Impact of COVID-19 Epidemics on Bed Requirements in a Healthcare Center Using Data-Driven Discrete-Event Simulation

Lay, Jules Le; Augusto, Vincent; Xie, Xiaolan; Alfonso-Lizarazo, Edgar; Bongué, Bienvenu; Célarier, Thomas; Gonthier, R.; Masmoudi, Mohamed

doi:10.1109/wsc48552.2020.9384093

Cited by 8 publications

(2 citation statements)

References 12 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The paper reports on the application of the simulation model in two Regions of Spain during the COVID-19 waves in 2020, where the model was used daily to inform regional healthcare planning teams. Le Lay et al (2020) propose a data-driven discrete-event simulation model to predict bed requirements in a healthcare centre during the COVID-19 pandemic. The model is based on data from a French university hospital and aims to provide a decision-aid tool for hospital managers to determine bed and staff requirements.…”

Section: Theoretical Frameworkmentioning

confidence: 99%

Hospitalization forecast to inform COVID-19 pandemic planning and resource allocation using discrete event simulation

Wikman-Jorgensen,

Ruiz,

Giner-Galvañ

et al. 2024

JIEM

View full text Add to dashboard Cite

Purpose: This study aims to address the pressing need for accurate forecasting of healthcare resource demands during the COVID-19 pandemic. It presents a novel approach that combines a stochastic Markov model and a discrete event simulation model to dynamically predict hospital admissions and daily occupancy of hospital and ICU beds.Design/methodology/approach: The research builds upon existing work related to predicting COVID-19 spread and patient influx to hospital emergency departments. The proposed model was developed and validated at San Juan de Alicante University Hospital from July 10, 2020, to January 10, 2022, and externally validated at Hospital Vega Baja. The model involves an admissions generator based on a stochastic Markov model, feeding data into a discrete event simulation model in the R programming language. The probabilities of hospital admission were calculated based on age-stratified positive SARS-COV-2 results from the health department's catchment population. The discrete event simulation model simulates distinct patient pathways within the hospital to estimate bed occupancy for the upcoming week. The performance of the model was measured using the median absolute difference (MAD) between predicted and actual demand.Findings: When applied to data from San Juan hospital, the admissions generator demonstrated a MAD of 6 admissions/week (interquartile range [IQR] 2-11). The MAD between the model's predictions and actual bed occupancy was 20 beds/day (IQR 5-43), equivalent to 5% of total hospital beds. For ICU occupancy, the MAD was 4 beds/day (IQR 2-7), constituting 25% of ICU beds. Evaluation with data from Hospital Vega Baja showcased an admissions generator MAD of 2.42 admissions/week (IQR 1.02-7.41). The MAD between the model's predictions and actual bed occupancy was 18 beds/day (IQR 19.57-38.89), approximately 5.1% of hospital beds. The ICU occupancy MAD was 3 beds/day (IQR 1-5), making up 21.4% of ICU beds.Practical implications: The dynamic predictions of hospital admissions, ward beds, and ICU occupancy for COVID-19 patients proved highly valuable to hospital managers, facilitating early and informed planning of resource allocation. Additionally, this study underscores the importance of utilizing simulation techniques to predict and manage hospital occupancy levels, thereby enhancing decision-making in hospital bed management, not only during pandemics but also during regular periods.Originality/value: This study introduces a novel hybrid approach that combines stochastic modeling and discrete event simulation to forecast healthcare resource demands during the COVID-19 pandemic. The methodology's effectiveness in predicting admissions and bed occupancy contributes to improved resource planning and situational awareness.

show abstract

Section: Theoretical Frameworkmentioning

confidence: 99%

Hospitalization forecast to inform COVID-19 pandemic planning and resource allocation using discrete event simulation

Wikman-Jorgensen,

Ruiz,

Giner-Galvañ

et al. 2024

JIEM

View full text Add to dashboard Cite

show abstract

“…They evaluate the number of beds needed in the emergency department as well as the number of ambulances required to maintain targeted response times for emergency patients and finally, they study the effects of emergency department (ED) boarding time for COVID-19 patients. In the same vein, Le Lay et al. (2020) develop a DES model to evaluate bed and staff requirements in response to the increased demand for beds during the pandemic.…”

Section: Literature Reviewmentioning

confidence: 99%

Hybrid RFID-IoT simulation modeling approach for analyzing scrubs’ distribution solutions in operating rooms

Maïzi

Bendavid

2023

BPMJ

View full text Add to dashboard Cite

PurposeAssess the realistic impacts of implementing an Radio Frequency Identification (RFID)/Internet of Things (IoT) uniforms’ distribution system for managing medical personnel’s scrubs in operating rooms. The authors use a hybrid simulation framework to address the following objectives and challenges: a) reduce and control operating rooms’ level of inventory; b) stabilize scrubs’ demand and c) improve infection control and prevention of cross-contamination (through scrubs over manipulation and hoarding).Design/methodology/approachThe authors adopt a Design Science approach. This methodological approach is used to design, develop, create and evaluate information technology “artifacts” (e.g. constructs, models, methods and instantiations) intended to solve organizational problems and make research contributions (Peffers et al., 2007). More specifically, the authors follow the Design Science Research Methodology process model which includes six steps: problem identification and motivation, definition of the objectives for a solution, design and development, demonstration, evaluation, and communication.FindingsTo assess the realistic impacts of implementing an RFID-IoT uniforms’ distribution system for managing medical personnel’s scrubs in operating rooms, the authors adopted a design science approach and initiated the research by documenting the business case and reviewed the existing literature to build a comparative analysis of existing uniforms’ distribution systems. The authors used a hybrid simulation model to assess the impact of three business cases: present mode of operation, implementing smart shelves or the smart distributors. The authors show that smart dispensers allow a greater control on scrubs’ utilization while eliminating the cross-contamination of the medical personnel.Practical implicationsThrough this research study, the authors provide hospitals’ managers a scientific support for uniforms’ (scrubs) distribution process improvement. The authors use a hybrid simulation model to compare innovative solutions for uniforms’ distribution systems in the form of “smart cabinets” supported by Radio Frequency Identification (RFID)/Internet of Things (IoT) technologies and choose the most appropriate design for the hospital to meet two main challenges: a) inefficiency of uniform replenishment-distribution system and b) noncompliancy with infection control regulations caused by the distribution system.Originality/valueFrom a methodological perspective, this paper addresses concerns from researchers calling quantitative research methods and using case-based research strategy to address IoT issues and assess the system in practice. From a broader point of view, this work confirms the predominant interest of RFID-IoT research work in the arena of supply chain management and logistics as the technology is used for tracking purpose and for monitoring applications. It is also one response to the research community suggesting that “hospitals should evaluate the medical effectiveness of the new technologies as well as the cost before adoption”.

show abstract

Artificial intelligence and discrete-event simulation for capacity management of intensive care units during the Covid-19 pandemic: A case study

Ortíz‐Barrios

Arias-Fonseca

Ishizaka

et al. 2023

Journal of Business Research

View full text Add to dashboard Cite

Impact of COVID-19 Epidemics on Bed Requirements in a Healthcare Center Using Data-Driven Discrete-Event Simulation

Cited by 8 publications

References 12 publications

Hospitalization forecast to inform COVID-19 pandemic planning and resource allocation using discrete event simulation

Hospitalization forecast to inform COVID-19 pandemic planning and resource allocation using discrete event simulation

Hybrid RFID-IoT simulation modeling approach for analyzing scrubs’ distribution solutions in operating rooms

Artificial intelligence and discrete-event simulation for capacity management of intensive care units during the Covid-19 pandemic: A case study

Contact Info

Product

Resources

About