Ambient Intelligence and Sensor Technology in Nursing Care: A Review of Applications for Fall Prevention, Elopement Management, and Subtle Change Detection
Abstract
Background: Patient safety incidents like falls and undetected clinical deterioration are ongoing challenges in healthcare, leading to increased morbidity and costs. Traditional methods, such as nurse call systems, are reactive and resource-heavy. Ambient intelligent (AmI) systems, using unobtrusive sensors and AI, enable continuous monitoring and proactive intervention.
Aim: This narrative review aims to synthesize and critically analyze the current state of evidence regarding ambient sensor technologies in nursing care, with a focused examination of their application in fall prevention, elopement management, and the detection of subtle physiological or behavioral changes.
Methods: A comprehensive literature search was conducted using PubMed, CINAHL, IEEE Xplore, and ACM Digital Library for peer-reviewed articles (2010-2024).
Results: Evidence shows that ambient systems have over 90% accuracy in detecting falls and alerting elopements in controlled environments. Although predictive analytics for fall prevention using movement patterns are developing, data on their real-world clinical effectiveness is scarce. There are ongoing concerns about data privacy, algorithmic bias, the loss of human interaction, and the ethics of constant surveillance, especially for vulnerable groups like dementia patients.
Conclusion: Ambient intelligence can significantly improve nursing care and patient safety, but its successful integration requires a balanced, evidence-based, and ethical approach. Key factors include co-design with end-users, robust privacy-by-design frameworks, thoughtful implementation to support nursing judgment, and further high-quality research to prove its clinical and economic benefits.
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Authors
Copyright (c) 2024 Jawaher Aied Alrashidy, Nader Hussain Alahmadi, Ogab Owaied Mohammad Almutairi, Majed Snid Alsehly, Massad Mubark Alsobhi, Najat Salem Reja Alblwi, Eman Mosa Alamri, Ibtisam Ali Al johni, Muslih Saleh Aljohani, Khaled Abdullah Ali Al-Ghammas, Ahmad Salim S Alrashedi, Abdulaziz Obidallah Salma Alahmada, Aisha Awad Alanazi
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