Review of Smart Hospital Services in Real Healthcare Environments

An Analysis of the Efficacy of the Health Butler Application and Intelligent Accompaniment Systems in Smart Outpatient Services Based on Patient Feedback

Objective

This study aims to explore the efficacy of a smart outpatient service system incorporating the Health Butler application and Intelligent Accompaniment based on patients' feedback.

Methods

A total of 1000 outpatients from Shenzhen Third People's Hospital were randomly selected in May 2023 and November 2023 to provide feedback before and after implementing the smart outpatient service system consisting of the Health Butler app and Intelligent Accompaniment. The changes in the patient's awareness of the medical treatment process, average waiting time, satisfaction with the experience of seeking medical treatment, and trust in medical services offered based on patient feedback were compared pre- and post-implementation.

Results

There were significant differences in outpatients' awareness of the process of medical treatment, average waiting time, satisfaction with the experience of seeking medical care, and trust in medical services before and after the implementation of the smart outpatient service system (P < 0.05).

Conclusion

The smart outpatient service system, which incorporated the Health Butler app and Intelligent accompaniment based on patient feedback, was effective in improving awareness of the process of medical treatment among outpatients, shortening their average waiting time, improving patient satisfaction with the experience of seeking medical care, and also enhancing their trust in medical services.

Forecasting the future of smart hospitals: findings from a real-time delphi study

BACKGROUND

In concert with other digital technologies, artificial intelligence (AI) is shaping the vision of smart hospitals. The transformation into smart hospitals, however, is all but trivial due to the lack of financial and human resources, digital skills, and supporting policies. Thus, the extent to which the vision of smart hospitals will eventually become reality is uncertain. In this context, our study provides a multidimensional conceptualization of the immediate future of smart hospitals to 2042.

METHODS

This study employs an iterative mixed-methods approach, including expert workshops and a Delphi study. We conducted a real-time Delphi study to forecast the evolution of smart hospitals in 5-year steps from 2027 to 2042. A total of 39 experts in healthcare, artificial intelligence, and management participated.

RESULTS

Our understanding of a technology-enabled smart hospital in this study includes four dimensions: artificial intelligence (AI), sustainability, ecosystems, and human-centeredness. Our findings underscore the critical need to address the shortage of hospital staff and general practitioners that models predict will peak by 2032. Additionally, our results show a significant shift to individualized medicine and home care. This shift indicates that smart hospitals are expected to leverage AI and digital technologies to tailor care to each patient. Furthermore, the roles and responsibilities of hospital staff will undergo significant changes. Healthcare personnel will have to adapt to new technologies that facilitate more efficient workflows and improve patient engagement in evolving healthcare environments. The results of our study suggest a shift in care to individualized medicine and home care, with corresponding changes in the roles and responsibilities of hospital staff who will employ new technologies.

CONCLUSIONS

The findings from our real-time Delphi study suggest that the vision of smart hospitals is gradually becoming reality over the next 20 years. Advancements in artificial intelligence should enhance operational efficiency and patient-centric care, while facilitating the integration of sustainability practices and fostering collaborative ecosystems. However, addressing challenges such as staff shortages, ethical considerations, and the need for robust digital skills will be essential. A deep pool of expert healthcare practitioners, clear ethical guidelines, and robust digital skills are essential to fully realize this vision and ensure that smart hospitals can meet the evolving needs of healthcare delivery.

[Factors Affecting the Intention to Use Smartmonitor-Based Mobile Health in Middle-Aged in Patients Applying the Technology Acceptance Model II]

PURPOSE

This study aimed to identify factors that influence the intention to use smart monitor-based mobile health (SBM) technology among middle-aged inpatients, based on the technology acceptance model II (TAM II).

METHODS

A total of 222 participants were surveyed. Data were analyzed using SPSS Statistics 23.0 and IBM SPSS Amos 23. Seven exogenous variables-social influence (SI), personal self-efficacy, (PSE), environmental self-efficacy (ESE), health literacy, health concerns, resistance to innovative technology (RIT), accessibility (AC)-and three endogenous variables-perceived ease of use (PEOU), perceived usability (PU), and intention to use (ITU)-were investigated.

RESULTS

The hypothesized path model demonstrated a good fit for the data. SI (β = .13, p = .042), PU (β = .46, p < .001), and PEOU (β = .16, p = .008) had significant direct effects on the ITU, which explained 39.5% of the variance. Additionally, SI (β = .27, p < .001), ESE (β = .16, p = .010), RIT (β = -.12, p = .026), AC (β = .28, p < .001), and PEOU (β = .20, p = .001) indirectly affected ITU through PU, which explained 50.7% of the variance. Furthermore, PSE (β = .38, p < .001) indirectly influenced ITU via PEOU, which explained 38.4% of the variance.

CONCLUSION

This study demonstrates that the TAM II can be used to effectively predict ITU in SBMs among middle-aged inpatients. To expand the intention to use SBMs, it is necessary to develop SBMs that include content and programs that promote PU, SI, and PEOU.

Digital innovative healthcare during a pandemic and beyond: a showcase of the large-scale and integrated Saudi smart national health command centre

INTRODUCTION

The increasing frequency of pandemics, demand for healthcare and costs of healthcare services require efficient health systems with integrated care via a command centre that ensures a centralised and coordinated approach to exercise effective leadership.

DESCRIPTION

We present a case study using the conceptual framework of Franklin to describe the novel system-based engineering approach of the Saudi National Health Command Centre (NHCC) including its features and outcomes measured.

DISCUSSION

The NHCC is structured into four departments and four zones with real-time data integration and visualisation on 88 dashboards. To empower leadership, it harnesses artificial intelligence affordances such as machine learning algorithms to enhance functionality, decision-making processes and overall performance. This allows for the rapid assessment of available resources and to monitor healthcare system efficiency at diverse levels of clinical and system indicators. Enhanced proactive capacity management has contributed to reducing lengths of stay, average supply chain lead time and surgery waiting list; early bending of the COVID-19 curve resulting in a low mortality rate; increasing bed capacity; deploying medical staff and mechanical ventilators rapidly; rolling out the COVID-19 vaccination programme and improving patient satisfaction.

CONCLUSION

Integrating a healthcare system with a command centre provides healthcare leaders with the necessary infrastructure to create synergy between people, processes and technologies. This substantially improves both patient and service outcomes. It also allows for immediate care coordination and resource allocations and safeguards ease of access to care.

[Digital twin hospitals: transforming the future of healthcare]

Since the concept of digital twin technology has been put forward, after decades of rapid development and wide application, it has not only made great achievements in many fields, but also brought broader prospects for the development of the medical field. As an important trend in the medical industry, digital twin hospitals play multiple roles by connecting physical hospitals and virtual hospitals and benefit the "patient-medical staff-hospital administrators", highlighting the immeasurable promising application of digital twin technology in smart hospitals. This review takes digital twin technology as an entry point, briefly introduces the progress of its application in various fields, focuses on the characteristics of digital twin technology, practical application cases in hospitals and their limitations, and also looks forward to its future development prospects, aiming to provide certain useful insights and guidance for the future of digital twin hospitals, and also expecting it to play an important role in changing the future of healthcare to a certain extent.

Green hospitals face to climate change: Between sobriety and resilience

Climate change poses a critical challenge to global health, influencing social and environmental determinants such as housing, air and water quality, and food security. This article explores the profound impact of climate change on health, projecting an additional 250,000 annual deaths from various climate-related diseases between 2030 and 2050. Healthcare systems significantly contribute to global carbon emissions. The concept of the "Green Hospital" is introduced as a paradigm shift in healthcare, focusing on optimizing resource efficiency and minimizing environmental impact. This concept encompasses renewable energy integration, natural lighting, sustainable materials, green roofs, and smart building management systems. Several challenges remain major, such as medical waste management, water conservation, chemical use, pollution, and plastic usage in healthcare settings. Moreover, obstacles to green hospital initiatives should be resolved, including system redundancy, regulatory compliance, operational demands, financial constraints, and cultural resistance. Conclusively, an urgent reformation of healthcare systems is needed to align with eco-friendly and sustainable practices, highlighting the necessity to reduce CO2 emissions and manage resources and waste more effectively to meet the evolving health needs of a growing and aging global population.

Potential of Assistive Robots in Clinical Nursing: An Observational Study of Nurses' Transportation Tasks in Rural Clinics of Bavaria, Germany

Transportation tasks in nursing are common, often overlooked, and directly impact patient care time in the context of staff shortages and an aging society. Current studies lack a specific focus on transportation tasks, a gap our research aims to fill. By providing detailed data on transportation needs in nursing, our study establishes a crucial foundation for the development and integration of assistive robots in clinical settings. In July and September 2023, we conducted weekly observations of nurses to assess clinical transportation needs. We aim to understand the economic impact and the methods nurses use for transportation tasks. We conducted a participant observation using a standardized app-based form over a seven-day observation period in two rural clinics. N = 1830 transports were made by nurses and examined by descriptive analysis. Non-medical supplies account for 27.05% (n = 495) of all transports, followed by medical supplies at 17.32% (n = 317), pharmacotherapy at 14.10% (n = 258) and other other categories like meals or drinks contributing 12.68% (n = 232). Most transports had a factual transport time of under a minute, with patient transport and lab samples displaying more variability. In total, 77.15% of all transports were made by hand. Requirements to collect items or connect transports with patient care were included in 5% of all transports. Our economic evaluation highlighted meals as the most costly transport, with 9596.16 € per year in the observed clinics. Budget-friendly robots would amortize these costs over one year by transporting meals. We support understanding nurses' transportation needs via further research on assistive robots to validate our findings and determine the feasibility of transport robots.

Advances in IoMT for Healthcare Systems

Nowadays, the demand for healthcare to transform from traditional hospital and disease-centered services to smart healthcare and patient-centered services, including the health management, biomedical diagnosis, and remote monitoring of patients with chronic diseases, is growing tremendously [...].

A-GSTCN: An Augmented Graph Structural-Temporal Convolution Network for Medication Recommendation Based on Electronic Health Records

Medication recommendation based on electronic health records (EHRs) is a significant research direction in the biomedical field, which aims to provide a reasonable prescription for patients according to their historical and current health conditions. However, the existing recommended methods have many limitations in dealing with the structural and temporal characteristics of EHRs. These methods either only consider the current state while ignoring the historical situation, or fail to adequately assess the structural correlations among various medical events. These factors result in poor recommendation quality. To solve this problem, we propose an augmented graph structural-temporal convolutional network (A-GSTCN). Firstly, an augmented graph attention network is used to model the structural features among medical events of patients' EHRs. Next, the dilated convolution combined with residual connection is applied in the proposed model, which can improve the temporal prediction capability and further reduce the complexity. Moreover, the cache memory module further enhances the model's learning of the history of EHRs. Finally, the A-GSTCN model is compared with the baselines through experiments, and the efficiency of the A-GSTCN model is verified by Jaccard, F1 and PRAUC. Not only that, the proposed model also reduces the training parameters by an order of magnitude.

The role of digital transformation in addressing health inequalities in coastal communities: barriers and enablers

Healthcare systems worldwide are striving for the "quadruple aim" of better population health and well-being, improved experience of care, healthcare team well-being (including that of carers) and lower system costs. By shifting the balance of care from reactive to preventive by facilitating the integration of data between patients and clinicians to support prevention, early diagnosis and care at home, many technological solutions exist to support this ambition. Yet few have been mainstreamed in the NHS. This is particularly the case in English coastal areas which, despite having a substantially higher burden of physical and mental health conditions and poorer health outcomes, also experience inequalities with respect to digital maturity. In this paper, we suggest ways in which digital health technologies (DHTs) can support a greater shift towards prevention; discuss barriers to digital transformation in coastal communities; and highlight ways in which central, regional and local bodes can enable transformation. Given a real risk that variations in digital maturity may be exacerbating coastal health inequalities, we call on health and care policy leaders and service managers to understands the potential benefits of a digital future and the risks of failing to address the digital divide.

What constitutes adequate legal protection for the collection, use and sharing of mobility and location data in health care in South Africa?

Mobile phone technology has been a catalyst that has added an innovative dimension in health care and created new opportunities for digital health services. These digital devices can be viewed as an extension of the person using them due to the deluge of personal information that can be collected and stored on them. Data collected on mobile phones are used extensively in health services and research. Personal, mobility and location data are constantly collected. The unique mobile phone architecture provides for an easy flow of data between various role players such as application developers and phone manufacturers. The collection, storage and sharing of personal information on mobile phones elicit various legal questions relating to the protection of privacy, consent, liability and the accountability of stakeholders such as health insurance providers, hospital groups and national departments of health.

Intelligent Intensive Care Unit: Current and Future Trends

There is a growing demand for intensive care units, but there is a relative shortage of medical staff. Intensive care work is heavy and stressful. Optimizing the working conditions and processes of the intensive care unit is of great significance for improving the work efficiency and the level of diagnosis and treatment in the intensive care unit. The intelligent intensive care unit is a new ward management model gradually developed on the basis of modern science and technology such as communication technology, internet of things, artificial intelligence, robots, and big data. Under this model, the potential risks caused by human factors are greatly reduced, and the monitoring and treatment of patients has been significantly improved. This paper reviews the progress in related fields.

Review on the Advancements of Stethoscope Types in Chest Auscultation

Stethoscopes were originally designed for the auscultation of a patient's chest for the purpose of listening to lung and heart sounds. These aid medical professionals in their evaluation of the cardiovascular and respiratory systems, as well as in other applications, such as listening to bowel sounds in the gastrointestinal system or assessing for vascular bruits. Listening to internal sounds during chest auscultation aids healthcare professionals in their diagnosis of a patient's illness. We performed an extensive literature review on the currently available stethoscopes specifically for use in chest auscultation. By understanding the specificities of the different stethoscopes available, healthcare professionals can capitalize on their beneficial features, to serve both clinical and educational purposes. Additionally, the ongoing COVID-19 pandemic has also highlighted the unique application of digital stethoscopes for telemedicine. Thus, the advantages and limitations of digital stethoscopes are reviewed. Lastly, to determine the best available stethoscopes in the healthcare industry, this literature review explored various benchmarking methods that can be used to identify areas of improvement for existing stethoscopes, as well as to serve as a standard for the general comparison of stethoscope quality. The potential use of digital stethoscopes for telemedicine amidst ongoing technological advancements in wearable sensors and modern communication facilities such as 5G are also discussed. Based on the ongoing trend in advancements in wearable technology, telemedicine, and smart hospitals, understanding the benefits and limitations of the digital stethoscope is an essential consideration for potential equipment deployment, especially during the height of the current COVID-19 pandemic and, more importantly, for future healthcare crises when human and resource mobility is restricted.

The field of human building interaction for convergent research and innovation for intelligent built environments

Human-Building Interaction (HBI) is a convergent field that represents the growing complexities of the dynamic interplay between human experience and intelligence within built environments. This paper provides core definitions, research dimensions, and an overall vision for the future of HBI as developed through consensus among 25 interdisciplinary experts in a series of facilitated workshops. Three primary areas contribute to and require attention in HBI research: humans (human experiences, performance, and well-being), buildings (building design and operations), and technologies (sensing, inference, and awareness). Three critical interdisciplinary research domains intersect these areas: control systems and decision making, trust and collaboration, and modeling and simulation. Finally, at the core, it is vital for HBI research to center on and support equity, privacy, and sustainability. Compelling research questions are posed for each primary area, research domain, and core principle. State-of-the-art methods used in HBI studies are discussed, and examples of original research are offered to illustrate opportunities for the advancement of HBI research.

Contexts of Consumption and Their Evolution in the Digital Age: Beyond the Service-Dominant Logic

Starting from the observation of a conceptual gap regarding the association between consumption and the contexts in which it occurs, the paper has two objectives. The first is to fill this gap by developing a framework that includes: the identification of consumption contexts based on their building blocks (actors, goods, relationships), the basic classification of their variety, and a knowledge-based reading of consumption contexts capable of explaining their functioning. The second aim is to show that the framework allows the understanding of the digital transformation of consumption contexts. We show that services are produced in two contexts: in the first type, consumers interact directly with goods; in the second, the intermediation of frontline personnel comes into play. Actors and goods present in the consumption contexts are knowledge-holders, and the relationships between them are learning relationships. The shift from traditional consumption contexts to contexts based on artificial intelligence and the internet of things introduces a major change in that learning relationships are no longer the domain of only (human) actors who learn by interacting with each other and using goods. Both types of contexts are in fact powered by smart goods capable of interacting with each other and with humans within a given context and endowed with structural cognitive connections outside that context.

Impact of IoT on Achieving Smart Primary Healthcare Building Facilities in Gauteng, South Africa

Processes and services undertaken in smart primary healthcare building facilities capture operational data through advanced monitoring and enable experts to use these building facilities for efficient healthcare service delivery. This study assessed the impact of Internet of Things (IoT) services on achieving efficient primary healthcare in the rural areas of South Africa. The study identified three (3) basic constructs of IoT services. They include IoT location recognition and tracking services, the application of the IoT high-speed communication network-based services, and the application of IoT-based services. The study is quantitative, and a questionnaire was used to collect data from the project managers and healthcare practitioners working with the primary healthcare agency in South Africa. The study found a variable degree of impact between the three (3) IoT constructs and the successful development of primary healthcare building facility services in South Africa. The study recommends adopting IoT essential services for achieving efficient primary healthcare services in the rural areas of South Africa and other developing countries facing similar primary healthcare delivery challenges.

Application of 5G network combined with AI robots in personalized nursing in China: A literature review

The medical and healthcare industry is currently developing into digitization. Attributed to the rapid development of advanced technologies such as the 5G network, cloud computing, artificial intelligence (AI), and big data, and their wide applications in the medical industry, the medical model is shifting into an intelligent one. By combining the 5G network with cloud healthcare platforms and AI, nursing robots can effectively improve the overall medical efficacy. Meanwhile, patients can enjoy personalized medical services, the supply and the sharing of medical and healthcare services are promoted, and the digital transformation of the healthcare industry is accelerated. In this paper, the application and practice of 5G network technology in the medical industry are introduced, including telecare, 5G first-aid remote medical service, and remote robot applications. Also, by combining application characteristics of AI and development requirements of smart healthcare, the overall planning, intelligence, and personalization of the 5G network in the medical industry, as well as opportunities and challenges of its application in the field of nursing are discussed. This paper provides references to the development and application of 5G network technology in the field of medical service.

Implementation of Personalized Scenic Spot Recommendation Algorithm Based on Generalized Regression Neural Network for 5G Smart Tourism System

On the basis of the analysis of the evolution dynamics and the process of smart tourism service, this paper constructs the evolutionary game model of smart tourism service and reveals the evolution mechanism of smart tourism service based on the network platform. Based on the strategic main line of “advantages,” it proposes the design ideas and overall framework of the smart tourism service model based on the network platform, including the smart tourism information interactive service model, the element collaborative service model, and the value cocreation service model. The comparison of recommendation results shows that the recommendation error of the genetically improved generalized regression neural network algorithm is reduced, and the recommendation accuracy is better than that of the unimproved generalized regression neural network algorithm. In the recommendation scenario of click-through rate recommendation, the existing recommendation models are difficult to meet the functions of memory and generalization at the same time and cannot fully mine and combine low-level features, and the model parameters of the deep learning model are difficult to learn under the high-dimensional sparse data set of the recommendation system. To solve the problem of generalization, this paper proposes a deep CTR recommendation model based on the gradient boosting tree and factorization machine. It can fully mine low-level feature information and automatically realize low-level feature combination, which can better learn model parameters on high-dimensional sparse data sets, and the recommendation results are no longer overgeneralized. In this paper, simulation experiments are carried out on the data set, and the related recommendation models are compared. The experimental results show that the model proposed in this paper achieves better results in both the AUC (area under ROC curve) evaluation index and the cross-entropy evaluation index.