Literature DB >> 23650696

Discrete event simulation for healthcare organizations: a tool for decision making.

Eric Hamrock1, Kerrie Paige, Jennifer Parks, James Scheulen, Scott Levin.   

Abstract

Healthcare organizations face challenges in efficiently accommodating increased patient demand with limited resources and capacity. The modern reimbursement environment prioritizes the maximization of operational efficiency and the reduction of unnecessary costs (i.e., waste) while maintaining or improving quality. As healthcare organizations adapt, significant pressures are placed on leaders to make difficult operational and budgetary decisions. In lieu of hard data, decision makers often base these decisions on subjective information. Discrete event simulation (DES), a computerized method of imitating the operation of a real-world system (e.g., healthcare delivery facility) over time, can provide decision makers with an evidence-based tool to develop and objectively vet operational solutions prior to implementation. DES in healthcare commonly focuses on (1) improving patient flow, (2) managing bed capacity, (3) scheduling staff, (4) managing patient admission and scheduling procedures, and (5) using ancillary resources (e.g., labs, pharmacies). This article describes applicable scenarios, outlines DES concepts, and describes the steps required for development. An original DES model developed to examine crowding and patient flow for staffing decision making at an urban academic emergency department serves as a practical example.

Entities:  

Mesh:

Year:  2013        PMID: 23650696

Source DB:  PubMed          Journal:  J Healthc Manag        ISSN: 1096-9012


  18 in total

1.  An Electronic Dashboard to Monitor Patient Flow at the Johns Hopkins Hospital: Communication of Key Performance Indicators Using the Donabedian Model.

Authors:  Diego A Martinez; Erin M Kane; Mehdi Jalalpour; James Scheulen; Hetal Rupani; Rohit Toteja; Charles Barbara; Bree Bush; Scott R Levin
Journal:  J Med Syst       Date:  2018-06-18       Impact factor: 4.460

2.  Understanding Emergency Care Delivery Through Computer Simulation Modeling.

Authors:  Lauren F Laker; Elham Torabi; Daniel J France; Craig M Froehle; Eric J Goldlust; Nathan R Hoot; Parastu Kasaie; Michael S Lyons; Laura H Barg-Walkow; Michael J Ward; Robert L Wears
Journal:  Acad Emerg Med       Date:  2017-09-21       Impact factor: 3.451

3.  A Machine Learning Based Discharge Prediction of Cardiovascular Diseases Patients in Intensive Care Units.

Authors:  Kaouter Karboub; Mohamed Tabaa
Journal:  Healthcare (Basel)       Date:  2022-05-24

4.  Real-time prediction of inpatient length of stay for discharge prioritization.

Authors:  Sean Barnes; Eric Hamrock; Matthew Toerper; Sauleh Siddiqui; Scott Levin
Journal:  J Am Med Inform Assoc       Date:  2015-08-07       Impact factor: 4.497

5.  Patients, primary care, and policy: Agent-based simulation modeling for health care decision support.

Authors:  Martin Comis; Catherine Cleophas; Christina Büsing
Journal:  Health Care Manag Sci       Date:  2021-05-25

6.  Big data simulations for capacity improvement in a general ophthalmology clinic.

Authors:  Christoph Kern; André König; Dun Jack Fu; Benedikt Schworm; Armin Wolf; Siegfried Priglinger; Karsten U Kortuem
Journal:  Graefes Arch Clin Exp Ophthalmol       Date:  2021-01-02       Impact factor: 3.117

Review 7.  Information systems to support surveillance for malaria elimination.

Authors:  Colin Ohrt; Kathryn W Roberts; Hugh J W Sturrock; Jennifer Wegbreit; Bruce Y Lee; Roly D Gosling
Journal:  Am J Trop Med Hyg       Date:  2015-05-26       Impact factor: 2.345

8.  A preliminary study of a novel emergency department nursing triage simulation for research applications.

Authors:  Steven L Dubovsky; Daniel Antonius; David G Ellis; Werner Ceusters; Robert C Sugarman; Renee Roberts; Sevie Kandifer; James Phillips; Elsa C Daurignac; Kenneth E Leonard; Lisa D Butler; Jessica P Castner; G Richard Braen
Journal:  BMC Res Notes       Date:  2017-01-03

9.  Designing optimal COVID-19 testing stations locally: A discrete event simulation model applied on a university campus.

Authors:  Michael Saidani; Harrison Kim; Jinju Kim
Journal:  PLoS One       Date:  2021-06-29       Impact factor: 3.240

10.  Using Six Sigma DMAIC Methodology and Discrete Event Simulation to Reduce Patient Discharge Time in King Hussein Cancer Center.

Authors:  Mazen Arafeh; Mahmoud A Barghash; Nirmin Haddad; Nadeem Musharbash; Dana Nashawati; Adnan Al-Bashir; Fatina Assaf
Journal:  J Healthc Eng       Date:  2018-06-24       Impact factor: 2.682

View more

北京卡尤迪生物科技股份有限公司 © 2022-2023.