AI in Healthcare

How Artificial Intelligence Is Changing IT Operations and Infrastructure Services

Robert Shimonski

• Verlag: Wiley
• Jahr: 2020
• ISBN:
  9781119680048
• Auflage: 1
• Thema: Technology & Engineering
• Thema: Biomedical Science

The best source for cutting-edge insights into AI in healthcare operations

AI in Healthcare: How Artificial Intelligence Is Changing IT Operations and Infrastructure Services collects, organizes and provides the latest, most up-to-date research on the emerging technology of artificial intelligence as it is applied to healthcare operations.

Written by a world-leading technology executive specializing in healthcare IT, this book provides concrete examples and practical advice on how to deploy artificial intelligence solutions in your healthcare environment. AI in Healthcare reveals to readers how they can take advantage of connecting real-time event correlation and response automation to minimize IT disruptions in critical healthcare IT functions.

This book provides in-depth coverage of all the most important and central topics in the healthcare applications of artificial intelligence, including:

• Healthcare IT
• AI Clinical Operations
• AI Operational Infrastructure
• Project Planning
• Metrics, Reporting, and Service Performance
• AIOps in Automation
• AIOps Cloud Operations
• Future of AI

Written in an accessible and straightforward style, this book will be invaluable to IT managers, administrators, and engineers in healthcare settings, as well as anyone with an interest or stake in healthcare technology.

CHAPTER 1
Healthcare IT and the Growing Need for AI Operations

Shall we play a game?

—Joshua (from WarGames)

In today's ever-changing business model of do it faster, do it better, and do it without flaws, there needs to be a balance between those who create the technology and technology having a mind of its own. As in the famous quote “Shall we play a game?” healthcare operations is anything but a game and lives hang in the balance. In today's organizations, that balance is being established as technologies such as artificial intelligence (AI) are being implemented. There needs to be a way to do more work efficiently and with greater intelligence while still ensuring that the work is performed correctly. With the boom of healthcare advancements and the need to keep up with technological change, those who rely on all of the newest technology for clinical operations need an enterprise system that ensures that the technology continues to work for us and not against us. That combination of technology and clinical advancement comes in the form of a successful merging of intelligence and strategy, using the correct tools for the job, and planning and designing a platform that works for you, not against you. This is AI operations (AIOps) in healthcare.

This chapter explores the healthcare market and how technology continually changes it, specifically within the realm of AIOps. In these pages I will discuss the growing need for technology in this space, how healthcare has been fundamentally (and forever) changed by the digital landscape, and all of the specifics revolving around AIOps. This includes how AIOps is being used to create efficiency, reduce downtime, increase time to respond to issues, improve the ability to automate efforts to reduce waste and time spent doing computation work, and ultimately create better customer experiences for all patients, clinicians, and everyone involved in the healthcare space.

In the first portion of this chapter, I will cover the basic history of artificial intelligence (AI) and machine learning (ML). Although some could say we have always been in a perpetual state of “machine learning” for as long as we have had machines and in a constant state of computational (or artificial) intelligence as long as we could compute things, there are some significant milestones in the ML and AI timeline. For one, as long as we have been playing games, there has always been a study of game theory and outcomes through games. Many military and war strategists believed in game theory, and this became even more apparent when IBM began testing ML theory with gaming to produce the first machine learning game in the 1950s when someone played checkers and the program was able to learn from the outcomes of the game, the players' choices, and so on. I think this real story was likely the predecessor to the movie WarGames decades later. Checkers, chess, backgammon, and other games were all tested to see how a machine could learn.

As more and more technology (machines) was created and advanced, the same questions and theories were applied to it. When cars were made, how could we get them to learn? What about if we made a robot? Could it learn? The same theories from a long time ago all waited until technology caught up and provided for computers, robotics, and other major technological advancements that could be fused with machines to allow them to learn. Once computers were added to cars, then cars could start to learn. Now we drive in cars that can predict a possible crash and take action. This development went way beyond the abilities of game theory, but it should be noted that the mathematical equations, usage, and logic behind it still remained the same. It was only advancing as quickly as the technology did and was expanded on.

Another major installment of ML and AI development came with the World Wide Web (WWW), the Internet, and the Internet of Things (IoT), where the interconnected nature and development of all of technology was able to fuse and share data as well as save it. The saving of large quantities of data (or big data theory) allowed for more math to be applied for machine learning capabilities. Also, the growth of large-scale search engines (like Google) continued to allow for even more ML and AI abilities due to the analytics that could be applied to “customize” an experience for every user. Augmented reality (AR), wearable technology, DNA collection, mobile technologies, social media, and so many other advancements bring us to a stage in life where ML and AI are able to be used not only in any one of these advancement areas but also across them as they too interconnect.

This is where we begin our journey into the development and fundamental layout of AI, ML, and AIOps in the healthcare world. Healthcare is the largest user of all the technologies I just mentioned and the biggest connector of intelligence usage to increase the use of treatment, medicine, and patient satisfaction. We now need to know how to keep all of these systems running and available, continue to perform their computations, and allow for the continued interconnection and learning to provide the best care possible now and in the future.

A Brief History of AI and Healthcare

No industry is bigger, more important, more dynamic, and exploding with change than healthcare. Some may argue that it is similar to the industrial revolution in its transformative scope. The electronic medical record (EMR) and other technological advancements are changing the way we see, deliver, and expect to receive our healthcare. One of the most interesting things about the healthcare industry is we are all our own clients, customers, and patients, which makes our industry unique in that it is something we hope we never have to use but absolutely must have in the form of benefits to cover our families and ourselves. Working in this field can be the most rewarding experience one can have, and seeing its growth and being a part of its transformation can be a once-in-a-lifetime experience.

As the healthcare field grows in every aspect, we must consider the technology used to bolster this revolutionary expansion. We call this technological field healthcare information technology (also known as healthcare IT or HIT). In this chapter, I will explain healthcare IT, some of its history, and why technology has expanded it exponentially. I will also start to talk about the need for another popular and growing technological advancement called artificial intelligence (AI). Beyond that, we will bridge the two technologies—healthcare IT and AI—and explore a third topic called healthcare operations so we can create a fusion between them all, which is known today as artificial intelligence operations (AIOps). Let's begin our journey in this chapter and this book by starting from the beginning, which is the expansion of healthcare as we know it today.

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THE CORONAVIRUS AND COVID-19

When the coronavirus (COVID-19) pandemic spread around the world in 2020, it affected the world of medicine in a dramatic way. As I will cover in Chapter 8, “The Future of Healthcare AI,” there have been radical changes to the use, delivery, and expectations of healthcare service.

With guidance by the World Health Organization (WHO), Centers for Disease Control and Prevention (CDC), and local, state, and government leaders, the world population had to make radical changes to slow the spread of COVID-19. For one, all healthcare systems (hospitals, practices, etc.) needed to find ways to reconfigure to handle the surge into the emergency department (ED) and intensive care unit (ICU) areas of the system. Temporary facilities were set up, and many new ways of practicing emergency medicine were considered. New medications, new treatments, and a world of new research into finding a cure were put in place. The world actively worked to slow the spread and focus on a cure. New technologies emerged, the importance of keeping systems up and running found a renewed priority level, and the use of older technologies saw a resurgence, like telemedicine.

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Healthcare IT Expansion and Growth

The radical expansion and growth of IT and healthcare IT was just the beginning. It provided the needed building blocks to get to where we are today so that we can collect data in large amounts (big data), analyze it, and make predictions and assessments to create better outcomes. Big data analysis helps our knowledge and handling of population health, the need to reduce hospital stays, what can be done inside an acute versus nonacute facility, and how to make predictions on outcomes in a geographic area. An example of a prediction in an area would be how seasonal flu may impact certain areas and why that may be. This only scratches the surface of what we can leverage big data for.

To get to this point, we needed to get all data into the computer systems. By creating the electronic medical record (EMR) and having clinical staff adding this data to these systems, the building block was in place to start expanding this practice across all health systems. This provided many benefits, one of which was leveraging the resources of the many instead of the few. The increasing cost of healthcare put a lot of stress on smaller hospitals that could no longer afford to continue to build technical systems while still expanding their operations outside of information technology. Other important factors include (but are not limited to) security, risk, and compliance where privacy became paramount by law.

Compliance, meaningful use, regulatory bodies, inspections, laws and even the dominance of the Health Insurance Portability and Accountability Act (HIPAA) passed by Congress in 1996 continued to drive more and more healthcare providers to join forces with others to share resources so they could stay afloat. One of the many benefits was the ability to leverage the administrative functions (like technology) between providers, clinicians, healthcare facilities, practices, hospitals, and even insurance. Another advantage was the chance to scale up on all of these shared resources, which allowed hospitals and practices to share operational data so that they could model best practices and standards to keep all of these technology systems operational, resilient, and well-positioned for future innovation.

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BIG DATA AND ITS IMPORTANCE

Data is the fundamental building block to everything that we do in technology. Think of a simple, traditional network. A network is useless unless you have something to share. Would you spend all of this money to set up countless connections simply for the sake of having connections? Of course not. You need to share data from printers, ...

Inhaltsverzeichnis

• Cover
• Table of Contents
• Title Page
• Introduction
• CHAPTER 1: Healthcare IT and the Growing Need for AI Operations
• CHAPTER 2: AI Healthcare Operations (Clinical)
• CHAPTER 3: AI Healthcare Operations (Operational Infrastructure)
• CHAPTER 4: Project Planning for AIOps
• CHAPTER 5: Using AI for Metrics, Performance, and Reporting
• CHAPTER 6: AIOps and Automation in Healthcare Operations
• CHAPTER 7: Cloud Operations and AIOps
• CHAPTER 8: The Future of Healthcare AI
• CHAPTER 9: The Convergence of Healthcare AI Technology
• APPENDIX Sample AIOps Use Cases and Examples
• Index
• Copyright
• Dedication
• About the Author
• About the Technical Editor
• Acknowledgments
• End User License Agreement