Using probabilistic patient flow modelling helps generate individualised intensive care unit operational predictions and improved understanding of current organisational behaviours

Abstract

Purpose: We sought a bespoke, stochastic model for our specific, and complex ICU to understand its organisational behaviour and how best to focus our resources in order to optimise our intensive care unit’s function. Methods: Using 12 months of ICU data from 2017, we simulated different referral rates to find the threshold between occupancy and failed admissions and unsafe days. We also modelled the outcomes of four change options. Results: Ninety-two percent bed occupancy is our threshold between practical unit function and optimal resource use. All change options reduced occupancy, and less predictably unsafe days and failed admissions. They were ranked by magnitude and direction of change. Conclusions: This approach goes one step further from past models by examining efficiency limits first, and then allowing change options to be quantitatively compared. The model can be adapted by any intensive care unit in order to predict optimal strategies for improving ICU efficiency.