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The application of predictive and prescriptive analytics can enable greater insights and solutions within healthcare delivery and enhance outcomes for our patients. As a national leader in data science, the University of Chicago has multiple constituent departments with expertise in the development and deployment of advanced analytics methodologies.

In 2019, the Analytic Interventions Unit (AIU) was formed to identify key clinical applications of predictive analytics and support the development, deployment and evaluation of such initiatives at UChicago Medicine.

The AIU fosters innovation through provisioning of accurate and actionable predictive tools that improve clinical decision support, and ensures safe and effective application that protects quality of care for our patients.

The Unit is comprised of leaders in clinical quality, informatics, data and analytics, and information technology who provide strategic direction and operational support needed for implementation of predictive models across the healthcare system.

Model Deployment Approach

The AIU works with clinical and operational leaders across the UChicago healthcare system to identify areas and processes where predictive interventions may help advance clinical delivery. The unit identifies clinical or operational sponsors for each operational initiative, and that sponsor works with the project team to guide project: prioritization; development or configuration of the predictive model; incorporation within the clinical workflow; validation of model data inputs and outputs; and integration of the model into the health system’s technical architecture.

The AIU supports each stage of the model development and deployment process outlined below and coordinates resources needed for the successful implementation and evaluation of each predictive initiative:

Current Focus Areas

The AIU is currently supporting a diverse portfolio of predictive analytics initiatives, either in-development or in-production, focused in the following clinical and operational areas:

                        • Sepsis risk (adult and pediatric)
                        • Hospital readmission risk
                        • Inpatient length of stay
                        • Inpatient risk of falls
                        • Ambulatory risk of no-show

AIU Team Members

Craig Umscheid, Unit Lead

John Fahrenbach, Data Scientist

Clara Guixa-Lluansi, IT Clinical Applications

John Moses, IT Research

Prasad Rao, IT Integration Architecture

Juan Rojas, Healthcare Delivery Science

Sachin Shah, Medical Informatics

Mary Kate Springman, Data and Analytics

Michael Wall, Data and Analytics

Jason Woods, IT Epic Application

Syed Zaffer, Program Management

For more information on the AIU or if you are interested in engaging our group on a predictive analytics initiative at UChicago Medicine, please contact Syed Zaffer