Engaging in Improving Patient Experience Through Analytics

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 take a deeper dive into the CAHPS survey, a common method for evaluating consumer satisfaction with their health plan and provider.

Now that we have reviewed making predictions in the previous module, we'll explore making recommendations based on these predictions. We'll start by looking at opportunities for outreach and intervention with individual patients. Then we'll examine the prevalent outreach strategies in the industry. Next, we'll examine different ways of allocating campaign resources. We'll then explore the methods and techniques that are needed in order to optimize outreach campaigns.

In this module, we'll focus on operational considerations that are vital if your decision support innovation is to be deployed successfully. We'll start by touching on using data and visualization to support investment in outreach and intervention campaigns. Then we'll examine the considerations you will need to take when embedding a decision support innovation into existing workflows and technology ecosystems. By the end of this module, you will have a solid grasp on issues and challenges you may face when trying to operationalize your experience management decision support.