Now that we have explored these trends and some of the consequences, we'll take a look at the data. We'll examine data that we have available and the ways in which we are currently using it in the industry. Then we'll explore some misconceptions that exist in the industry about the uses of big data. We'll review social drivers of health outcomes and how we can get at that data. We'll also delve into how to address missing information within the data as well as bias that may not be readily apparently.

Integral to maintaining high-quality care for patients, is remembering that despite all the data points, each patient is an individual with their own challenges. In this module, we'll focus on humanizing the patient by looking holistically at patient needs, burdens and motivations. We'll look at risk factors and data attribution. We'll explore the importance of taking patient opinions into account when making decisions. Then we'll look at attribution methods and AI-based modeling techniques.

In this module, we'll be looking at experience oriented predictions and some of the different methods and techniques we can bring to bear when building them. We'll examine making predictions for individual patient outcomes, for example: how likely they are to have preventable complications, non-compliance with medication regimens or how likely they are to be satisfied with their provider. We'll introduce the the CAHPS survey, a common method for evaluating consumer satisfaction with their health plan and provider.