The field of digital health aims to improve healthcare through the integration of digital technologies such as Internet of Things (IoT), EHRs, and telemedicine into clinical practice and patient care. Its goal is to provide healthcare providers with the tools and information needed to make better-informed decisions and deliver more personalized care. The increasing demand for hospital and healthcare management in India due to medical tourism, hygiene perception, and the need for efficient resource allocation highlights the importance of education and training in this field. Healthcare 4.0 is an emerging area that builds on digital health and aims to create an intelligent and interconnected healthcare system using emerging technologies such as artificial intelligence (AI), blockchain, and the Internet of Medical Things (IoMT) to transform the entire healthcare value chain. The main objective of Healthcare 4.0 is to improve patient outcomes, enhance the quality of care, and reduce costs by leveraging the power of these advanced technologies. This chapter discusses the recent trends, methodology, scope, and limitations in Healthcare 4.0. The potential scope of Healthcare 4.0 is vast and varied, ranging from personalized medicine, telemedicine, remote monitoring, and smart healthcare systems to drug discovery and development, clinical trials, and supply chain management.

Humans have a basic requirement for nutrition, which is also necessary for a healthy life. Getting enough nutrients on a regular basis is essential for maintaining human well-being. Malnutrition is mostly to blame for the majority of health problems. Diseases including stunted growth, eye issues, diabetes, and heart disease are primarily brought on by inadequate nutrition consumed on a regular basis. Malnutrition is not only a result of poverty; it is also a result of people's ignorance of the nutrients in the foods they eat. In this day and age, it is crucial to recognize food correctly and to be able to identify its characteristics and nutritional value. The objective of this study is to identify the best techniques to recognize the food item and its nutrient content by using deep learning (DL) algorithms. Through this, a point-by-point analysis of malnutrition and treatment for malnutrition are reviewed. Different algorithms such as machine learning, DL, and various convolutional neural network architectures are reviewed, for recognizing the food items from food images. Inception V4 gives more accuracy when compared with other techniques. Food 101 is the selected dataset for comparing the accuracy of the food image identification. The nutrient contents of all the 101 classes of food items are also reviewed.

The heart is one of the most important organs in the human body. It supports metabolic processes and circulates oxygen and other vital nutrients throughout the body. Because of this, even minor heart conditions can have a significant effect on the entire body. Researchers are heavily utilising data analysis to assist doctors in foretelling heart issues. In order to anticipate who will develop coronary heart disease in the near future, this paper can explore the many machine learning techniques used in predictive analytics with an emphasis on heart disease prediction. Using Tableau, a predictive model with the highest degree of accuracy is created, allowing users to enter values and obtain predictions about the likelihood of the existence or absence of heart disease. Quick treatment will result, in saving the lives of the sufferers. The prognosis and aetiology of heart illness, as well as the classification of infections in patients who have just undergone surgery, are among the findings of this study.

In recent years, data privacy issues have become increasingly prevalent in the healthcare industry. With the increasing amount of data being generated by healthcare providers, there are now more opportunities for sensitive information to be compromised. Electronic medical records (EMRs) are becoming increasingly popular in healthcare management due to their many benefits, such as easier access to patient data, improved patient safety, and increased efficiency. However, implementing and using EMRs also present several challenges, including cost, staff training, data security, and interoperability. An effective healthcare data management requires a holistic approach that addresses the various challenges in collecting, managing, and using healthcare data. The adoption of emerging technologies such as blockchain and the development of interoperability standards and data governance frameworks are critical to achieving this goal. The uniqueness of the chapter is that it discusses the commercial models along with the research models in the survey. This chapter summarizes the challenges in EMR maintenance, a few benchmarking existing approaches, and a brief study of blockchain-based models.

The objective of this review is to provide an overview of biomedical applications of augmented reality (AR) and virtual reality (VR). The main focus will be on AR and VR methodologies for dealing with patient anxiety disorders. As a traditional approach, music was used to remove anxiety in certain patients. However, the music-based approaches could not provide an immersive experience to patients. To circumvent this challenge, AR/VR-based methods came into existence. AR-based approaches were used to treat phobias. In recent years, due to the advent of VR headsets, VR-based approaches are preferred in a variety of applications ranging from healthcare education to patients anxiety management.

In modern civilization, healthcare is a significant problem. The Internet of Things (IoT) technology is appealing to everyone because it has the ability to change the present healthcare system and address the problems that the aging population and the steady rise in chronic sickness are posing for the healthcare system. This chapter focuses on the conventional healthcare system that has been used in the past to deliver healthcare services as well as the integration of IoT, a new technology, into the healthcare system to modernize patient care. To provide services more quickly and effectively, this chapter illustrates how IoT has changed the conventional approach to monitoring healthcare. Finally, a study on different IoT-based healthcare monitoring systems will be conducted, along with a comparison of numerous IoT-based healthcare systems to show their advantages and disadvantages. The industry's digital transformation is piquing the curiosity of academics and healthcare practitioners alike. In this work, we attempt to examine the research question about the management and commercial uses of digital technology by different stakeholders. This chapter examines IoT applications for medical purposes, the different ways it is affecting the healthcare industry, and some potential future routes for its growth, such as Bio-IoT and Nano-IoT or the Internet of Nano Things. From the perspective of monitoring patients' vital signs, wireless body area networks (WBANs) are crucial components of a system. The WBANs consist of tiny smart devices that communicate wirelessly and are implanted within or on top of the patient. We analyze the literature on digital transformation in healthcare to answer this question. According to our findings, previous research can be grouped into five clusters: organizational characteristics, patient-centered approaches, operational efficiency of healthcare providers, and research techniques. These clusters are linked to illustrate how various technology adoption approaches enhance service providers' operational effectiveness. Research in a variety of directions is recommended, with implications for management as well.

Over the years, healthcare has shifted its approach from doctor centric to patient centric. Recently, the focus has been not only on disease treatment but also on developing prevention-oriented medicine with remote accessibility and capabilities. This requires not only to have real-time capabilities but also to automate the process from patient data management to disease prediction to its treatment to post care. Industrial Revolution 4.0 has been driven by the adoption of cyber-physical systems with the help of technological developments in mobile Internet speed, cloud technology, artificial intelligence (AI)-enabled automation, and data analytics. The objective of this chapter is to review these Industry 4.0 technologies at play which are crucial to improve performance, productivity, efficiency, and security of healthcare services without sacrificing reliability or accessibility. It can only be achieved with patient-centric smart hospitals that are automated on every step right from patient admission to data management to discharge procedures for effective management of the crowd so that the patients are served efficiently. The hospitals are not only required to manage patients but also require optimal utilization of the medical equipment, physical assets and services, not only for the admitted patients but also for extensive care of discharged patients in real-time remotely at their comfort. Mobile wearable sensors are greatly useful for tracking self-health which can be integrated into hospital systems and practitioners can observe the patients' vital parameters remotely to actively intervene in emergencies. Science and technology have advanced to increase the life span of humans which requires critical maintenance of human organs and sometimes, even replacement. The availability of human organs for donation is limited but bioprinting of human organs is a breakthrough that will definitely save the life of many critically ill patients. In such cases, skilled practitioners can extend their services to remote locations with the use of cloud services and augmented reality devices with high capabilities. With the introduction of these new technologies in different realms of healthcare for automation and monitoring, a large set of heterogeneous data is collected with needs to be securely saved, accessed, and used. This data is also useful for scientific studies not only for improving disease prediction, for hospital management and remote patient care but developing new trends and equipment.