research-article

A modeling framework that combines markov models and discrete-event simulation for stroke patient care

Authors:

Ken FullertonAuthors Info & Claims

ACM Transactions on Modeling and Computer Simulation (TOMACS), Volume 21, Issue 4

Article No.: 25, Pages 1 - 26

https://doi.org/10.1145/2000494.2000498

Published: 02 September 2011 Publication History

Abstract

Stroke disease places a heavy burden on society, incurring long periods of hospital and community care. Also stroke is a highly complex disease with heterogeneous outcomes and multiple strategies for therapy and care. In this article we develop a modeling framework that clusters patients with respect to their length of stay (LOS); phase-type models are then used to describe patient flows for each cluster. In most cases, there are multiple outcomes, such as discharge to normal residence, nursing home, or death. We therefore derive a novel analytical model for the distribution of LOS in such situations. A model of the whole care system is developed, based on Poisson admissions to hospital, and results obtained for expected numbers in different states of the system at any time. We can thus describe the whole integrated system of stroke patient care, which will facilitate planning of services. We also use the basic model to build a discrete-event simulation, which incorporates back-up queues to model delayed discharge. Based on stroke patients' data from the Belfast City Hospital, various scenarios are explored with a focus on the potential efficiency gains if LOS, prior to discharge to a private nursing home, can be reduced. Predictions for bed occupancy are also provided. The overall modeling framework characterizes the behavior of stroke patient populations, with a focus on integrated system-wide planning, encompassing hospital and community services. Within this general framework we can develop either analytic or simulation models that take account of patient heterogeneity and multiple care options.

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cover image ACM Transactions on Modeling and Computer Simulation

ACM Transactions on Modeling and Computer Simulation Volume 21, Issue 4

August 2011

115 pages

ISSN:1049-3301

EISSN:1558-1195

DOI:10.1145/2000494

Issue’s Table of Contents

Copyright © 2011 ACM.

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Publication History

Published: 02 September 2011

Accepted: 01 June 2010

Revised: 01 October 2009

Received: 01 May 2009

Published in TOMACS Volume 21, Issue 4

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Ortiz-Barrios MPetrillo AArias-Fonseca SMcClean Sde Felice FNugent CUribe-López S(2024)An AI-based multiphase framework for improving the mechanical ventilation availability in emergency departments during respiratory disease seasons: a case studyInternational Journal of Emergency Medicine10.1186/s12245-024-00626-017:1Online publication date: 2-Apr-2024
https://doi.org/10.1186/s12245-024-00626-0
Gorelova AMeliá SGadzhimusieva D(2024)A Discrete Event Simulation of Patient Flow in an Assisted Reproduction Clinic With the Integration of a Smart Health Monitoring SystemIEEE Access10.1109/ACCESS.2024.338002112(46304-46318)Online publication date: 2024
https://doi.org/10.1109/ACCESS.2024.3380021
Ortiz-Barrios MIshizaka ABarbati MArias-Fonseca SKhan JGul MYücesan MAlfaro-Saíz JPérez-Aguilar A(2024)Integrating discrete-event simulation and artificial intelligence for shortening bed waiting times in hospitalization departments during respiratory disease seasonsComputers & Industrial Engineering10.1016/j.cie.2024.110405194(110405)Online publication date: Aug-2024
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