Discrete-Event Simulation Modeling of Critical Care Flow: New Hospital, Old Challenges

Williams, Elizabeth; Szakmány, Tamás; Spernaes, Izabela; Muthuswamy, Babu; Holborn, Penny

doi:10.1097/cce.0000000000000174

Cited by 13 publications

(11 citation statements)

References 26 publications

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“…There was only one paper with research conducted on how systems would manage if a sudden rise in frail and elderly patients were to occur [6]. Sudden increase in demand is not a novel area to healthcare modelling with a high quantity of papers investigating this issue, e.g., intensive care units [17,44,79].…”

Section: Discussionmentioning

confidence: 99%

A survey of OR/MS models on care planning for frail and elderly patients

Williams

Gärtner

Harper

2021

Operations Research for Health Care

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Section: Discussionmentioning

confidence: 99%

A survey of OR/MS models on care planning for frail and elderly patients

Williams

Gärtner

Harper

2021

Operations Research for Health Care

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“…Thus, the modelling of business processes, a sequence of events (activities), is discrete event modelling. (Williams, Szakmany, Spernaes, Muthuswamy & Holborn, 2020) applied DES in analyzing the impact of various scenarios on critical care bed occupancy. (Hajrizi & Berisha, 2019) applied DES and multi-objective optimization simulation to reduce the length of stay and waiting times at an emergency department.…”

Section: Business Processes and Discrete Event Modellingmentioning

confidence: 99%

Hospital energy demand forecasting for prioritisation during periods of constrained supply

Munsamy

Telukdarie²,

Igusa³

et al. 2023

JIEM

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Purpose: Sustaining healthcare operations without adequate energy capacity creates significant challenges, especially during periods of constrained energy supply. This research develops a clinical and non-clinical activity-based hospital energy model for electrical load prioritization during periods of constrained energy supply.Design/methodology/approach: Discrete event modelling is adopted for development of the hospital energy model (HEM). The basis of the HEM is business process mapping of the hospitals clinical and non-clinical activities. The model prioritizes the electrical load demand as Priority 1, 2 and 3; with Priority 1 activities essential to the survival of patients, Priority 2 activities are critical activities that are required after one to four hours, and Priority 3 activities can run for several hours without electricity.Findings: The model was applied to a small, medium, and large hospital. The results demonstrate that Priority 2 activities have the highest energy demand, followed by Priority 1 and Priority 3 activities, respectively for all hospital sizes. For the medium and large hospitals, the top three contributors to energy demand are lighting, HVAC, and patient services. For the small hospital, it is patient services, lighting, and HVAC, respectively.Research limitations/implications: The model is specific to hospitals but can be modified for other healthcare facilities.Practical implications: The resolution of the electrical energy demand down to the business activity level, enables hospitals to evaluate current practices for optimization. It facilitates multiple energy supply scenarios, enabling hospital management to conduct feasibility studies based on available power supply optionsSocial implications: Improved planning of capital expenditure and operational budgets and during constrained energy supply. This reduces risk to hospitals and ensures consistent quality of service. Originality/value: Current hospital energy models are limited, especially for operations management under constrained energy supply. A simple to use model is proposed to assist in planning of activities based on available supply.

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“…In addition to reduction in mortality, the Randomised, Embedded, Multi-factorial, Adaptive Platform Trial for Community-Acquired Pneumonia (REMAP-CAP) trial has found significant reduction in organ support needs and reduction in length of ICU stay in the IL-6 receptor blockade arms, while the RECOVERY trial reported significantly less progression to mechanical ventilation or death in those patients who only needed supplemental oxygen on randomization ( 6 , 12 ). This is especially important as these data could inform further modeling of healthcare resource needs during the pandemic, where stretched ICU capacity has been linked to adverse outcomes ( 13 , 14 ).…”

Section: To the Editormentioning

confidence: 99%