Northeastern University
Intro to Improving the Patient Experience Through Analytics
Northeastern University

Intro to Improving the Patient Experience Through Analytics

Taught in English


Gain insight into a topic and learn the fundamentals

Craig Johnson

Instructor: Craig Johnson

Beginner level
No prior experience required
19 hours to complete
3 weeks at 6 hours a week
Flexible schedule
Learn at your own pace

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There are 4 modules in this course

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's included

10 videos22 readings2 quizzes2 discussion prompts

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.

What's included

5 videos12 readings3 quizzes1 peer review

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.

What's included

1 video11 readings2 quizzes1 discussion prompt

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.

What's included

1 video8 readings2 quizzes1 discussion prompt


Craig Johnson
Northeastern University
4 Courses7,571 learners

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