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BACKGROUND: Improving health care in cities with a diverse, international population is crucial for ensuring health equity, particularly for foreigners facing challenges due to cultural and language barriers. This situation is especially relevant in China, a major destination for expatriates and travelers, where optimizing health care services and incorporating international standards in the public sector are vital. Achieving this involves understanding the operational details, cultural and linguistic nuances, and advancing medical digitalization. A strategic approach focusing on cultural competence and awareness of health care systems is essential for effectively navigating health care for foreigners and expatriates in China. OBJECTIVE: The aim of this study was to perform an in-depth analysis of the subjective and objective experiences of local and international patients in public hospitals in China to provide a basis for enhancing the medical experience of all patients. METHODS: A structured questionnaire was provided to patients at an international outpatient service of a top-tier university hospital in China. Qualitative analysis of the survey responses was performed to methodically categorize and analyze medical treatment, focusing on patient demand and satisfaction across four main category elements ("high demand, high satisfaction"; "high demand, low satisfaction"; "low demand, high satisfaction"; and "low demand, low satisfaction"), enabling a detailed cross-sectional analysis to identify areas for improvement. RESULTS: Elements falling under "high demand, high satisfaction" for both Chinese and international patients were primarily in the realms of medical quality and treatment processes. In contrast, elements identified as "high demand, low satisfaction" were significantly different between the two patient groups. CONCLUSIONS: The findings highlight the importance of systematic, objective research in advancing the quality of international health care services within China's leading academic medical centers. Key to this improvement is rigorous quality control involving both patients and providers. This study highlights the necessity of certifying such centers and emphasizes the role of digital platforms in disseminating information about medical services. This strategy is expected to cater to diverse patient needs, enhancing the overall patient experience. Furthermore, by developing comprehensive diagnosis and treatment services and highlighting the superior quality and costs associated with international health care, these efforts aim to foster a sense of belonging among international patients and increase the attractiveness of China's medical services for this demographic.

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Smart hospitals are poised to greatly enhance life quality by offering persistent health monitoring capabilities. Remote healthcare and surgery, which are highly dependent on low latency, have seen a transformative improvement with the advent of 5G technology. This has facilitated a new breed of healthcare services, including monitoring and remote surgical procedures. The enhanced features of 5G, such as Enhanced Mobile Broadband (eMBB) and Ultra-Reliable Low Latency Communications (URLLC), have enabled the development of advanced healthcare systems. These systems reduce the need for direct patient contact in hospitals, which is especially pertinent as 5G becomes more widespread. This research presents novel hybrid detection algorithms, specifically QR decomposition with M-algorithm maximum likelihood-minimum mean square error (QRM-MLD-MMSE) and QRM-MLD-ZF (zero forcing), for use in Massive MIMO (M-MIMO) technology. These methods aim to decrease the latency in MIMO-based Non-Orthogonal Multiple Access (NOMA) waveforms while ensuring optimal bit error rate (BER) performance. We conducted simulations to evaluate parameters like BER and power spectral density (PSD) over Rician and Rayleigh channels using both the proposed hybrid and standard algorithms. The study concludes that our hybrid algorithms significantly enhance BER and PSD with lower complexity, marking a substantial improvement in 5G communication for smart healthcare applications.

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BACKGROUNDS: Healthcare is a social and economic challenge in many countries, exacerbated by today's increasing demand. Many studies demonstrate that hospitals that move towards smartness, and some of their processes are smart, can provide more appropriate treatments and deal with problems more flexibly. It is axiomatic that implementing smart hospitals and healthcare tools requires a clear objective. However, the concept of a smart hospital lacks a comprehensive and broadly accepted definition, leading to varied interpretations and misconceptions. Many developments touted as 'smart' merely digitize existing hospital environments without truly embracing the full potential of smart technology. Furthermore, research studies have neglected to consider industrial perspectives, which will soon cause a gap between industry and academics in this concept. OBJECTIVES: This research aims to explore the attributes of a smart hospital and use them to propose a definition for it, considering both scholarly and industrial viewpoints. METHOD AND RESULTS: The PRISMA method is employed to select academic and practical papers providing definitions and insights into smart hospitals or healthcare. 17 studies are analyzed, and a total, 83 characteristics are identified to describe the smart hospital. These features are categorized into three primary categories: "technologies", "services", and "goals". The most important features are determined by analyzing the frequencies of these characteristics across all the studies. In the results section, these data are presented in graphical form, highlighting both academic and industrial perspectives separately, as well as a combined analysis. Furthermore, an attempt is made to uncover trends in smart hospitals from 2015 to 2023. CONCLUSION: A comprehensive definition of the smart hospital, encompassing both academic and industrial perspectives, is proposed using the investigated characteristics. This study also presents research opportunities and discusses the existing gap between academia and industry concerning smart hospitals.

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As the state vigorously promotes the high-quality development of hospitals and improves people's medical ex-perience,the patient-centered hospital should make full use of technological resources such as the Internet,5 G and artificial in-telligence,vigorously develop mobile medical services and artificial intelligence services,transform and optimize the diagnosis and treatment process,and streamline all links before,during and after diagnosis so as to provide better data services to the pa-tient to ensure that they can get things done with greater ease.The patients'medical experience and hospital management effi-ciency can be greatly improved.In this study,specific measures to create a new diagnosis and treatment model through the con-struction of smart hospital platform were expounded in order to provide references for related research and policy formulation of other Chinese medical institutions.

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The National Health Commission's initiative of"Enhancing Medical Services and Patient Experience"neces-sitates a comprehensive enhancement of the precision,scientific rigor,and standardization of medical quality and safety manage-ment.It also calls for an optimized allocation of medical resources and service balance,and the promotion of new service models for admission management.This paper discusses the multiple aspects of hospital admission management.Through digital transfor-mation,we aimed to establish a centralized operation model encompassing multiple tasks,responsibilities,and uniform services across all sectors of a hospital,maintaining simplicity without compromising quality.Innovative service methods were employed to facilitate both online and offline admission procedures.Leveraging digital tools,we implemented the"beds for patients"model to directly address patient medical experience challenges.This enabled the application of information intelligence in the operation and management of large,multi-campus tertiary hospitals.The paper provided insights into the methods,specific tasks,and fu-ture value of implementing admission management and operation in large-scale comprehensive hospitals based on smart hospital models.These insights can serve as valuable references for admission management and operation in hospitals.

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The construction of smart hospital is an important part of modern hospital management system,and it is also the key way to build the new system of high-quality hospital development.In terms of building smart hospitals,multi-campus hos-pitals face more difficulties and challenges than single campus hospitals,such as the lack of top-level design,the difficulty of in-tegrated management,the uneven development of hospitals and the widespread phenomenon of information islands.This study summarizes and analyzes the difficulties encountered in the construction and application of smart hospitals in multi-hospital areas.Guided by problems,it puts forward countermeasures and suggestions for the construction of refined and high-quality smart hospi-tals in multi-campus hospitals,including strengthening overall and forward-looking awareness,integrating management according to hospital conditions,characteristic development under demand guidance,establishing a data integration center for smart hospi-tals,scientific planning of talent reserve and discipline layout,etc.

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Rapid advances in digital technology and the promising potential of artificial intelligence (AI) are changing our everyday lives and have already impacted on hospital procedures. The use of AI applications, in particular, enables a wide range of possible uses and has considerable potential for improving medical and nursing care. In radiological diagnostics, for example, there are already many well-researched applications for AI-based image evaluation. In this article further AI developments are presented, which can help to relieve medical staff in order to create more time for direct patient care. In addition, essential aspects regarding the development and transfer of AI-based applications are highlighted. It is crucial that the integration of AI into medical practice is carried out with the utmost care and prudence. Data protection and ethical aspects need to be considered and respected at all times. Ensuring the reliability and integrity of AI systems is essential to earn the trust of both patients and healthcare professionals. A comprehensive inspection for possible bias within the underlying data and algorithms is indispensable. In this field of tension between promising possibilities and ethical challenges, the digital transformation in medicine and care can be designed to increase patient safety and to relieve staff.

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Recently, in order to comprehensively promote the development of medical institutions and solve the nationwide problems in the healthcare fields, the government of China developed an innovative national policy of "Trinity" smart hospital construction, which includes "smart medicine," "smart services," and "smart management". The prototype of the evaluation system has been established, and a large number of construction achievements have emerged in many hospitals. In this article, the summary of this field was performed to provide a reference for medical workers, managers of hospitals, and policymakers.

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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.

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Smart hospitals aim to advance digitalization to provide better and safer care and increase user satisfaction by minimizing documentation burden. The aim of this study is to investigate the potential impact and its logic of user participation and self-efficacy on the pre-usage attitude and behavioural intention towards IT for smart barcode scanner-based workflows. A cross-sectional survey was conducted in a system of 10 hospitals in Germany that are in the process of implementing intelligent workflow technology. Based on the answers of 310 clinicians, a partial least squares (PLS) model was developed which explained 71.3% of the variance in pre-usage attitude and 49.4% of the variance in behavioural intention. User participation significantly determined pre-usage attitude through perceived usefulness and trust, while self-efficacy significantly did so through effort expectancy. This pre-usage model sheds light on how users' behavioural intention towards using smart workflow technology could be shaped. It will be complemented by a post-usage model according to the two-stage model of Information System Continuance.

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The advent of Artificial Intelligence (AI) and the Internet of Things (IoT) have recently created previously unimaginable opportunities for boosting clinical and patient services, reducing costs and improving community health. Yet, a fundamental challenge that the modern healthcare management system faces is storing and securely transferring data. Therefore, this research proposes a novel Lionized remora optimization-based serpent (LRO-S) encryption method to encrypt sensitive data and reduce privacy breaches and cyber-attacks from unauthorized users and hackers. The LRO-S method is the combination of hybrid metaheuristic optimization and improved security algorithm. The fitness functions of lion and remora are combined to create a new algorithm for security key generation, which is provided to the serpent encryption algorithm. The LRO-S technique encrypts sensitive patient data before storing it in the cloud. The primary goal of this study is to improve the safety and adaptability of medical professionals' access to cloud-based patient-sensitive data more securely. The experiment's findings suggest that the secret keys generated are sufficiently random and one of a kind to provide adequate protection for the data stored in modern healthcare management systems. The proposed method minimizes the time needed to encrypt and decrypt data and improves privacy standards. This study found that the suggested technique outperformed previous techniques in terms of reducing execution time and is cost-effective.

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Objective: The popularity of the â€‹"bring your own device (BYOD)" â€‹concept has grown in recent years, and its application has extended to the healthcare field. This study was aimed at examining nurses' acceptance of a BYOD-supported system after a 9-month implementation period. Methods: We used the technology acceptance model to develop and validate a structured questionnaire as a research tool. All nurses (n â€‹= â€‹18) responsible for the BYOD-supported wards during the study period were included in our study. A 5-point Likert scale was used to assess the degree of disagreement and agreement. Statistical analysis was performed in SPSS version 24.0. Results: The questionnaire was determined to be reliable and well constructed, on the basis of the item-level content validity index and Cronbach α values above 0.95 and 0.87, respectively. The mean constant values for all items were above 3.95, thus suggesting that nurses had a positive attitude toward the BYOD-supported system, driven by the characteristics of the tasks involved. Conclusions: We successfully developed a BYOD-supported system. Our study results suggested that nursing staff satisfaction with BYOD-supported systems could be effectively increased by providing practical functionalities and reducing clinical burden. Hospitals could benefit from the insights generated by this study when implementing similar systems.

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The growing and aging of the world population have driven the shortage of medical resources in recent years, especially during the COVID-19 pandemic. Fortunately, the rapid development of robotics and artificial intelligence technologies help to adapt to the challenges in the healthcare field. Among them, intelligent speech technology (IST) has served doctors and patients to improve the efficiency of medical behavior and alleviate the medical burden. However, problems like noise interference in complex medical scenarios and pronunciation differences between patients and healthy people hamper the broad application of IST in hospitals. In recent years, technologies such as machine learning have developed rapidly in intelligent speech recognition, which is expected to solve these problems. This paper first introduces IST's procedure and system architecture and analyzes its application in medical scenarios. Secondly, we review existing IST applications in smart hospitals in detail, including electronic medical documentation, disease diagnosis and evaluation, and human-medical equipment interaction. In addition, we elaborate on an application case of IST in the early recognition, diagnosis, rehabilitation training, evaluation, and daily care of stroke patients. Finally, we discuss IST's limitations, challenges, and future directions in the medical field. Furthermore, we propose a novel medical voice analysis system architecture that employs active hardware, active software, and human-computer interaction to realize intelligent and evolvable speech recognition. This comprehensive review and the proposed architecture offer directions for future studies on IST and its applications in smart hospitals.

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The COVID-19 pandemic has accelerated a long-term trend of smart hospital development. However, there is no consistent conceptualization of what a smart hospital entails. Few hospitals have genuinely reached being "smart," primarily failing to bring systems together and consider implications from all perspectives. Hospital Intelligent Twins, a new technology integration powered by IoT, AI, cloud computing, and 5G application to create all-scenario intelligence for health care and hospital management. This communication presented a smart hospital for all-scenario intelligence by creating the hospital Intelligent Twins. Intelligent Twins is widely involved in medical activities. However, solving the medical ethics, protecting patient privacy, and reducing security risks involved are significant challenges for all-scenario intelligence applications. This exploration of creating hospital Intelligent Twins that can be a worthwhile endeavor to assess how to inform evidence-based decision-making better and enhance patient satisfaction and outcomes.

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BACKGROUND: Despite the increasing adoption rate of tracking technologies in hospitals in the United States, few empirical studies have examined the factors involved in such adoption within different use contexts (eg, clinical and supply chain use contexts). To date, no study has systematically examined how governance structures impact technology adoption in different use contexts in hospitals. Given that the hospital governance structure fundamentally governs health care workflows and operations, understanding its critical role provides a solid foundation from which to explore factors involved in the adoption of tracking technologies in hospitals. OBJECTIVE: This study aims to compare critical factors associated with the adoption of tracking technologies for clinical and supply chain uses and examine how governance structure types affect the adoption of tracking technologies in hospitals. METHODS: This study was conducted based on a comprehensive and longitudinal national census data set comprising 3623 unique hospitals across 50 states in the United States from 2012 to 2015. Using mixed effects population logistic regression models to account for the effects within and between hospitals, we captured and examined the effects of hospital characteristics, locations, and governance structure on adjustments to the innate development of tracking technology over time. RESULTS: From 2012 to 2015, we discovered that the proportion of hospitals in which tracking technologies were fully implemented for clinical use increased from 36.34% (782/2152) to 54.63% (1316/2409), and that for supply chain use increased from 28.58% (615/2152) to 41.3% (995/2409). We also discovered that adoption factors impact the clinical and supply chain use contexts differently. In the clinical use context, compared with hospitals located in urban areas, hospitals in rural areas (odds ratio [OR] 0.68, 95% CI 0.56-0.80) are less likely to fully adopt tracking technologies. In the context of supply chain use, the type of governance structure influences tracking technology adoption. Compared with hospitals not affiliated with a health system, implementation rates increased as hospitals affiliated with a more centralized health system-1.9-fold increase (OR 1.87, 95% CI 1.60-2.13) for decentralized or independent hospitals, 2.4-fold increase (OR 2.40, 95% CI 2.07-2.80) for moderately centralized health systems, and 3.1-fold increase for centralized health systems (OR 3.07, 95% CI 2.67-3.53). CONCLUSIONS: As the first of such type of studies, we provided a longitudinal overview of how hospital characteristics and governance structure jointly affect adoption rates of tracking technology in both clinical and supply chain use contexts, which is essential for developing intelligent infrastructure for smart hospital systems. This study informs researchers, health care providers, and policy makers that hospital characteristics, locations, and governance structures have different impacts on the adoption of tracking technologies for clinical and supply chain use and on health resource disparities among hospitals of different sizes, locations, and governance structures.

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Researchers, regulatory agencies, and the pharmaceutical industry are moving towards precision pharmacovigilance as a comprehensive framework for drug safety assessment, at the service of the individual patient, by clustering specific risk groups in different databases. This article explores its implementation by focusing on: (i) designing a new data collection infrastructure, (ii) exploring new computational methods suitable for drug safety data, and (iii) providing a computer-aided framework for distributed clinical decisions with the aim of compiling a personalized information leaflet with specific reference to a drug's risks and adverse drug reactions. These goals can be achieved by using 'smart hospitals' as the principal data sources and by employing methods of precision medicine and medical statistics to supplement current public health decisions.

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The third industrial revolution has radically impacted the transformation of hospitals. Through the adoption of key digital technologies, hospitals have become more accessible, flexible, organised, responsive and able to deliver more personalised care. The digitalisation of patient health records, one of the most remarkable achievements to date in healthcare management, has enabled new opportunities, including the idea of hospitals evolving to become artificially intelligent. In parallel, the adoption of electronic and mobile internet technologies in hospitals has introduced new structural concepts, seeing a variety of terms blossom such as 'smart', 'intelligent', 'green' and 'liquid'. Now in the early fourth industrial revolution, driven by AI and internet-of-things technologies, this article unveils a new concept adapted to the upcoming era.

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BACKGROUND: The smart hospital's concept of using the Internet of Things (IoT) to reduce human resources demand has become more popular in the aging society. OBJECTIVE: To implement the voice smart care (VSC) system in hospital wards and explore patient acceptance via the Technology Acceptance Model (TAM). METHODS: A structured questionnaire based on TAM was developed and validated as a research tool. Only the patients hospitalized in the VSC wards and who used it for more than two days were invited to fill the questionnaire. Statistical variables were analyzed using SPSS version 24.0. A total of 30 valid questionnaires were finally obtained after excluding two incomplete questionnaires. Cronbach's α values for all study constructs were above 0.84. RESULT: We observed that perceived ease of use on perceived usefulness, perceived usefulness on user satisfaction and attitude toward using, and attitude toward using on behavioral intention to use had statistical significance (p < .01), respectively. CONCLUSION: We have successfully developed the VSC system in a Taiwanese academic medical center. Our study indicated that perceived usefulness was a crucial factor, which means the system function should precisely meet the patients' demands. Additionally, a clever system design is important since perceived ease of use positively affects perceived usefulness. The insight generated from this study could be beneficial to hospitals when implementing similar systems to their wards.

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The spread of the Coronavirus (COVID-19) pandemic across countries all over the world urges governments to revolutionize the traditional medical hospitals/centers to provide sustainable and trustworthy medical services to patients under the pressure of the huge overload on the computing systems of wireless sensor networks (WSNs) for medical monitoring as well as treatment services of medical professionals. Uncertain malfunctions in any part of the medical computing infrastructure, from its power system in a remote area to the local computing systems at a smart hospital, can cause critical failures in medical monitoring services, which could lead to a fatal loss of human life in the worst case. Therefore, early design in the medical computing infrastructure's power and computing systems needs to carefully consider the dependability characteristics, including the reliability and availability of the WSNs in smart hospitals under an uncertain outage of any part of the energy resources or failures of computing servers, especially due to software aging. In that regard, we propose reliability and availability models adopting stochastic Petri net (SPN) to quantify the impact of energy resources and server rejuvenation on the dependability of medical sensor networks. Three different availability models (A, B, and C) are developed in accordance with various operational configurations of a smart hospital's computing infrastructure to assimilate the impact of energy resource redundancy and server rejuvenation techniques for high availability. Moreover, a comprehensive sensitivity analysis is performed to investigate the components that impose the greatest impact on the system availability. The analysis results indicate different impacts of the considered configurations on the WSN's operational availability in smart hospitals, particularly 99.40%, 99.53%, and 99.64% for the configurations A, B, and C, respectively. This result highlights the difference of 21 h of downtime per year when comparing the worst with the best case. This study can help leverage the early design of smart hospitals considering its wireless medical sensor networks' dependability in quality of service to cope with overloading medical services in world-wide virus pandemics.

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