A Generic Simulation-Based DSS for Evaluating Flexible Ward Clusters in Hospital Occupancy Management

Helbig, Karsten; Stoeck, Thomas; Mellouli, Taïeb

doi:10.1109/hicss.2015.354

Cited by 5 publications

(3 citation statements)

References 24 publications

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“…Most of these studies used computer simulations such as agent-based modeling (Dadgar et al, 2013). Helbig, Stoeck, and Mellouli (2015) conducted a simulation of a decision support system for evaluating flexible ward clusters in hospital occupancy management. Nsakanda et al (2015) performed a simulation approach at a hospital in Ottawa to understand workflow changes when deploying a new computerized physician order entry system (CPOE).…”

Section: Figure 4 Frequencies Of Used Methodologymentioning

confidence: 99%

The Trajectory of IT in Healthcare at HICSS: A Literature Review, Analysis, and Future Directions

Samhan¹,

Crampton²,

Ruane³

2018

CAIS

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Research has extensively demonstrated that healthcare industry has rapidly implemented and adopted information technology in recent years. Research in health information technology (HIT), which represents a major component of the Hawaii International Conference on System Sciences, demonstrates similar findings. In this paper, review the literature to better understand the work on HIT that researchers have conducted in HICSS from 2008 to 2017. In doing so, we identify themes, methods, technology types, research populations, context, and emerged research gaps from the reviewed literature. With much change and development in the HIT field and varying levels of adoption, this review uncovers, catalogs, and analyzes the research in HIT at HICSS in this ten-year period and provides future directions for research in the field.

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Section: Figure 4 Frequencies Of Used Methodologymentioning

confidence: 99%

The Trajectory of IT in Healthcare at HICSS: A Literature Review, Analysis, and Future Directions

Samhan¹,

Crampton²,

Ruane³

2018

CAIS

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“…Simulation models have been widely used in research articles focusing on hospital bed management (Ahmad et al, 2014;Clissold et al, 2015;Hajlasz & Mielczarek, 2020;He et al, 2019;Helbig et al, 2015;Holm et al, 2013;Khanna et al, 2016;Landa et al, 2017;Mallor & Azcarate, 2014;Monks et al, 2016;Oliveira et al, 2020;Qin et al, 2017;Seymour et al, 2015;Varney et al, 2019). These computer-based tools mimic real-world systems and processes and are particularly useful for predicting and analyzing patient flow and resource utilization in hospital settings.…”

Section: Theoretical Frameworkmentioning

confidence: 99%

“…The study found that a 6% reduction in admissions to the pediatric ward resulted in a decrease in average bed utilization from 57.90% to 54.06%, while a 12% increase in admissions to the geriatric ward led to an increase in bed utilization from 68.88% to 75.59%. Helbig, Stoeck and Mellouli (2015) focused on improving hospital efficiency by clustering softened patient arrival peaks and reduced bed bottlenecks. Holm, Luras and Dahl ( 2013) developed a simulation model using discrete event simulation to minimize crowding and improve bed utilization in a Norwegian general hospital.…”

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

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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.

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A Two-Dimensional Categorization Scheme for Simulation/Optimization-Based Decision Support in Hospitals Applied to Overall Bed Management in Interdependent Wards Under Flexibility

Stoeck

Mellouli

2020

Operations Research and Simulation in Healthcare

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A Generic Simulation-Based DSS for Evaluating Flexible Ward Clusters in Hospital Occupancy Management

Cited by 5 publications

References 24 publications

The Trajectory of IT in Healthcare at HICSS: A Literature Review, Analysis, and Future Directions

The Trajectory of IT in Healthcare at HICSS: A Literature Review, Analysis, and Future Directions

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

A Two-Dimensional Categorization Scheme for Simulation/Optimization-Based Decision Support in Hospitals Applied to Overall Bed Management in Interdependent Wards Under Flexibility

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