This book takes an in-depth look at the emerging technologies that are transforming the way clinicians manage patients, while at the same time emphasizing that the best practitioners use both artificial and human intelligence to make decisions. AI and machine learning are explored at length, with plain clinical English explanations of convolutional neural networks, back propagation, and digital image analysis. Real-world examples of how these tools are being employed are also discussed, including their value in diagnosing diabetic retinopathy, melanoma, breast cancer, cancer metastasis, and colorectal cancer, as well as in managing severe sepsis. With all the enthusiasm about AI and machine learning, it was also necessary to outline some of criticisms, obstacles, and limitations of these new tools. Among the criticisms discussed: the relative lack of hard scientific evidence supporting some of the latest algorithms and the so-called black box problem. A chapter on data analytics takes a deep dive into new ways to conduct subgroup analysis and how it's forcing healthcare executives to rethink the way they apply the results of large clinical trials to everyday medical practice. This re-evaluation is slowly affecting the way diabetes, heart disease, hypertension, and cancer are treated. The research discussed also suggests that data analytics will impact emergency medicine, medication management, and healthcare costs. An examination of the diagnostic reasoning process itself looks at how diagnostic errors are measured, what technological and cognitive errors are to blame, and what solutions are most likely to improve the process. It explores Type 1 and Type 2 reasoning methods; cognitive mistakes like availability bias, affective bias, and anchoring; and potential solutions such as the Human Diagnosis Project. Finally, the book explores the role of systems biology and precision medicine in clinical decision support and provides several case studies of how next generation AI is transforming patient care.

The complex challenges facing healthcare require innovative solutions that can make patient care more effective, easily available, and affordable. One such solution is the digital reconstruction of medicine that transitions much of patient care from hospitals, clinics, and offices to a variety of virtual settings. This reconstruction involves telemedicine, hospital-at-home services, mobile apps, remote sensing devices, clinical data analytics, and other cutting-edge technologies. The Digital Reconstruction of Healthcare: Transitioning from Brick and Mortar to Virtual Care takes a deep dive into these tools and how they can transform medicine to meet the unique needs of patients across the globe. This book enables readers to peer into the very near future and prepare them for the opportunities afforded by the digital shift in healthcare. It is also a wake-up call to readers who are less than enthusiastic about these digital tools and helps them to realize the cost of ignoring these tools. It is written for a wide range of medical professionals including: Physicians, nurses, and entrepreneurs who want to understand how to use or develop digital products and services IT managers who need to fold these tools into existing computer networks at hospitals, clinics, and medical offices Healthcare executives who decide how to invest in these platforms and products Insurers who need to stay current on the latest trends and the evidence to support their cost effectiveness Filled with insights from international experts, this book also features Dr. John Halamka's lessons learned from years of international consulting with government officials on digital health. It also taps into senior research analyst Paul Cerrato's expertise in AI, data analytics, and machine learning. Combining these lessons learned with an in-depth analysis of clinical informatics research, this book aims to separate hyped AI "solutions" from evidence-based digital tools. Together, these two pillars support the contention that these technologies can, in fact, help solve many of the seemingly intractable problems facing healthcare providers and patients.

The complex challenges facing healthcare require innovative solutions that can make patient care more effective, easily available, and affordable. One such solution is the digital reconstruction of medicine that transitions much of patient care from hospitals, clinics, and offices to a variety of virtual settings. This reconstruction involves telemedicine, hospital-at-home services, mobile apps, remote sensing devices, clinical data analytics, and other cutting-edge technologies. The Digital Reconstruction of Healthcare: Transitioning from Brick and Mortar to Virtual Care takes a deep dive into these tools and how they can transform medicine to meet the unique needs of patients across the globe. This book enables readers to peer into the very near future and prepare them for the opportunities afforded by the digital shift in healthcare. It is also a wake-up call to readers who are less than enthusiastic about these digital tools and helps them to realize the cost of ignoring these tools. It is written for a wide range of medical professionals including: Physicians, nurses, and entrepreneurs who want to understand how to use or develop digital products and services IT managers who need to fold these tools into existing computer networks at hospitals, clinics, and medical offices Healthcare executives who decide how to invest in these platforms and products Insurers who need to stay current on the latest trends and the evidence to support their cost effectiveness Filled with insights from international experts, this book also features Dr. John Halamka's lessons learned from years of international consulting with government officials on digital health. It also taps into senior research analyst Paul Cerrato's expertise in AI, data analytics, and machine learning. Combining these lessons learned with an in-depth analysis of clinical informatics research, this book aims to separate hyped AI "solutions" from evidence-based digital tools. Together, these two pillars support the contention that these technologies can, in fact, help solve many of the seemingly intractable problems facing healthcare providers and patients.

The Transformative Power of Mobile Medicine: Leveraging Innovation, Seizing Opportunities, and Overcoming Obstacles of mHealth addresses the rapid advances taking place in mHealth and their impact on clinicians and patients. It provides guidance on reliable mobile health apps that are based on sound scientific evidence, while also offering advice on how to stay clear of junk science. The book explores the latest developments, including the value of blockchain, the emerging growth of remote sensors in chronic patient care, the potential use of Amazon Alexa and Google Assistant as patient bedside assistants, the use of Amazon's IoT button, and much more. This book enables physicians and nurses to gain a deep understanding of the strengths and weaknesses of mobile health and helps them choose evidence-based mobile medicine tools to improve patient care.

This book takes an in-depth look at the emerging technologies that are transforming the way clinicians manage patients, while at the same time emphasizing that the best practitioners use both artificial and human intelligence to make decisions. AI and machine learning are explored at length, with plain clinical English explanations of convolutional neural networks, back propagation, and digital image analysis. Real-world examples of how these tools are being employed are also discussed, including their value in diagnosing diabetic retinopathy, melanoma, breast cancer, cancer metastasis, and colorectal cancer, as well as in managing severe sepsis. With all the enthusiasm about AI and machine learning, it was also necessary to outline some of criticisms, obstacles, and limitations of these new tools. Among the criticisms discussed: the relative lack of hard scientific evidence supporting some of the latest algorithms and the so-called black box problem. A chapter on data analytics takes a deep dive into new ways to conduct subgroup analysis and how it's forcing healthcare executives to rethink the way they apply the results of large clinical trials to everyday medical practice. This re-evaluation is slowly affecting the way diabetes, heart disease, hypertension, and cancer are treated. The research discussed also suggests that data analytics will impact emergency medicine, medication management, and healthcare costs. An examination of the diagnostic reasoning process itself looks at how diagnostic errors are measured, what technological and cognitive errors are to blame, and what solutions are most likely to improve the process. It explores Type 1 and Type 2 reasoning methods; cognitive mistakes like availability bias, affective bias, and anchoring; and potential solutions such as the Human Diagnosis Project. Finally, the book explores the role of systems biology and precision medicine in clinical decision support and provides several case studies of how next generation AI is transforming patient care.