Emerging Computational Approaches in Telehealth and Telemedicine: A Look at The Post-COVID-19 Landscape

The term ‘telemedicine’ is referred to as healing remotely with the help of digital technologies by healthcare providers to detect and treat sufferers. Due to necessary physical distancing and lack of appropriate treatments during the Covid-19 pandemic times, telemedicine has proven to be a secure interactive mechanism between patients and medical professionals. The telemedicine framework is part of the Internet of Medical Things (IoMT) since it allows many medical devices to connect and share data. IoT has a lot of benefits in Telemedicine. It aids doctors in gaining access to vital data from medical devices, real-time monitoring of patients, assisting sick and elderly people, and distant medical support. Apart from benefits for patients, it also benefits hospitals and insurance companies. Moreover, distant monitoring of a patient's condition tends to shorten hospital stays. It has a huge effect on lowering healthcare costs and enhancing treatment methods. Many wearable devices, like heart rate monitoring devices, blood pressure monitoring devices, glucometers, etc., provide a way to access the patient’s health information. The proposed study revealed different applications of IoT in healthcare for various diseases and disorders, various medical sensors, and notable wearable devices in healthcare.

The COVID-19 pandemic has created a new culture in the working style in all fields across the globe. While trauma is continuing to rise in many parts of the world, some countries are now working to sustain the stress and are recreating their health and economies. In a few developing countries, there is a shortage of doctors, and treatment is not being provided to all patients due to the lack of time that doctors can spend with patients. In such a situation, getting appointments and treatment is always a challenge for patients and elderly people who cannot resist longer waiting times. Another rising problem is the restriction on visiting the hospitals during the pandemic spread time, except in emergencies. To overcome this problem, we are proposing a Remote Health Monitoring Application for patients, which helps all categories of people consult the doctor over the telephone, explain their symptoms, and get their required treatment. In this chapter, we will discuss a Health Monitoring Application that employs Artificial Intelligence, which enables the patient to consult the doctor remotely and get treatment through digital mode. 

Nowadays, computational technology is given great importance in the health care system to understand the importance of advanced computational technologies. Skin cancer or skin disease (melanoma) has been considered in this chapter. As we know, the detection of skin lesions caused by exposure to UV rays over the human body would be a difficult task for doctors to diagnose in the initial stages due to the low contrast of the affected portion of the body. Early prediction campaigns are expected to diminish the incidence of new instances of melanoma by lessening the populace's openness to sunlight. While beginning phase forecast campaigns have ordinarily been aimed at whole campaigns or the public, regardless of the real dangers of disease among people, most specialists prescribe that melanoma reconnaissance be confined to patients who are in great danger of disease. The test for specialists is the way to characterise a patient's real danger of melanoma since none of the rules, in actuality, throughout the communities offer an approved algorithm through which melanoma risk may be assessed. The main objective of this chapter is to describe the employment of the deep learning (DL) approach to predict melanoma at an early stage. The implemented approach uses a novel hair removal algorithm for preprocessing. The kmeans clustering technique and the CNN architecture are then used to differentiate between normal and abnormal skin lesions. The approach is tested using the ISIC International Skin Imaging Collaboration Archive set, which contains different images of melanoma and non-melanoma.

Telemedicine is a part of e-Health that employs information communication technologies (ICT) to transmit healthcare information required for educational and therapeutic purposes. Telehealth attempts to overcome the challenges in the delivery of health services due to distance, time, and challenging landscapes. It plays a significant role during floods and earthquakes. It enables better access and cost-effectiveness in both developing and developed world locations. The health sector has been dramatically influenced and affected by the Covid-19 pandemic with the adoption of improved technology that has allowed many people to access healthcare from the comfort of their homes. Remote follow-up and monitoring are also provided through Telemedicine as postoperative care. The possible scope and application of Artificial Intelligence techniques in the Telehealth area are discussed in this paper. The paper also focuses on different computational solutions involving machine learning and Artificial Intelligence to tackle the crisis. The methods focus on two major areas: 1) improvement in the quality of existing clinical practices, and service delivery. 2) the growth besides the support of innovative models for healthcare. The methods to improve quality include digital storage of patient data and large datasets, automation of manual tasks for CT scans, conducting X-rays and handling the emergency, and electronic consultation for diagnosis, treatment, and monitoring of patients. Innovative methods such as ICT and technology such as accelerometers, GPS, gyroscopes, motion sensors, and so on, are used in healthcare.

Pandemics are large-scale infectious disease outbreaks that can dramatically increase morbidity and mortality over a wider geographic region and trigger substantial economic, social, and political damage. Currently, the world is facing the coronavirus (COVID-19) pandemic. COVID-19 is considered a dangerous disease affecting all entire humanity and reports death cases in the thousands each day (as per the source from Wikipedia, it is 3,690,000 deaths and 172,000,000 cases identified as COVID-19 positive as of 04-June-2021) and quietly throws dangerous bells on the entire humanity, causing health emergencies in every country, worldwide. Due to the ongoing pandemic, the healthcare infrastructure has been stretched. With the limited healthcare infrastructure and the number of COVID-19 cases spiking up, many countries have opted to treat their patients from the patient home, providing at-home medical facilities and continuous monitoring by medical officials at regular intervals. Health is of considerable importance in the new global situation. Providing smart healthcare is important for all people to monitor continuously and maintain good health. A powerful new mobile application and the usage of machine learning techniques can be an innovative solution to the healthcare problems in these pandemic times for patient management and disease management. This solution can directly impact clinical decision-making. The proposed mobile application is a utility tool for COVID-19 patients during and after the quarantine period/home isolation. This application is aimed at being a friendly interface that can record every detail of COVID-19 patient activity from the day of admission to the day of discharge. This facilitates the proposed system to record all symptoms, medication, responses to medicine, diet aspects, and physical and mental aspects of the patients. The proposed system is designed in such a way that we can get the data from the application that monitors the person’s health activities, and that data will be used for the analysis to extract useful information by using machine learning techniques. The data that is collected from each patient is provided to the machine learning domain to find common features and patterns that help us to gain further insights into the disease and could help to develop better medications, vaccine development, immunisation knowledge base, recovery aspects, and symptomatic approaches for the future generation. This knowledge extracted from the machine learning techniques can be used for better treatment and prediction of disease at the initial stages, which could mitigate the life risk and help to stop the spread of the disease.

The Indian economy has been witnessing remarkable economic growth in recent times, but the increasing healthcare overheads are still a major challenge. Telemedicine is a distant health service that uses electronic methods to diagnose, treat, and prevent disease and damage, as well as to conduct research and educate health care practitioners. Because of the diversified geography, challenging terrain, and a large number of people, it helps to bridge the gap between resources and demand in the healthcare system. It provides a chance for successful collaboration between primary and secondary healthcare centers, as well as reaching out to the rural masses. The Internet of Things (IoT), artificial intelligence (AI), machine learning (ML), and big data science have emerged as promising technologies. All patient data may be transferred to the cloud for continued monitoring, which can then be consulted by medical specialists at any time and from anywhere. With the increasing use of cyberspace for teleconsultation, data storage, data protection, and confidentiality obligations become imperative. The Telemedicine Guidelines of India (2020) are the first step in standardizing teleconsultation services in India. A case study was conducted to determine the level of satisfaction among the patients from the telemedicine aspect. A sample size of 100 individuals was collected on various parameters namely, age, marital status, education, gender, income, income category, and telemedicine satisfaction. IBM’s SPSS tool was used to understand the statistical aspects. In almost all cases, the survey showed that the patients had good feedback which shows that telemedicine is the way forward where patients can consult with doctors in the comfort of their homes instead of visiting clinics. The goal of the chapter is to explore the current state of telemedicine in India, as well as its uses, problems, and future potential. 

IoT and cloud are the fastest growing technologies today. The convergence of IoT and cloud opens up various new horizons. In the last few years, this convergence of IoT and cloud architecture has dominated not only the research field but also the business sector. The compatibility of these two entirely versatile ideas is their ability to manage the app, user interfaces, and the data stream simultaneously, especially in a high-performance support structure. But along with the advantages comes some disadvantages as well. The major concerns in the IoT - cloud convergence are privacy and security. Therefore, confidentiality must be maintained at all costs. The chapter discusses some prime benefits of IoT and cloud convergence issues and also the challenging concerns of those. This chapter discusses the issues concerned with IoT - cloud convergence and possible solutions to overcome those issues. Later privacy issues of IoT are presented. Then, a case study denoting a pathology tracking model using big data analytics is presented in detail.