Deep Learning and Sustainable Telemedicine

Deep Learning and Sustainable Telemedicine

Edward T. Chen (University of Massachusetts – Lowell, USA)
Copyright: © 2020 |Pages: 18
DOI: 10.4018/978-1-7998-0047-7.ch008

Abstract

The need for healthcare is increasing on a global scale. The lack of medical professionals available to fill this need has increased interest in deep learning and sustainable telemedicine technologies. Telemedicine has been shown to be financially beneficial to both patients and healthcare facilities, provided that government regulations and insurance companies recognize them as a reimbursable expense. Advancements in cloud computing, deep learning, and telemedicine are creating a global standard for healthcare, and at the same time increasing the need for these services.
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Introduction

The use of telemedicine in the healthcare industry has been around for over a century. Since the introduction of long-distance communication methods such as the radio and the telephone, telemedicine has been used by doctors to assist patients in regions where medical personnel are scarce. As communication technology improved, so did telemedicine. The introduction of the Internet has drastically improved a person’s ability to receive medical treatment without visiting a healthcare facility. As of 2016, 47% of the world’s population has access to the Internet, which is a 4% increase over the previous year (Taylor, 2016). The growth of Internet capable devices such as desktops, laptops, smartphones, and tablets has drastically increased a person’s ability to use telemedicine.

Telemedicine is on the rise as telehealth services are becoming increasingly available to patients across the U.S. FAIR Health, a nonprofit organization, drew on its database of over 25 billion privately billed claim records to develop the FH Healthcare Indicators resource, which evaluates changes in demographics, utilization, diagnoses, procedures and costs. The report showed that from 2011 to 2016, telehealth service use increased substantially, especially in rural areas (960 percent). In comparison, telehealth use grew by 629 percent in urban areas, and by 643 percent nationally. However, more recently urban usage has grown to match and even surpass rural usage. Between 2015 and 2016, urban areas saw a jump from just over 25 percent to over 45 percent, while rural growth increased from 35 percent in 2015 to just over 40 percent in 2016 (Fair Health, 2018).

Machine learning and artificial intelligence have many exciting applications in the medical field in general. IBM Watson Health program is able to analyze millions of pages of medical information within nanoseconds, and draw conclusions that can be used for diagnosis, comparison and recommendation. The enormous power of supercomputers to search through large volumes of data is combined with analytical and decision-making prowess in machine learning and artificial intelligence technologies (Hoyt, Snider, Thompson, & Mantravadi, 2016). Beyond just dealing with information, though, machine learning and artificial intelligence can also bring new capabilities to patient examination. For example, in radiology, machine learning algorithms can look at radiology scans and other resources to find evidence of outcomes and realities that can guide human decision-makers (Sennaar, 2019a).

There are also other formative examples of the power of machine learning and diagnosis in telemedicine. National Institute of Health provides automated analysis of retinal imaging, which can help detect certain types of sight loss linked to diabetes (Sim, Keane, Tufail, Egan, Aiello, & Silva, 2015). Google also teams up with National Health Service in UK to use Google DeepMind to help spot early signs of eye conditions that human eye care experts might miss at Moorfields Eye Hospital. Specifically, Moorfields is applying DeepMind's algorithms to 1 million anonymous OCT (Optical Coherence Tomography) scans. The aim is to determine whether the algorithms can learn to spot early signs of age-related macular degeneration and sight loss that occurs as a result of diabetes (Shead, 2017). NIH or NHS can save those diagnoses and OCT scan images in their repositories for future research and preventive treatment (Sim et al., 2015; Shead, 2017).

Recently, advancements in machine learning have brought about the creation of deep learning. The use of machine learning and deep learning with decision support tools have significantly improved a medical professional’s ability to accurately diagnose and treat patients within a minimal amount of time. Combining deep learning with telemedicine will create opportunities for a vastly larger portion of the population to receive immediate healthcare without needing to enter a medical facility. Doctors will review it and sign on – instead of being only supported by videoconferencing, doctors will also be supported by key assistive technologies that are thinking and learning on their own (Sennaar, 2019b). As mentioned previously, a deep learning program is an decision support tool for physicians to provide effective healthcare services to telemedicine patients. The goal of deep learning technology is not to replace their jobs. According to a report published in the Physician Leadership Journal, physician leaders projected a 18.5% increase in telemedicine jobs in 2018, compared to 2012 (Anonymous, 2018).

Key Terms in this Chapter

Videoconferencing: A video conference is a live, visual connection between two or more people residing in separate locations for the purpose of communication. At its simplest, video conferencing provides transmission of static images and text between two locations. At its most sophisticated, it provides transmission of full-motion video images and high-quality audio between multiple locations.

Algorithms: An algorithm is a well-defined procedure that allows a computer to solve a problem. A particular problem can typically be solved by more than one algorithm. Optimization is the process of finding the most efficient algorithm for a given task.

Decision Support System: A decision support system (DSS) is a computerized information system used to support decision-making in an organization or a business. A DSS lets users sift through and analyze massive amounts of data, and compile information that can be used to solve problems and make better decisions.

Information Accessibility: The right of a patient to seek, receive and impart information and ideas concerning health issues. This access to information, however, should not impair the right to have personal health data treated with confidentiality.

Telemedicine: Telemedicine is the use of telecommunication and information technology to provide clinical health care from a distance. It has been used to overcome distance barriers and to improve access to medical services that would often not be consistently available in distant rural communities.

Machine Learning: Machine learning is an application of artificial intelligence (AI) that provides systems the ability to automatically learn and improve from experience without being explicitly programmed. Machine learning focuses on the development of computer programs that can access data and use it learn for themselves.

Telehealth: The delivery and facilitation of health and health-related services including medical care, provider and patient education, health information services, and self-care via telecommunications and digital communication technologies.

Artificial Intelligence: The theory and development of computer systems able to perform tasks normally requiring human intelligence, such as visual perception, speech recognition, decision-making, and translation between languages.

EHealth: An emerging field in the intersection of medical informatics, public health and business, referring to health services and information delivered or enhanced through the Internet and related technologies.

Deep Learning: Deep learning is a collection of algorithms used in machine learning, used to model high-level abstractions in data through the use of model architectures, which are composed of multiple nonlinear transformations. It is part of a broad family of methods used for machine learning that are based on learning representations of data.

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