Artificial Intelligence-Enabled Blockchain Technology and Digital Twin for Smart Hospitals E-Book
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- Herausgeber: John Wiley & Sons
- Kategorie: Wissenschaft und neue Technologien
- Sprache: Englisch
The book uniquely explores the fundamentals of blockchain and digital twin and their uses in smart hospitals.
Artificial Intelligence-Enabled Blockchain Technology and Digital Twin for Smart Hospitals provides fundamental information on blockchain and digital twin technology as effective solutions in smart hospitals. Digital twin technology enables the creation of real-time virtual replicas of hospital assets and patients, enhancing predictive maintenance, operational efficiency, and patient care. Blockchain technology provides a secure and transparent platform for managing and sharing sensitive data, such as medical records and pharmaceutical supply chains. By combining these technologies, smart hospitals can ensure data security, interoperability, and streamlined operations while providing patient-centered care. The book also explores the impact of collected medical data from real-time systems in smart hospitals, and by making it accessible to all doctors via a smartphone or mobile device for fast decisions.
Inevitable challenges such as privacy concerns and integration costs must, of course, be addressed. However, the potential benefits in terms of improved healthcare quality, reduced costs, and global health initiatives makes the integration of these technologies a compelling avenue for the future of healthcare.
Some of the topics that readers will find in this book include:
Wireless Medical Sensor Networks in Smart Hospitals ● DNA Computing in Cryptography ● Enhancing Diabetic Retinopathy and Glaucoma Diagnosis through Efficient Retinal Vessel Segmentation and Disease Classification ● Machine Learning-Enabled Digital Twins for Diagnostic And Therapeutic Purposes ● Blockchain as the Backbone of a Connected Ecosystem of Smart Hospitals ● Blockchain for Edge Association in Digital Twin Empowered 6G Networks ● Blockchain for Security and Privacy in Smart Healthcare ● Blockchain-Enabled Internet of Things (IoTs) Platforms for IoT-Based Healthcare and Biomedical Sector ● Electronic Health Records in a Blockchain ● PSO-Based Hybrid Cardiovascular Disease Prediction for Using Artificial Flora Algorithm ● AI and Transfer Learning Based Framework for Efficient Classification And Detection Of Lyme Disease ● Framework for Gender Detection Using Facial Countenances ● Smartphone-Based Sensors for Biomedical Applications ● Blockchain for Improving Security and Privacy in the Smart Sensor Network ● Sensors and Digital Twin Application in Healthcare Facilities Management ● Integration of Internet of Medical Things (IoMT) with Blockchain Technology to Improve Security and Privacy ● Machine Learning-Driven Digital Twins for Precise Brain Tumor and Breast Cancer Assessment ● Ethical and Technological Convergence: AI and Blockchain in Halal Healthcare ● Digital Twin Application in Healthcare Facilities Management ● Cloud-based Digital Twinning for Structural Health Monitoring Using Deep Learning.
Audience
The book will be read by hospital and healthcare providers, administrators, policymakers, scientists and engineers in artificial intelligence, information technology, electronics engineering, and related disciplines.
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Table of Contents
Cover
Table of Contents
Title Page
Series Page
Copyright Page
Preface
Part 1: Basic Fundamentals and Principles
1 Introduction to Smart Hospital
1.1 Introduction
1.2 Conclusion
1.3 Demerits of Smart Hospitals
References
2 Wireless Medical Sensor Networks in Smart Hospitals
2.1 Introduction
2.2 Wireless Sensor Network
2.3 Application in Healthcare
2.4 Benefits
2.5 Technical Challenges
2.6 Conclusion
References
3 Introduction of DNA Computing in Cryptography
3.1 Introduction
3.2 Steganography
3.3 Related Work on DNA
3.4 DNA Computing
3.5 Essence of DNA Computing
3.6 Role of DNA Computing in Cryptography
3.7 Applications of DNA Computing
3.8 Related Work on DNA-Based Cryptography (
Document)
3.9 Limitations
3.10 Cryptography Methods Based on DNA
3.11 Experimental Analysis
3.12 Conclusions and Future Work
References
Part 2: Methods and Applications
4 Enhancing Diabetic Retinopathy and Glaucoma Diagnosis through Efficient Retinal Vessel Segmentation and Disease Classification
4.1 Introduction
4.2 Literature Review
4.3 Existing System
4.4 Proposed System
4.5 Experimental Results
4.6 Conclusion
Conflicts of Interest
References
5 Machine Learning–Enabled Digital Twins for Diagnostic and Therapeutic Purposes
5.1 Introduction
5.2 Conceptualization of Digital Twin and Machine Learning
5.3 State-of-the-Art Works
5.4 Applications of Digital Twins Enabled With Deep Learning Models and Reinforcement Learning
5.5 Limitations and Challenges
5.6 Opportunities/Future Scope
5.7 Concluding Remarks
References
6 Blockchain as the Backbone of a Connected Ecosystem of Smart Hospitals
6.1 Introduction
6.2 Smart Hospitals
6.3 Foundations of Blockchain Technology
6.4 Literature Survey
6.5 Integration of Blockchain in Healthcare
6.6 Digital Twin Technology in Smart Hospitals
6.7 Benefits and Challenges
6.8 Building A Connected Ecosystem
6.9 Regulatory Considerations
6.10 Case Study
6.11 Future Trends and Innovation
6.12 Conclusion
References
7 Blockchain for Edge Association in Digital Twin Empowered 6G Networks
7.1 Introduction
7.2 Digital Twin Technology
7.3 Edge Computing in 6G Networks
7.4 The Blockchain Technology
7.5 Blockchain, Digital Twin, and Edge Computing Integration
7.6 Case Studies from Multiple Domains
7.7 Prospects for Future Directions and Research
References
8 Blockchain for Security and Privacy in the Smart Healthcare
8.1 Brief Overview of Medical Records and Their Confidentiality
8.2 Basics of Blockchain Technology
8.3 Benefits of BC Regarding the Protection of Medical Data
8.4 Principles of Using Blockchain for Medical Records
8.5 IAM on the Blockchain
8.6 Encrypted Medical Information Exchange via Blockchain
8.7 Insurance User Intelligence and Power in Blockchain-Enabled Services
8.8 Governmental and Moral Thoughts
8.9 Selected Experiences and Recommended Approaches
8.10 Prospects and Hurdles in Advancing Blockchain-Based Health Record Security
8.11 Conclusion and Future Prospects
References
9 Conceptual and Empirical Evidence for the Implementation of Blockchain Technology as a Solution for Healthcare Service Providers in India
Introduction
Review of Literature
Conclusion
Acknowledgments
References
Annexures
10 Blockchain-Enabled Internet of Things (IoTs) Platforms for IoT-Based Healthcare and Biomedical Sectors
10.1 Introduction
10.2 Various Applications of Blockchain and Internet of Things in Healthcare and Biomedical Sectors
10.3 Internet of Things Supported Blockchain Platforms in Healthcare and Biomedical Sectors
10.4 Blockchain Technology for Healthcare and Biomedical Sectors
10.5 Storage Capacity and Scalability for Electronic Health Records (EHR)
10.6 Security Issues in Healthcare and Biomedical Sectors: Weaknesses and Threats in Blockchain-Based Internet of Things
10.7 Privacy Issues in Healthcare and Biomedical Sectors: Weaknesses and Threats in Blockchain-Based Internet of Things
10.8 Trust Issue in Healthcare and Biomedical Sectors: Weaknesses and Threats in Blockchain-Based Internet of Things
10.9 Other Issues Healthcare and Biomedical Sectors Rather than Security, Privacy, and Trust
10.10 Technical and Non-Technical Challenges in Healthcare and Biomedical Sectors
10.11 Future Work Toward Healthcare and Biomedical Sectors
10.12 Conclusion
References
11 Electronic Health Records in a Blockchain
Introduction
Conclusion
References
12 A PSO-Based Hybrid Cardiovascular Disease Prediction for Using Artificial Flora Algorithm
12.1 Introduction
12.2 Literature Review
12.3 Proposed Methodology
12.4 Machine Learning Algorithms
12.5 Experimental Setup
12.6 Conclusion
References
13 AI and Transfer Learning–Based Framework for Efficient Classification and Detection of Lyme Disease
13.1 Introduction
13.2 Literature Survey
13.3 Methodologies
13.4 Proposed Work
13.5 Results and Analysis
13.6 Conclusion
References
14 Framework for Gender Detection Using Facial Countenances
14.1 Introduction
14.2 Objectives
14.3 Methodology
14.4 Architecture
14.5 Raining and Evaluation
14.6 Conclusion
References
Part 3: Issues and Challenges
15 Unveiling the Challenges and Limitations in COVID-19 Health Data Prediction with Convolutional Neural Networks: A Data Science Research Perspective
15.1 Introduction
15.2 Literature Review
15.3 Methodology
15.4 Challenges in COVID-19 Health Data Prediction
15.5 Limitations of Convolutional Neural Networks
15.6 Mitigation Strategies
15.7 Case Study: An Empirical Analysis of CNNs for COVID-19 Health Data Prediction
15.8 Conclusion
Data Availability
Funding Statement
Conflict of Interest
Author Contributions
References
Part 4: Future Opportunities
16 Cloud-Based Digital Twinning for Structural Health Monitoring Using Deep Learning
16.1 Introduction to Cloud-Based Digital Twinning
16.2 Evolution of Structural Health Monitoring (SHM)
16.3 Digital Twinning: Concept and Applications
16.4 Integration of Cloud Computing in SHM
16.5 Deep Learning Techniques for Sensor Data Analysis
16.6 Leveraging Convolutional Neural Networks (CNNs) in Cloud Environment
16.7 Recurrent Neural Networks (RNNs) for Anomaly Detection
16.8 Proactive Maintenance and Early Fault Detection
16.9 Collaboration and Data Sharing in Cloud-Based Deployment
16.10 Anticipated Outcomes and Implications
16.11 Advancing SHM Technologies with Cloud-Based Solutions
16.12 Promoting Resilience and Sustainability through Intelligent SHM Systems
16.13 Conclusion
References
17 Smartphone-Based Sensors for Biomedical Applications
17.1 Introduction to Smartphone-Based Sensors and Its Importance in Biomedical Applications
17.2 Smartphone-Based Sensors for Biomedical Applications
17.3 Benefits, Limitations, Issues, and Challenges of Smartphone-Based Sensors in Biomedical Application
17.4 Ensuring Data Security and Privacy in Biomedical Applications by Using Smartphone-Based Sensors
17.5 Sensor Technologies and Communication Protocols in Biomedical Applications
17.6 Data Processing and Analysis Using Emerging Technologies in Biomedical Applications
17.7 Future Research Directions in Biomedical Sensing Using Smartphone-Based Sensors
17.8 Conclusion
References
18 Blockchain for Improving Security and Privacy in the Smart Sensor Network
18.1 Introduction to Smart Sensor Networks and Blockchain Technology
18.2 Blockchain for Improving Security and Privacy in the Smart Sensor Network
18.3 Real-World Examples of Blockchain in Smart Sensor Networks
18.4 Issues and Challenges with Recommended Solutions of Using Blockchain in the Smart Sensor Network for Improving Security and Privacy in this Smart Era
18.5 Future Opportunities with Emerging Technologies in Blockchain for Smart Sensor Networks
18.6 Potential Advancements in Security and Privacy Using Emerging Technologies for Smart Sensor Networks
18.7 Incorporating Blockchain in Existing Sensor Networks for Better Efficiency
18.8 Conclusion
References
19 Sensors and Digital Twin Application in Healthcare Facilities Management
19.1 Introduction to Healthcare Facilities Management
19.2 Digital Twins: Concepts and Applications
19.3 Facilities Management with Digital Twins for Effective Healthcare Facilities
19.4 Benefits and Disadvantages of Emerging Technologies in Modern Healthcare Facilities
19.5 Security and Privacy Issues in Modern Healthcare Facilities
19.6 Data Security in Healthcare Facilities
19.7 Real-World Examples of Sensor and Digital Twin Implementation/Solution for Better Healthcare Facilities Management
19.8 Challenges and Recommended Solutions for Better Healthcare Facilities Management
19.9 Future Trends and Innovations Toward Better Healthcare Facilities Management
19.10 Conclusion
References
20 Integration of Internet of Medical Things (IoMT) with Blockchain Technology to Improve Security and Privacy
20.1 Introduction
20.2 Motivation
20.3 Background
20.4 State of the Art
20.5 Technical Challenges
20.6 Significant Future Trends of Blockchain in Healthcare
20.7 Conclusion
References
21 Advancing Healthcare Diagnostics: Machine Learning–Driven Digital Twins for Precise Brain Tumor and Breast Cancer Assessment
21.1 Introduction
21.2 Digital Twin
21.3 The Contribution of Machine Learning and Deep Learning to the Advancement of Digital Twins in Healthcare
21.4 Machine Learning in Cancer and Brain Prediction
21.5 Materials and Methods
21.6 Experimental Results and Analysis
21.7 Conclusion and Recommendation
References
22 Digital Twin Applications in Healthcare Facilities Management
22.1 Introduction to Digital Twin Technology in Healthcare
22.2 Adoption of Digital Twins in Healthcare Facility Management
22.3 Evolution from Engineering and Manufacturing to Healthcare
22.4 Real-Time Virtual Duplicates for Facility Management
22.5 Features of Digital Twins: Sensors, Data Analytics, and Simulation
22.6 Challenges in Healthcare Facility Management
22.7 Resource Allocation and Patient Safety
22.8 Operational Efficiency in Healthcare Facilities
22.9 Monitoring Infrastructure, Equipment, and Patient Movement
22.10 Integration of Sensor Data EHRs, and Other Sources
22.11 Empowering Stakeholders with Insights from Digital Twins
22.12 Continuous Improvement and Adaptive Management in Healthcare
22.13 Conclusion
References
23 Ethical and Technological Convergence: AI and Blockchain in Halal Healthcare
23.1 Introduction
23.2 Balancing Ethics and Faith: AI and Blockchain in Halal Healthcare
23.3 AI-Driven Halal Healthcare: Navigating Compliance and Technological Integration
23.4 Streamlining Halal Healthcare via Blockchain
23.5 AI and Blockchain in Halal Healthcare: Regulatory Frontiers
23.6 Conclusion
Acknowledgment
References
Index
End User License Agreement
List of Tables
Chapter 2
Table 2.1 Loss rate values for the in-house patient.
Chapter 3
Table 3.1 Several cryptographic systems that use DNA technology.
Table 3.2 Encode plaintext.
Table 3.3 Nucleotides to number.
Table 3.4 Nucleotides to number.
Chapter 5
Table 5.1 State-of-the-art deep learning models in the diagnostic...
Table 5.2 State-of-the-art DTs in the diagnostic and therapeutic industry.
Table 5.3 State-of-the-art combinations of digital twin and deep...
Table 5.4 State-of-the-art reinforcement learning models in the...
Chapter 6
Table 6.1 Key elements associated with each aspect of a smart hospital.
Table 6.2 Comprehensive overview of the differences between...
Chapter 8
Table 8.1 Integrate blockchain-based additional services.
Table 8.2 Valuable BC contributions to the medical sector.
Table 8.3 Using BC in novel ways to secure patient...
Table 8.4 Standards for the safeguarding of medical information...
Table 8.5 New paths in the security of health records stored on blockchain.
Table 8.6 Challenges and contemplations.
Chapter 9
Table 9.1 Breakup of various paramedics participated in the survey.
Table 9.2 The mean weight value of perception for various categories...
Annexure-1: Perception of Technicians -23.
Annexure-2: Perception of Laboratory Assistants -22.
Annexure-3: Perception of Nurses -26
Annexure-4: Perception of Administrative staff -18
Chapter 11
Table 11.1 Components of EHR.
Table 11.2 Categories of EHR.
Table 11.3 EHR adopted hotspots.
Chapter 12
Table 12.1 Dataset value for different attributes.
Table 12.2 Feature set values.
Table 12.3 Holdout results.
Table 12.4 Relevant feature set by dimension reductionality.
Table 12.5 Accuracy results for PSOAFA.
Chapter 13
Table 13.1 Results – proposed CNN (sequential) - epoch and losses.
Table 13.2 Results – MobileNetV2 - epoch and losses.
Table 13.3 Results – InceptionNet-V3 - epoch and losses.
Table 13.4 Results – VGG-19 - epoch and losses.
Table 13.5 Comparison results of used approach.
Chapter 16
Table 16.1 Accuracy values comparison of the model.
Chapter 17
Table 17.1 Smartphone-based sensors in biomedical applications.
Chapter 20
Table 20.1 Domain in healthcare, existing problems,...
Table 20.2 Various applications.
Table 20.3 Examining the present prospects...
Chapter 21
Table 21.1 Classification report obtained from the evaluation of the MobileNetV2...
Chapter 23
Table 23.1 Ethical and technological convergence with faith: AI...
Table 23.2 Regulatory frontiers in AI and blockchain for Halal healthcare.
List of Figures
Chapter 1
Figure 1.1 Hierarchy structure of administrative unit in a smart hospital...
Figure 1.2 The management system of a smart hospital. Courtesy: Springer...
Figure 1.3 Workflow in a smart hospital. Courtesy: Smart Hospital Assets...
Chapter 2
Figure 2.1 Wireless sensor network (WSN).
Figure 2.2 Measurement of heart rate using wireless sensor.
Figure 2.3 Calculation of blood pressure.
Figure 2.4 Wireless transmission of rate of body temperature.
Figure 2.5 Respiratory monitoring in both conditions of breathing...
Chapter 3
Figure 3.1 Key encryption process.
Figure 3.2 Structure of DNA.
Figure 3.3 Process.
Figure 3.4 Process of DNA with RSA algorithm.
Figure 3.5 Encryption and decryption time plot.
Chapter 4
Figure 4.1 Proposed architecture for eye disease diagnosis.
Figure 4.2 Feature importance vs. feature index.
Figure 4.3 Predicted labels vs. true labels for confusion matrix.
Figure 4.4 Accuracy vs. loss for the model accuracy.
Chapter 5
Figure 5.1 Explaining the generation process of the digital twin.
Figure 5.2 Schematic representation of the interconnections between...
Figure 5.3 Figure explaining reinforcement learning process with an example.
Figure 5.4 Figure with a brief overview of key limitations and...
Chapter 6
Figure 6.1 Smart hospital ecosystem.
Chapter 7
Figure 7.1 Edge computing architecture.
Figure 7.2 Blockchain technology.
Figure 7.3 Integrated platform of blockchain, digital twin, and edge computing.
Chapter 8
Figure 8.1 Basic functional schematic of e-clinical care scheme.
Figure 8.2 Medical care system using blockchain.
Figure 8.3 Overview of IAM functionalities.
Figure 8.4 BC-based process for medical-care uses.
Chapter 9
Figure 9.1 Key elements of blockchain. Source: H. Sami Ullah
et al.
Figure 9.2 Peer to peer network of block chain transactions. Source- H...
Figure 9.3 Application of blockchain in the healthcare system. Source- H...
Figure 9.4 Sampling frame. Source: Table 9.1
Figure 9.5 Weighted mean of perceptions of various paramedic. Source: Table 9.2
Figure 9.6 SWOT analysis.
Chapter 11
Figure 11.1 Block diagram.
Figure 11.2 Structure of EHR.
Figure 11.3 Hotspots of metro and non-metro.
Chapter 12
Figure 12.1 Proposed work for the PSOAFA model.
Figure 12.2 Particle swarm optimization space particle results.
Figure 12.3 Graphical implementation results for the dataset.
Figure 12.4 Results for different datasets.
Figure 12.5 Performance results for cross-validation values.
Chapter 13
Figure 13.1 Basic working and architecture of a deep neural network [22].
Figure 13.2 Plot of sigmoid function.
Figure 13.3 Plot of tanh function.
Figure 13.4 Plot of ReLU function.
Figure 13.5 Overall process for image classification by transfer learning [30].
Figure 13.6.1 Identity block in ResNet model [14].
Figure 13.6.2 Basic architecture of ResNet-50 model [14].
Figure 13.7 Basic architecture of VGG-19 [15].
Figure 13.8 Basic architecture of MobileNetV2 [16].
Figure 13.9 Basic architecture of InceptionNet-V3 [18].
Figure 13.10.1 Raw and resized image.
Figure 13.10.2 Different augmentations of image.
Figure 13.11 Flowchart of steps followed in the sequential model method.
Figure 13.12 CNN model architecture.
Figure 13.13 Flowchart of steps followed in the transfer learning method.
Figure 13.14 Original image.
Figure 13.15 Grad-CAM VGG-19.
Figure 13.16 Grad-CAM ResNet-50.
Figure 13.17 Accuracy plot for training and validation.
Chapter 14
Figure 14.1 Basic CNN process [7].
Figure 14.2 Before and after image processing [16]...
Figure 14.3 CVlib is a powerful and user-friendly object detection...
Figure 14.4 CNN architecture [35].
Figure 14.5 Live demonstration.
Figure 14.6 Plot.
Chapter 15
Figure 15.1 Model performance metrics.
Figure 15.2 Training and validation accuracy.
Figure 15.3 Model performance comparison.
Figure 15.4 Execution flow of the proposed CNN model.
Figure 15.5 Final output of the proposed CNN model.
Figure 15.6 Accuracy vs. epoch of the proposed CNN model.
Figure 15.7 Loss vs. epoch of the proposed CNN model.
Chapter 16
Figure 16.1 Integration of cloud computing in SHM through IoT.
Figure 16.2 Accuracy values comparison of the model.
Chapter 17
Figure 17.1 Sensors compatible with smartphones.
Figure 17.2 Sensors for biomedical applications.
Figure 17.3 Data security and privacy in biomedical applications.
Figure 17.4 Data processing and analysis using emerging technologies.
Figure 17.5 Future research directions.
Chapter 20
Figure 20.1 Example of different IoMT contexts and entities.
Figure 20.2 A cloud-based IoMT application’s architecture.
Figure 20.3 Guidelines for the exchange of electronic medical records.
Figure 20.4 Methods for managing security and privacy concerns in HER.
Figure 20.5 Ethical concerns with electronic health records.
Figure 20.6 Primary objectives for blockchain use in the medical field.
Figure 20.7 Blockchain’s effects on healthcare.
Figure 20.8 Applications of blockchain technology in the medical field.
Figure 20.9 Blockchain’s technical hurdles.
Chapter 21
Figure 21.1 Digital twin representation.
Figure 21.2 Confusion matrix obtained from the evaluation of the model.
Figure 21.3 ROC obtained from the evaluation of the model.
Chapter 22
Figure 22.1 Application of digital twin in healthcare.
Figure 22.2 Evolution from engineering and manufacturing to healthcare.
Figure 22.3 Overview for patient movement monitoring.
Chapter 23
Figure 23.1 AI-driven Halal healthcare...
Figure 23.2 Blockchain applications in Halal healthcare.
Guide
Cover Page
Series Page
Title Page
Copyright Page
Preface
Table of Contents
Begin Reading
Index
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Scrivener Publishing100 Cummings Center, Suite 541JBeverly, MA 01915-6106
Publishers at ScrivenerMartin Scrivener ([email protected])Phillip Carmical ([email protected])
Artificial Intelligence-Enabled Blockchain Technology and Digital Twin for Smart Hospitals
Edited by
Amit Kumar Tyagi
Department of Fashion Technology, National Institute of Fashion Technology, New Delhi, India
This edition first published 2024 by John Wiley & Sons, Inc., 111 River Street, Hoboken, NJ 07030, USA and Scrivener Publishing LLC, 100 Cummings Center, Suite 541J, Beverly, MA 01915, USA© 2024 Scrivener Publishing LLCFor more information about Scrivener publications please visit www.scrivenerpublishing.com.
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Library of Congress Cataloging-in-Publication Data
ISBN 978-1-394-28739-0
Cover image: supplied by Adobe FireflyCover design by Russell Richardson
Preface
The latest technologies like artificial intelligence, blockchain, edge computing, digital twin, quantum computing, etc., are used extensively in the healthcare sector. Among these technologies, blockchain is considered essential to Industry 5.0, while AI provides intelligence to computer systems with its efficient learning techniques. The integration of digital twin and blockchain technologies in smart hospitals present a promising path toward transforming healthcare systems. Digital twin technology enables the creation of real-time virtual replicas of hospital assets and patients, enhancing predictive maintenance, operational efficiency, and patient care. Blockchain technology provides a secure and transparent platform for managing and sharing sensitive data, such as medical records and pharmaceutical supply chains. By combining these technologies, smart hospitals can ensure data security, interoperability, and streamlined operations while providing patient-centered care.
Inevitable challenges such as privacy concerns and integration costs must be addressed, but the potential benefits in terms of improved healthcare quality, reduced costs, and global health initiatives make the integration of these technologies a compelling avenue for the future of healthcare.
Today, medical data is collected in real-time from all devices and systems in a smart hospital. This data can be integrated into smart insight using analytics or machine learning software, which is in turn accessible to the medical professional via a smartphone or mobile device to facilitate fast decisions. This book explains how blockchain technology can improve health services in smart hospitals, especially during pandemics. Furthermore, it explores how blockchain and digital twins can be effectively combined in smart hospitals with profound benefits for society.
I extend a heartfelt thanks to all those who have contributed to this book, and I am most grateful to Martin Scrivener of Scrivener Publishing for his help and making this book possible.
Amit Kumar Tyagi
June 2024
Part 1BASIC FUNDAMENTALS AND PRINCIPLES
1Introduction to Smart Hospital
R. Bhuvana1, R. J. Hemalatha1*, S. Baskar1 and Krishnakumar Kosalaram2
1Vels Institute of Science, Technology and Advanced Studies, Chennai, India
2University of Technology and Applied Sciences - UTAS-Ibri, Ibri, Sultanate of Oman
Abstract
The idea of “smart hospitals” has developed as a result of the incorporation of cutting-edge technology into the fast-changing environment of the healthcare industry. The utilization of cutting-edge technologies like artificial intelligence (AI), big data analytics, the Internet of Things (IoT), and cutting-edge communication systems allows these forward-thinking healthcare facilities to improve patient care, optimize operational efficiency, and revolutionize the overall experience of receiving medical care. By providing a complete overview of the essential components and features that distinguish smart hospitals, this brief offers a comprehensive picture. It is possible to monitor patients in real time thanks to the incorporation of IoT devices, which also makes it easier to execute individualized treatment plans and preventative healthcare therapies. It has been discovered that the contributions of AI-driven systems to predictive analytics, quicker decision-making processes, and diagnostic accuracy all contribute to an improvement in clinical outcomes. In addition, the application of big data analytics makes it possible for experts working in the healthcare industry to get important insights from huge datasets. The implementation of methods that are supported by evidence, the optimization of resources, and eventually the improvement of patient outcomes are all outcomes that are facilitated by this strategy. The networked architecture of smart hospitals makes it possible for medical staff to communicate and work together in a fluid manner, which, in turn, encourages a holistic and patient-centered approach to the delivery of healthcare. The obstacles and issues that are involved with the adoption of smart hospital technology are also discussed in this abstract. These challenges and considerations include topics such as data security, interoperability, and ethical concerns. In addition, it sheds light on the potential contributions that smart hospitals might make to the overall quality of patient care, as well as the cost-effectiveness and accessibility of healthcare. In essence, the introduction of smart hospitals represents a paradigm change in the delivery of healthcare. These hospitals make use of sophisticated technology to construct an environment that is distinguished by intelligence, connection, and a concentration on patient-centered care. The development of these technologies offers enormous potential for bringing about dramatic changes in the results and practices of healthcare, therefore ushering in a new age that will be defined by precision medicine and creative healthcare solutions.
Keywords: Medicine, patient-centered care, innovative healthcare, the Internet of Things, and artificial intelligence are some of the key phrases you should know
1.1 Introduction
One of the most significant changes that the healthcare business is going through right now is the introduction of smart hospitals, which is taking place in this age of fast technological innovation [1]. The use of cutting-edge technology by these establishments helps to improve the quality of care provided to patients, simplify processes, and increase overall efficiency [2]. For the purpose of establishing an environment that places a premium on the health and well-being of patients as well as the provision of medical treatment, smart hospitals include data analytics, artificial intelligence (AI), and networked technologies. The purpose of this essay is to shed light on the bright future of healthcare by offering an examination of the fundamental components and advantages of smart hospitals [3].
1.1.1 Aspects That Make Up Intelligent Hospitals
Integration of the Internet of Things:
Smart hospitals depend extensively on the Internet of Things (IoT) to link various pieces of technology, including wearables, medical devices, and other gadgets
[8]
. For the purpose of providing medical experts with data that is both prompt and accurate, this connection makes it possible to monitor the vital signs of patients in real time
[4]
. A patient’s heart rate, blood pressure, and other vital metrics may be monitored by wearable devices, which enable pre-emptive intervention and individualized treatment
[5]
. For example, wearable devices can monitor key parameters.
Artificial Intelligence (AI) and Machine Learning (ML):
The algorithms that are used in smart hospitals are very important since they analyze large quantities of healthcare data in order to develop insights that may be put into action
[6]
. The diagnosis of illnesses, the prediction of patient outcomes, and the optimization of treatment programs are all aided by this innovative technology. Chatbots that are driven by AI are being used for first-patient engagement. This allows for speedier replies to be given to questions and reduces the workload of healthcare professionals
[7]
.
Telemedicine and Remote Monitoring:
Intelligent hospitals are embracing telemedicine systems, which make it possible to conduct virtual consultations and to monitor patients remotely
[8]
. This is especially helpful for those who are afflicted with chronic ailments and need regular medical attention. Patient monitoring via remote access not only improves accessibility to medical treatment but also lowers the number of hospital admissions and readmissions, which eventually results in a reduction in the expenses associated with medical care
[9]
.
1.1.2 Advantages That Smart Hospitals Offer
Improved Patient Treatment:
The incorporation of cutting-edge technology into contemporary hospitals leads to an improvement in the quality of treatment provided to patients. Real-time monitoring, diagnostics helped by AI, and individualized treatment plans all contribute to the delivery of healthcare that is both more accurate and more efficient
[10]
. Patients report improved results and a higher level of satisfaction with their entire healthcare experience with the healthcare system.
Smart hospitals
are able to improve their operational efficiency by using digital technologies, which allows them to simplify their operations
[11]
. Administrative processes may be optimized, mistakes can be reduced, and overall efficiency can be improved via the use of automated workflows, EHRs, and analytics powered by AI. Not only does this save time for medical personnel but also guarantees that the patient’s path is one that is more streamlined and devoid of errors
[12]
.
Decision-Making That Is Driven By Data:
The abundance of data that is produced in smart hospitals makes it easier to make decisions based on the data. In order to enhance overall healthcare delivery, hospitals may utilize this information to discover patterns, efficiently manage resources, and improve overall healthcare delivery
[13]
. Through the use of predictive analytics, future healthcare problems may be managed in a pre-emptive manner, which ultimately results in a more proactive and preventative strategy
[14]
.
The future of healthcare is represented by smart hospitals, which are defined by the seamless integration of technology with patient care in order to provide a system that is both more efficient and effective. These institutions are transforming the landscape of healthcare by adopting cutting-edge technology such as AI, the IoT, and others. In addition to better patient outcomes, the advantages include increased operational efficiency, cost savings, and a more individualized approach to healthcare. Overall, the benefits are substantial [15]. As smart hospitals continue to develop, it is probable that they will have a revolutionary effect on the healthcare business. This will pave the way for a future that is both healthier and more technologically sophisticated [16].
The intelligent hospital concept has the potential to completely transform the healthcare industry. It is able to address a number of urgent problems that are hurting both the management of hospitals and the practitioners of healthcare. This includes everything from managing the ever-increasing number of vehicle fleets to analyzing the vast amounts of medical data. With the help of our network of solution partners, Intel is working on the development of complex medical devices [17]. It is possible to make the smart hospital a reality. These technological developments are being driven by the development of technologies that make AI possible [18].
The analysis of situations utilizing a variety of different modes of perception, computer resources that are situated at the edge of the network, and cloud computing resources that have been developed expressly for edge computing are all examples of ubiquitous computing practices with capabilities that extend all the way from the edge to the cloud. Through collaborative efforts, we are identifying ways to improve the performance of providers, enhancing efficiency and increasing patient outcomes while simultaneously reducing expenditures and downtime is a goal that deserves attention. The provision of cognitive capabilities, connectedness, and protection were provided [19].
Patients all around the world are gaining access to breakthrough tests and therapies that might save their lives as a result of the fast advancement of healthcare technology. Applications that require a significant amount of effort, such as medical imaging, are producing amounts of data that have never been seen before. All of this information has to be saved, analyzed, and accessible in a safe manner throughout the whole organization, from the perimeter of the hospital network to the cloud. The integration of AI and analytics in clinical applications and medical equipment is becoming more common. In this way, quicker and more accurate diagnoses are being made possible, as well as more informed decision-making. On the other hand, despite the fact that hospitals continue to reap the benefits of new developments in healthcare technology, these smart point solutions are often not linked to one another [20]. This results in isolated data silos, bottlenecks in workflow, and clinical user experiences that are not cohesive with one another. A new generation of intelligent hospitals is rapidly developing as a solution to the technical difficulties that are now being faced. Smart hospitals are able to provide workflow agility among healthcare partners, providers, and systems by using technical frameworks that are extendable. Organizations that prioritize digital transformation are now in the process of altering their infrastructure by implementing the seamless integration that is necessary to greatly improve connection, intelligence, and automation. Utilizing digital twins to monitor the state of patients, equipment, and the hospital itself is a strategy that is being implemented.
The advancement of medical technology is rapidly advancing, which is bringing about the introduction of revolutionary and life-saving diagnostic procedures and treatments to patients all over the globe. A quantity of data that has never been seen before is being produced as a consequence of the use of medical imaging and other applications that demand a significant amount of work. The information must be saved, processed, and made available throughout the whole of the organization, beginning at the edge of the hospital network and continuing all the way up to the cloud. The use of AI and analytics into clinical applications and medical equipment is becoming more common. This not only makes it easier to make decisions by offering more extensive information but also makes it possible to make diagnoses more quickly and with a high degree of accuracy. However, despite the undeniable benefits that hospitals continue to reap from the most recent developments in healthcare technology, these sophisticated individual solutions often fail to integrate with one another. Consequently, the healthcare sector is characterized by the presence of distinct and disjointed data sources, delays in workflow, and fragmented user experiences. In order to overcome these technological challenges, a new generation of intelligent hospitals is fast emerging onto the scene. In order to improve the flexibility of processes among healthcare partners, providers, and systems, smart hospitals make use of technological frameworks that are flexible. Modern businesses that place a high priority on digital technology are now in the process of rebuilding their infrastructure in order to create a seamless integration that will result in a significant improvement in connectivity, intelligence, and automation.
It is a concept known as the digital twin that is being used by contemporary hospitals in order to improve their ability to manage and make use of the vast amounts of data that are associated with patients and valued assets. The technology in question is capable of producing a virtual representation of a system, institution, or even a person who is seeking medical care. Through the use of predictive data and the facilitation of care optimization, the virtualized digital twin offers improved visibility and makes it possible to employ predictive data. In the beginning, the concept of the digital twin, which was first developed for use in industrial applications, is becoming more popular [21].
Smart hospitals are equipped with various features and technology that are designed to improve patient care, streamline operations, and enhance healthcare delivery as a whole. Smart hospitals have the following important features:
Integration With the Internet of Things (IoT):
Smart hospitals use the IoT to link and connect different pieces of equipment, wearable tech, and medical devices. The ability to track assets, monitor patients’ vitals in real time, and efficiently manage hospital resources are all made possible by this connectedness. Connectivity to the IoT improves the precision of data and makes preventative healthcare treatments easier.
The Field of Artificial Intelligence and Machine Learning Use Cases:
Smart hospitals rely heavily on AI and ML. Diagnostic assistance, predictive analytics, customized treatment programs, and medical picture analysis are some of the many uses for these technologies. Healthcare providers may benefit greatly from the insights provided by AI-powered algorithms, which can learn and adapt, in order to make better decisions.
EHRs:
In order to digitize and consolidate patient information, smart hospitals use EHRs. EHRs provide better data management, reduced paperwork, and easier information exchange between healthcare providers. Better patient care that is both organized and efficient is encouraged by this.
Telemedicine and Remote Monitoring:
“Smart” hospitals use telemedicine technology to monitor patients remotely and conduct virtual consultations. With the use of telemedicine, doctors can see patients even when they are far away, and with remote monitoring, they can keep tabs on their health at all times, cutting down on the frequency of hospital visits.
Technology That Is Worn by the User:
Devices like smartwatches and fitness trackers are used to continuously monitor the user’s vital signs and activity. The information gathered by these gadgets is priceless for the purposes of preventative medicine and the early diagnosis of illness.
Automated Lighting
, climate control, and energy management systems are just a few examples of how the hospital’s physical infrastructure is improved using smart technology. These help optimize energy use while also making the atmosphere more pleasant for both personnel and patients.
RPA:
Appointment scheduling, billing, and data input are just a few examples of the repetitive and regular processes that may be automated using RPA. That way, doctors and nurses may devote their whole attention to the most important and intricate parts of patient care.
Platforms for Patient Engagement:
Digital platforms and mobile apps enable smart hospitals to actively include patients in their healthcare journey. Access to one’s medical history, appointment calendar, prescription reminders, and instructional materials are all made available via these platforms
[22]
.
Cybersecurity Measures:
Smart hospitals emphasize strong cybersecurity measures to secure patient information, considering the sensitive nature of healthcare data. Protecting the privacy and authenticity of patients’ medical records requires measures such as encryption, access limits, and regular security assessments.
Utilizing Predictive Analytics for Resource Management:
With the use of predictive analytics, hospitals are able to better manage their inventory, allocate resources more efficiently, and anticipate patient admission rates. Improved operational efficiency and decreased expenses are the results of this data-driven strategy.
Optimizing Patient movement:
By using technology, smart hospitals are able to streamline patient movement inside the institution, cutting down on wait times and enhancing the overall experience for patients. Automated check-ins, smart scheduling, and real-time information for relatives and patients are all part of this.
Smart hospitals
use communication systems that allow healthcare workers to work together seamlessly, which brings us to our 12th point: collaborative communication platforms. Better patient care coordination and faster decision-making are outcomes of these systems’ immediate and secure connectivity.
To sum up, smart hospitals are built with features that aim to create a healthcare environment that is networked, data-driven, and patient-centric. More effective healthcare delivery, better patient outcomes, and a new standard of care are all results of cutting-edge technology’s incorporation into the industry.
Smart hospitals make use of cutting-edge technology to improve the quality of care that is provided to patients as well as to the medical professionals who deliver that care. These are some of the benefits that smart hospitals offer:
Smart hospitals offer continuous monitoring of patients via the use of wearable devices and sensors, which enables healthcare personnel to measure vital signs and health indicators in real time [23].
Improved Patient Care Is One of the Benefits of Smart Hospitals
- “Predictive Analytics”: The use of advanced analytics to forecast the occurrence of prospective health problems enables early intervention and the development of individualized treatment strategies.
Effective Workflow
- Digital Records: Smart hospitals make use of EHRs to preserve and communicate patient information in a seamless manner, therefore lowering the amount of paperwork and enhancing accuracy.
Automation of regular activities, such as appointment scheduling and invoicing, leads to enhanced efficiency and decreased administrative load.
Automated processes: Automation of routine tasks leads to increased efficiency.
“Enhanced Communication
” - “Telemedicine”: “Smart hospitals facilitate remote consultations, allowing patients to access healthcare services from anywhere. This is especially beneficial for patients who are in remote areas or who are experiencing an emergency.”
Internal Communication: The use of sophisticated communication systems helps to simplify communication among healthcare workers, which, in turn, leads to faster decision-making and enhanced coordination.
“
Resource Optimization” - “Asset Tracking”:
Smart hospitals utilize IoT devices to monitor the location and utilization of medical equipment. This helps to reduce the amount of time spent looking for resources and ensures that they are used to their full potential.
Maintenance That Is Predictive: Predictive maintenance for medical equipment helps to avoid failures that were not anticipated, so guaranteeing that the equipment is constantly in a state of operation.
Patient Engagement
- Health applications and Portals: Smart hospitals give patients the ability to actively engage in their own healthcare, access information, and connect with healthcare practitioners via the use of mobile applications and internet portals.
Patient education is facilitated by digital platforms, which provide patients access to educational materials, hence fostering health literacy and focusing on illness prevention.
Data Security and Privacy
- Secure Systems: Smart hospitals use comprehensive cybersecurity measures to secure sensitive patient data and guarantee compliance with any privacy legislation that may be in place.
Access Controls: The access to patient information is rigorously managed, which reduces the danger of data breaches or unauthorized access to the information.
Research and creation: -
Big Data Analytics: Smart hospitals make use of big data analytics to analyze enormous datasets, which contributes to the advancement of medical research, the creation of new drugs, and the improvement of healthcare procedures.
In the field of clinical decision support systems, AI-driven solutions provide assistance to medical practitioners in making well-informed judgments by using the most recent research and patient data. When it comes to environmental sustainability, energy efficiency is the eighth priority. The implementation of sustainable practices in smart hospitals, such as energy-efficient lighting and heating, ventilation, and air conditioning (HVAC) systems, contributes to the preservation of the environment and the reduction of costs. In conclusion, smart hospitals provide a multitude of advantages by using technology to improve patient care, simplify operations, boost communication, and contribute to breakthroughs in the field of healthcare [24].
1.1.3 Hierarchical Structure of Smart Hospital
In most cases, a smart hospital will have a hierarchical structure that includes many layers of administration, personnel, and integrated technology. Figure 1.1 represents the hierarchy structure of the administrative unit in a smart hospital. The following are some instances of intelligent hospital hierarchies:
The “Executive Level” consists of the “Chief Executive Officer” (CEO), who is accountable for the overall administration and strategy of the hospital.
As the Chief Information Officer (CIO), you are responsible for overseeing the administration and execution of various technological solutions.
The Chief Medical Information Officer (CMIO) is responsible for ensuring that clinical processes are supported by the integration of medical information systems and technology.
The Hospital Administrator is responsible for managing day-to-day operations and ensuring that the hospital’s strategic objectives are realized. This position falls under the management-level classification.
The Director of Operations is responsible for overseeing a number of departments and ensuring that coordinated and collaborative efforts run smoothly.
The Chief Nursing Officer (CNO) is responsible for managing the nursing team and ensuring that patients get high-quality care.
At the clinical level, the Medical Director is responsible for providing leadership to the medical team and ensuring that the appropriate level of clinical care is provided.
Department Heads who are in charge of certain medical departments, such as Surgery, Cardiology, and Pediatrics, are responsible for managing patient care within their respective areas of expertise.
The Chief Information Officer (CIO) is in charge of overseeing the hospital’s information technology strategy. The Information Technology (IT) Department is responsible for this.
Information Technology Managers (such as Network Managers and Database Managers) are responsible for managing certain components of the hospital’s information technology infrastructure.
The Health Information Management (HIM) Director is responsible for guarding the confidentiality and authenticity of patient medical data.
The fifth category of clinical support staff consists of nurses who are responsible for providing direct patient care and using technology for the purposes of documentation, communication, and monitoring.
When it comes to the management and distribution of medications, pharmacists should make use of intelligent systems.
Laboratory Technicians are expected to make use of cutting-edge technologies while conducting diagnostic tests.
“Operational Support Staff” - “Administrative Staff”: Provide assistance with day-to-day operations consisting of activities such as scheduling, invoicing, and administrative responsibilities.
Facility Management is responsible for overseeing the daily operations and upkeep of the hospital’s physical infrastructure.
Patient Interaction and Monitoring: Patient Engagement Specialists: Make use of technology to improve patient education and communication (Patient Interaction and Monitoring).
Telehealth and Remote Monitoring Teams: These teams are responsible for providing virtual consultations and maintaining remote patient monitoring.
It is the responsibility of IoT and Wearable Device Technicians to manage and repair devices that monitor the health of patients in real time [25].
Data Scientists: Analyze healthcare data in order to generate insights for the purpose of improving patient outcomes and operational efficiency. This is the responsibility of the Artificial Intelligence (AI) and Analytics Teams.
Developers and maintainers of AI software for diagnostics, predictive analytics, and customized medicine are the responsibility of AI specialists.
Security and Compliance: Chief Security Officer (CSO): Responsible for overseeing the security measures used by the hospital to secure patient data and maintain compliance.
Privacy Officers are responsible for ensuring that the hospital complies with privacy legislation and safeguards the confidentiality of patient information.
“Patient Experience and Quality Improvement”: “Patient Experience Officers” should center their efforts on enhancing the entire patient experience by implementing technological and procedural improvements.
Quality Improvement Teams are responsible for monitoring and putting into action initiatives that aim to improve the quality of care that is given
[26]
.
Figure 1.1 Hierarchy structure of administrative unit in a smart hospital.
Courtesy: EdrawMax
In a smart hospital, these hierarchical layers work together to create an environment that is linked and makes use of technology to improve the results for patients, the efficiency of operations, and the overall delivery of healthcare.
1.1.4 Smart Hospital Management System
A smart hospital management system makes use of technology to simplify and improve different elements of hospital operations, eventually leading to improvements in patient care, efficiency, and overall management. Figure 1.2 represents the management system of a smart hospital [27]. The following is a list of important components and features that it is possible to include in a smart hospital management system:
Figure 1.2 The management system of a smart hospital.
Courtesy: Springer Data Science in Societal Applications pp. 77–106
The EHR system provides a centralized and secure location for the preservation of patient information.
Real-time access to patient records by authorized healthcare practitioners.
Integration of diagnostic systems, laboratories, and other departments is a must.
Appointment Scheduling and Management: the second point
The ability to schedule appointments for patients online.
Reminders sent via email or text message for scheduled appointments.
Optimized scheduling for medical professionals and organizations.
Telehealth services include video consultations and remote monitoring for those who do not need immediate medical attention
[28]
.
The integration with EHRs allows for the interchange of information without any obstacles
[29]
.
Automated tracking of medical supplies and equipment is the fourth component of inventory management.
Alerts for low stock levels and expiry dates are also included.
Integration with procurement systems in order to ensure effective replenishment.
Shift scheduling and management are included in the fifth category of staff management.
The use of biometrics or card systems for the purpose of monitoring attendance. Figure 1.3 represents workflow in a smart hospital. Staff members are provided with training and certification monitoring [30].
Processes of billing and invoicing that are automated are included in the sixth category of billing and financial management
[31]
.
Integration with insurance systems for claims processing. Financial reporting and analytics are also included.
Real-time monitoring of the patient’s vital signs is the seventh component of patient monitoring.
Alerts for urgent circumstances.
Integration with EHRs will allow for extensive patient data.
IoT devices for monitoring patient health at home are the eighth component of IoT integration: instruments that may be worn to monitor vital signs and other health indicators [32].
“Mobile Applications”: “Apps that are directed toward patients, such as those that allow for appointment scheduling, access to medical records, and telehealth”
[33]
.
Mobile applications allowing users to obtain patient information while they are on the go.
Data analytics for the purpose of performance assessment and decision-making is included in the category of “Analytics and Reporting.”
Reports that may be customized for each of the departments
[34]
.
Secure and compliant access controls to safeguard patient information are the 11th and 12th security and compliance measures.
Adherence to rules governing healthcare, such as the Health Insurance Portability and Accountability Act (HIPAA).
“Feedback and Survey System:” - Information gathered from patients for the purpose of making ongoing improvements.
In order to evaluate the level of care and services provided, surveys are conducted.
“Emergency Response System”: “Alerts and notifications for emergency situations that are automatically generated when they occur.”
Integration with emergency services and response teams.
Figure 1.3 Workflow in a smart hospital.
Courtesy: Smart Hospital Assets Research Gateway.
Integration with healthcare providers, laboratories, and pharmacies located outside of the organization is the 14th step in the integration process [35].
The capacity to communicate with health information exchange networks on a nationwide or regional scale.
The implementation of a smart hospital management system requires careful planning, the consideration of security measures, and coordination with healthcare experts in order to guarantee that the system will be efficient in enhancing patient care and operational efficiency [36].
1.2 Conclusion
In conclusion, the introduction of smart hospitals ushers in a new era of revolutionary change in the healthcare industry, bringing about a revolution in the manner in which medical services are provided and patient care is administered. There has been a shift toward a new standard of efficiency, accuracy, and patient centricity as a result of the incorporation of cutting-edge technology such as the IoT, AI, and sophisticated data analytics [37].
The ability of smart hospitals to streamline operations, maximize resource use, and improve overall healthcare outcomes has been proved by these institutions. These intelligent technologies have provided healthcare personnel with fast, data-driven insights, which have resulted in better-informed decision-making and enhanced patient care. These insights range from the real-time monitoring of patient vital signs to predictive analytics for disease prevention [38, 39].
Smart hospitals provide a number of benefits, one of the most significant of which is the seamless communication and interoperability present across the different equipment and systems. The interconnection of these systems helps to cultivate a collaborative environment in which healthcare practitioners are able to easily communicate information with one another. This, in turn, leads to quicker diagnoses, more individualized treatment regimens, and, ultimately, better results for patients. To guarantee that patient information is available across the whole of the healthcare continuum, the integration of EHRs is essential. This not only helps to maintain continuity of treatment but also reduces the probability of medical mistakes occurring.
Patient interaction is another important aspect of smart hospitals that should not be overlooked. It is now possible for people to take an active role in the management of their own healthcare via the use of mobile applications, wearable devices, and telehealth solutions. Not only does this foster a feeling of empowerment but also makes it possible for healthcare practitioners to collect vital data provided by patients, which can then be used to develop treatment plans that are more thorough and individualized.
In addition, the deployment of smart hospitals has shown to be an important factor in maximizing the usage of available resources. The use of automated systems for inventory management, asset monitoring, and staff scheduling contributes to the cost-effectiveness and operational efficiency of an organization. This, in turn, enables healthcare institutions to distribute their resources in a more prudent manner, which ultimately benefits not just the institution but also the people that they serve.
In conclusion, the many uses of smart hospitals have completely transformed the landscape of healthcare, therefore ushering in a new age that is characterized by high levels of innovation and efficiency. Because technological improvements are still being made, there are an infinite number of opportunities for additional developments in intelligent healthcare solutions. The ongoing collaboration between healthcare professionals, technology developers, and policymakers will be crucial in harnessing the full potential of smart hospitals, ensuring that they continue to enhance patient care, improve outcomes, and contribute to the overall advancement of the healthcare industry. In the pursuit of a healthcare environment that is both more intelligent and more connected, there is little question that the path will be both thrilling and hopeful [40].
1.3 Demerits of Smart Hospitals
There are a number of obstacles and drawbacks associated with smart hospitals, despite the fact that they have a number of benefits in terms of efficiency, patient care, and overall healthcare administration. The following are some of the most significant drawbacks:
Exorbitantly High Initial Expenses: Initial investments of a large amount are required in order to successfully use smart technology in hospitals. The expense of obtaining and integrating sophisticated technology, such as EHRs, AI systems, and IoT devices, may be rather high.
Concerns Regarding the Safety of Data: Smart hospitals are significantly dependent on digital data, which raises issues about the confidential nature of patient information and the protection of their privacy. Threats to cybersecurity, breaches in data security, and illegal access to sensitive health data are all serious issues that need to be addressed.
Problems Accompanying Interoperability: When integrating a variety of smart technologies from different suppliers, there is the potential for interoperability issues to arise. When it comes to the efficiency of smart hospitals, it is very necessary to guarantee that there is no disruption in communication and data exchange between the various systems and equipment.
Resistance Training and Conditioning for Staff: When new technology is introduced, it is sometimes necessary for healthcare personnel to undergo training. A hurdle that might be encountered in the effective adoption of smart systems is resistance to change among staff members. This resistance can result in delays and poor utilization of the technology.
Dependence on technology means that smart hospitals are susceptible to system failures, technical problems, or downtime. This vulnerability renders smart hospitals prone to downtime. In critical circumstances, any disruption in the operation of intelligent systems may have an effect on the care provided to patients and the operations of the hospital.
Concerns on the Legal and Ethical Fronts: Ethical problems are raised when AI is used in the healthcare industry. These concerns include the possibility of depersonalization in patient care, the existence of biases in algorithms, and responsibility for decision-making. In order to meet the problems that are offered by evolving technology, legal frameworks may also need to be modified.
In addition, there is restricted access for vulnerable populations: It is possible that some patient groups, especially those located in underserved or distant locations, may have difficulties in gaining access to intelligent healthcare technology owing to factors such as a lack of internet connection or insufficient infrastructural provisions [41].
An Excessive dependence on Technology: An excessive dependence on technology may result in a decrease in the amount of human connection and individualized care that is provided. When the human touch is overpowered by technology, patients may experience feelings of being ignored.
Preventative Measures and Maintenance: It is necessary for smart hospitals to undergo continuous maintenance, updates, and upgrades in order to guarantee that the technologies will continue to be operational. This may result in increased expenses as well as difficulties in terms of logistics.
