A Tale of Two AI Implementations in Healthcare
A Tale of Two AI Implementations in Healthcare


Implementing artificial intelligence into healthcare is a very difficult task. In this presentation, we will discuss how we implemented two predictive models based on machine learning at Fresenius Medical Care North America, a company that provides dialysis to approximately 200,000 patients with kidney failure. The two models are: 1) predicting which patients are at risk for all-cause hospitalization in the next 7 days and 2) which patients are at risk for peritonitis (i.e. infection) in the next 30 days. One of these models had high performance on area-under-the-curve (AUC), the other did not. One had actionable reasons, the other did not. One was rolled out abruptly, the other gradually. One integrated end-user feedback into the final product, the other did not. One had thousands of users, the other had less than a hundred. One had actionable metrics tracked throughout development, the other did not. One was productionized through a different department, the other was not. One was expanded, the other was terminated. Come to the talk to listen to challenges and lessons learned about implementing AI in healthcare.


Caitlin Monaghan is a Data Scientist in the Applied Advanced Analytics team at Fresenius Medical Care. Caitlin has a master’s degree in Psychology from University of Oregon and a Ph.D. in Neuroscience from Boston University. She was a post-doctoral fellow at McLean Hospital / Harvard Medical School where she used EEG-derived biomarkers, clinical variables, and other data sources to predict functional outcome in individuals who recently experienced a first episode of psychosis. More recently she was a fellow in the Insight Health Data Science program. In her current role, Caitlin performs predictive modeling and other advanced analytical activities in an effort to improve outcomes and provide better care to patients with chronic kidney disease.

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