Mar. 12th, 2020 (Thursday 12:00 am)Gabriel Zayas-Caban (University of Wisconsin-Madison)

  • A latent variable approach to potential outcomes for emergency department admission decisions Show Abstract

    [Abstract] In emergency departments (EDs), care providers continuously weigh admissions against continued monitoring and treatment often without knowing their condition and health needs. To understand the decision process and its causal effect on outcomes, an observational study must contend with unobserved/missing information and a lack of exchangeability between admitted and discharged patients. Our goal was to provide a general framework to evaluate admission decisions from electronic healthcare records (EHRs). We describe admission decisions as a decision‐making process in which the patient’s health needs is a binary latent variable. We estimate latent health needs from EHR with only partial knowledge of the decision process (ie, initial evaluation, admission decision, length of stay). Estimated latent health needs are then used to understand the admission decision and the decision’s causal impact on outcomes. For the latter, we assume potential outcomes are stochastically independent from the admission decision conditional on latent health needs. As a case study, we apply our approach to over 150 000 patient encounters with the ED from the University of Michigan Health System collected from August 2012 through July 2015. We estimate that while admitting a patient with higher latent needs reduces the 30‐day risk of revisiting the ED or later being admitted through the ED by over 79%, admitting a patient with lower latent needs actually increases these 30‐day risks by 3.0% and 7.6%, respectively.

    [Bio] Gabriel Zayas-Caban is an Assistant Professor in the Industrial and Systems Engineering Department at the University of Wisconsin-Madison. Before that, he was a President’s Postdoctoral Fellow at the University of Michigan. He graduated from Cornell with a PhD from the Center for Applied Mathematics under the mentorship and guidance of Dr. Mark E. Lewis. His interests are in queueing theory, Markov decision processes, scheduling, and causal inference, with a focus on healthcare delivery.

Mar. 13th, 2020 (Friday 11:00 am)Fatma Kilinc-Karzan (Carnegie Mellon University)

  • TBD Show Abstract

    [Abstract] TBD

    [Bio] TBD