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Thursday, April 11 • 3:25pm - 3:40pm
Modeling and Forecasting Hospital Workforce Supply to Improve Outcomes

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I will present initial findings from a project involving a large academic medical center looking to develop a predictive system that optimizes nurse staffing. The hospital is looking to reduce costs and improve patient care by staffing the right number of nurses, with the right skills and experience, on every shift, across every unit. I will discuss how we are integrating nurse workforce data to understand labor supply variations and compare this to patient demand — including volume predictions as well as patient condition metrics. Using forecasting models, optimization, and machine learning we are building a system that will recommend optimal staffing levels for each unit so that nurse managers can staff more accurately, reducing over- or under-staffing for a given period. 

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avatar for John Harris

John Harris

Data Scientist, Cogit Analytics
John is the chief data scientist for Cogit Analytics. He has extensive experience designing and building advanced analytics, machine learning, and other quantitative solutions in supply chain, finance, and healthcare.


Thursday April 11, 2019 3:25pm - 3:40pm EDT
Violet Crown, Theater 1 200 W Main St, Charlottesville, VA 22902, USA