Introduction

Today's systems, network, data, and critical infrastructure are all dependent on becoming more efficient and "self-aware," while also self-healing. Artificial intelligence (AI) in operational models has become the de facto standard of how services will be designed and deployed in the future. This fusion of AI and operations has spawned the newly infamous acronym AIOps. As operations becomes the foundation of integrity that critical enterprise systems rely on, it's no mystery that AIOps has become incredibly popular as of late.

Service delivery is the lifeblood of all companies looking to be and remain competitive. This is especially true in highly critical services such as those delivered by healthcare where lost time, mistakes, and red tape impact the patient experience negatively. With more push toward zero downtime and less impact to 24/7/365 systems, having the intelligence to weather this storm is one of the most exciting prospects turned reality of our time and can be fully realized with the use of AI-based systems. Using this book, you will not only learn about AI and its importance, but be given practical examples and advice on how to actually deploy it in your environment to achieve these positive outcomes. It is also within this book that years of experience not only in other verticals but in the specific nature of healthcare delivery becomes evident as you progress through the chapters.

In this publication we explore what AI and ML are and how they are used in companies today. While this book covers machine learning and artificial intelligence concepts in depth, it also covers the practicality of deploying them into your enterprise. Another critical concept that remains a constant through this book is what AI and ML concepts are and how they apply to the myriad of tools, systems, and services offered today where corporate executives and technical engineers need to understand the "how to" when they need to make a decision and deploy the correct toolsets for the best outcomes. There is too much confusion and not enough real data surrounding how to select a good tool (or tools) to get this accomplished in operational departments today. This book also focuses on the primary sector where AI and ML are creating some of the biggest impacts today: healthcare IT (HIT).

Some of the key target areas that this book covers in detail include the foundations of AI and ML and what you need to know to be able to recommend and then deploy the technology. Concepts such as AI and how it fused with IT operations and specifically healthcare IT are covered in depth. There is also a deep look into clinical operations and how infrastructure services and IT operations support the clinical role and how both can interact successfully for mutual advantage, leverage AI for maximum potential, and increase successful outcomes for clinicians and their client or patients. Key target areas include but are not limited to the following:

• Healthcare IT
• AI clinical operations
• AI operational infrastructure
• Project planning
• Metrics, reporting, and service performance
• AIOps in automation
• AIOps cloud operations

The goal of this book is to help build confidence in deploying a technology that will radically change how operations are done today. It requires understanding the versions of available AIOps platforms and systems, the vendors involved, and what infrastructure is needed. Once it is completely defined and understood from this perspective, we will explore applications of AI and ML in specific settings (or verticals) such as healthcare and how to strategize for these deployments in a cost-effective manner. The key to doing this well is to know how to use project management fundamentals to create a successful project. Project planning is the primary focus here with all of the planning done up front before the rollout to maximize the ROI for these large-scale deployments of technology. In the healthcare setting, there is an enterprise operational use of AI, and there is a clinical use, and it's important to know how they differ and how they can be integrated. This is where true innovation takes place. Although the book does cover many concepts of clinical AI and how it works in the grand scheme of IT, operational use of AI for the integrity of IT and healthcare assets, proactive and automated systems based on learned data, how to gauge service performance, and ultimately how to better deliver services (service delivery) of healthcare via IT is the primary focus and how it relates to AIOps.

Lastly, the book will cover a brief history of AI from 30 years ago until today, including where it came from, where it grew from, and ultimately where it is going (the direction it is heading). It is important to know how AI, ML, and AIOps work with cloud technologies, IoT, and other emerging technologies so that there are no missed opportunities due to lack of knowledge. It is the goal of this book to prepare you not only to understand but to be successful at a current and future rollout, implementation, and ongoing support framework for operations using AIOps in your healthcare setting.

What Does This Book Cover?

This book covers the following topics:

Chapter 1, "Healthcare IT and the Growing Need for AI Operations," opens the book by providing a brief but thorough history on artificial intelligence (AI), machine learning (ML), and healthcare information technology (HIT). The chapter brings focus to current operations and how HIT is expanding and growing and how the digital transformation of providing healthcare requires a more focused view on technology operations, infrastructure services, providing care through technology, and how to innovatively change the digital footprint to provide reliable services for patient care. Other important topics covered are how artificial intelligence operations (AIOps) brings these different functions together and gives the users of the technology more insight into their technology investments.

Chapter 2, "AI Healthcare Operations (Clinical)," builds on what was covered in Chapter 1 by providing a different view into AI and ML and how it directly impacts clinical operations. Topics such as intelligent cloud, data analytics, informatics, convergence, and other methods to merge innovative efforts between technology support and clinical operations under the AIOps umbrella are discussed in great detail. Other topics include the need for security in the clinical technology space and why service performance is critical to providing reliable patient care on stable systems.

Chapter 3, "AI Healthcare Operations (Operational Infrastructure)," covers the strategy pillars and fundamentals required to get started with developing, strategizing, conceptualizing, and selecting products and vendors for your AIOps deployment in your organization. Topics covered include creating the project scope for vendor selection; selecting platforms, products, and services from tool vendors; and sizing the request correctly. Product vendors such as ServiceNow, Dynatrace, and Splunk are covered to help you design the correct deployment for your enterprise. Other topics include event and fault management and how these functions tie into advanced workflow and automation topics to help bridge the gap between AI and manual intervention.

Chapter 4, "Project Planning for AIOps," builds on the concepts learned in Chapter 3 when project scope was introduced to help with vendor selection. In this chapter, you learn how to finish building the project plan and how to bring AIOps into enterprises consisting of large infrastructures. Project management concepts are covered, such as how to select a good project manager, how to build the project team (and why it's critical to success), what a good project plan looks like, and how to build a program into your portfolio. The chapter also discusses deploying AIOps in your environment using a project plan, communicating status updates, and keeping executives informed of project milestones.

Chapter 5, "Using AI for Metrics, Performance, and Reporting," details what you need to know post-deployment. Once you have deployed AIOps and are running it in your organization, you need to ensure your return on investment (ROI) by covering service performance metrics, KPIs, CSFs, and other important metrics that show how your investment in AIOps is creating a positive impact in both your IT environment and your clinical environment. Using AI for metrics, performance, and reporting allows you to feel confident in your AIOps platform by looking at how well it is performing and by building and viewing dashboards that help tell a story of success. Other tools helpful to building and showing metrics are covered as well as what you can pull directly from tools such as ServiceNow and Splunk.

Chapter 6, "AIOps and Automation in Healthcare Operations," discusses how to develop advanced automation for real-world healthcare operations. By looking at tools such as ServiceNow and others, building processes, designing workflows, and other automation functions, you get a full understanding of how this helps to reduce outages, increase visibility, and increase the availability of systems. Through warning detection, incident engagement, and event handling, the framework for automation is covered in great detail to help create good-quality control in your environment. The chapter includes advanced discussions on how to create and build machine learning into process automation, how...