Patient-Centered Digital Healthcare Technology explores the creative intersection of novel, emerging technologies and medicine. This convergence is transforming the landscape of healthcare with the overarching objectives of improving clinical outcomes and advocating wellness. The concept of encountering or treating a medical condition when it has already become disturbingly manifest is being replaced by earlier awareness, diagnosis, and proactive intervention enabled by technologies. This book features a range of innovations in health information systems and big data, artificial intelligence and machine learning, real-time home monitoring tools, smartphone apps, medical robotics and intelligent machines, virtual and augmented realities, genome sequencing, blockchain and gamification in healthcare. Intuitive digital health solutions motivate end-users to become active partners in their care, thus enhancing patient engagement and empowerment. This may perhaps lead to yet the most gratifying consequence of novel and emerging technologies: the democratization of healthcare. This book offers a valuable resource for healthcare providers, engineers, data and computer scientists, researchers, practitioners in academia, biomedical industry and multi-disciplinary clinical settings. Subjects include informatics, computing, networking, wireless and mobile applications, and robotic sensing that collectively improve healthcare access and delivery.
Inspec keywords: health care; mobile computing; patient care; medical computing
Other keywords: patient-centered digital healthcare technology; mental health; public health; next generation healthcare systems; information technologies; computerized medical records; DHTs; clinical outcomes; digital health technologies; mobile apps; medical needs
Subjects: Mobile, ubiquitous and pervasive computing; Biology and medical computing; Medical administration; General and management topics
In this chapter, we acknowledge that information systems in health cannot be addressed under a single disciplinary umbrella, and indeed that the variety of umbrellas that exist exhibit a degree of arbitrariness in terms of the best mix of disciplines to consider and how to combine them. We explore conceptual frameworks to address the information needs of individuals in healthcare systems, and the needs of networks of people if they are to make systems or services function well. We draw these ideas together with some recommendations on critical questions to ask in evaluating systems now, or in specifying systems for the future.
Health information technologies (HIT) including electronic health records (EHRs), bio-medical device interfaces, and a broad array of clinical software applications suffer from usability flaws that impact patient safety, clinician efficiency, and health outcomes . In response, informaticians, systems engineers, and human factors experts have hard fought to raise awareness among stakeholders including clinicians, health administrators, policy makers. The design of health information technology, are often neglected or abbreviated in a misguided effort to reduce production costs, close functionality gaps, or keep pace with software development schedules. The usability specialists (1) assess the UX maturity of their organization; (2) evangelize the importance of evidence-based design; (3) become conversant in a variety of usability techniques and (4) strategically apply hybrid strategies throughout the software design lifecycle. One such problem that affects the industry as it heads toward a paper-less environment is ensuring that decision support tools in the electronic medical record are both safe and effective.
The embracing of "big data" approaches is providing useful methods for public health research, surveillance, and intervention. Information is increasingly collected from traditional data sources, such as electronic health records, laboratory databases, and surveys, as well as in real time from personal mobile devices, internet use, social media, and environmental sensors - resulting in large and complex databases for analyses. Advances in technologies and data analytic techniques provide powerful ways to predict human behavior (such as health risk behaviors) or the outbreak of disease in a population. These techniques can also be used to provide helpful recommendations for risk reduction and guide clinical intervention and treatments. Importantly, the use of these modern techniques and data collection methods also raise important ethical concerns such as those associated with misuse of data, privacy, lack of transparency, and inaccurate prediction. This chapter provides an overview of "big data analytics" used in public health as well as current and emerging ethical issues and risks. Recommendations for the fields of public health and healthcare in general are provided.
Vision is one of the most valued of human sensory perceptions, and vision loss is associated with a significant decrease in quality of life as well as serious medical, psychological, social and financial consequences. Due to the high value people place on vision, ophthalmologists often find that patients are motivated to take an active role in reducing their risk for vision loss. Age-related macular degeneration (AMID) is the leading cause of irreversible vision loss in the western world. Despite major advances in treating this condition over the past two decades, additional efforts are needed to significantly alter current rates of visual decline due to AMID. This unmet need provides an opportunity to utilise home monitoring technology to enable self-aware AMID patients to preserve their vision. Remote patient monitoring is growing in clinical applicability generally, and AMID is an excellent target for this valuable approach to patient care. The ForeseeHome®preferential hyperacuity perimeter is a telemedicine home-based monitoring system and has been proven to improve visual outcomes in patients suffering from AMID. The development of ForeseeHome is the result of a global cooperative effort to change the lives of people with AMID by using a simple at-home test. As is true for other successes in biomedicine, this program was founded on excellent basic science, strong engineering, an experienced, dedicated team and well-designed clin-ical trials showing unquestionable efficacy.
Mobile phone applications (apps) are a particularly valuable form of mHealth, as they have many advantages not true for websites and text messaging. This chapter reviews the current behavioral health technology landscape and focuses specifically on one form of technology, mHealth apps, for one serious public health concern, suicidal behavior.
The World Health Organization defines mental health as the foundation for physical health and well-being and effective functioning. Mental health encompasses the self and others within an environment that promotes emotional, social, and cognitive well-being. Further, improvement of mental health is not an elusive ideal to be reached, but a priority to be intentionally addressed and maintained. Traditional mental health models are not reaching the amount of children and adolescents in need of services. Technology, however, may offer a unique platform for the creation of innovative solutions to reach a broader number of children globally given the number of children connected to various forms of digital platforms. Therefore, programming that integrates the fields of child development, psychology, learning, and gaming offer a significant potential to address the pro-motion of mental health and wellness.
Diabetes is a chronic metabolic disease in which the body has trouble regulating blood sugar due to a lack of insulin production by the pancreas (Type I diabetes) or by a resistance to the insulin that is produced (Type II diabetes). Over time, elevated levels of blood sugar (glucose) can cause serious damage to the heart, blood vessels, eyes, kidneys and nerves. The global prevalence of diabetes is currently 8.5% (up from 4.8% in 1980) or 422 million adults worldwide and is expected to continue increasing as the world's population ages. In the United States, the prevalence is slightly higher: 30.3 million people (or 9.4% of the general population) had diabetes in 2015, but this is a problem that gets worse with age: an estimated 25.2% of adults over 65 in the United States are diabetic. European rates of Type II diabetes range from 2.4% in Moldova to 14.9% in Turkey, with an estimated rate of undiagnosed diabetes in high-income European countries (Denmark, Finland, and the United Kingdom) of a staggering 36.6%. Although the rate of new diagnoses remains steady in higher income countries, diabetes prevalence continues to rise in low- and middle-income countries. Unfortunately, the WHO reports that 1.5 million deaths were directly attributable to diabetes in 2012, and a further 2.2 million deaths were caused by higher than optimal blood glucose, which caused death by cardiovascular and other related diseases. As a result, diabetes is one of four priority noncommunicable diseases targeted for action by world leaders.
Robots are taking on more significant roles in modern healthcare. With their con-stantly advancing capabilities, robotics can improve medical care. Robotics has the precision to perform the most careful procedures, such as those required for minimally invasive surgery (MIS). Robotics can enhance productivity by allowing people to focus on clinical decision-making tasks rather than on non-value-added tasks, such as in logistics and delivery of supplies. Advances in artificial intelligence (Al) and human-machine interfaces offer opportunities for robots to engage with patients at an emotional level and to provide companionship or service. A grand vision for medical robotics is one where devices are fully decen-tralized, connected seamlessly via health informatics systems and networks to provide continuity of patient care across the entire health-care continuum. This chapter provides examples of key areas of innovation and growth in medical robotics. Each section describes the trends, exemplified with cases, design and implementation challenges, and future opportunities.
Surgical training methods are evolving with the technological advancements, including the application of virtual reality (VR) and augmented reality. However, 28 to 40% of novice residents are not confident in performing a major surgical procedure. VR surgery, an immersive VR (iVR) experience, was developed using Oculus Rift and Leap Motion devices to address this challenge. Our iVR is a multisensory, holistic surgical training application that demonstrates a maxillofacial surgical technique, the Le Fort I osteotomy. The main objective of the present study was to evaluate the effect of using VR surgery on the self-confidence and knowledge of surgical residents.
Since 2016, interest in blockchain has emerged across all sectors of healthcare. This interest has been expressed in the form of collaborative initiatives led by notable organizations within the healthcare industry. Some showed promising results, but none has yet achieved scale in adoption and economic sustainability to be considered an unqualified success. After reading this chapter, the reader will be able to describe what blockchain is and the new business models it can enable. In addition, the reader will have a framework to consider blockchain for a use case and to understand the potential barriers to adoption. Finally, the chapter will review multiple use cases being considered across diverse healthcare sectors, the early industry experiments exploring how blockchain can be used, and what ideal end states could look like. Please note that this chapter has been authored with a focus on healthcare in the United States, and as a result, most examples will be presented with a US perspective.
Following visible successes on a wide range of predictive tasks, machine learning techniques are attracting substantial interest from medical researchers and clinicians. We address the need for capacity development in this area by providing a conceptual introduction to machine learning alongside a practical guide to developing and evaluating predictive algorithms using freely-available open source software and public domain data.
Personalized Medicine (PM) moves away from a "one size fits all" approach to the treatment and care of individuals. PM has a powerful impact in management of disease risks [2], in therapeutic treatments accounting for individual drug efficacy.This approach is transforming medicine by providing an individual's genetic profile that guides decision-making in prevention, diagnosis, and treatment of diseases. This approach also maximizes health benefits and minimizes the risks of disease progres-sion by enabling diagnostic and prognostic information based on genetic profile not previously available.
Around December 2019, the emergence of a cluster of pneumonia of unknown etiology started appearing in Wuhan City, Hubei Province of China. Subsequent virus isolation from patients and genetic analysis showed that the pathogen was a new coronavirus.How could digital healthcare technologies alleviate the deleterious impacts of this public health crisis or facilitate strategies geared at quashing this pandemic? Digital healthcare technologies have been adopted by many countries in the management of COVID-19 pandemic. Applications of novel and emerging technologies have been implemented in various facets of the response, such as surveillance, testing, contact tracing, quarantine, drug and vaccine development, clinical management, continuity of care, logistics and personnel training.