Defining the concepts of a smart nursing home and its potential technology utilities that integrate medical services and are acceptable to stakeholders: a scoping review

Zhao, Yuanyuan; Rokhani, Fakhrul Zaman; Sazlina, Shariff-Ghazali; Su, Jing; Chew, Boon How

doi:10.1186/s12877-022-03424-6

Cited by 19 publications

(24 citation statements)

References 159 publications

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“…16 As mentioned earlier, nurses are the most overburdened positions as they directly face patients. The IN model based on the IoT cloud computing platform facilitates the maximum deployment of nursing resources, and through real-time data exchange and sharing, enables each nurse on duty to have all the information related to his or her work as quickly as possible and to do his or her job with the highest efficiency and best results, thus alleviating the pressure faced by the healthcare system to a certain extent, 17 which is another important contribution of the IN model.…”

Section: Discussionmentioning

confidence: 99%

Application of intelligent nursing based on cloud computing of internet of things in children with pneumonia and sepsis treated with human gamma globulin

Qin¹,

Liu

Shao³

2023

Eur J Inflamm

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Purpose: To explore the application of intelligent nursing (IN) based on the Internet of Things (IoT) in children with pneumonia and sepsis treated with human gamma globulin (HGG). Methods: A total of 200 children with pneumonia combined with sepsis who attended the First People’s Hospital of Shangqiu from January 1, 2020 to February 13, 2022 were consecutively collected. Children were randomly divided into IN group and routine nursing (RN) group, with 100 children in each group. All children received standard anti-infection treatment along with intravenous HGG. In IN group, IN measures based on the IoT cloud computing platform monitored the treatment process of children with HGG throughout the whole process, while children in the RN group only received RN measures. Information on both groups was collected from the medical records, such as gender, age, duration of hospitalization, fever, antibiotic use, serological indicators, pulmonary function indicators, immune function indicators and adverse effects of HGG. Multi-factorial logistic regression was performed to access the correlation between IN and the duration of hospitalization and a range of other factors studied above. Results: After adjusting for numerous confounding factors, multifactorial logistic regression revealed that the application of IN was associated with a shorter duration of hospitalization ( p = .030) and lower white blood cell (WBC) and creatinine (Cr) levels in post-treatment children ( p = .003, p = .010). It was also associated with higher levels of forced expiratory volume in one second (FEV1), forced vital capacity (FVC) and peak expiratory flow (PEV) after treatment ( p = .014, p = .001, p = .002) and higher levels of immune CD4+/CD8+ ratio after treatment ( p = .001) and reduced symptoms of vomiting among the adverse effects ( p = .047). Conclusion: The IoT cloud-based IN model significantly improved the efficacy of HGG in the treatment of pneumonia sepsis in children and reduce occurrence of some adverse reactions.

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Section: Discussionmentioning

confidence: 99%

Application of intelligent nursing based on cloud computing of internet of things in children with pneumonia and sepsis treated with human gamma globulin

Qin¹,

Liu

Shao³

2023

Eur J Inflamm

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“…To study advances in ICT, however, these challenges can now be met head‐on in the form of remote monitoring and management of chronic diseases (ICT). “Remote monitoring” 3 means collecting and sending patient health data to healthcare providers for feedback, instruction, or action. Remote monitoring for chronic diseases can improve patient satisfaction, hospitalizations, and self‐management.…”

Section: Introductionmentioning

confidence: 99%

Internet of things‐enabled intelligent systems for remote chronic disease monitoring

Tai,

Rajawat,

Goyal

et al. 2023

Trans Emerging Tel Tech

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The possibility of remotely monitoring and managing chronic diseases is made feasible by the Internet of Things (IoT), which enables the collection and analysis of vast amounts of data from various sources such as sensors, devices, and wearables. However, conventional machine learning approaches often encounter difficulties when dealing with IoT data due to its noise, heterogeneity, and high dimensions. In this study, we introduce a novel technique for developing human‐centric intelligent systems for remote monitoring of chronic diseases using deep learning in an IoT context. This technique is capable of handling complex and diverse IoT data, providing accurate and comprehensible predictions and recommendations. We outline a standardized architecture for these systems, comprising of three distinct phases: data acquisition, data processing, and data visualization. To demonstrate the effectiveness of our methodology, we apply it to a case study involving the development and evaluation of a human‐centered intelligent system for remote monitoring of hypertension and diabetic patients, involving real users. Our findings reveal a substantial enhancement in prediction accuracy when employing our proposed hybrid algorithm. When compared to traditional algorithms, our proposed hybrid algorithm exhibits superior performance, achieving an accuracy of 93.5%, surpassing the Restricted Boltzmann Machine (RBM) with an accuracy of 88.2%, Long Short‐Term Memory (LSTM) networks at 90.4%, and Convolutional Neural Networks (CNN) at 91.6%. This improvement in accuracy is critical for ensuring reliable disease monitoring and management, highlighting the effectiveness of our approach. In terms of precision, scalability, and user satisfaction, we demonstrate the effectiveness and efficiency of our method. Furthermore, we address the social and ethical implications inherent in our methodology and suggest avenues for future research in this domain.

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“…In hospital settings, smart technologies are used to enhance clinical decision-making [7], while in home-based care, they help with self-management and the remote monitoring of chronic diseases [8,9]. In nursing home settings, technologies are predominantly implemented to provide person-centred care services and integrate medical services from remote hospitals [10]. The use of smart technologies holds the potential to manage a substantial number of older adults in both home-based and institutional care [11].…”

Section: Introductionmentioning

confidence: 99%

“…Moreover, many older adults have a negative attitude towards smart technologies, perceiving them as challenging to use and being expensive [14]. Exploring the expectations and acceptability of smart nursing homes within a de ned service scope and associated technologies [10] among stakeholders, particularly older adults, will provide a better understanding of the future development and implementation of smart nursing home models. Expectations, in this context, generally encompass the desires of consumers regarding what they expect a smart nursing home to provide [15], while acceptability refers to the intention to use services when they are available and meet the criteria of target users willing to adopt smart nursing homes [16].…”

Section: Introductionmentioning

confidence: 99%

“…Speci cally, a precise de nition of smart nursing homes and the comprehensive implementation of functional technologies is needed. A comprehensive scoping review has de ned a smart nursing home as characterised by the incorporating of functional information technologies, encompassing the IoT, digital health, big data, AI, cloud computing technologies, and information management system (IMS) that enable the monitoring of abnormal events, provision of remote clinical services, establishment of health information databases, enhancement of decision making processes, analysis of clinical data, and facilitation of activities of daily living for older residents [10]. It may integrate medical services from remote hospitals or healthcare experts, using telemedicine, mHealth, and other electronic clinical information, to manage complex health conditions among their inmates and ensure their overall well-being within a safe and costeffective environment [10].…”

Section: Introductionmentioning

confidence: 99%

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The expectations and acceptability of a smart nursing home model among Chinese older adults: A mixed methods study

Zhao,

Sazlina,

Rokhani

et al. 2023

Preprint

Self Cite

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Background Integrated smart technologies in nursing homes can enhance the quality of care. This study expands upon prior research, including a scoping review defining smart nursing homes and a qualitative study exploring the expectations and acceptability of such facilities among Chinese older adults and their families. It investigates the levels of these expectations, acceptability, and related sociodemographic factors. Methods This study presents the development and validation of the 24-item Expectation and Acceptability of Smart Nursing Homes Questionnaire (EASNH-Q), and a cross-sectional study conducted in four major cities in China in an exploratory sequential mixed methods study design. Results The newly developed EASNH-Q with Likert Scale from 1 (strongly disagree) to 5 (strongly agree) demonstrated commendable validity, reliability, and stability. The overall mean (SD) scores for expectations and acceptability were 4.0 (0.60) for each. The highest tertile of expectations (62/70, 88.6%, ꭓ2=28.89, p<0.001) and acceptability (68/83, 81.9%, ꭓ2=25.64, p<0.001) towards smart nursing homes were significantly associated with the willingness to relocate to such facilities. Older adults with self-efficacy in applying smart technologies (OR: 28.0, 95% CI: 5.92-132.66) and those expressing a willingness to move to a nursing home (OR: 3.0, 95% CI: 1.06-8.37) were more likely to have the highest tertile of expectations compared to those in the lowest tertile. Similarly, older adults with self-efficacy in applying smart technologies were more likely to be in the highest tertile of acceptability of smart nursing homes compared to those in the lowest tertile (OR: 13.8, 95% CI: 4.33-43.95). Conclusions The majority of Chinese older adults were willing to move to a smart nursing home. Self-efficacy in applying smart technologies links to the high expectations and acceptability of smart nursing homes. The willingness to relocate to a nursing home increased the high expectations of smart nursing homes.

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Defining the concepts of a smart nursing home and its potential technology utilities that integrate medical services and are acceptable to stakeholders: a scoping review

Cited by 19 publications

References 159 publications

Application of intelligent nursing based on cloud computing of internet of things in children with pneumonia and sepsis treated with human gamma globulin

Application of intelligent nursing based on cloud computing of internet of things in children with pneumonia and sepsis treated with human gamma globulin

Internet of things‐enabled intelligent systems for remote chronic disease monitoring

The expectations and acceptability of a smart nursing home model among Chinese older adults: A mixed methods study

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