Impact assessment of neonatal care interventions on regional neonatal care capacity: a simulation study based on...

To analyse the impact of selected neonatal care interventions on regional care capacity.DesignDiscrete event simulation modelling based on clinical data.

Neonatal care in the southwest of the Netherlands, consisting of one tertiary-level neonatal intensive care unit (NICU), four hospitals with high-care neonatal (HCN) wards and six with medium-care neonatal (MCN) wards.

44 461 neonates admitted to at least one hospital within the specified region or admitted outside of the region but with a residential address inside the region between 2016 and 2021.

The impact of three interventions was simulated: (1) home-based phototherapy for hyperbilirubinaemia, (2) oral antibiotic switch for culture-negative early onset infection and (3) changing tertiary-level NICU admission guidelines.

Regional neonatal capacity defined as: (1) occupancy per ward level, (2) required operational beds per ward level to provide care to all inside region patients at maximum 85% occupancy, (3) proportion rejected, defined as outside region transfers due to no capacity to provide local care and (4) the weekly rejections in relation to occupancy to provide a combined analysis.

In the current situation, with many operational beds closed due to nurse shortages, occupancy was extremely high at the NICU and HCNs (respectively 91.7% (95% CI 91.4 to 92.0) and 98.1% (95% CI 98.0 to 98.2)). The number of required beds exceeded available beds, resulting in >20% rejections for both NICU and HCN patients. Although the three interventions individually demonstrated effect on capacity, clinical impact was marginal. In combination, NICU occupancy was reduced below the 85% government recommendation at the cost of an increased burden for HCNs, highlighting the need for redistribution to MCNs.

Our model confirmed the severity of current neonatal capacity strain and demonstrated the potential impact of three interventions on regional capacity. The model showed to be a low-cost and easy-to-use method for regional capacity impact assessment and could provide the basis for making informed decisions for other interventions and future scenarios, supporting data-driven neonatal capacity planning and policy development.