Description
Predictive analytics has the potential to improve health and healthcare, but implementing applications that are utilized in a clinically meaningful way remains challenging. We present key findings from interviews conducted with operational, informatics, and front-line personnel related to a predictive algorithm currently under development. Our results demonstrate that successful integration of predictive models depend on a diverse set of factors related to institutional infrastructure, hardware/software constraints, the model itself, and the needs of front-line personnel.
Learning Objective: Consider the challenges to implementing clinically meaningful and actionable predictive algorithms in a healthcare setting, and formulate strategies for mitigating the challenges discussed
Authors:
Natalie Benda (Presenter)
Weill Cornell Medicine
Erika Abramson, Weill Cornell Medicine
Lala Tanmoy Das, Weill Cornell Medicine
Katherine Blackburn, University of Florida
Amy Thoman, Weill Cornell Medicine
Rainu Kaushal, Weill Cornell Medicine
Jessica Ancker, Weill Cornell Medicine
Presentation Materials: