Maximising patient benefit from triage policies for scarce critical care resource allocation during periods of intense pressure: an empirically informed systems modelling and simulation study

Existing triage policies aimed at prioritising and rationing critical care resources are underpinned by the four fundamental principles: maximizing the benefits produced by scarce resources, treating people equally, promoting and rewarding instrumental value, and giving priority to the worst off. In practice, when triage criteria are applied to individual patients at the time of ICU admission, their priorities are determined mainly by individual attributes based on single or some of those values, including the illness severity and the near-term prognosis after discharge. Nevertheless, they do not consider the dynamic illness pathway of individual patients over the duration of treatment in ICU. Further, they ignore the overall mixture of current patient profiles and the uncertainty in the number of patients who become critical ill over time. Above all, the existing triage criteria do not adopt and operationalise all these values simultaneously, leading to suboptimal policies that challenge the principle of fairness in resource rationing.

To address these limitations, we developed an empirically informed algorithmic model that assigns patient priority based on ICU operational data from one NHS trust. The performance of our prioritisation policies is evaluated against existing triage benchmarks in a comprehensive computer simulation study.

Speaker Details

Christos Vasilakis, Li Ding and  Dong Li

Acknowledgement: This research was funded by UKRI/EPSRC Covid-19 Rapid Response Grant (Grant Ref: EP/V050761/1).

NOTE: This talk is geared towards health professionals