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Healthcare Analytics for Quality and Performance Improvement by Trevor L. Strome

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CHAPTER 11

Advanced Analytics in Healthcare

The best way to predict the future is to invent it.

—Alan Kay

Analytical systems have the potential to provide healthcare leaders much more information and understanding of their organizations than simply reporting on past or current performance. In fact, just knowing what has happened is usually not enough to make transformational decisions. Healthcare decision makers must now leverage the growing volumes of data being collected by electronic medical records and other systems to gain insight into future performance and resource requirements. This is now possible by using advanced analytical tools that apply algorithms and other mathematical methods to better understand how quality and performance are likely to vary given a change in process, policy, or patient need. This chapter will discuss the tools and techniques commonly associated with data mining and “predictive analytics,” identify where these algorithms can be employed within a healthcare setting, and uncover obstacles and pitfalls associated with relying on computerized prediction models.

Overview of Advanced Analytics

Because the use of data mining, text mining, and predictive algorithms is relatively new in healthcare, healthcare organizations (HCOs) are at different stages of their use of predictive and other advanced analytics. Most HCOs are likely in the early stages of using these types of analytics, while HCOs at the other end of the spectrum may be using data mining ...

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