The Impact of Predictive Analytics and CI on Patient Satisfaction

Andrea Grgic | 5/11/2023

Topics: Continuous Improvement, Predictive Analytics, Minitab Statistical Software, CART, Minitab Engage

Think about your most recent doctor's visit. Was the hospital staff friendly? Was the waiting area comfortable? How long did you wait for the physician to see you? Did your nurse show empathy throughout your visit?  All these factors contribute to the overall patient experience, one of the most important aspects of healthcare.   
In this blog, we’ll cover the importance of patient satisfaction and experience, along with ways that predictive analytics and continuous improvement tools can help healthcare providers improve their overall patient satisfaction.  

Patient Satisfaction vs. Patient Experience: What’s the Difference? 

Patient satisfaction refers to whether a patient’s expectations were met throughout their visit with a healthcare provider. It is one of the key indicators for providers and hospitals to assess where to improve as an organization and is directly linked to overall success. For these reasons, it is a top priority across all healthcare organizations.  

Patient experience focuses on improving patient satisfaction and outcomes, reducing time to treatment, and improving patient care coordination. According to the 2023 Gartner CIO and Technology Executive Survey, operational excellence and patient experience are the primary objectives for digital investments for healthcare providers.  

Brainstorming Patient Satisfaction Contributors 

When thinking about the overall patient experience, it is important to consider the factors that impact your patients. An easy way to get started is to brainstorm with your administrative and clinical staff and “map out” these factors with a mind map.  

A mind map helps you visually organize related ideas and concepts, so that you can better understand the central concept and potential solutions.  

In our example above, we used Minitab Engage to create a mind map to help us understand the factors we want to focus on to improve patient satisfaction. Now that we have our factors mapped out, let’s determine how to address patient satisfaction.  

One of the most effective and cost-efficient ways for healthcare providers to measure patient satisfaction is to send a survey.  

Patient Satisfaction Survey: Taking a Closer Look  

Patient satisfaction survey results provide a deeper understanding of why your patients may or may not be satisfied with your service. Let’s review an example.  

By looking at our sample patient data set, we can see that patients were asked to rate their overall satisfaction with a healthcare provider. They were also asked to rate other important factors of their care such as nurse and doctor empathy, room appearance, on-time appointments, and amenities, which we will revisit later in this blog.   

Our sample survey results show that 55% of patients were satisfied with their experience, which tells us that overall, most patients are satisfied with their healthcare provider’s experience.   
Patient Satisfaction Bar Chart

Patient Satisfaction: A Predictive Approach

It is a good start to know that, in general, patients are satisfied with this healthcare provider’s services. Now let’s dive into why these patients are satisfied and how their experience compares to a dissatisfied or neutral patient.  
By leveraging the predictive analytics module in Minitab Statistical Software, the healthcare provider can easily identify the key drivers of patient satisfaction. For our example, we’ll use CART®. 
CART®, or Classification and Regression Trees, is a decision tree algorithm used to find important patterns and relationships in data variables. If the question or challenge you're facing has a binomial or multinomial categorical response, use CART Classification, while anything that has a continuous response with many categorical or continuous predictors should use CART Regression
In our sample survey, we are categorizing customers into two groups, whether they are satisfied or not satisfied with this healthcare provider, so we will use CART Classification. Minitab Statistical Software automatically finds the best decision tree for you and provides model statistics, so you can understand if the model is useful for your analysis.  

As you can see below, nurse empathy and keeping a patient informed are the most important variables when predicting patient satisfaction, followed by doctor empathy and outcome of procedure, which also ranked highly as important. 

  Variable Importance

Using Tree Diagrams to Understand Patient Data 

To start, we’d like to mention that nurse empathy is measured on a 5-point scale, where 5 indicates a very positive evaluation. Looking at the tree in greater detail, we can see that when nurse empathy was rated greater than 3.5, approximately 82% of patients rate their experience as satisfied. We can also see that when patients rated their nurse empathy less than 3.5, they were more satisfied if they were better informed by the provider, but much less satisfied if they were not informed.   
 Smaller Tree
By looking at the tree above, healthcare providers can see that their patients want an empathetic nurse and expect to be informed throughout their visit — but knowing that even if their nurse does not show empathy, they can keep patients happy by keeping them informed — is an important insight. 


Patient satisfaction is only a portion of the overall patient experience. Data-powered insights from predictive analytics, along with brainstorming tools, can help healthcare providers reach optimal patient care.  

Ready to improve patient satisfaction across your organization?   
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