Simulating wait time in healthcare: Accounting for transition process variability using survival analyses

Scott Levin, Maxim Garifullin

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Wait or queuing time is a principal performance measure for many discrete-event simulation (DES) models in healthcare. However, variation in wait time is often caused by both occupied downstream servers (e.g., beds) and organizational and human transition processes. DES models that attribute wait solely to occupied servers, ignoring transition process variability, face challenges in adequate baseline validation. Embedding regression models for survival data in DES to estimate patient wait times is a method capable of integrating the effects of transition processes with queuing. Developing these models as a sub-component is further valuable in understanding the socio-technical system factors that drive prolonged waits. These general methods are exhibited in a DES for a large urban hospital with a primary output of wait time in the emergency department (ED) for transfer to an inpatient bed (boarding time). Simulated boarding time is compared before and after accounting for transition processes using survival analysis.

Original languageEnglish (US)
Title of host publication2015 Winter Simulation Conference, WSC 2015
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages1252-1260
Number of pages9
ISBN (Electronic)9781467397438
DOIs
StatePublished - Feb 16 2016
EventWinter Simulation Conference, WSC 2015 - Huntington Beach, United States
Duration: Dec 6 2015Dec 9 2015

Publication series

NameProceedings - Winter Simulation Conference
Volume2016-February
ISSN (Print)0891-7736

Other

OtherWinter Simulation Conference, WSC 2015
CountryUnited States
CityHuntington Beach
Period12/6/1512/9/15

ASJC Scopus subject areas

  • Software
  • Modeling and Simulation
  • Computer Science Applications

Fingerprint Dive into the research topics of 'Simulating wait time in healthcare: Accounting for transition process variability using survival analyses'. Together they form a unique fingerprint.

  • Cite this

    Levin, S., & Garifullin, M. (2016). Simulating wait time in healthcare: Accounting for transition process variability using survival analyses. In 2015 Winter Simulation Conference, WSC 2015 (pp. 1252-1260). [7408250] (Proceedings - Winter Simulation Conference; Vol. 2016-February). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/WSC.2015.7408250