Clinical practice guidelines (CPGs) are patient management tools that synthesize medical knowledge into an actionable format. CPGs are disease specific with limited applicability to the management of complex patients suffering from multimorbidity. For the management of these patients, CPGs need to be augmented with secondary medical knowledge coming from a variety of knowledge repositories. The operationalization of this knowledge is key to increasing CPGs' uptake in clinical practice. In this work, we propose an approach to operationalizing secondary medical knowledge inspired by graph rewriting. We assume that the CPGs can be represented as task network models, and provide an approach for representing and applying codified medical knowledge to a specific patient encounter. We formally define revisions that model and mitigate adverse interactions between CPGs and we use a vocabulary of terms to instantiate these revisions. We demonstrate the application of our approach using synthetic and clinical examples. We conclude by identifying areas for future work with the vision of developing a theory of mitigation that will facilitate the development of comprehensive decision support for the management of multimorbid patients.

This innovative study presents ADscreen, a cutting-edge speech processing algorithm designed to automatically identify patients with Alzheimer's disease and related dementias (ADRD). The algorithm is built on five core components that analyze both acoustic and linguistic aspects of patients' speech. A total of five major machine learning architectures were used, integrating data from acoustic, linguistic, and contextual word embedding vectors. Performance metrics demonstrated an F1-score of 89.55 and AUC-ROC of 93.89 for the test dataset, indicating the algorithm's high level of accuracy. The study emphasizes the potential of ADscreen to be incorporated into clinical workflows, serving as a crucial tool for the timely diagnosis and care of patients with cognitive impairments.

This retrospective observational cohort study investigated racial differences in judgment language usage in clinical notes and the relationship between judgment language and time spent on home visits in home health care. Data from 45,384 patients who received home health care services in 2019 were analyzed using a natural language processing algorithm. Results showed that 38% of patients had judgment language in their clinical notes, with the highest usage in Hispanic patients, followed by Black, White, and Asian patients. Black and Hispanic patients were 14% more likely to have judgment language in their notes compared to White patients. Additionally, the use of judgment language reduced the length of home health care visits by 21 minutes. The study highlights the need for further research on the impact of judgment language on care quality and the implementation of policy, education, and clinical practice improvements to address biases.

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.

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.

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.

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.

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. 

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.