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

  • Wikman-Jorgensen, Philip Erick [4] ; Ruiz, Angel [1] ; Giner-Galvañ, Vicente [4] ; Llenas-García, Jara [2] ; Seguí-Ripoll, José Miguel [5] ; Salinas Serrano, Jose María [6] ; Borrajo, Emilio [7] ; Ibarra Sánchez, José María [8] ; García-Sabater, José P. [3] Árbol académico ; Marin-Garcia, Juan A. [3]
    1. [1] Laval University

      Laval University

      Canadá

    2. [2] Hospital Universitario San Juan de Alicante

      Hospital Universitario San Juan de Alicante

      Alicante, España

    3. [3] Universidad Politécnica de Valencia

      Universidad Politécnica de Valencia

      Valencia, España

    4. [4] Internal Medicine Department, Hospital Universitario San Juan de Alicante-FISABIO
    5. [5] Internal Medicine Department, Hospital Universitario San Juan de Alicante-FISABIO (Spain) Clinical Medicine Department, Universidad Miguel Hernández de Elche (Spain)
    6. [6] IT Department - Hospital San Juan de Alicante
    7. [7] Microbiology Department, Hospital Vega Baja-FISABIO, Orihuela
    8. [8] Documentation and Admission Department, Hospital Vega Baja-FISABIO, Orihuela
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  • Localización: Journal of Industrial Engineering and Management, ISSN-e 2013-0953, Vol. 17, Nº. 1, 2024, págs. 168-181
  • Idioma: inglés
  • DOI: 10.3926/jiem.6404
  • Enlaces
  • Resumen

    • 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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