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

Stat Med. 2019 Sep 10;38(20):3911-3935. doi: 10.1002/sim.8210. Epub 2019 Jun 11.


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.

Keywords: causal inference; emergency department admission decisions; latent variables; potential outcomes.

Publication types

  • Research Support, N.I.H., Extramural

MeSH terms

  • Clinical Decision-Making / methods*
  • Decision Support Systems, Clinical
  • Emergency Service, Hospital*
  • Humans
  • Michigan
  • Models, Statistical*
  • Organizational Case Studies
  • Patient Admission*
  • Treatment Outcome