(Virtual) Seminar Series | Rational Transplant Program Response to Outcome-based Regulation in Lung Transplantation

Speaker: Saumya Sinha, Postdoctoral Researcher

Department of Computational and Applied Mathematics, Rice University

Abstract:

Organ transplantation is an increasingly common therapy for end-stage organ failure. In the past twenty years, transplant programs in the United States have seen increased scrutiny under two sets of federal regulations, whereby programs are evaluated based on their patients’ post-transplant survival outcomes. The intent of these regulations was to expand organ utilization and improve post-transplant outcomes. However, some transplant volume at some programs started to decline soon after the enactment of the regulations. Some researchers have attributed this decline to risk-averse patient selection, while others have questioned whether such a strategy would be rational.

In this talk, we consider the perspective of a transplant program that seeks to maximize its transplant volume while controlling the risk of penalization under the regulatory criteria. We formulate the problem as a chance-constrained mixed-integer program. Through analytical results and numerical experiments calibrated with real transplant data, we demonstrate that it may in fact be rational for a transplant program to curtail its transplant volume to avoid penalization. We also highlight other shortcomings of the regulations, such as their disproportionate impact on medium-sized programs.

Bio:

Saumya Sinha is a postdoctoral researcher in the Department of Computational and Applied Mathematics at Rice University, and a visiting postdoctoral fellow at Houston Methodist Hospital. Her recent work uses optimization and analytics to study the incentives created by federal policies in organ transplantation. Prior to this, she received a PhD in Applied Mathematics with an ‘Advanced Data Science’ option from the University of Washington in 2018. Her research interests lie in sequential decision-making and optimization under uncertainty, with a focus on applications in healthcare operations and health policy.