Literature DB >> 19789019

Using simulation to determine the need for ICU beds for surgery patients.

Philip Marc Troy1, Lawrence Rosenberg.   

Abstract

BACKGROUND: As the need for surgical ICU beds at the hospital increases, the mismatch between demand and supply for those beds has led to the need to understand the drivers of ICU performance.
METHOD: A Monte Carlo simulation study of ICU performance was performed using a discrete event model that captured the events, timing, and logic of ICU patient arrivals and bed stays.
RESULTS: The study found that functional ICU capacity, ie, the number of occupied ICU beds at which operative procedures were canceled if they were known to require an ICU stay, was the main determinant of the wait, the number performed, and the number of cancellations of operative procedures known to require an ICU stay. The study also found that actual and functional ICU capacity jointly explained ICU utilization and the mean number of patients that should have been in the ICU that were parked elsewhere.
CONCLUSION: The study demonstrated the necessity of considering actual and functional ICU capacity when analyzing surgical ICU bed requirements, and suggested the need for additional research on synchronizing demand with supply. The study also reinforced the authors' sense that simulation facilitates the evaluation of trade-offs between surgical management alternatives proposed by experts and the identification of unexpected drawbacks or opportunities of those proposals.

Entities:  

Mesh:

Year:  2009        PMID: 19789019     DOI: 10.1016/j.surg.2009.05.021

Source DB:  PubMed          Journal:  Surgery        ISSN: 0039-6060            Impact factor:   3.982


  7 in total

1.  Transforming Healthcare Delivery: Integrating Dynamic Simulation Modelling and Big Data in Health Economics and Outcomes Research.

Authors:  Deborah A Marshall; Lina Burgos-Liz; Kalyan S Pasupathy; William V Padula; Maarten J IJzerman; Peter K Wong; Mitchell K Higashi; Jordan Engbers; Samuel Wiebe; William Crown; Nathaniel D Osgood
Journal:  Pharmacoeconomics       Date:  2016-02       Impact factor: 4.981

2.  A Conceptual Framework for Improving Critical Care Patient Flow and Bed Use.

Authors:  Kusum S Mathews; Elisa F Long
Journal:  Ann Am Thorac Soc       Date:  2015-06

Review 3.  Operations research in intensive care unit management: a literature review.

Authors:  Jie Bai; Andreas Fügener; Jan Schoenfelder; Jens O Brunner
Journal:  Health Care Manag Sci       Date:  2016-08-12

4.  Recovery bed planning in cardiovascular surgery: a simulation case study.

Authors:  Yariv N Marmor; Thomas R Rohleder; David J Cook; Todd R Huschka; Jeffrey E Thompson
Journal:  Health Care Manag Sci       Date:  2013-03-19

5.  Addressing the variation of post-surgical inpatient census with computer simulation.

Authors:  Theodore Eugene Day; Albert Chi; Matthew Harris Rutberg; Ashley J Zahm; Victoria M Otarola; Jeffrey M Feldman; Caroline A Pasquariello
Journal:  Pediatr Surg Int       Date:  2014-01-30       Impact factor: 1.827

6.  Decreased length of stay after addition of healthcare provider in emergency department triage: a comparison between computer-simulated and real-world interventions.

Authors:  Theodore Eugene Day; Abdul Rahim Al-Roubaie; Eric Jonathan Goldlust
Journal:  Emerg Med J       Date:  2012-03-07       Impact factor: 2.740

7.  Machine Learning-Based Hospital Discharge Prediction for Patients With Cardiovascular Diseases: Development and Usability Study.

Authors:  Tae Joon Jun; Young-Hak Kim; Imjin Ahn; Hansle Gwon; Heejun Kang; Yunha Kim; Hyeram Seo; Heejung Choi; Ha Na Cho; Minkyoung Kim
Journal:  JMIR Med Inform       Date:  2021-11-17
  7 in total

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