Decision Tree Predictive Learner-Based Approach for False Alarm Detection in ICU

Alarm fatigue in nurses working in intensive care units: A multicenter study

BACKGROUND: Technological progress improves health care efficiency, quality, safety, and cost, supporting clinical activity in various scenarios, such as Intensive Care Unit (ICU). A timely response to alarms from monitors and other ICU electromedical devices is therefore crucial. The number of false alarms tend to desensitize care providers increasing the risk of experiencing alarm fatigue and, at times, lead to severe consequences for patients. OBJECTIVES: To assess the psychometric properties of the Italian version of the Alarm Fatigue Questionnaire and to explore the phenomenon of alarm fatigue among nurses working in intensive care settings. METHODS: The CVI-I was calculated to evaluate the validity of the content of the tool. Construct validity was investigated through exploratory factor analysis. Cronbach’s alpha coefficient (α) was used to examine the internal consistency of the scale and Spearman’s rho coefficient to test for stability. We designed a multicentre cross-sectional survey. A convenience sample of nurses from 4 Major Italian hospitals was recruited. The nurses completed the Italian version of the Alarm Fatigue Questionnaire. RESULTS: The content validity index CVI-S of the scale (CVI-S) was 91.11%; Cronbach’s alpha coefficient was 0.71. The Italian version of the tool explained 67.18%of the overall variance. 396 nurses were enrolled (79.84%). The overall level of alarm fatigue was Me = 29 [22;30]. 42.17%of the sample reported prior experience with alarm fatigue incidents. CONCLUSIONS: The extension of alarm fatigue requires the adoption of a preventive intervention plan. The Italian version of the Alarm Fatigue Questionnaire shows promising psychometric properties.

Application Scenarios for Artificial Intelligence in Nursing Care: Rapid Review

Background

Artificial intelligence (AI) holds the promise of supporting nurses’ clinical decision-making in complex care situations or conducting tasks that are remote from direct patient interaction, such as documentation processes. There has been an increase in the research and development of AI applications for nursing care, but there is a persistent lack of an extensive overview covering the evidence base for promising application scenarios.

Objective

This study synthesizes literature on application scenarios for AI in nursing care settings as well as highlights adjacent aspects in the ethical, legal, and social discourse surrounding the application of AI in nursing care.

Methods

Following a rapid review design, PubMed, CINAHL, Association for Computing Machinery Digital Library, Institute of Electrical and Electronics Engineers Xplore, Digital Bibliography & Library Project, and Association for Information Systems Library, as well as the libraries of leading AI conferences, were searched in June 2020. Publications of original quantitative and qualitative research, systematic reviews, discussion papers, and essays on the ethical, legal, and social implications published in English were included. Eligible studies were analyzed on the basis of predetermined selection criteria.

Results

The titles and abstracts of 7016 publications and 704 full texts were screened, and 292 publications were included. Hospitals were the most prominent study setting, followed by independent living at home; fewer application scenarios were identified for nursing homes or home care. Most studies used machine learning algorithms, whereas expert or hybrid systems were entailed in less than every 10th publication. The application context of focusing on image and signal processing with tracking, monitoring, or the classification of activity and health followed by care coordination and communication, as well as fall detection, was the main purpose of AI applications. Few studies have reported the effects of AI applications on clinical or organizational outcomes, lacking particularly in data gathered outside laboratory conditions. In addition to technological requirements, the reporting and inclusion of certain requirements capture more overarching topics, such as data privacy, safety, and technology acceptance. Ethical, legal, and social implications reflect the discourse on technology use in health care but have mostly not been discussed in meaningful and potentially encompassing detail.

Conclusions

The results highlight the potential for the application of AI systems in different nursing care settings. Considering the lack of findings on the effectiveness and application of AI systems in real-world scenarios, future research should reflect on a more nursing care–specific perspective toward objectives, outcomes, and benefits. We identify that, crucially, an advancement in technological-societal discourse that surrounds the ethical and legal implications of AI applications in nursing care is a necessary next step. Further, we outline the need for greater participation among all of the stakeholders involved.

A Hybrid Scheme for Heart Disease Diagnosis Using Rough Set and Cuckoo Search Technique

Large volumes of raw data are created from the digital world every day. Acquiring useful information from these data is challenging, and it turned into a prime zone of momentum explore. More research is done in this direction. Further, in disease diagnosis, many uncertainties are involved in the information system. To handle such uncertainties, intelligent techniques are employed. In this paper, we present an integrated scheme for heart disease diagnosis. The proposed model integrates cuckoo search and rough set for inferencing decision rules. At the underlying phase, we employ a cuckoo search to discover the main features. Further, these main features are analyzed using rough set generating rules. An empirical analysis is carried out on heart disease. Besides, a comparative study is also presented. The comparative study demonstrates the feasibility of the proposed model.