Using stochastic simulation modelling to study occupancy levels of decentralised admission avoidance units in Norway

Area of Science:

• Healthcare Management
• Health Services Research
• Public Health Policy

Background:

• Decentralised municipal acute units (MAUs) were established in Norway to divert low-acuity patients from hospitals.
• MAUs have faced challenges including low occupancy and limited impact on hospital pressures.
• Identifying effective strategies for MAU optimization is crucial for healthcare systems.

Purpose of the Study:

• To develop a simulation model to test scenarios for increasing MAU occupancy.
• To estimate the number of patients turned away due to lack of capacity.
• To assess the impact of merging MAUs on bed capacity and service provision.

Main Methods:

• A discrete time simulation model was developed to represent admissions and discharges to MAUs.
• Scenarios were tested to evaluate strategies for increasing absolute mean occupancy.
• The model was used to estimate unconstrained demand for beds in the absence of historical data.

Main Results:

• Mergers alone are unlikely to significantly increase MAU absolute mean occupancy due to generally low unmet demand.
• Merging MAUs could allow for up to a 20% reduction in bed capacity.
• Service provision would not be negatively affected by these capacity reductions.

Conclusions:

• Merging MAUs offers potential for resource optimization and bed capacity reduction.
• The developed simulation model provides a method for estimating demand in data-scarce environments.
• Findings are relevant for other admissions avoidance units and healthcare planning.