Intro to Improving the Patient Experience Through Analytics

Overview

This course is best suited for individuals currently in the healthcare sector, as a provider, payer, or administrator. Individuals pursuing a career change to the healthcare sector may also be interested in this course. In this course, you will have an opportunity to explore concepts and topics related to improving the patient experience and reducing pain points in healthcare processes through analytic and decision support frameworks. After learning about the problems facing patients in today's health system, you will survey the kind of data that is used to make effective decision support choices, following up with information on how to use the data to predict outcomes. Throughout this course, you will be given the opportunity to apply the course concepts to operational improvements in your own organization.

Syllabus

Problem: The Patient Experience Today

This module begins with an overview of the course and the experience management and decision support system innovation project. Next, we will learn some of the latest trends in healthcare that are impacting the patient experience. We will explore the unintended consequences of these well intentioned trends. We'll explore some of the drivers that have been shaping these trends. Then we'll examine the patient journey and what impact these trends have on this journey. Then, we will discuss the issues and potential for solutions to some of these systemic problems that negatively impact the patient experience and ability to manage their own care.

What Do We Have for Data?

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.

Humanizing With Data

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.

Predicting Experiences

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.