Computational Methods for Healthcare Access Modeling

In this month’s edition of the NOVA SBE HEALTH ECONOMICS & MANAGEMENT SEMINAR SERIES, Nicoleta Serban, Peterson Professor of Pediatric Research at the H. Milton Stewart School of Industrial and Systems Engineering at Georgia Tech, whose research focuses on model-based data mining, spatio-temporal data, and nonparametric statistical methods, will discuss:

Computational Methods for Healthcare Access Modeling

ABSTRACT:

This seminar will begin with an introduction of the multidimensional construct of healthcare access, providing a well-established definition and common objectives in access measurement and inference. Different approaches will be presented, focusing on rigorous mathematical models to estimate access, including optimization and simulation under uncertainty of the model inputs. Important aspects will be covered including spatial dependence in the decision parameters of optimization models used to estimate healthcare access and Bayesian hierarchical models used to specify the sampling distributions of model inputs. The models will be illustrated for modeling access to mental healthcare in Georgia, United States.

ABOUT THE SPEAKER:
Nicoleta Serban is the Peterson Professor of Pediatric Research in the H. Milton Stewart School of Industrial and Systems Engineering at Georgia Tech. Dr. Serban's most recent research focuses on model-based data mining for functional data, spatio-temporal data with applications to industrial economics, and nonparametric statistical methods motivated by recent applications from proteomics and genomics.

She received her B.S. in Mathematics and an M.S. in Theoretical Statistics and Stochastic Processes from the University of Bucharest. She went on to earn her Ph.D. in Statistics at Carnegie Mellon University.