Ambient intelligence–based monitoring of staff and patient activity in the intensive care unit

Chan, Peter; Tay, Andrew; Chen, David C.; Freitas, Maria De; Millet, Coralie; Nguyen-Duc, Thanh; Duke, Graeme; Lyall, Jessica; Nguyen, John; McNeil, John J; Hopper, Ingrid

doi:10.1016/j.aucc.2022.08.011

Cited by 6 publications

(4 citation statements)

References 16 publications

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“…An observational pilot study reported that delirious patients had significantly different expressions of facial action units, head pose movement, and other environmental factors such as visitation frequency measured by CV (n = 22) [156]. A different study examining the frequency of caregiver actions reported that delirious patients had more caregiver activity overall, which was most concentrated from 8:00 p.m. to 12:00 a.m. [195]. These observational differences between non-delirious and delirious patients can be automated to aid in the recognition of early warning signs of delirium, the measurement of delirium severity, and aid in subtyping and phenotyping efforts.…”

Section: Detection and Monitoring Of Brain Healthmentioning

confidence: 99%

“…These tasks were then examined over time for trends. The second study recognized and then categorized patient and caregiver movement (i.e., workload) over the course of 24 h in an adult ICU [195]. The study reported significant differences in patient and caregiver movement throughout the 24 h period, between intubated and non-intubated, delirious and non-delirious, and settings (high dependency unit vs. ICU).…”

Section: Quantification Of Workload In the Icumentioning

confidence: 99%

“…This system was validated against manual observation with a correlation of r = 0.882 [222]. Improving the quantification and understanding of caregiver workload and function throughout a time period was the focus of these studies as existing monitoring systems are resource intensive and subjective [195,222,226].…”

Section: Quantification Of Workload In the Icumentioning

confidence: 99%

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Applied Artificial Intelligence in Healthcare: A Review of Computer Vision Technology Application in Hospital Settings

Lindroth,

Nalaie,

Raghu

et al. 2024

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Computer vision (CV), a type of artificial intelligence (AI) that uses digital videos or a sequence of images to recognize content, has been used extensively across industries in recent years. However, in the healthcare industry, its applications are limited by factors like privacy, safety, and ethical concerns. Despite this, CV has the potential to improve patient monitoring, and system efficiencies, while reducing workload. In contrast to previous reviews, we focus on the end-user applications of CV. First, we briefly review and categorize CV applications in other industries (job enhancement, surveillance and monitoring, automation, and augmented reality). We then review the developments of CV in the hospital setting, outpatient, and community settings. The recent advances in monitoring delirium, pain and sedation, patient deterioration, mechanical ventilation, mobility, patient safety, surgical applications, quantification of workload in the hospital, and monitoring for patient events outside the hospital are highlighted. To identify opportunities for future applications, we also completed journey mapping at different system levels. Lastly, we discuss the privacy, safety, and ethical considerations associated with CV and outline processes in algorithm development and testing that limit CV expansion in healthcare. This comprehensive review highlights CV applications and ideas for its expanded use in healthcare.

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Section: Detection and Monitoring Of Brain Healthmentioning

confidence: 99%

Section: Quantification Of Workload In the Icumentioning

confidence: 99%

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Applied Artificial Intelligence in Healthcare: A Review of Computer Vision Technology Application in Hospital Settings

Lindroth,

Nalaie,

Raghu

et al. 2024

J. Imaging

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“…For example, ref. [11] demonstrated the utilization of AMI to track and monitor patients in intensive care units. Additionally, ref.…”

mentioning

confidence: 99%

Smart Healthcare: Exploring the Internet of Medical Things with Ambient Intelligence

Sarkar,

Lee,

Sahoo

2024

Electronics

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Ambient Intelligence (AMI) represents a significant advancement in information technology that is perceptive, adaptable, and finely attuned to human needs. It holds immense promise across diverse domains, with particular relevance to healthcare. The integration of Artificial Intelligence (AI) with the Internet of Medical Things (IoMT) to create an AMI environment in medical contexts further enriches this concept within healthcare. This survey provides invaluable insights for both researchers and practitioners in the healthcare sector by reviewing the incorporation of AMI techniques in the IoMT. This analysis encompasses essential infrastructure, including smart environments and spectrum for both wearable and non-wearable medical devices to realize the AMI vision in healthcare settings. Furthermore, this survey provides a comprehensive overview of cutting-edge AI methodologies employed in crafting IoMT systems tailored for healthcare applications and sheds light on existing research issues, with the aim of guiding and inspiring further advancements in this dynamic field.

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Critical care workforce in crisis: A path forward

Costa

Hammond

2023

Australian Critical Care

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Ambient intelligence–based monitoring of staff and patient activity in the intensive care unit

Cited by 6 publications

References 16 publications

Applied Artificial Intelligence in Healthcare: A Review of Computer Vision Technology Application in Hospital Settings

Applied Artificial Intelligence in Healthcare: A Review of Computer Vision Technology Application in Hospital Settings

Smart Healthcare: Exploring the Internet of Medical Things with Ambient Intelligence

Critical care workforce in crisis: A path forward

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