Discrete event simulation model for planning Level 2 “step-down” bed needs using NEMS

Abstract

This is the peer reviewed version of the following article: F. Rodrigues, G.S. Zaric, D.A. Stanford, Discrete event simulation model for planning Level 2 "step-down" bed needs using NEMS, In Operations Research for Health Care, 2017, which has been published in final form at https://doi. AbstractIn highly congested hospitals it may be common for patients to overstay at Intensive Care Units (ICU) due to blockages and imbalances in capacity. This is inadequate clinically, as patients occupy a service they no longer need; operationally, as it disrupts ow from upstream units; and nancially as ICU beds are more expensive than ward beds.Step-down beds, also known as Level 2 beds, have become an increasingly popular and less expensive alternative to ICU beds to deal with this issue. We developed a discrete event simulation model that estimates Level 2 bed needs for a large university hospital. The model innovates by simulating the entirety of the hospital's inpatient ow and most importantly, the ICU's daily stochastic ows based on a nursing workload scoring metrics called "Nine Equivalents of Nursing Manpower Use Score" (NEMS). Using data from a large academic hospital, the model shows the benets of Level 2 beds in improving both patient ow and costs.

Patient Flow in Congested Intensive Care Unit /Step-down Unit system: Premature Step-down or not?

“…Metrics have been developed by the clinical community as systematic criteria to evaluate patient health severity status. Rodrigues et al (2018), working with a large dataset from an academic hospital, used a discrete event simulation to show the benefits of SDU beds in optimizing hospital expenses and patient flow at a jam-packed facility and developed a metric called the Nine Equivalents of Nursing Manpower (NEMS). Shmueli et al (2003) used Acute Physiology and Chronic Health Evaluation II (APACHE II) to evaluate patient severity.…”

Hospital preparedness during epidemics using simulation: the case of COVID-19

“…The ultimate goal of these models is to match resource availability with demand in order to provide high-quality patient care while maintaining adequate human and technological resource provision. Some of the problems analyzed in this framework are patient flow (Shahani et al 2008 ; Kolker 2009 ), bed planning (Ridge et al 1998 ; Zhu et al 2012 ; Rodrigues et al 2018 ), health service design (Mallor et al 2016 ), and medical staff scheduling (Erhard et al 2018 ), among others. Despite reports in the medical literature of discrepancies between assumptions in mathematical simulation models and the behavior of real healthcare systems (Azcarate et al 2020 ), there is no doubt about the usefulness of simulation models for the analysis of relevant problems in complex healthcare systems.…”

Hospital preparedness in epidemics by using simulation. The case of COVID-19

“…The ultimate goal of these models is to reconcile resource availability with demand in order to provide high-quality healthcare to patients while maintaining a reasonable level of human and technological resources. Problems analyzed into this framework are patient flow [8,9], bed planning [10][11][12], health service design [13] and medical staff scheduling [14], among others. Although discrepancies between assumptions made in mathematical simulation models and behavior of real health systems reported in the medical literature have been pointed out [15], there is no doubt about the usefulness of simulation models for the analysis of relevant problems in complex health systems.…”