An Integrated Stochastic-Forecasting Model for Capacity Management of Intensive Care Units Publisher



Akbari M ; Ketabi S ; Ghandehari M ; Abbasi S
Source: Health Systems Published:2026

Abstract

Introduction: Intensive-Care-Units (ICUs) play a vital role in healthcare systems to deliver life-saving care to critical-patients, emergency-patients, and elective-surgical-patients who require specialized post-surgery facilities. Arrivals to this unit and Length-of-Stays(LOS) are both stochastic and affect the occupied capacity of the unit. Because of limited ICU resources, capacity management has paramount importance, involving optimal utilization of available resources. Objectives: This paper proposes a stochastic model for capacity management aimed at reducing congestion in ICU through a two-step approach. Methodology: A Multilayer-Perceptron Artificial Neural Network(MLP-ANN) model is employed based on time-series data to address the uncertainty associated with the admission of various patient types and the daily-occupied capacity of the ICU. Then, a stochastic model is developed to determine the ICU occupancy level over the study period. The first stage predictive models’ outputs are utilized along with appropriate probability distribution functions for LOSs to account for the uncertainty inherent in serving patients. Results: We recommend policies to control the arrival of the elective-surgical-patients who require admission to ICU, to manage bed capacity. The results indicate a reduction (at least 1.6% per day) in congestion within the unit. Practical Implications: We provide guidelines (e.g. protocol-driven OR-ICU-coordination) for decision-makers to more efficiently manage the ICU. © 2026 The Operational Research Society.