Unfortunately, a definition of healthcare analytics is not in Webster's dictionary yet. However, our own definition of healthcare analytics is the use of advanced computing technology to improve medical care. Let's break down this definition phrase by phrase.

At the time of this writing, we are approaching the year 2020, and computers and mobile phones have taken over many aspects of our lives, the healthcare industry being no exception. Most of our healthcare data is being migrated from paper charts to electronic ones, in many cases motivated by massive governmental incentives for doing so. Meanwhile, countless medical mobile applications are being written to track vital signs, including heart rates and weights, and even communicate with doctors. While this migration is not trivial, it will allow for the application of advanced computing techniques hopefully to unlock doors toward improving medical care for everyone.

What are some of these advanced computing technologies? We will discuss them in the upcoming sections.

If you're looking for a book that demonstrates the use of machine learning to predict the year of the apocalypse, unfortunately, this is not it. Healthcare analytics is all things healthcare.

So far, we are using computers to do something in healthcare. What exactly are we doing? We are trying to improve medical care. Well that's broad, isn't it? The effectiveness of medical care is commonly measured using the so-called healthcare triple aim: improving outcomes, reducing costs, and ensuring quality (although we've seen different words used here). Let's look at each of these aims in turn.

On a personal level, everyone can relate to better healthcare outcomes. We yearn for better outcomes in our own lives whenever we visit a doctor or a hospital. Specifically, here are some of the things about which we are concerned:

• Accurate diagnosis: When we see a physician, usually it is for a medical problem. The problem may be causing some amount of pain or anxiety in our lives. What we care about is that the cause of this problem will be accurately identified so that the problem may be effectively treated.
• Effective treatment: Treatment may be expensive, time-consuming, and may cause adverse side-effects; therefore, we want to be sure that the treatment is effective. We don't want to have to take another vacation day to see a doctor or be admitted to the hospital for the same problem two months from now–such an experience would be costly, in terms of both time and money (either through medical bills or tax dollars).
• No complications: We don't want to come down with a new infection or take a dangerous fall while we are seeking care for the current ailment.
• An overall improved quality of life: To summarize the concept of better health outcomes, while governmental bodies and physician organizations may have different ways of measuring outcomes, what we aim for is an improved quality and longevity of life that is pain- and worry-free.

So the goal is better health outcomes, right? Unfortunately, we can't provide 24-7 medical care to everyone all the time, because our economy would break down. We can't order whole-body x-rays to detect every cancer in advance. There is a careful balance between achieving better outcomes and decreasing costs in healthcare. The idea with healthcare analytics is that we will be able to do more with less expensive techniques. A CT scan of the chest to screen for lung cancer may cost thousands of dollars; however, doing mathematical calculations on a patient's medical history to screen for lung cancer costs much less. In this book, the plan is to show you how to make those calculations.

Healthcare quality encompasses the satisfaction level of the patient after he or she receives medical care. In a capitalist system (such as the healthcare system of the United States), a tried-and-true method of improving the quality involves fair and objective measurement of how different providers are performing so that patients can make more informed decisions about their care.