Modeling the Impact of Community First Responders

In community first responder (CFR) systems, traditional emergency service response is augmented by a network of trained volunteers who are dispatched via an app. A central application of such systems is out-of-hospital cardiac arrest (OHCA), where a very fast response is crucial. For a target performance level, how many volunteers are needed, and from which locations should they be recruited? We model the presence of volunteers throughout a region as a Poisson point process, which permits the computation of the response-time distribution of the first-arriving volunteer. Combining this with known survival-rate functions, we deduce survival probabilities in the cardiac arrest setting. We then use convex optimization to compute a location distribution of volunteers across the region that optimizes either the fraction of incidents with a fast response (a common measure in the industry) or patient survival in the case of OHCA. The optimal location distribution provides a bound on the best possible performance with a given number of volunteers. This can be used to determine whether introducing a CFR system in a new region is worthwhile or can serve as a guide for additional recruitment in existing systems. Effective target areas for recruitment are not always obvious because volunteers recruited from one area may be found in various areas across the city depending on the time of day; we explicitly capture this issue. We demonstrate these methods through an extended case study of Auckland, New Zealand. This paper was accepted by Carri Chan, healthcare management. Funding: This research was financed in part by the Netherlands Organization for Scientific Research (NWO) in the form of a Rubicon grant (019.172EN.016) to C. J. Jagtenberg and a Veni grant (VI.Veni.191E.005) to P. L. van den Berg. S. G. Henderson and H. Li were supported in part by National Science Foundation Grant CMMI-2035086. Vrije Universiteit Amsterdam and Erasmus University received funding from TKI Dinalog Grant 2023-1-307TKI. Some of this work has been executed under the TKI Dinalog Grant 2023-1-307TKI. Part of this work was completed during a research visit made possible by a Distinguished Visitor Award from the University of Auckland. Supplemental Material: The online appendix and data files are available at https://doi.org/10.1287/mnsc.2022.04024 .