Jan. 19, 2021 – The students designed an approach grounded in finding “unobvious and of interest” information. First, they determined what defined a “typical” patient at Hazelden Betty Ford—age, gender, race, profession, etc. Then they looked at how often that patient came to Hazelden Betty Ford, what those visits were like, the diagnoses they received, and the patient’s feelings about the experience.

As part of the process, the team built a dashboard to show the data in an easy-to-understand, visual way. Building that tool has provided Hazelden Betty Ford with the ability to continue analyzing its data in the future. It also allowed the team to take the next step in its work: understanding who returned to using substances after their initial treatment, a key success metric for Hazelden Betty Ford.

“It was great to be able to work on an important cause,” says team member Batool Fatima. “It was a type of project that I had never experienced before, but I quickly found that you can apply data analytics knowledge to solve all sorts of problems.”

Team members found that gender, age, type of substance used, and the number of substances used all correlated—in varying degrees—to a patient’s risk for returning to use.



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