Kakad, M;
Utley, M;
Dahl, FA;
(2023)
Using stochastic simulation modelling to study occupancy levels of decentralised admission avoidance units in Norway.
Health Systems
, 12
(3)
pp. 317-331.
10.1080/20476965.2023.2174453.
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Abstract
Identifying alternatives to acute hospital admission is a priority for many countries. Over 200 decentralised municipal acute units (MAUs) were established in Norway to divert low-acuity patients away from hospitals. MAUs have faced criticism for low mean occupancy and not relieving pressures on hospitals. We developed a discrete time simulation model of admissions and discharges to MAUs to test scenarios for increasing absolute mean occupancy. We also used the model to estimate the number of patients turned away as historical data was unavailable. Our experiments suggest that mergers alone are unlikely to substantially increase MAU absolute mean occupancy as unmet demand is generally low. However, merging MAUs offers scope for up to 20% reduction in bed capacity, without affecting service provision. Our work has relevance for other admissions avoidance units and provides a method for estimating unconstrained demand for beds in the absence of historical data.
| Type: | Article |
|---|---|
| Title: | Using stochastic simulation modelling to study occupancy levels of decentralised admission avoidance units in Norway |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.1080/20476965.2023.2174453 |
| Publisher version: | https://doi.org/10.1080/20476965.2023.2174453 |
| Language: | English |
| Additional information: | Copyright © 2023 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. |
| Keywords: | Admission avoidance; community-based; healthcare; discrete event simulation; Erlang; regression |
| UCL classification: | UCL UCL > Provost and Vice Provost Offices > UCL BEAMS UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Maths and Physical Sciences UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Maths and Physical Sciences > Dept of Mathematics UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Maths and Physical Sciences > Dept of Mathematics > Clinical Operational Research Unit |
| URI: | https://discovery-pp.ucl.ac.uk/id/eprint/10167240 |
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