Exploring financially sustainable initiatives to address out-of-area placements in psychiatric ICUs: a computer simulation study

Abstract

Background

Transferring individuals for treatment outside their geographic area occurs when healthcare demand exceeds local supply. This can result in significant financial cost while impacting patient outcomes and experience.

Aims

The aim of this study was to assess initiatives to reduce psychiatric intensive care unit (PICU) out-of-area bed placements within a major healthcare system in South West England.

Methods

Discrete event computer simulation was used to model patient flow across the healthcare system’s three PICUs. A scenario analysis was performed to estimate the impact of management plans to decrease admissions and length of stay. The amount of capacity required to minimise total cost was also considered.

Results

Without increasing in-area capacity, mean out-of-area bed requirement can be reduced by 25.6% and 19.1% respectively through plausible initiatives to decrease admissions and length of stay. Reductions of 34.7% are possible if both initiatives are employed. Adjusting the in-area bed capacity can also lead to aggregate cost savings.

Conclusions

This study supports the likely effectiveness of particular initiatives in reducing out-of-area placements for high-acuity bedded psychiatric care. This study also demonstrates the value of computer simulation in an area that has seen little such attention to date.

Keywords:

• Systems modelling
• computer simulation
• psychiatric intensive care

Acknowledgements

The authors are grateful to the contributions of Simon Bailey, Simon Cole, Emma Gara, and Toby Rickard. The authors also acknowledge the comments and suggestions from the anonymous reviewers.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Data and material

This study utilised a discrete event simulation tool purpose built for modelling patient pathways. Model code used for this study is freely available at https://github.com/nhs-bnssg-analytics/PathSimR.

Additional information

Funding

This work was partially supported by The Health Foundation in the UK (Evidence into Practice award).