AI applications. Frequently, we as nurses do not recognize them as such. AI applications are being incorporated into clinical care, and in many situations, awareness of their impact is uncertain due to a lack of understanding on how they are developed.

Citation: Pruinelli L. Nursing and Data: Powering Nursing Leaders for Big Data Science. Rev Bras Enferm. 2021 Jul 14;74(4):e740401. English, Portuguese, Spanish. doi: 10.1590/0034-7167.2021740401. PMID: 34287564.


More comprehensive knowledge and improved data science skills are needed by nurses and nursing leaders who conduct analytics using large sets of nursing data with the goal of improving population outcomes. Nursing informatics competencies, not just for nurse informaticians, but for nurse leaders as well, need to move beyond the current formal training to a more competency-based model, including education and practice. Very few efforts have been made to advance nursing understanding and skillsets required for using data for clinical applications in real-world scenarios. Below are some concepts and current initiatives to address, inform, and discuss future directions in nursing big data science are presented.