Smart Hospital Research Articles

Digital twinning for smart hospital operations: Framework and proof of concept

Analyzable data capturing physical entity activities is a prerequisite for complex hospital operational decision-makings. However, there is a lack of effective strategies to integrate real-time data from multiple sources for efficient clinical and non-clinical operations in healthcare settings. Drawing on previous explorations of digital twins that facilitate real-time feedback of physical entities, we propose a conceptual framework of digital twinning for smart hospitals, with identified information needs and enabling technologies. A pilot platform is developed and tested in a Shanghai municipal hospital. The results indicate that the digital twinning method enables continuous real-time control of related operational tasks, and further promotes the development of digitization, automation, and intelligence in hospital operations.

Robust Medical Image Watermarking Scheme Using PSO, LWT, and Hessenberg Decomposition

Digital imaging is a technology that is extensively employed in diverse diagnostic examinations such as magnetic resonance imaging (MRI), computed tomography (CT), and ultrasound imaging, among other modalities. Transferring a patient’s diagnostic images and medical data to a specialist physician in a distinct geographical location is conducted to facilitate an accurate diagnosis. The safeguarding of patient data privacy and confidentiality is ensured through the utilisation of smart hospital applications for medical data security. The current research presents the effective utilisation of lifting wavelet transform (LWT) and Hessenberg-based particle swarm optimization in order to generate resilient and safeguarded watermarks on ultrasound images. The empirical evidence suggests that our innovative approach outperforms our prior methodology, established through extensive testing. The watermark’s imperceptibility and accuracy are exemplified by its capacity to sustain a superior structural similarity index measure (SSIM) and peak signal-to-noise ratio (PSNR), even amidst diverse image processing assaults.

Blockchain-assisted authenticated key agreement scheme for IoT-based healthcare system

IoT devices are widely used in smart hospitals, digital health, and automated pathology laboratories to generate a large amount of heterogeneous data. These devices, coupled with medical sensors, enable healthcare professionals to monitor patients’ health and medical device performance in real-time. The collected health data is transmitted to centralized cloud servers for analysis and diagnosis. However, the wireless communication channels used by these devices are vulnerable to security threats such as unauthorized access, denial-of-service attacks, and man-in-the-middle attacks. Existing solutions rely on a single trusted authority for computing and storage, which can lead to issues such as higher latency, centralization, and a single point of failure. To address these concerns, we propose a new protocol called Blockchain-based IoMT Authenticated Key Exchange (BIoMTAKE) that establishes a private/consortium blockchain-based distributed environment using Hyperledger Fabric. This protocol eliminates the need for a single trusted authority and ensures secure access to data generated by IoMT devices. Before sharing or accessing any data from the distributed healthcare system, the BIoMTAKE scheme establishes a secured shared session for authenticated devices to prevent unauthorized access. The proposed methodology has been subjected to formal security analysis using the Scyther tool and informal security analysis, which demonstrates its strong security. Additionally, our performance analysis using Hyperledger Fabric and cryptography libraries shows that the proposed scheme is computationally and communicatively efficient.

IoT-Gov: A structured framework for internet of things governance

The rise of artificial intelligence and smart devices with decision-making capabilities has highlighted the critical need for governance in the field of IoT. Effective governance can ensure accountability, reliability, security, and privacy in the development and use of these technologies, as well as safeguard against unethical or malicious activities. Moreover, it can drive innovation and growth in the IoT and AI industries, while fostering public trust in these technologies. The objective of this paper is to propose a new governance framework for IoT, called IoT-Gov. To achieve this objective, the paper first defines the requirements for IoT governance and assesses the compatibility of existing IT and IoT governance frameworks with these requirements. The limitations of current frameworks prove the need for a new framework, and IoT-Gov is presented as a solution. The paper explains the layers and processes of the proposed framework and provides a high-level simulation of its deployment. Specifically, the paper illustrates how the proposed framework can be applied in an example scenario of access control in a smart hospital parking, enabled with Blockchain technology. Finally, the paper concludes with an exploration of potential challenges and future research directions for the framework.

PREVALENCE RATE OF ESBL AMONG ENTEROBACTERIACEAE ISOLATED FROM UTI PATIENTS IN SULAIMANI PROVINCE

Background Extended-Spectrum β-Lactamase are enzymes that provide resistance against third-and fourth generation Cephalosporins and Monobactams, and they are distributed among the Enterobacteriaceae family.
 Objectives To describe the prevalence of Extended-Spectrum β-Lactamase among Enterobacteriaceae causing urinary tract infections in Sulaimani province.
 Patients and MethodsOne hundred bacterial isolates of Enterobacteriaceae from patients with urinary tract infections attending Smart Hospital (inpatients and outpatients). Urine samples were inoculated onto different culture media. Colony morphology, gram staining, and BD Phoenix™ system were used for bacterial identification. Antibiotic profile and Extended-Spectrum β-Lactamase were observed phenotypically by antibiotic profile results, double disk synergy test, and confirmed by combined disk test methods and BD Phoenix™ system.
 ResultsOut of one hundred isolates of Enterobacteriaceae, Escherichia coli was the commonest isolate (89), followed by Klebsiella pneumoniae (10) and one isolate of Proteus mirabilis. According to the antibiotic profile, the most effective antibiotic among all three isolates was Imipenem and Nitrofurantoin, while the most resistant antibiotic was Nalidixic acid and third generation Cephalosporin. The prevalence rate of Extended-Spectrum β-Lactamase -producing Enterobacteriaceae was 69% by the screening tests and 48% by the confirmatory tests.
 ConclusionIn this study, Extended-Spectrum β-Lactamase prevalence was shown to be at an alarming rate that must be considered. The high priority of public health justifies further investigation to properly establish annual surveillance systems that can aid in selecting an appropriate antibiotic upon ESBL detection.

IoT-based patient stretcher movement simulation in smart hospital using type-2 fuzzy sets systems

The healthcare facilities in the modern age are the key challenge in developing countries where remote areas lack immense-quality hospitals and medical experts. The idea here is to provide an intelligent smart health system using some sensors and fuzzy controllers which are implemented in stretchers, and the update of the patient stretcher monitoring is implemented to monitor the immovable stretchers and rectify the problem of immovable stretchers, which can be solved by Type-2 fuzzy sets and systems, which is featured by three-dimensional characteristics. This study proposes IoT-based Patient Stretcher Movement Simulation for Smart Hospitals using Type-2 fuzzy sets and systems. Type-2 fuzzy logic is a generalisation of conventional fuzzy logic in which the stretcher movement simulation and exact location of the stretcher are monitored by the sensor-based fuzzy set and let the model minimise the effects of uncertainties which is found to have very wide applicability in rule-based fuzzy logic systems. The results presented in this paper prove the concept and provide the IoT-based patient stretcher creation. The results of the experiments also show that the proposed approach is more reliable and intelligent enough to provide efficient monitoring applications through the type-2 fuzzy logic systems.

Learning as the best medicine: Proposal for SMART schooling for hospitalized children

This research paper explores learning alternatives for chronically ill students, considering the challenges they encounter in their learning processes due to prolonged or intermittent absences from school. International practices and recent research findings on this topic will be reviewed in the contexts of hospital schools, SMART hospitals, and SMART learning technologies to highlight their primary characteristics. The current situation of hospitalized students, particularly those in Dubai, will be discussed to propose an alternative education program to aid them: the Edu-Med Care Model. This model is built on SMART education and healthcare approaches to support the goal of helping students overcome barriers that prevent them from accessing conventional learning spaces. The strengths and limitations of the Edu-Med Care Model will be evaluated.

Computer and Information Literacy in Nursing as a Prerequisite for Financial Sustainability

Abstract This paper examines information technology and information literacy of employees as one of the more essential prerequisites for creating smart hospitals, which are, on the other hand, a prerequisite for sustainable financial operations. Since nursing is the backbone of every hospital, research is focused on the nursing profession. Namely, if medical personnel do not know the basics of information and communication technology (ICT) and are incapable of recognizing, finding and structuring information into new knowledge and exchanging it with colleagues, an intelligent hospital is impossible, and the business is worse. For this reason, research was conducted to determine the state of computer and information literacy in the nursing field in hospitals in north-western Croatia. It was carried out in 2021, which determined the ratio of administrative work and health care and examined the level of ICT knowledge of nurses and technicians. Analysing the secondary, higher and higher medical school curriculum determined the current situation and required improvements. The research results showed that computer and information literate staff spend less time on administration; they estimate that there is not enough content on computer literacy in the existing curricula. The hospital's operations will also be efficient because the competencies of the nurses will enable better implementation of business processes and a better financial result for the hospital.

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.

A Hybrid Stacked CNN and Residual Feedback GMDH-LSTM Deep Learning Model for Stroke Prediction Applied on Mobile AI Smart Hospital Platform

Artificial intelligence (AI) techniques for intelligent mobile computing in healthcare has opened up new opportunities in healthcare systems. Combining AI techniques with the existing Internet of Medical Things (IoMT) will enhance the quality of care that patients receive at home remotely and the successful establishment of smart living environments. Building a real AI for mobile AI in an integrated smart hospital environment is a challenging problem due to the complexities of receiving IoT medical sensors data, data analysis, and deep learning algorithm complexity programming for mobile AI engine implementation AI-based cloud computing complexities, especially when we tackle real-time environments of AI technologies. In this paper, we propose a new mobile AI smart hospital platform architecture for stroke prediction and emergencies. In addition, this research is focused on developing and testing different modules of integrated AI software based on XAI architecture, this is for the mobile health app as an independent expert system or as connected with a simulated environment of an AI-cloud-based solution. The novelty is in the integrated architecture and results obtained in our previous works and this extended research on hybrid GMDH and LSTM deep learning models for the proposed artificial intelligence and IoMT engine for mobile health edge computing technology. Its main goal is to predict heart-stroke disease. Current research is still missing a mobile AI system for heart/brain stroke prediction during patient emergency cases. This research work implements AI algorithms for stroke prediction and diagnosis. The hybrid AI in connected health is based on a stacked CNN and group handling method (GMDH) predictive analytics model, enhanced with an LSTM deep learning module for biomedical signals prediction. The techniques developed depend on the dataset of electromyography (EMG) signals, which provides a significant source of information for the identification of normal and abnormal motions in a stroke scenario. The resulting artificial intelligence mHealth app is an innovation beyond the state of the art and the proposed techniques achieve high accuracy as stacked CNN reaches almost 98% for stroke diagnosis. The GMDH neural network proves to be a good technique for monitoring the EMG signal of the same patient case with an average accuracy of 98.60% to an average of 96.68% of the signal prediction. Moreover, extending the GMDH model and a hybrid LSTM with dense layers deep learning model has improved significantly the prediction results that reach an average of 99%.

Introducing a Service Model in IoT -based Smart Hospital

Introduction: Nowadays, the world of technology is facing a growing phenomenon called the Internet of Things (IoT). The purpose of this study is to provide the infrastructure requirements, service model, and conceptual model for a smart hospital based on IoT, which is a safe and reliable method due to the integration of exchanges between objects. Method: In this strategic applied study, the infrastructure requirements of a smart hospital based on IoT are explained explains. First, basic requirements were prepared through library studies and, by setting up a questionnaire for medical staff and specialists in medical informatics, information technology, and health information technology, they were evaluated and then finalized using the Delphi technique. Then, based on extracted requirements, the service model of a smart hospital was presented. Finally, the conceptual model of IoT -based smart hospital infrastructure was suggested. Results: Thirty functional and non -functional requirements for IoT -based smart hospital infrastructure were determined. After that, the proposed model of services that can be provided in the hospital, including infrastructure, core and value -added services, was presented. Then, a conceptual model that includes the health ecosystem of the country, consisting of the levels of communication and information infrastructure, digital service systems, and the health system was provided. Conclusion: Agreement on the requirements, service and conceptual model , will help to design and implement the IoT -based smart hospital and m ake it more accurate and simple, according to factors affecting such as precision, speed and reliability in healthcare .

Kaohsiung Chang Gung Memorial Hospital, Recipient of the 2022 National Healthcare Quality Award - Distinguish Institution Award Pursuing Innovation and Excellence in Healthcare Quality: Emphasis on Quality Management and Error Free in Patient Safety

<p>高雄長庚紀念醫院創立於1986年，深耕南臺灣37年，從專業走向卓越，重視醫療品質；以王創辦人「要做就做最好的」為共同目標，自2003年開始參與財團法人醫院評鑑暨醫療品質策進會（以下簡稱醫策會）主辦「NHQA國家醫療品質獎」，19年期間屢獲佳績，2016年榮獲醫策會國家醫療品質獎頒發智慧醫療（Smart Hospital）全機構標章，近年連續獲得「醫品標竿獎」、「熱心分享獎」、「防疫貢獻獎」、「實證醫學類持續參與特別獎」、「主題類深耕品質獎」，更獲得3項「卓越中心」獎項，於2022年榮獲「特優機構」獎項。</p> <p>本院在院長、副院長、執行長等領導下，全員投入持續改善，整合教學、研究、辯論與知識轉譯等多元面向的發展，體現實證醫學於提升醫療品質之價值；透過高擬真情境訓練增強臨床知識、專業精神、決策能力和信心，確保病人的品質和安全；超前部署精進創新，打造屬於高雄長庚之專業品牌形象。追求品質永無止盡，需要實事求是、追根究柢，以展現卓越之價值與提供卓越的醫療服務。</p> <p> </p><p>Founded in 1986, the Kaohsiung Chang Gung Memorial Hospital has rooted its operation in Taiwan to pursue professionalism and excellence while focusing on healthcare quality over the past 37 years. The common goal set by Yung-Ching Wang, the founder of the Hospital, for all staff members is to complete every task to the best of their ability. The Hospital began participating in the National Healthcare Quality Award launched by the Joint Commission of Taiwan (JCT) in 2003 and has received multiple awards. Notably, the Hospital received the Smart Hospital Award - Whole Institution from JCT in 2016. It proceeded to win the Healthcare Benchmark Award, Experience Sharing Award, Pandemic Prevention Contribution Award, Special Award for Continual Participation in Evidencebased Medicine, and Special Theme Quality Awards consecutively in recent years, in addition to receiving three Excellent Center awards. In 2022, the Hospital won the Distinguish Institution Award. Under the leadership of the superintendent, deputy superintendent, and chief executive officer, all members of the Hospital continue to improve every aspect of its operation and ensure multifaceted development in the areas of medical education, research, debates, and knowledge translation, thereby highlighting the value of evidence-based medicine in improving healthcare quality. Hospital staff also participate in high-fidelity simulation to ensure patient care quality and safety by enhancing their clinical knowledge, professionalism, decision-making ability, and confidence. Relevant strategies have been deployed in advance to create a professional brand image for the Hospital, which continues to pursue outstanding healthcare quality, seek truth from facts, foster adequate values, and provide excellent healthcare services.</p> <p> </p>

IoT-Enabled Healthcare Data Analysis in Virtual Hospital Systems Using Industry 4.0 Smart Manufacturing

Background: The world is transitioning to Industry 4.0, representing the transition to digital, fully machine-driven environments and cyberphysical systems. Industry 4.0 comprises various technologies and innovations that enable development in multiple perspectives, which are implemented in many different sectors. Problem: The major challenges are the high cost, high rate of failure, security and privacy issues, and there is a need for highly skilled labor for applying healthcare data analysis. Aim: To resolve these issues, we employ the proposed system of Industry 4.0 smart manufacturing for IoT-enabled healthcare data analysis in virtual hospital systems with machine learning (ML) techniques. Methods: The proposed system contains five alternative solutions under smart manufacturing. First, the healthcare data analysis is applied for Weber’s syndrome. That is, this will be used to analyze Weber’s syndrome during its consistent treatment. Second, the IoT-enabled healthcare data handling system works based on edge-assisted edge computing that is used to apply IoT to the healthcare data handling system. The healthcare data analysis in virtual hospital systems uses machine learning for driving data synthesis. Finally, the Industry 4.0 smart manufacturing is applied to the IoT-enabled healthcare data analysis to realize efficient data digitization, especially in smart hospitals with smart sensors for virtual IoT-enabled devices surveillance of Weber’s syndrome. Result: The data digitization based on Industry 4.0 smart manufacturing analysis is considered for data processing, storage and transmission. The proposed system is 62% more efficient than the other analyzed methods. The identification of Weber’s syndrome is 69.8% more efficient than the existing midbrain stroke syndrome identification. The processing and storage of data results are 45.78% more efficient than the current encryption method. Finally, the priority-aware healthcare data analysis based on ML provides 63.4% efficient, faster and more accurate diagnoses in the personalized treatment.

Design smart hospital system based on cloud computing

<span lang="EN-US">Smart healthcare is a program that provides services to patients and healthcare assistants at any time, it is used smartly with cloud computing. A smart mobile device can collect data from a wide range of receiving sets (Wi-Fi and cloud), underwired sensors, the internet of things (IoT). It permits doctors to monitor and diagnose patients with small embedded devices that are unobtrusive, wireless, lightweight, and capable of detecting, interpreting and communi-cating physiological signs easily in a timely manner without the need for a doctor present. This paper develops integrated smart records and reports and is shared via the cloud. A variety of small devices have been used that detect the object to be sensed and then tell the person monitoring the patient's condition, for example, body and room temperature, oxygen percentage, heart rate). This data is sent to the server via the Internet. To store details and monitor the patient through it. In the event that any error occurs in any of this data, such as a decrease or increase in the normal value, a signal will be sent via Telegram to the phone of the individual monitoring the patient's condition to alert and to take necessary action.</span>

Investigation and Countermeasures Research of Hospital Information Construction of Tertiary Class-A Public Hospitals in China: Questionnaire Study

Medical informatization has initially demonstrated its advantages in improving the medical service industry. Over the past decade, the Chinese government have made a lot of effort to complete infrastructural information construction in the medical and health domain, and smart hospitals will be the next priority according to policies released by Chinese government in recent years. To provide strategic support for further development of medical information construction in China, this study aimed to investigate the current situation of medical information construction in tertiary class-A public hospitals and analyze the existing problems and countermeasures. This study surveyed 23 tertiary class-A public hospitals in China who voluntarily responded to a self-designed questionnaire distributed in April 2020 to investigate the current medical information construction status. Descriptive statistics were used to summarize the current configurations of hospital information department, hospital information systems, hospital internet service and its application, and the satisfaction of hospital information construction. Interviews were also conducted with the respondents in this study for requirement analysis. The results show that hospital information construction has become one of the priorities of the hospitals' daily work, and the medical information infrastructural construction and internet service application of the hospitals are good; however, a remarkable gap among the different level of hospitals can be observed. Although most hospitals had built their own IT team to undertake information construction work, the actual utilization rate of big data collected and stored in the hospital information system was not satisfactory. Support for the construction of information technology in primary care institutions should be increased to balance the level of development of medical informatization in medical institutions at all levels. The training of complex talents with both IT and medical backgrounds should be emphasized, and specialized disease information standards should be developed to lay a solid data foundation for data utilization and improve the utilization of medical big data.

An Intelligent Collaborative Image-Sensing System for Disease Detection

With the growth of smart medical devices and applications in smart hospitals, home care facilities, nursing, and the Internet of Medical Things (IoMT) are becoming more ubiquitous. It uses smart medical devices and cloud computing services, and basic Internet of Things (IoT) technology, to detect key body indicators, monitor health situations, and generate multivariate data to provide just-in-time healthcare services. In this article, we present a novel collaborative disease detection system based on IoMT amalgamated with captured image data. The system can be based on intelligent agents, where every agent explores the interaction between different medical data obtained by smart sensor devices using reinforcement learning as well as targets to detect diseases. The agents then collaborate to make a reliable conclusion about the detected diseases. Intensive experiments were conducted using medical data. The results show the importance of using intelligent agents for disease detection in healthcare decision-making. Moreover, collaboration increases the detection rate, with numerical results showing the superiority of the proposed framework compared with baseline solutions for disease detection.

A Framework for Securing Saudi Arabian Hospital Industry: Vision-2030 Perspective

Recent transformation of Saudi Arabian healthcare sector into a revenue producing one has signaled several advancements in healthcare in the country. Transforming healthcare management into Smart hospital systems is one of them. Secure hospital management systems which are breach-proof only can be termed as effective smart hospital systems. Given the perspective of Saudi Vision-2030, many practitioners are trying to achieve a cost-effective hospital management system by using smart ideas. In this row, the proposed framework posits the main objectives for creating smart hospital management systems that can only be acknowledged by managing the security of healthcare data and medical practices. Further, the proposed framework will also be helpful in gaining satisfactory revenue from the healthcare sector by reducing the cost and time involved in managing the smart hospital system. The framework is based on a hybrid approach of three key methods which include: employing the Internet of Medical Things (IoMT) and blockchain methodologies for maintaining the security and privacy of healthcare data and medical practices, and using big data analytics methodology for raising the funds and revenue by managing the bulk volume of healthcare data. Moreover, the framework will also be helpful for both the patients and the doctors, thus enabling the Kingdom of Saudi Arabia (KSA) to meet its goals of Vision-2030 by ensuring low cost, yet credible, healthcare services.

New Hybrid Deep Learning Models to Predict Cost From Healthcare Providers in Smart Hospitals

New Hybrid Deep Learning Models to Predict Cost From Healthcare Providers in Smart Hospitals

Deep Learning Enabled Intelligent Healthcare Management System in Smart Cities Environment

In recent times, cities are getting smart and can be managed effectively through diverse architectures and services. Smart cities have the ability to support smart medical systems that can infiltrate distinct events (i.e., smart hospitals, smart homes, and community health centres) and scenarios (e.g., rehabilitation, abnormal behavior monitoring, clinical decision-making, disease prevention and diagnosis postmarking surveillance and prescription recommendation). The integration of Artificial Intelligence (AI) with recent technologies, for instance medical screening gadgets, are significant enough to deliver maximum performance and improved management services to handle chronic diseases. With latest developments in digital data collection, AI techniques can be employed for clinical decision making process. On the other hand, Cardiovascular Disease (CVD) is one of the major illnesses that increase the mortality rate across the globe. Generally, wearables can be employed in healthcare systems that instigate the development of CVD detection and classification. With this motivation, the current study develops an Artificial Intelligence Enabled Decision Support System for CVD Disease Detection and Classification in e-healthcare environment, abbreviated as AIDSS-CDDC technique. The proposed AIDSS-CDDC model enables the Internet of Things (IoT) devices for healthcare data collection. Then, the collected data is saved in cloud server for examination. Followed by, training and testing processes are executed to determine the patient’s health condition. To accomplish this, the presented AIDSS-CDDC model employs data pre-processing and Improved Sine Cosine Optimization based Feature Selection (ISCO-FS) technique. In addition, Adam optimizer with Autoencoder Gated Recurrent Unit (AE-GRU) model is employed for detection and classification of CVD. The experimental results highlight that the proposed AIDSS-CDDC model is a promising performer compared to other existing models.

Study on the Informatization Construction of Public Stomatological Medical Institutions in China

With the deepening of healthcare system reform in China, the competition in the oral healthcare market is becoming stronger day by day. The public hospital is the main body of the medical service system in China, its degree of informatization greatly affects rational market competition and then affects the allocation of resources and the quality of medical service. By analyzing the problems existing in the current informatization of China’s public stomatological medical institutions, this paper discusses how to strengthen the informatization of China’s public stomatological medical institutions, and puts forward targeted optimization measures, to provide a reference for the innovation and development of smart hospital construction of the stomatological industry.