Smart Hospital Research Articles

In-Depth Examination of the Functionality and Performance of the Internet Hospital Information Platform: Development and Usability Study.

Internet hospitals (IHs) have rapidly developed as a promising strategy to address supply-demand imbalances in China's medical industry, with their capabilities directly dependent on information platform functionality. Furthermore, a novel theory of "Trinity" smart hospital has provided advanced guidelines on IH constructions. This study aimed to explore the construction experience, construction models, and development prospects based on operational data from IHs. Based on existing information systems and internet service functionalities, our hospital has built a "Smart Hospital Internet Information Platform (SHIIP)" for IH operations, actively to expand online services, digitalize traditional health care, and explore health care services modes throughout the entire process and lifecycle. This article encompasses the platform architecture design, technological applications, patient service content and processes, health care professional support features, administrative management tools, and associated operational data. Our platform has presented a set of data, including 82,279,669 visits, 420,120 online medical consultations, 124,422 electronic prescriptions, 92,285 medication deliveries, 6,965,566 prediagnosis triages, 4,995,824 offline outpatient appointments, 2025 medical education articles with a total of 15,148,310 views, and so on. These data demonstrate the significant role of IH as an indispensable component of our physical hospital services, with deep integration between online and offline health care systems. The upward trends in various data metrics indicate that our IH has gained significant recognition and usage among both the public and healthcare workers, and may have promising development prospects. Additionally, the platform construction approach, which prioritizes comprehensive service digitization and the 'Trinity' of the public, healthcare workers, and managers, serves as an effective means of promoting the development of Internet Hospitals. Such insights may prove invaluable in guiding the development of IH and facilitating the continued evolution of the Internet healthcare sector.

Evaluating Software-Based Methods for Replacing Manual Collection of Midnight Totals: A Study from Sri Lanka

The manual collection of midnight totals in hospitals has long been used to calculate key performance indicators such as bed occupancy rates. Despite technological advancements, many hospitals still rely on labour-intensive manual methods prone to inaccuracies at multiple stages. This study introduces and evaluates the Smart Hospital Metrics Calculator, a tool developed to automate the calculation of midnight totals and same-day discharges using patient admission and discharge dates from the electronic Indoor Morbidity and Mortality Return (eIMMR). Both manual and software-based methods exhibited inaccuracies, but errors in software-based calculations were primarily due to issues with entering dates, which can be mitigated by improving workflows and refining the eIMMR system. In contrast, improving manual methods requires addressing challenges across multiple levels. Data from three hospitals were analysed, and while the results do not yet demonstrate these outcomes, it is postulated that with enhanced date entry processes, software-based methods, such as the Smart Hospital Metrics Calculator, could achieve near-zero inaccuracies. These findings suggest that transitioning to this automated approach has the potential to streamline data collection, enable real-time monitoring, and improve hospital data management—without requiring additional workload or significant investment. If successfully implemented, this change could represent a seamless transformation, realizing benefits without disrupting current operations.

Outpatient scheduling problem in smart hospital with two-agent deep reinforcement learning algorithm

As the internet and digital technologies revolutionize the healthcare sector, the development of smart hospitals is pivotal for improving medical service efficiency. This research presents a mathematical model for the outpatient scheduling problem and introduces the Graph-based Two-Agent Deep Reinforcement Learning algorithm (GTARS-DRL) to address the escalating demand for medical services and the scarcity of resources. The GTARS-DRL leverages graph neural networks for feature extraction of patient medical activities and applies the PDQPN algorithm to train agents for optimizing patient sequencing and service desk allocation, effectively minimizing wait times. Our experiments indicate that GTARS-DRL outperforms traditional scheduling rules in solution quality and matches the performance of Genetic Algorithms (GA), with a notable advantage in computational efficiency, boasting an ACPU of 0.762 s and an ARPT of 0.327 s. The algorithm’s efficacy is further supported by its successful application to larger-scale instances, showcasing its potential for real-world use. This study contributes a novel approach to outpatient scheduling in smart hospitals, providing theoretical backing and practical direction for the optimization of medical resource allocation, and is poised to significantly enhance service efficiency and patient satisfaction.

Leveraging digital platforms to improve Public Health in hospital smart buildings

Abstract The integration of digital platforms into hospital infrastructure represents a significant leap forward in public health management. This session explores how smart hospital buildings, equipped with advanced digital technologies, can enhance public health outcomes through more effective disease surveillance, improved environmental controls and enhanced patient care management. Smart hospital buildings utilize a range of new technologies such as IoT sensors, AI-driven analytics and big data solutions to monitor and optimize various aspects of hospital operations and care. These technologies enable real-time data collection on environmental conditions, patient health metrics and resource utilization, creating an ecosystem that not only supports the immediate health needs of patients but also contributes to broader public health goals. The session will address four key areas where digital platforms can make an impact: -Disease Surveillance and Management: Digital tools can track health data trends and patterns within hospital settings, enabling early detection of infectious outbreaks and more precise tracking of patient outcomes. -Environmental Monitoring: IoT sensors can continuously assess conditions such as air quality, temperature and humidity, which are critical for preventing hospital-acquired infections and ensuring a safe environment for patients and staff. -Resource Optimization: AI algorithms can analyse data to optimize resource allocation, ensuring that medical supplies and human resources are used efficiently, which is crucial in health emergencies. -Patient-Centred Care: Digital platforms can facilitate personalized patient care through real-time health monitoring and data analysis, ensuring timely medical interventions and better health management. This session will highlight case studies demonstrating successful implementation of digital technologies in smart hospital buildings and discuss the challenges and lessons learned from these initiatives.

Assessment of Smartness Status in Public Hospitals and Its Relationship with Characteristics and Performance Indicators

Background: The need for smartification is increasingly recognized in public hospitals, making it crucial to identify the current status, gaps, and factors influencing hospital smartness. Objectives: This study aimed to assess the smartness status of public hospitals and investigate its correlation with hospital characteristics and performance indicators. Methods: This descriptive-analytical cross-sectional study was conducted in seven public hospitals in Alborz Province, Iran, during 2023 - 2024. We assessed the characteristics and performance indicators of the hospitals using a researcher-developed questionnaire and evaluated their smartness status with the standardized "Smart Hospital Evaluation" checklist. Data analysis was performed using SPSS24 software, utilizing independent t-tests, Pearson's correlation, Spearman's correlation, and analysis of variance (ANOVA) tests. Results: The smartness status of the hospitals was found to be moderate, with 71% of hospitals falling into this category. A significant correlation was observed between the smartness of hospital units, the type of hospital (teaching or non-teaching), and the hospital's specialization (specialized or general) (P = 0.013). Significant and direct correlations were also found between information technology and hospital units (P = 0.043), management systems and clinical processes (P = 0.008), and overall smartness and clinical processes (P = 0.049). Conclusions: Public hospitals need improvements in smartness indicators. Factors such as whether the hospital is teaching or non-teaching and whether it is specialized or general should be taken into account. Moreover, hospital smartification efforts should adopt a comprehensive approach, considering all dimensions of smartness.

Empowering healthcare education: A multilingual ontology for medical informatics and digital health (MIMO) integrated to artificial intelligence powered training in smart hospitals.

Objective: A comprehensive understanding of professional and technical terms is essential to achieving practical results in multidisciplinary projects dealing with health informatics and digital health. The medical informatics multilingual ontology (MIMO) initiative has been created through international cooperation. MIMO is continuously updated and comprises over 3700 concepts in 37 languages on the Health Terminology/Ontology Portal (HeTOP). Methods: We conducted case studies to assess the feasibility and impact of integrating MIMO into real-world healthcare projects. In HosmartAI, MIMO is used to index technological tools in a dedicated marketplace and improve partners' communication. Then, in SaNuRN, MIMO supports the development of a "Catalog and Index of Digital Health Teaching Resources" (CIDHR) backing digital health resources retrieval for health and allied health students. Results: In HosmartAI, MIMO facilitates the indexation of technological tools and smooths partners' interactions. In SaNuRN within CIDHR, MIMO ensures that students and practitioners access up-to-date, multilingual, and high-quality resources to enhance their learning endeavors. Conclusion: Integrating MIMO into training in smart hospital projects allows healthcare students and experts worldwide with different mother tongues and knowledge to tackle challenges facing the health informatics and digital health landscape to find innovative solutions improving initial and continuous education.

Smart Hospital Patient Monitoring System Aided by Edge-Fog-Cloud Continuum: A Performability Evaluation Focusing on Distinct Sensor Sources

Smart Hospital Patient Monitoring System Aided by Edge-Fog-Cloud Continuum: A Performability Evaluation Focusing on Distinct Sensor Sources

Enhancing Pharmaceutical Supply Chains through Blockchain Technology on Hyperledger Fabric Implementation

Background: The complexity and dynamism in business environments necessitate the adoption of advanced technologies to enhance supply chain (SC) processes and routines. In the pharmaceutical industry, maintaining high standards of safety and visibility within SCs is critical. Challenges arise from the proliferation of online medical stores and the need for uniform medication safety standards. The shift towards digital SCs has brought about significant changes, demanding improvements in efficiency and transparency. This study explores the potential of Blockchain technology (BcT) to revolutionize SC processes, particularly in the pharmaceutical sector. By integrating BcT, which offers transparency and security, pharmaceutical SCs can address issues related to data integrity, cost management, and operational efficiency. Methods: A qualitative content analysis was conducted to assess the impact of BcT on SC practices, using transaction cost economics. The study evaluated how BcT can enhance visibility, reduce transaction costs, and improve the management of pharmaceutical SCs. Hyperledger Fabric, a BcT platform, was used to design and implement a secure medicine distribution system in a smart hospital setting. Results: Implementation of BcT through Hyperledger Fabric demonstrated significant improvements in transaction speed and efficiency. Performance tests showed consistent reaction times and transaction rates even with increasing user loads. For instance, the system maintained a reaction time increase of only 20 milliseconds with 300 users and achieved a mean transaction processing rate of 120 transactions per second with a user group of 300. Conclusion: The integration of BcT in pharmaceutical SCs addresses key issues related to safety, transparency, and efficiency. By utilizing BcT and Hyperledger Fabric, the study provides a robust framework for managing pharmaceutical supply chains, improving data integrity, and reducing transaction costs.

Healthcare Resilience in Trinidad and Tobago: A short report on the Healthy Hospital Initiative at the Sangre Grande Hospital

Objective: To describe the establishment of the Healthy Hospital Initiative (HHI), its activities and accomplishments, challenges and future steps. Methods: The initiative was implemented at the Sangre Grande Hospital in the Eastern Regional Health Authority (ERHA), one of five Regional Health Authorities in Trinidad and Tobago. A review of personal records maintained by members of the Healthy Hospital Initiative was undertaken. These records included descriptions of the activities, accomplishments and obstacles that the team experienced. Results: The Healthy Hospital Initiative was launched in November 2019 with the aim of creating an environmentally sustainable health system and improving the physical and mental health of staff and patients. A senior doctor led the initiative and team members included a range of staff, including both clinical and non-clinical professionals who were all volunteers. This allowed for a multi-directional, inclusive approach and created a sense of ownership amongst the staff. In 2022, several activities were implemented: green spaces, recycling bins, a kitchen garden and staff exercise sessions. In addition, team members participated in training on Pan-American Health Organization (PAHO) Smart hospitals and collaborated with a local non-governmental organisation as well as the University of the West Indies, St. Augustine campus engineering department to develop further projects. Conclusion: Next steps include promoting similar projects in community health facilities, developing a hydroponics system and, assessing the disaster resilience and carbon footprint of the hospital.

Online complaint management system: a smart hospital challenge after the COVID-19 pandemic

Online complaint management system: a smart hospital challenge after the COVID-19 pandemic

Smart Hospitals: Integrating AI for Enhanced Patient Outcomes

The use of information technology and artificial intelligence in the healthcare sector has brought about smart hospitals. These hospitals incorporate AI, IoT, big data analytics, and robotics to increase productivity, optimize patient care, and decrease expenses. This paper aims at discussing the current trends in application of advanced IT in hospitals and the role of AI in enhancing the quality of patient care, clinical outcomes and financial performance. The study used a retrospective cohort design in two large hospitals in New York City and Los Angeles to evaluate the adoption of AI technologies from 2019 to 2023. The EHRs and interviews with the healthcare providers and patients were used to assess the mortality, complication, length of stay, and readmission rates before and after the implementation of AI. The findings show that there are statistically significant positive changes in patient outcomes after the integration of AI in smart hospital solutions. Issues related to data integration, privacy, and clinicians’ acceptance of AI-generated suggestions are also considered. This research contributes to the knowledge of the changes that AI brings to the healthcare system and the directions for the development of smart hospitals.

ZTCloudGuard: Zero Trust Context-Aware Access Management Framework to Avoid Medical Errors in the Era of Generative AI and Cloud-Based Health Information Ecosystems

Managing access between large numbers of distributed medical devices has become a crucial aspect of modern healthcare systems, enabling the establishment of smart hospitals and telehealth infrastructure. However, as telehealth technology continues to evolve and Internet of Things (IoT) devices become more widely used, they are also increasingly exposed to various types of vulnerabilities and medical errors. In healthcare information systems, about 90% of vulnerabilities emerge from medical error and human error. As a result, there is a need for additional research and development of security tools to prevent such attacks. This article proposes a zero-trust-based context-aware framework for managing access to the main components of the cloud ecosystem, including users, devices, and output data. The main goal and benefit of the proposed framework is to build a scoring system to prevent or alleviate medical errors while using distributed medical devices in cloud-based healthcare information systems. The framework has two main scoring criteria to maintain the chain of trust. First, it proposes a critical trust score based on cloud-native microservices for authentication, encryption, logging, and authorizations. Second, a bond trust scoring system is created to assess the real-time semantic and syntactic analysis of attributes stored in a healthcare information system. The analysis is based on a pre-trained machine learning model that generates the semantic and syntactic scores. The framework also takes into account regulatory compliance and user consent in the creation of the scoring system. The advantage of this method is that it applies to any language and adapts to all attributes, as it relies on a language model, not just a set of predefined and limited attributes. The results show a high F1 score of 93.5%, which proves that it is valid for detecting medical errors.

Towards a smart hospital: Smart infrastructure integration

In today’s business world, digital technologies have become vital for implementing processes in any company, including the healthcare industry. By utilizing the latest technologies to collect, process, analyze, and transmit data through smart devices and IT infrastructure, it is possible to implement trends in medicine as a value approach, personalization, predictability, and participation. Considering these trends, it is necessary to develop a methodology for creating a management system for medical organizations that can comprehensively and interconnectively develop various elements of the system. This includes business process systems, information systems and application architectures, hardware, and "smart" medical equipment that can fully participate in hospital information exchange. These elements should be developed within a single model. An architectural approach can be used to create a model of a medical organization. This paper examines the integration of smart infrastructure in medical organizations, develops requirements for smart infrastructure services, and creates architectural models based on the findings and requirements. Architecture models were developed for the following elements of the Smart Infrastructure: smart wards, wearable devices and telemedicine stands as the most necessary elements in everyday healthcare. The key discovery of the study is the architecture models of the mentioned elements and their integration with the medical organization IT architecture, described in the form of visual models and data exchange tables. The proposed models can be used both for further research or for practical implementation as the reference models to check up and adopt to each individual case. The solutions proposed in the paper give an example of open innovation philosophy, as they are developed on the basis of open information about the innovation activities of various medical organizations, and, at the same, time the results themselves are openly published and open for adaptation by the scientific and practicing medical community.

Analysis of Hospital Budget Management Control Based on Smart Hospital

Hospitals are crucial healthcare facilities where patients seek treatment, and effective budget management within hospitals significantly impacts their operational efficiency and financial performance. In the age of information technology and advanced healthcare solutions, the emergence of smart hospitals represents a new trend in the medical industry’s evolution. Leveraging modern information technology can enhance the development of hospital IT systems and drive budget management toward greater intelligence. This paper begins by analyzing the influence of smart hospitals on hospital budget control. It then examines the current state of budget management control within smart hospitals. Finally, it proposes several strategies for budget management control in smart hospitals, aiming to provide guidance for relevant stakeholders.

Enhancing Hospital Efficiency and Patient Care: Real-Time Tracking and Data-Driven Dispatch in Patient Transport

Inefficient patient transport in hospitals often leads to delays, overworked staff, and suboptimal resource utilization, ultimately impacting patient care. Existing dispatch management algorithms are often evaluated in simulation environments, raising concerns about their real-world applicability. This study presents a real-world experiment that bridges the gap between theoretical dispatch algorithms and real-world implementation. It applies process capability analysis at Taichung Veterans General Hospital in Taichung, Taiwan, and utilizes IoT for real-time tracking of staff and medical devices to address challenges associated with manual dispatch processes. Experimental data collected from the hospital underwent statistical evaluation between January 2021 and December 2021. The results of our experiment, which compared the use of traditional dispatch methods with the Beacon dispatch method, found that traditional dispatch had an overtime delay of 41.0%; in comparison, the Beacon dispatch method had an overtime delay of 26.5%. These findings demonstrate the transformative potential of this solution for not only hospital operations but also for improving service quality across the healthcare industry in the context of smart hospitals.

Challenges of ambient WiFi backscatter systems in healthcare applications

In healthcare, patients commonly use medical devices that require batteries and wired connections, and do not have timely access to monitoring data. Ambient WiFi backscatter systems are attracting attention in healthcare owing to their ultra-low power consumption, wireless communication method, battery-free mode, and the ability for continuous monitoring. However, there is a lack of investigations on it. In this paper, we focus on discovering ambient WiFi backscatter systems’ applications in healthcare. Therefore, in this paper, we provide a comprehensive survey of ambient WiFi backscatter in healthcare. Firstly, we present some existing ambient WiFi backscatter systems to help us better understand the operating principle, and then classify them based on the use of commercial WiFi receivers. After that, we survey their latest applications and discuss the challenges in healthcare, starting from two general directions: smart hospitals and medical devices. Finally, we foresee the development of ambient WiFi backscatter technology and expand it in other fields. The research aims to receive a comprehensive understanding of the applications and challenges of ambient WiFi backscatter systems in healthcare, and thus promoting their further development.

Smart Hospital Management System: Streamlining Healthcare Operations with SQL Integration

This research delves into the development and deployment of a Smart Hospital Management System (HMS), a pivotal solution for enhancing administrative efficiency and elevating patient care standards within healthcare institutions. Motivated by the pressing need to optimize hospital operations, the system seamlessly integrates modules encompassing patient registration, appointment scheduling, medical records management, billing, and inventory control. Leveraging modern technologies, particularly database management systems and web-based platforms, the HMS aims to bolster the efficiency, accuracy, and accessibility of healthcare services while prioritizing data security and regulatory compliance. The study scrutinizes the benefits and challenges associated with HMS adoption, shedding light on its potential impact on healthcare delivery and organizational performance. Furthermore, it underscores the contributions of this work to the research community, delineates future avenues for exploration, and offers insights into the promising prospects of smart healthcare systems.

Advancements in Telehealth: Enhancing Breast Cancer Detection and Health Automation through Smart Integration of IoT and CNN Deep Learning in Residential and Healthcare Settings

The rapid evolution of telehealth, or telemedicine, has spurred crucial technological advancements aimed at addressing the early stages of complex cancer conditions, where conventional diagnostic methods face challenges. This research introduces a cancer detection system that utilizes Internet of Things (IoT)-based patient records and machine learning. The primary objective is to automate real-time breast cancer monitoring and detection in residential institutions and smart hospitals, thus enhancing the delivery of quality cancer healthcare. Background: Traditional diagnostic methods, particularly physical inspection, exhibit inherent limitations in identifying breast cancer at early stages. This research responds to this challenge by leveraging innovative technologies, such as IoT and deep learning-based techniques, to overcome the constraints of conventional approaches. Objective: The primary goal of this study is to develop and implement a cancer detection system that integrates IoT-based patient records and machine learning for real-time breast cancer monitoring in residential and healthcare settings. Method: The research employs a synergistic combination of IoT technology for collecting images of residential users and Convolutional Neural Network (CNN), a deep learning technique, for early cancer prediction. The focus lies on contributing to the overall well-being of individuals who may unknowingly be living with cancer. Result: Simulated outcomes after 25 epochs are presented, emphasizing the training accuracy of the model and its validation accuracy using the proposed VGG16 classifier. Graphical representations of the results indicate consistent performance metrics, with both validation and training accuracy exceeding 99%. Specifically, the training accuracy measures at an impressive 99.64%, while the validation accuracy stands at 99.12%. Main Findings: The study demonstrates the effectiveness of the integrated IoT and deep learning techniques in achieving high accuracy rates for early breast cancer prediction. The findings affirm the potential of this approach to assist dermatologists in identifying breast malignancies at treatable stages. Conclusion: This research establishes a foundational framework for the integration of IoT and deep learning techniques, presenting a promising avenue for advancing early cancer detection in smart healthcare systems. The proposed cancer detection system holds significant potential for improving healthcare outcomes and contributing to the overall well-being of individuals at risk of breast cancer.

Planar antenna design for contactless energizing applications: resonance analysis

Advances in medical technology suggest that the application of implantable medical devices or Smart Hospital Devices (SHD) can enhance the control and treatment of chronic disorders. However, most of these developments require an electrical power source for operation, making the use of batteries unfeasible. This paper presents the design and resonance analysis of four different geometries for the fabrication of planar antennas as an alternative to a contactless power supply. Previously published software [19] was used to calculate the electrical parameters for each coil design. Operating trials were executed under laboratory conditions, and a bracket manufactured using polylactic acid (PLA) through a 3D printer was used to accurately define the distances between coils to achieve correct alignment between them. Based on the experimental results, it was possible to calculate the resonant frequency of the inductive links at a 5 mm spacing. Similarly, it is possible to calculate the capacitive effect necessary to improve the resonant circuit in different transmitter-receiver planar antenna pairs. The most efficient combination to induce voltage in these links is to use the hexagonal profile design as a transmitter and receiver antenna, applying a sinusoidal signal with 8 MHz frequency.

IoT Based Hospital Management

Nowadays, Internet of Things (IoT) is invading almost all sectors of life since it is based on connecting living or non-living things together through computer technology. It is responsible for connecting physical objects together through the internet. Healthcare and hospitals are one of the most important sectors that require a lot of attention to transfer their old form of documentation into SMART management systems. It is essential to analyze health data in order to increase the quality of patient’s care. Egypt being a development country is starting to substitute its old governmental systems into electronic SMART technology. IoT devices produce different types of data and transfer them to the cloud computing for storage and analysis. The benefits of using IoT in collecting, transferring, and analyzing patients’ data for the hospitals are attracting a lot of researchers. Therefore, the arrangement of smarter and more money saving healthcare services are becoming highly required. Security and privacy, device communication, and data collection and management are some of the challenges that face the IoT technology especially when used with hospital’s data. Accordingly, a proposed reference model for making SMART hospital management system is under construction in order to achieve the best performance. The model is taking into consideration both the functional and non-functional requirements of the different participants involved in the hospital management system