EURO 2024 Copenhagen
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1914. How should volunteers be dispatched to out-of-hospital cardiac arrests?

Invited abstract in session TD-31: Analytics for Combinatorial Problems from Health Care to the Food Industry, stream Analytics.

Tuesday, 14:30-16:00
Room: 046 (building: 208)

Authors (first author is the speaker)

1. Caroline Jagtenberg
Supply Chain Analytics, Vrije Universiteit Amsterdam

Abstract

Survival for out-of-hospital cardiac arrest (OHCA) can be significantly improved through bystander efforts. To shorten the time to good-quality cardiopulmonary resuscitation, some emergency call centers use mobile phone technology to rapidly locate and alert nearby trained volunteers. Several such community first responder (CFR) systems are active worldwide, for example GoodSAM, which operates in the UK, Australia and New Zealand.

GoodSAM sends so-called phased alerts: they notify increasingly many volunteers with built-in time delays. The policy that defines these delays affects (1) response times - which have a direct relation to survival - (2) CFR workload and (3) the number of redundant CFR arrivals. We start by comparing policies through Monte Carlo Simulation, in which we use bootstrapped values from historical GoodSAM responses, estimating the three KPIs above. CFR app managers can use those results to identify a policy that displays a desirable trade-off between the performance measures.

We continue by using machine learning to predict the best policy to use, given where the volunteers are observed in relation to the patient. We do this by formulating the problem as a multiclass classification problem, for which we train a tree on the results from the simulations above. We compare the performance of the tree against a policy designed by dynamic programming. Finally, we look into optimal trees which go beyond the heuristic nature of machine learning algorithms.

Keywords

Status: accepted

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